International Journal of Information and Communication Technology (74 papers in press)
Research on highway vehicle detection algorithm based on video image
by Yucai Zhou, Zhimin Lv, Yuelin Li, Jiangchun Mo
Abstract: In order to solve problem that vehicle detection rate can affected in complex scenarios, authors put forward the adaptive method based on GMM which sets up and updates the background, and use the average neighborhood based on HSV fast shadow elimination algorithm which improves the speed of shadow elimination. For occluded vehicles, authors use the recognition algorithm based on Kalman Filter which blocks vehicle identification, then authors adopts pyramid hierarchical search algorithm based on the template matching which segments the occluded vehicles. The experimental results show that the algorithm is simple and effective, and the detection rate of vehicle is 97%, which meets the requirements of vehicle detection completely.
Keywords: vehicle occlusion; GMM; shadow removal; The Kalman Filter; vehicle detection.
Parallel Mining Method for Symbol Application Features of Complex Network Images
by Chiyu Pan
Abstract: Aiming at the problems of poor denoising effect, low recognition rate and longtime of feature mining in current methods, a parallel feature mining method for symbol application features of complex network images based on neural network learning control is proposed. Firstly, the standard deviation of image symbol noise is calculated, and the denoising is carried out by using filtering parameters. The gray value of image and the two-dimensional histogram of image symbol are calculated. The discrete measure matrix between image background and object is defined, and the occurrence probability of image background and object is obtained. On this basis, the two-dimensional maximum inter-class variance method is used to segment image symbols. The wavelet basis function in the neural network is replaced by wavelet basis function. By introducing momentum coefficient and learning efficiency to calculate the parameters of the neural network, and using the improved simulated annealing algorithm to adjust the learning efficiency of the neural network, the parallel mining of symbolic application features of complex network images is finally realized. The experimental results show that the proposed method has good image denoising effect, high image recognition rate and short time for feature mining. The above results verify the comprehensive effectiveness of the proposed method.
Keywords: Complex networks; Image symbols; Application features; Parallel mining;.
Trust circle recommended model based on different fields
by Yun Bai, Wandong Cai
Abstract: A trust-based recommendation system recommends the resources needed for users by system rating data and users trust relationship. In current relevant work, an over-generalized trust relationship is likely to be considered without exploiting the relationship between trust information and interest fields, affecting the precision and reliability of the recommendation. This research, therefore, proposes a users interest-field-based trust circle model. Based on different interest fields, it exploits potential implicit trust relationships in separated layers. Besides, it conducts user rating by combining explicit trust relationships. This model not only considers the matching between trust information and fields, but also explores the implicit trust relationships between users in specific fields, thus it is able to improve the precision and coverage of rating prediction. The experiments made with the Epinions data set proved that the recommendation model based on trust circle exploiting in users interest fields proposed in this research, compared with the traditional recommendation algorithm based on generalized trust relationship, is able to effectively improve the precision and coverage of the recommendation rating prediction. rnrn
Keywords: trust relationship; interest field; recommendation algorithm; trust circle; social network.
Research on Improved modulation Routing and Spectrum Allocation Algorithm in Elastic Optical Networks
by Li Li
Abstract: With the rapid development of services such as mobile Internet, high-definition video, and cloud computing, users' bandwidth requirements are increasing, and the demand for bandwidth granularity is becoming more diverse. In order to solve this problem, the researchers proposed the concept of elastic optical networks. The Elastic optical network adopts the flexible grid transmission mode, which can flexibly allocate spectrum resources while meeting high bandwidth and diversity requirements. Considering the modulation, routing and spectrum allocation (RMSA) of modulation mode selection on different paths is an extremely important problem in elastic optical networks. This paper presents a RMSA algorithm for Elastic Optical Networks with a Tradeoff between consumed resources and interval with boundary (Tradeoff-RnI). For a connection, the amount of resources consumed (R) and the interval between the spectrum blocks and the boundary (I) are indicators that need to be minimized. For these two indicators, the RMSA problem is first modeled as a multi-objective optimization model, and then a linear combination αR+ (1-α) I of R and I is designed. The target RMSA algorithm takes a compromise between R and I. The simulation results show that the Tradeoff-RnI algorithm has a much lower connection request blocking rate than the First-Last Fit algorithm and the Block-Assignment algorithm.
Keywords: Elastic optical network (EON); Routing; Spectrum Allocation; Improved; Tradeoff-RnI Algorithm; Blocking Probability.
An Energy Efficient and Load Balanced Sink Mobility for Wireless Sensor Networks
by Vivekanand Jha, Amar Mohapatra, Nupur Prakash
Abstract: The uniform energy dissipation of sensor nodes is a key objective to optimize network lifetime of Wireless sensor networks (WSNs). In recent studies, a mobile sink implementation has been used instead of static sink and it offers better energy balanced network. This approach used four design constraints of sink mobility in WSN to achieve uniform energy dissipation of nodes in the network. These design constraints are: sink mobility pattern, node deployment technique, scheduling of node activities, and routing of data to the sink. In the literature, the existing approaches have utilized only subset of these design constraints. In our proposed approach all four design constraints have been used in an integrated manner and have been implemented in a three steps solution to achieve the energy load balanced sink mobility in the WSNs. The results have been compared using performance metrics such as coverage density, network lifetime, and energy balancing condition. These metrics are derived and further implemented through simulation. The simulation results confirm that the proposed solution outperforms the existing sink mobility patterns.
Keywords: energy hole; coverage hole; sink mobility; network lifetime; wireless sensor networks; routing; energy efficient.
Research on security monitoring system for wind-solar complementary power generation based on internet of things
by Bin Dai
Abstract: When traditional system is used to monitor wind-solar complementary power generation, there are problems such as large errors in temperature and wind speed acquired and high power consumption of nodes when the sensor transmits signals. Therefore, a security monitoring system for wind-solar complementary power generation based on Internet of Things (IoT) is designed. The system hardware is composed of I/O control panel, sensor, GPRS and data processing board. The system software mainly acquires data and processes the acquisition interrupt program. On this basis, the security monitoring of wind-solar complementary power generation is finally realized. The analysis of simulation experiments shows that compared with the traditional system, the data acquisition error of the proposed system is small, which is in good agreement with the actual value. Moreover, the power consumption of nodes is only 15.8 mw when the sensor transmits the signal, indicating that the system has strong reliability and practical application value.
Keywords: Internet of Things (IoT); wind-solar complementary; sensor; data acquisition; power generation security; monitoring system.
Interrupt Nesting Method Based On Time Slice In Embedded Software
by Yingjie Wang, Kuanjiu Zhou, Jie Pan, Mingchu Li
Abstract: For the limitation of embedded system resources, processing capacity, storage capacity, and executing time are important factors to evaluate an interrupt handler. For an interrupt handler, the more numbers of the interrupt thread rotations, the more times of the increase in record information on the interrupted site, and consumption of additional time and space. When the high-priority interrupt is preempting the low-priority interrupt and the low-priority interrupt deadline is approaching, this paper proposed an interrupt handler with a time slice, which is enabled to complete the executing sections of low priority interrupt within the time period of the time slice. This method could not only reduce the numbers of interrupt threads rotations but also save the space to record the interruption scene for interrupt execution. The experimental part used the two types of interrupt handlers by the ordinary queuing model and inserting time slices consecutively, to simulate two-priority interrupt processing in a system. The comparison of simulation experiments shows that: the interrupt handler with time slices could obviously reduce the rotation number of interrupt handling thread switched, thereby saving the space for the interruption of site preservation and providing a reference for further research on the interruption of embedded systems.
Keywords: Interrupt priority; Interrupt time slice; Queuing model.
Using Priced Timed Automata for the Specification and Verification of CSMA/CA in WSNs
by Zohra Hmidi, Laid Kahloul, Saber Benharzallah
Abstract: Several contention-based MAC protocols for WSNs have been proposed. The control channel is accessed with CSMA/CA (Carrier Sense Multiple Access with Collision Avoidance) method. The complexity of this method and its criticality motivate the formal specification and verification of its basic algorithms. Most existing works do not deal with all possible aspects such as topology, number of nodes, node behavior, and number of possible retransmissions. In this paper, we propose a stochastic generic model for the 802.11 MAC protocol for an arbitrary network topology which is independent of the number of sensors.rnIn addition to the qualitative evaluation that proves the correctness of the model, we will make a quantitative evaluation using the statistical model checking to measure the probabilistic performance of the protocol.
Keywords: WSNs; CSMA/CA; Statistical model checking; Priced timed automata; Formal Modeling; Formal Verification; UPPAAL.
Reduced Complexity Per-Survivor Iterative Timing Recovery using Max-Log-MAP algorithm
by Chuan Hsian Pu, Ezra Morris Abraham Gnanamuthu, Fook Loong Lo
Abstract: In this paper, we propose a reduced-complexity (RC) per-survivor-processing (RC-PSP) iterative timing recovery scheme. The objective is to lessen the computational burden while approaching the optimal system performance of the existing full-complexity (FC) Log-MAP (FC-PSP) iterative recovery scheme. This is achieved by utilizing the Max-Log-MAP framework, which converts the multiplications and additions to maximum operations. According to Spasov, Gushev and Ristov, such framework consumes less power, reduces space on a chip and increases decoding speed. Robertson et al. showed that Max-Log-MAP PSP requires only 5
Keywords: Max-Log-MAP; MAP; maximum-a-posteriori; SNR; TED; timing error detection; turbo-codes; trellis; timing recovery; PSP; per-survivor processing; synchronizations; complexity; BER; bit error rate; timing errors; Mueller and Müller; LDPC; low-density parity-check.
Research on Anomaly Detection Method for Hybrid Big Data Subarea based on Ant Colony Algorithm
by Shu Xu
Abstract: Due to the problems of low accuracy and poor degree of freedom of the existing big data anomaly detection methods, a mixed big data partition anomaly detection method based on ant colony algorithm is proposed. The number of common neighborhood between nodes in weighted network is redefined and the mixed big data sub-region is realized. Combining the operation, vulnerability and threat of the database, the security situation value is substituted into the abnormal location part to form the coordinate matrix. The pheromone concentration of each region was calculated, and the region where the concentration was reduced was defined as the abnormal region to complete the big data anomaly detection. Experimental results show that this method has high accuracy, freedom of anomaly location and good accuracy performance, which is a great progress of big data anomaly detection technology. In the future, an effective method to repair abnormal data and improve the specific application scope of this method should be developed on the basis of this method.
Keywords: Mixed type; Big data; Subarea; Anomaly detection;.
Construction of Performance Monitoring Model for Cloud Computing Service Platform based on Label technology
by Lishou Zhang, Yiyi Liu, Shaojun Shen
Abstract: To solve the problems of low service efficiency, large change in total system requests and high blocking probability of the current performance monitoring model for cloud computing service platform, a performance monitoring model for cloud computing service platform based on label technology is proposed. By using the model, the concept and characteristics of cloud computing are analyzed, the purpose of performance management and performance monitoring structure of cloud computing service platform are summarized, the service efficiency, time characteristics, resource utilization and efficiency compliance of cloud computing service platform are evaluated, the structure model of cloud computing service platform is established, and the label technology is introduced, to build the performance monitoring model for cloud computing service platform. Finally, the performance monitoring of cloud computing service platform is realized. The experimental results show that the proposed model has high service efficiency, stable change of the total number of system requests and low blocking probability, which verifies the validity and superiority of the proposed model, and provides a new idea for the development and progress of performance monitoring technology of cloud computing service platform.
Keywords: Label technology; Cloud computing; Service platform; Performance monitoring model;.
Symbolic Feature Extraction of Art Graphics based on Time Constraint
by Xiaofeng Liu
Abstract: Aiming at the problem of time-consuming and large error in current image feature extraction results, a symbolic feature extraction method for art graphics based on time constraint is proposed. Gauss filtering method is used to filter out the image noise, and Laplacian sharpening operation is used to enhance the edge of graphic symbols. According to the enhancement results, an active contour model is introduced to define a closed contour, and a driving force is used to realize the evolution of the contour to the target boundary to complete the graph segmentation. The geometric transformation of the segmentation results is analyzed by translation transformation, scale transformation and rotation transformation. Based on graphics enhancement, segmentation and geometric transformation analysis, time constraints are introduced. By analyzing the mean value and variance level of time-constrained laminar flow features, the average value and divergence degree of time-constrained laminar flow features are analyzed, and the time-constrained symbols are regarded as a vector to realize the symbolic feature extraction of fine arts graphics. The experimental results show that the proposed method is efficient and accurate, and it is a reliable method for extracting symbolic features of fine arts graphics.
Keywords: Time constraint; Art graphics; Symbolization; Feature extraction.
Analysis of Data Mining Method for Short-term Wind Measurement of Wind Farm based on Multi-technology Fusion
by Jianfeng CHE, Bo Wang, Shitao Chen
Abstract: Aiming at the problems of poor noise reduction effect of current methods for short-term wind measurement data, poor fitting between wind measurement values and real values, and long running time of the methods, a data mining method for short-term wind measurement of wind farm based on multi-technology fusion is proposed. The anomaly points in the short-term wind data are found and corrected. The short-term wind data are de-noised by wavelet decomposition and normalized. The short-term wind speed measurement of wind farms at each time is carried out, and the short-term wind measurement data mining of wind farms is finally realized. The experimental results show that the proposed method has better noise reduction effect for short-term wind measurement data, the fitting degree between wind measurement value and real value is higher, and the running time of the method is shorter. All the above results verify the effectiveness of the proposed method.
Keywords: Multi-technology fusion; Wind farm; Short-term wind measurement; Data mining;.
Network Teaching Target Classification System Based on Cloud Computing Technology
by Tianming Feng, Lin Chen, Dahang Fan
Abstract: Aiming at the problems of low security, low storage utilization and low classification accuracy of the current network teaching target classification system, a classification system based on cloud computing technology is proposed. Considering the above design functions from the perspective of teachers and managers, the teacher's functions are resource management, sub-category management, information induction, and student management. The functions of managers are unit management, classification management, subject management, teacher management. Through the classification management method and resource management method, the classification of teaching resources is realized. The TEM based on cloud computing technology adjusts the resource classification result through clear and detailed resource data structure, and combines feature extraction, training algorithm and classification algorithm to complete the network teaching resources. classification. The experimental results show that the security, storage utilization and classification accuracy are high, which provides a guarantee for the development of the network teaching target classification system.
Keywords: cloud computing technology; network teaching; teaching objectives; classification system.
Modeling the Impact of Communication Technology on E-Commerce Operation Mode
by Chaosheng Han
Abstract: In order to solve the problems of high modeling complexity and low coincidence degree of traditional methods of studying the relationship between communication technology and e-commerce operation mode, a new analysis model of the influence of communication technology on e-commerce operation mode is proposed. This paper from China, Alexa.com and the selected site object data, using online desktop data acquisition method for part of open data comparison, The experimental results show that the approximate mean square error of the model is lower than 0.035, the fitting index is higher than 0.9, and the complexity is lower than 184, which proves that the method in this paper has higher accuracy and lower complexity, and the practical application effect is better.
Keywords: Communication technology; E-commerce; Operation mode; Impact modeling.
Identity Authentication System for Mobile Terminal Equipment based on SDN Network
by Hao Yang, Wen Cai, Yibo Xia, Wenhua Ouyang, Xin Xie
Abstract: Aiming at the disadvantages of the existing mobile terminal equipment identity authentication system, such as poor anti-attack performance and slow speed, the mobile terminal equipment identity authentication system based on SDN network is designed. The architecture of mobile terminal device authentication system consists of sensor, data center and management center. This paper analyzes the requirements and performance of mobile terminal device identity authentication system, processes the key information through the combination of hashing algorithm and public/key system, and determines whether the mobile terminal device is attacked, so as to realize the identity authentication of mobile terminal device. Experimental results show that when the number of network attacks is 30, the designed system can resist about 92% of the attacks, and the speed of identity authentication is always below 2.7s, indicating that the system has good anti-attack performance and fast speed of identity authentication.
Keywords: SDN Network; Mobile terminal equipment; Identity authenticationrnrn.
Impact of co-channel interference on performance of Power domain based multiple access
by Thuan Do, Tu-Trinh Nguyen
Abstract: Power domain based Non-orthogonal multiple access (PDMA) is one emerging technology thanks to higher system capacity, lower latency, and massive connectivity. Such PDMA is proposed to address several challenges in the fifth-generation wireless systems. In this paper, we first reveal that the NOMA techniques have evolved from fairness assurance under impact of co-channel interference (CCI). Then, we comprehensively investigate on signal to noise ratio (SNR) to detect two separated signals, then outage performance evaluations of the two users are conduced. Monte- Carlo simulations are deployed to verify exactness of outage probabilities.
Keywords: Power Domain based Non-orthogonal multiple access (PDMA); co-channel interference (CCI); outage probability.
Research on Detection Method of Abnormal Capital Transfer in Electronic Commerce Based on Machine Learning
by Guiming Zhu
Abstract: In order to overcome the problems of long detection time, low detection efficiency and high false alarm rate, a new method based on machine learning is proposed. Data mining in e-commerce platform. The improved k-means algorithm was used to cluster the data, and the five steps of preparation, detection, location acquisition, modification and verification were used to clean up the clustering results and remove redundant data. The machine learning method is used to determine whether there are suspicious transaction fragments in the database through four steps: data preprocessing, generating reference sequence and query sequence, calculating similarity and sequence classification, and to complete abnormal fund transfer detection in e-commerce. Experimental results show that the detection time of this method is kept below 3s, the highest false detection rate is only 11%, and the detection rate is always higher than 90%, with high detection efficiency, low false alarm rate, high detection rate.
Keywords: Machine Learning; E-Commerce; Capital Transfer; Anomaly Detection;.
Inter-contact delay and location information based routing (ICDLIR) with adaptive threshold buffer management for delay tolerant networks
by Savita , Daya Krishan Lobiyal
Abstract: Most of the routing algorithms in delay tolerant networks use multicopy approach and messages stay for long time in the buffer because of no end-to-end connectivity between source to destination node. On one hand these activities ensure the high delivery probability, while on the other hand they introduce high resource overheads per message in the network. Therefore, limited storage capacity of buffer bound us to use intelligent message control buffer management policies. In this paper, we have proposed a combination of buffer management scheme for location information and inter-contact based routing approach. The proposed buffer management scheme is a combination of message scheduling and message drop policy grounded on the concept of inter-contact delay based on delivery probability, hop count and number of copies of a message in the network. We also used adaptive threshold scheme which depends on average transferred bytes in the network to prioritize messages. It has been observed that our schemes results in reduction of overheads as compared to previous schemes. The probabilistic analysis of location dependent and independent encounters of nodes has been made to gain better insights of encounter timings. Experimental results obtained demonstrate the effectiveness of our proposed scheme.
Keywords: delay tolerant networks (DTNs); power control; opportunistic networks; overhead; buffer management.
Research on Automatic Annotation of Pathological Image Detail Information Based on Machine Learning
by Xiang SUN, Qianmu LI
Abstract: Aiming at the inaccuracy of pathological image diagnosis results of traditional models, the machine learning technology is introduced and a new pathological image diagnosis method is then proposed. In this method, the accuracy of traditional pathological image diagnosis is transformed into the accuracy of detailed information annotation of pathological image. Pathological image pooling is completed through convolutional neural network, and some pathological images with dimensional blurring phenomenon are sharpened. On the basis of the pooling and sharpening of pathological image, the features of pathological image based on convolutional neural network are extracted. And then machine learning technology is used to annotate the extracted pathological image features and complete the automatic annotation of pathological image details. The experimental results show that the proposed model can improve the accuracy of pathological image feature annotation, and can accurately detect the pathological image of cancer cells, providing a new solution for pathological image diagnosis.
Keywords: pathological image; machine learning; information annotation; automatic detection.
Deep Features Based Dialect and Mood Recognition Using Assamese Telephonic Speech
by Mridusmita Sharma, Kandarpa Kumar Sarma
Abstract: Learning aided methods are popular for modeling the phenomena associated with auditory signals and design of Automatic Speech Recognition (ASR) systems. A vast majority of the works have used shallow models like conventional Artificial Neural Network (ANN) and Hidden Markov Model (HMM) in combination with Mel Frequency Cepstral Coefficients (MFCC) and other relevant features for speech recognition applications. Although these shallow models are effective but there is a need to increase the efficiency by incorporating deep features in the mechanism for speech processing applications. Considerable amount of works have been reported on the design of deep learning topologies and training paradigms in supervised domain. Despite these works, exploring new horizons with speech applications in mind, very few works have concentrated on deep features which are essential to capture detailed information of speech especially while dealing with telephonic content. This work focuses on the generation of deep features using stacked auto-encoder for normal and time shifted telephonic speech samples in Assamese language with mood and dialect variations. The deep features are then tested with softmax classifier which has been integrated to certain deep learning topology. Experimental results show that the deep features learned by the stacked auto-encoder performs better while it is configured for Assamese speech recognition with mood and dialect variations.
Keywords: Deep features; Auto-encoder; Assamese language; Dialects; Moods; Recognition; Artificial Neural Network.
MISO Assisted Multiple Access By Removing Orthogonal: Enabling D2D Transmission And Performance Anlysis
by Thuan Do, Minh-Sang Nguyen, Thi-Anh Hoang
Abstract: As a breakthrough system model adopted in the fifth generation (5G) wireless networks, Multiple Input Single Output (MISO) based Non-orthogonal multiple access (NOMA) has been introduced and investigated. With device-to-device (D2D) transmission taken into account, we consider a NOMA-based downlink network under Rayleigh fading in this paper. To look main metric, we study the system outage behavior, and close-form expressions for the exact outage probability of two NOMA users are obtained. By further evaluating the outage probability under different power allocation factors, fairness in NOMA still is guaranteed. Finally, numerical results are presented to demonstrate the validity of our analysis and show the advantages of NOMA scheme.
Keywords: MISO; non-orthogonal multiple access (NOMA); transmit antenna
Multi-Agent approach for data mining based Bagging Ensembles to improve the decision process for Big Data
by Ahmed Ghenabzia, Okba Kazar
Abstract: Today, data growth is accelerating to create a Big Data in various fields, such as social media, websites, emails, finance, and medicine. It needs analysis and knowledge extraction. In addition, Data Mining is a technology whose purpose is to promote information and knowledge extraction from a Big Data. In this paper, a multi-layered approach based on agents is proposed to extract knowledge from Big Dataset with Bagging algorithm. To achieve this, we call the paradigm of a multi-agent system in Hadoop to distribute the complexity and processing of large data sets across several autonomous entities called agents. The goal is to predict the target class or value for each case in the data using the Bagging technique that is dedicated to the task of Classification or Regression. This proposition will help decision-makers to take right decisions and provide a perfect response time by the use of the multi-agent system in Hadoop. Therefore, to implement the proposed architecture, It is more convenient to use the Apache Hadoop Framework, Apache Spark MLlib framework for building scalable Machine Learning algorithms and JADE platform which provides a complete set of services and agents.
Keywords: Big Data; Apache Hadoop; Apache Spark MLlib; Multi-Agents system (MAS); Bagging; JADE.
AN EFFECTIVE SECURE DATA RETRIEVAL APPROACH USING TRUST EVALUATION: HBSEE-CBC
by Rosy Swami, Prodipto Das
Abstract: In recent times, cloud computing has become more predominant and the secret data are centralized into the cloud to a great extent. But, for the security and privacy of the data, it is essential to encrypt the data before outsourcing; this generates the effective utilization of data as a critical task. In traditional works, the utilization of data based on the keyword search was in practice. Since normal keyword searches and search using encrypted query over encrypted data is very difficult, hence, facilitating an encrypted cloud data searching scheme is supreme prominence. But it has the major drawback of security while retrieving the information. Though many encryption algorithms are utilized for information security, still it has some issues like increased time consumption for encryption, decryption and key generation. Hence to avoid these issues, an Effective Secure Data Retrieval Approach (HSBEE-CBC) is introduced in this work. Here, the data are encrypted by using novel Elliptic Curve Cryptography (ECC) algorithm namely Hierarchical Scalar Based ECC Encryption (HSBEE). Depending on the cosine similarity the encrypted data are clustered by using Cosine Based Clustering (CBC) and transferred to the server. For improving the security, trust evaluation is carried out for the user who intends to retrieve the information. After evaluating the trust of the user, the data is decrypted in order to retrieve it. The performance of the HSBEE-CBC is analysed and compared with traditional approaches. The experimental analysis revealed the superiority of this proposed approach by providing effective results.
Keywords: Cloud Computing; Information Retrieval; Elliptic Curve Cryptography; Cosine Similarity; Trust Evaluation.
LoRaWAN Possible Attacks, Proposed Countmeasures and Enhanced Solution
by Sarra Naoui, Mohamed Houcine Elhdhili, Leila Azouz Saidane
Abstract: The Internet of things (IoT) is pervading our lives to provide a comfortable and smarter human living space by connecting sensors and objects directly with one another without human intervention. The requirements for the deployment of millions of IoT objects are increasing rapidly with a major security concern. To face this challenge, security solutions based on cryptography might be used. In this paper, we investigate one of the widespread IoT security solutions, that is LoRaWAN technology. We evaluate its security by gathering the possible attacks. Afterwards, we propose an enhanced LoRaWAN architecture that faces these attacks. We evaluate the proposed solution in terms of security and performance parameters and compare it to existing solutions.
Keywords: LoRaWAN; Vulnerabilities; Security; Countermeasures.
Fast Road Scenarios Recognition of Intelligent Vehicles by Image Processing
by Huawei Wu, Yicheng Li, Jie Zhang, Yong Kuang
Abstract: Road scenarios recognition is a key point for intelligent vehicles (IVs) self-localization. This paper proposes a fast road scenarios recognition method by image processing, which is based on a visual road model. First, a sequence of images is used to set up the visual road model. Second, we use the classic ORB (Oriented FAST and Rotated BRIEF) method to encode both holistic and local features of a query image. In the step of holistic feature extraction, each query image is treat as a feature and the feature size is only 63
Keywords: intelligent vehicle; computer vision; road scenario recognition; holistic feature; local feature;hybird KNN.
Security Protocol of Internet of Things based on NTRU Encryption Model
by Ying Li
Abstract: In view of the low security and reliability of the current Internet of things security protocols, a NTRU encryption model based Internet of things security protocol method is proposed. This method uses NTRU-PGP to construct the security protocol of the Internet of Things, reduces the amount of data through PKZIP algorithm, introduces AES symmetric encryption algorithm to encrypt the information data that need to be transmitted in the Internet of Things, re-encrypts the encrypted information according to NTRU encryption algorithm, and implements information signature by NTRUSign algorithm to ensure the security of the Internet of Things. The experimental results show that when the proposed method is attacked by active attack, passive attack, eavesdropping data, counterfeiting and other types of attacks, the estimated attack time is over 1025, which is significantly higher than other data, which proves that the protocol is more secure and reliable.
Keywords: NTRU; Internet of Things; Security protocol.
Lightweight Information Integration Model of IOT based on VMware Cluster Technology
by Ying Li
Abstract: In order to overcome the problems existing in the current Internet of things information integration model, Lightweight Information Integration Model of IOT based on VMware Cluster Technology was proposed. This model designs the standard coded identification for each node in the Internet of things, constructs the category of node attributes and node object categories of the Internet of things, and the node LDAP information model, and constructs the node management platform of the Internet of things. VMware cluster technology is introduced, and VMware Infrastructure is used to realize the virtualization and integration of lightweight Internet of things information from server, network and storage. The results show that the number of servers used in this paper is 4, with the least number. The server occupies less space than 8U; The power consumption of the server is lower than 3500W/h, and the power consumption is lower, showing a good performance.
Keywords: VMware cluster technology; Lightweight; IOT information; Integration.
The Research of Human Interaction Recognition Based on Fusion Features of Key Frame Feature Library
by Haiyan Zhang, Shangbing Gao
Abstract: Some issues such as computational complexity and low recognition accuracy still exist in human interaction recognition. In order to solve the problem, the paper has proposed innovative and effective method based on fixed features of key frame feature library. Firstly, GIST feature and HOG feature were extracted from the pre-processed videos. Secondly, training videos with different kinds of actions were clustered by the K-means algorithm respectively to get key frame feature of each action for constructing key frame feature library. And similarity measure was utilized to calculate the frequency of different key frames in every interactive video, and statistical histogram representation of interactive videos were obtained. Finally, the decision level fusion was achieved by using SVM classifier based on histogram intersection kernel to obtain impressive results on UT-interaction dataset. The correct recognition rate of the proposed algorithm is 85%, which indicates that the proposed algorithm is simple and effective than others.
Keywords: GIST Feature; HOG Feature; Key Frame Feature Library; Histogram Intersection Kernel; UT-interaction Dataset.
Research on Stability Modeling of High Speed Electronic Communication Based on Hierarchical Optimal Mining Algorithms
by Lin Tang
Abstract: In order to solve the problems of low accuracy, bad network topological data integrity and low communication transmission efficiency in the current stability modeling methods for high-speed electronic communication, a new stability modeling method for high-speed electronic communication based on hierarchical optimization mining algorithm is proposed. This paper analyzes the transmission characteristics of high-speed signals in the process of electronic communication, constructs a wireless channel model by using hierarchical optimization mining algorithm, and carries out distance correspondence based on the wireless channel model. The stability of high-speed electronic communication is modeled and analyzed by using one-step transition probability matrix, node relative distribution and link duration. Experimental results show that this method has the advantages of high model accuracy, good topological data integrity and high communication transmission efficiency.
Keywords: Hierarchical optimization mining algorithm; High-speed electronic communication; Stability; Modeling and analysis; Transfer probability matrix;.
Self-adaptive Process Optimization Method for SBS Cloud Application based on Reinforcement Learning
by Haiyan Hu, Chang Su
Abstract: To solve the problem of poor comprehensive performance of the traditional SBS cloud application adaptive process optimization method, a new SBS cloud application adaptive process optimization method based on reinforcement learning was proposed. Establish the adaptive action type selection model, realize the optimal choice of operation type, build the cloud application adaptive process optimization model for resource cost, convert the problem into the corresponding mathematical model, solve and make the mathematical model of the algorithm, and obtain the best adaptive process optimization scheme SBS cloud application. The simulation results show that the predicted load value of the method is the closest to the actual load value, the relative error of the prediction is less than 21.03, and the average time is less than 2.3s, indicating that the method has good performance and high practical application value.
Keywords: Enhanced learning; SBS cloud application; self-adaptive process optimization; system performance optimizationrnrn.
Cross-domain Single Sign-on Authentication of Information Security in Network Environment
by Ajun Cui, Wei WANG, Hua-feng ZHANG, Yan-hong MA, Chen LI, Xiao-ming WANG
Abstract: Aiming at the problems of long response time, poor security and information integrity in cross-domain single sign-on authentication research of network security, this paper proposes a cross-domain single sign-on authentication method based on SAML, including the design of login authentication control and revocation authentication scheme. In login authentication, efficient interaction between identity providers and service providers reduces unnecessary steps to achieve single sign-on. At the same time, a revocation scheme based on cumulative function is proposed to ensure that the local root signature is used for system parameter information, thus further ensuring network security. The experimental results show that the login time of the proposed method is below 400ms, and the login response takes a short time. The success rate of the proposed method against false login is over 90%, and the security is good. The data packet loss rate of this method is below 2%, high integrity and reliability.
Keywords: Network security; cross-domain single sign-on; parameter information; revocation of authentication.
Machine Learning Method Based on Improved Drosophila Optimization Algorithm
by Haiying Wang
Abstract: Aiming at the problems of poor classification effect and high CPU ratio of traditional machine learning methods, a machine learning method based on improved drosophila optimization algorithm was proposed.The rank one data mapping and the low order data are established.In low rank support vector set, CP rank organization of traditional support vector machine is used to improve data security.The traditional drosophila algorithm was improved and optimized to increase the number of data iterations, ensure the compatibility of rank one data, improve the optimal calculation of drosophila, and increase the density clustering.The decomposition process is designed to evaluate the objective function value of the optimal solution.In the evaluation process, support vector machine is used to complete the label classification of learning data.Experimental data show that this method performs well in data classification effect, low-rank data storage dimension characteristic performance and CPU operation proportion performance.
Keywords: Machine learning; Drosophila algorithm; Low rank data; Support vector machine.
Research on Blurred Edge Information Segmentation of Image based on Computer Vision
by Xu Zhang
Abstract: In the process of image fuzzy edge information segmentation by traditional methods, the segmentation effect is not ideal, the completion time is long and the accuracy is low.A fuzzy edge information segmentation method based on computer vision is proposed.After image denoising, image sharpening is carried out to extract image fuzzy edge information features.By designing a super-pixel grid, the pixels of the fuzzy edge information features of the image are matched, the inverse tensor information of the fuzzy edge of the image is analyzed, and the multi-threshold values are normalized.The processing results are overlaid on the single object in the image to realize the fuzzy edge information segmentation.Experimental results show that the proposed method has better segmentation effect, shorter completion time and higher accuracy, and is of practical significance.
Keywords: Computer vision; Image blurring; Edge information; Multi-threshold segmentation.
Multi-Tag Cleaning Software For Cyclic Redundant Data Stream Based On Rfid In Cloud Computing
by Yujiang Fei
Abstract: In order to overcome the problems of poor accuracy and recall and low data integrity existing in the current research results of multi-tag cleaning for cyclic redundant data streams, this paper designs a multi-tag cleaning software for cyclic redundant data streams in cloud computing based on RFID. According to the factor graph model, the software models cloud computing network, infers the overall quality of network link connection by cyclic reliability propagation method, and realizes data acquisition by probability graph reasoning. The improved RFID reader reads the information of the tag data to realize the preliminary processing of the cyclic redundant data stream. Multi-label cleaner and RFID reader cooperate to complete multi-label cleaning of cyclic redundant data stream under cloud computing. The experimental results show that the proposed multi-tag cleaning software for cyclic redundant data streams has higher accuracy and recall than the results of references, and has higher data integrity.
Keywords: RFID; Cloud computing; Cyclic redundancy; Data stream; Multi-tag; Cleaning;.
Intelligent Evaluation Model Of E-Commerce Transaction Volume Based On The Combination Of K-Means And Som Algorithms
by Junjie Niu
Abstract: Due to the lack of steps to process sensitive data in the traditional intelligent evaluation model of e-commerce transaction volume, which leads to poor evaluation effect, an intelligent evaluation model of e-commerce transaction volume based on the combination of k-mean and SOM algorithm is proposed.Taking system intelligent clustering as the core, the transaction volume data in the Web log is collected through the collection system.The desensitization rule is used to establish the data transmission model of trading volume.The detection of sensitive data adopts signal detection technology and analytical processing.Based on k-means algorithm, data desensitization is accomplished to achieve data denoising, and a clustering evaluation model is established to complete data mining and analysis.The experimental results show that the model has a good denoising effect, and the ROC curve is closer to the upper left corner with an AUC value of 0.9712, which verifies the effectiveness and superiority of the model.
Keywords: System intelligent clustering; E-commerce transaction volume; Evaluation model; K-means; SOM.
A PSO Based Improved Clustering Algorithm for Lifetime Maximization in Wireless Sensor Networks
by Santar Pal Singh, S.C. Sharma
Abstract: A wireless sensor network (WSN) is comprised of enormous amount of miniature devices known as sensor nodes. Sensor nodes are highly energy restrained due to their battery dependent operations and because of the harsh area deployment it is not feasible to change or revitalize their batteries. For maximizing the network lifetime, energy conservation is a crucial measure of performance improvement of WSNs. So, energy consumption and network lifetime are crucial issues in WSNs. Large portion of energy are consumed in communication process of sensor network. Several routing algorithms have been projected to accurately rout the packet in WSNs. Usually classification of routing algorithms can be done into two categories: based on network structure or based on protocol operations. Based on network structure, these algorithms are broadly divided into flat based, hierarchy based or cluster based, and location based routing algorithms. Due to energy efficiency and network stability, cluster based routing is the best choice. In this paper, we propose a swarm optimization based clustering algorithms for WSNs. We perform the analysis of our new clustering algorithms with LEACH, LEACH-C, and HEED on the basis of performance metrics that contain energy, number of alive nodes, delay, PDR, and throughput. Simulation outcomes confirm that proposed clustering algorithm outperform the other ones.
Keywords: wireless sensor network; clustering algorithm; energy efficiency; network stability; network lifetime.
Virtual Machine Allocation Method For Cloud Computing Based On Multi-Objective Evolutionary Algorithm
by Jinling Ma
Abstract: In order to overcome the problems of data access delay and high energy consumption in virtual machine configuration of cloud computing, a new virtual machine allocation method for cloud computing based on multi-objective evolutionary algorithm is proposed in this paper. Based on multi-objective evolutionary algorithm, the objective of energy consumption and access delay optimization is replaced by an objective model, which is used as the allocation objective model of computational virtual machine. The ant colony algorithm and K-NN local search method are introduced to obtain the optimal solution of the allocation target model and realize the output of the optimal allocation scheme for cloud computing virtual machines. The experimental results show that the access delay and energy consumption of the proposed method are significantly lower than those of other methods, which proves that the proposed method has better performance.
Keywords: Multi-objective evolution; Cloud computing; Virtual machine; Allocation.
Automatic Repair of Pollution Attacks in SDN Topology Network Based on SOM Algorithm
by Xiaoliang Yang, Yi Dai, A. Lun
Abstract: When using the current method to recover the vulnerabilities caused by pollution attacks in SDN network, there are some problems, such as low coverage, low accuracy of vulnerability detection results and low repair efficiency. To this end, an automatic repair method for topology pollution attacks in SDN network based on SOM algorithm is proposed. This paper analyzes the security vulnerabilities in SDN architecture and SDN network, and extracts the attack flow characteristics of topology pollution attacks in SDN network. The extracted attack flow features are input into SOM algorithm to detect and identify vulnerabilities caused by pollution attacks. Combining the theory of vertex concavity and convexity and geometric triangle, the detected vulnerabilities are repaired to realize the automatic repair of topology pollution attacks in SDN network. The experimental results show that the proposed method has high coverage, high accuracy of vulnerability detection and high efficiency of vulnerability repair.
Keywords: SDN network; Pollution attack; Vulnerability detection; Vulnerability repair; SOM algorithm;.
Unlabeled Facial Expression Capture Method In Virtual Reality System Based On Big Data
by Feng Gao
Abstract: In view of the problems of high error rate and low efficiency in the traditional method of facial expression capture without markers, a method of facial expression capture without markers based on large amount of data is proposed. Haar feature is used to determine the initial position of human face, and active shape model is used to extract unmarked facial feature points. The extracted feature points and the generated triangle mesh are tracked by the optical flow tracking method. The displacement of the face feature points is used to promote the overall change of the mesh and complete the unmarked facial expression capture. The experimental results show that the error rate of this method is in the range of 1.2% - 1.7%, the error rate is small, and it needs 20s-34s to capture facial expression, which is more practical and efficient.
Keywords: Big data; Virtual reality system; Unlabeled; Facial expression capture.
BB84 with Several Intercepts and Resend Attacks using Partially Non-orthogonal Basis States through a Depolarizing channel
by Mustapha DEHMANI, El Mehdi Salmani, Hamid Ez-Zahraouy, Abdelilah Benyoussef
Abstract: The paper presents the partially non-orthogonal basis states effect in presence of several intercept and resend attacks through a depolarizing channel. The quantum error and the mutual information are computed for arbitrary angles of the non-orthogonal basis states and the depolarizing channel parameter. It is found that the information security depends strongly on the non-orthogonal basis states angle, the depolarizing channel parameter, the intercepts and resend attack probability and the eavesdropper number. Besides, it is found that for any eavesdroppers number and basis states angle; the protocol is more secure if the depolarization parameter is lower than a threshold value and the maximum value of the quantum error decreases by increasing the depolarizing parameter and / or the eavesdroppers number. While for the depolarization parameter above the threshold value the protocol is not secured independently of any values of the non-orthogonal basis states angle and/or the eavesdroppers number.
Keywords: Quantum cryptography; Intercepts- resend attacks; Non-orthogonal basis states; depolarizing channel; Quantum error; Information Security.
Research On Web Users Behavior Data Mining Based On Feature Orientation
by Hui Zhang, Xiao-ling Jiang, Fa Zhang
Abstract: In view of the problems of long mining time and high error rate in the existing network user behavior data mining methods, a network user behavior data mining method based on feature preference is proposed.The interaction relationship in social network is analyzed as the constraint condition of feature selection.Laplasian operator is used to construct the feature selection model of network user correlation and to quantify the relationship between users.The improved ant colony algorithm is used to obtain the optimal feature subset to realize the network user behavior data mining.The experimental results show that, compared with the traditional methods, the mining time of the proposed method is shorter and the mining error rate is lower under the condition of low and high excavation strength, which verifies the effectiveness of the proposed method.
Keywords: Feature orientation; Web user’s behavior; Data mining.
Research On Global Tourism Information Query Method Based On Association Mining
by Zhongqi Zhao, Yanjie Tan
Abstract: The traditional global tourism information query method can not mine the intrinsic relevance of the discrete information because of its strong discreteness, which results in inaccurate query results and long query response time. To overcome this problem, a global tourism information query method based on association mining is proposed. Using SAS/ME tools, data missing processing, data normalization, correlation analysis and data filtering of global tourism information data in the data are carried out, and the global tourism information data set is constructed according to the results of data processing. This paper uses C4.5 decision tree algorithm and SAS Miner Enterprise to mine the association of global tourism information in data centralization. According to the result of association mining, a global tourism information query system is established to realize the global tourism information query. The experimental results show that the method has the shortest query response time and higher query accuracy, which shows that the method is more suitable for global tourism information query.
Keywords: Association mining; Global; Tourism information; Query; Normalization processing; Discrete attributes;.
Abnormal State Recognition Method for Online Intelligent Examination based on Improved Genetic Algorithm
by Bo Yang, Hongbin Li, Huan Xie, Jianyong Zhao, Rong Zhu, Lei Zhao
Abstract: In order to overcome the defects of the traditional test monitoring scheme, this paper proposes a new method of online intelligent test abnormal state recognition based on improved genetic algorithm.This method sets the test status parameters and defines the criteria of abnormal status, collects the online test information and builds the information database.Image preprocessing is realized from two aspects of image segmentation and grayscale processing.The improved genetic algorithm is used to analyze and collect data intelligently and search image feature points to obtain abnormal feature extraction results.Match the extracted feature results with the established database information to realize the abnormal state recognition results, and start the corresponding abnormal alarm program.The experimental results show that the accuracy of the proposed method is 18.57% higher than that of the traditional method, indicating that the method has a better application prospect.
Keywords: Improved genetic algorithm; Online examination system; Intelligent examination; Abnormal state; State recognition;.
Analysis of Intelligent Test Paper Generation Method for Online Examination based on UML and Particle Swarm Optimization
by Bo Yang, Huan Xie, Kuan Ye, Huan Qin, Rong Zhu, Anchang Liu
Abstract: In order to overcome the problems of unclear objective function and inaccurate optimal solution in traditional test paper generation methods, an online intelligent test paper generation method based on UML and particle swarm optimization is proposed. A mathematical model of test paper generation based on UML modeling tool is established, and the objective function of test paper generation is obtained. The improved particle swarm optimization algorithm is used to solve the objective function of test paper generation. The optimal solution of the objective function is introduced into the question bank. The test questions in the test bank are combined and imported into the online examination system to realize the intelligent test paper formation of online examination. The experimental results show that this method has strong adaptability and can achieve better performance than the traditional intelligent test paper generation method.
Keywords: UML; Particle swarm optimization; Online examination; Intelligent test paper generation; Constrained project indicators; State object matrix.
Research on Dynamic Performance Regulation Method of Bidirectional Relay Network Based on SDN Architecture
by Jianxin Qiu
Abstract: In order to overcome the shortcomings of the traditional dynamic regulation methods of bidirectional relay network, such as low resource utilization and high energy consumption of nodes, a dynamic regulation method of bidirectional relay network performance based on SDN architecture is proposed. SDN architecture is used to construct the bidirectional relay network model. The subcarrier pairing algorithm is used to pair the subcarriers, and then the model of two-way relay network is optimized. The optimal configuration of node power and subcarrier power is carried out respectively, and the joint energy consumption control model of two-way relay network is constructed by using game theory. In this way, the performance of bidirectional relay network can be dynamically adjusted. Through experiments, compared with the traditional method, the proposed method improves resource utilization, reduces node energy consumption, and has better performance.
Keywords: SDN Architecture; Bidirectional; Relay; Performance; Dynamic; Adjustment;.
Intrusion Detection of Hierarchical Distribution Network System based on Machine Computation
by Xiaohong HE
Abstract: In order to solve the problems of low detection accuracy and long detection time of traditional hierarchical distributed system intrusion detection method, a hierarchical distributed system intrusion detection method based on machine computing is proposed. By judging the Chinese protocol type of IP message and the control bit value of TCP, the network traffic is transformed into different sub-flows, and the characteristic parameters of traffic behavior are extracted from the sub-flows. Based on the invasion behavior characteristics obtained, the vector with six - dimensional characteristics is selected as the important symbol of invasion. By combining rule detection method, support vector machine and machine learning classification, the intrusion detection of hierarchical distribution network system is realized by embedding detection modules at different levels. Experimental results show that this method can effectively reduce intrusion detection time and improve detection accuracy.
Keywords: Machine computation; Hierarchical distribution network; System intrusion detection.
Digital English Teaching Resource Sharing System Based On Logical Database
by Fengtian Xu
Abstract: The traditional English teaching resource sharing system has the problems of low distributed computing performance and low data steady-state transmission rate. This paper proposes a design method of digital English teaching resource sharing system based on logical database. The hardware part of the system constructs the logical database of the system, designs the business layer of the logical database in the system, and reads and writes the resources. Establish a database resource search engine, reduce the calculation process and realize the effective use of digital English teaching resources and course recommendation. Using multi type system development software, the system file configuration and service address are developed to complete the system design. The experimental results show that compared with the traditional teaching resource sharing system, the distributed computing performance of the designed system is improved by 37%, and the data steady-state transmission rate is increased by 29%, which has distinct advantages.
Keywords: Logical database; Digitalization; English teaching; Resource sharing; Virtual server.
Research on High Accuracy Mandarin Emotion Recognition Method Based on Local Optimal Algorithms
by Caihua Chen
Abstract: In order to overcome the problem that the traditional Mandarin emotion recognition method does not optimize the result of emotion classification, which leads to low accuracy and recognition efficiency, a high-precision Mandarin emotion recognition method based on local optimization algorithm is proposed. The emotion database of Putonghua is constructed by the dimension emotion model. On this basis, the emotion signals of Putonghua speech are preprocessed and extracted. Through clustering analysis, emotion features are statistically calculated and classified, and local optimization algorithm is used to optimize the classification results to achieve high-precision recognition of Mandarin emotion. The experimental results show that compared with the traditional method, the accuracy and recognition efficiency of the proposed method have been improved to a certain extent, and the shortest recognition time is only 19 ms.
Keywords: Local Optimization Algorithm; High Precision; Mandarin Emotion Recognition; Emotion Feature Extraction.
Construction Of Load Balancing Scheduling Model For Cloud Computing Task Based On Chaotic Ant Colony Algorithm
by Jie Yu
Abstract: In order to overcome the problem of low scheduling balance and long time in traditional load scheduling model for cloud computing task, a load balancing scheduling model for cloud computing task based on chaotic ant colony algorithm is proposed. Task scheduling strategy is selected through task scheduling framework to achieve parallel task scheduling. Based on chaotic ant colony algorithm, cloud computing resources are deployed, and the load objective function of cloud computing task is constructed. Based on the constructed objective function, a load balancing scheduling model for cloud computing tasks is established, thereby achieving load balancing scheduling for cloud computing tasks. The experimental results show that the model has a high scheduling balance, the scheduling time is always less than 5 ms, and the scheduling efficiency is high. This model is more suitable for the balanced scheduling of cloud computing resources, which is feasible.
Keywords: Chaotic ant colony algorithm; Cloud computing; Task load; Balanced scheduling model;.
A Sample De-noising Method for FCM Clustering Induced by Gauss Kernel
by Yunxing Wang, Liyan Li, Zhicheng Wen
Abstract: Aiming at the problem of instability of clustering result because of the existence of noise samples in general FCM algorithm, an improved FCM algorithm induced by Gauss kernel induced is proposed. Firstly, the influence of samples distribution on clustering is analyzed, and the Gauss kernel function is used to nonlinear map of the samples, then the improvement of the objective function of the FCM algorithm and clustering of samples are achieved, thus the purpose of suppressing noise is achieved. The experimental comparison shows that compared with other FCM algorithms, the proposed algorithm's successful classification rate is about 10% higher and the partition coefficient is about 10% lower than other FCM algorithms, indicating that the algorithm has higher clustering effectiveness.
Keywords: Fuzzy C-Means; Gaussian Kernel Function; Noise Sample; Samples.
Research On Personalized Recommendation Method Of Popular Tourist Attractions Routes Based On Machine Learning
by Hui Wang
Abstract: In view of the problems existing in the current tourist route recommendation methods, this paper proposes a personalized tourist attraction route recommendation method based on machine learning.The tourism heterogeneous information data is collected by using Html parser, and the captured data are used as machine learning training samples.The characteristics and user interest characteristics under the rating were extracted, and the target user interest characteristics were taken as the starting point, combining the features of scenic spots and tourist routes.Recommend scenic spots to users to realize personalized recommendation of popular scenic spots.The experimental results show that the method proposed in this paper has a recommendation accuracy of more than 95%, a recommendation time of between 115ms-130ms and a user satisfaction rate of over 89%.With high recommendation accuracy and user satisfaction, and less time to calculate the recommendation, it is a reliable method to recommend tourist routes.
Keywords: Machine Learning; Tourist Attractions Routes; Recommendation.
Study On Geographic Disaster Information Monitoring In Sensitive Areas Based On Spatial Analysis
by Caijian Mo, Fengqiang Wu, Li Chen
Abstract: In order to overcome the problems of low monitoring accuracy and efficiency in the current risk information monitoring methods, this paper proposes a spatial analysis based monitoring method for geographical risk information in sensitive areas. Firstly, this method constructs a real-time monitoring platform with monitoring terminal, information data collection, real-time monitoring and analysis, and designs and analyzes the functional modules. Using spatial analysis technology and target location, the real-time collection of hazard information data and target centroid location are carried out. C / S model is used to manage the geo hazard information data in sensitive areas. The concept of E-R chart is used to build a database, which provides data support for the monitoring process. The experimental results show that the accuracy and efficiency of the proposed method are high, and the highest monitoring accuracy is 98.7%.
Keywords: Spatial analysis; Sensitive area; Geographic disaster information; Monitoring.
Research On Self-Incremental Expansion Method Of Knowledge Base Based On Deep Learning
by QIANG YU, LANLAN LIU
Abstract: In order to overcome the problems of low recall, low precision and long time consuming in current knowledge-base expansion algorithms, a new knowledge-base self-increment expansion algorithm based on deep learning is proposed. The data in the knowledge base is preprocessed by concept stratification, and the running example diagram of the knowledge base is designed according to concept stratification theory. Deep learning tool is used to expand the initial query words of knowledge base, and Word2vec is used to train the document set to build the knowledge base by calculating the cosine similarity. Based on the knowledge base, the noise detection model is constructed by convolution neural network. Through the deep learning, the extended words are filtered to realize the self-expanding of knowledge base. Experimental results show that the proposed algorithm has high recall rate, precision rate, and the algorithm takes less time, which verifies the effectiveness of the proposed algorithm.
Keywords: Deep learning; Knowledge base; Self-incremental expansion; Cosine similarity.
High-Load Reliable Transmission Method For Wireless Network Hybrid Sink Node Data
by Fan-li Meng
Abstract: Data transmission of a hybrid sink node in a wireless network is susceptible to multipath interference, which results in poor output reliability. Therefore, a high-route reliable transmission method of data onto a hybrid sink node in a wireless network is proposed. A channel model is constructed, and a multi-channel cascade matched filtering method is used to suppress data transmission interference. The spatial spectrum feature is extracted and the spectrum beam method is used to control the data transmission load gain. Adopt spatial link equalization configuration to control channel adaptive equalization. The channel spread spectrum model is established to complete the high-route reliable transmission optimization of the data of the wireless convergence node. Simulation results show that this method can provide a higher load for node data, high reliability, and low output error rate, thereby improving the reliability of data transmission.
Keywords: Wireless network; hybrid sink node; data; high load; reliable transmission.
Agimm Tracking Filter Algorithm Based On Maneuvering Feature Correction
by HUANG Qinlong, Xu-fang ZHU, Jia-wei XIA
Abstract: Considering the fast speed and strong maneuvering of small offshore targets, traditional filtering algorithms cannot effectively track the filtering due to the unstable grid center of weak maneuvering target and slow tracking convergence of strong maneuvering target. To overcome this problem, an adaptive grid interaction multiple model (AGIMM) tracking filter algorithm based on maneuvering feature correction was developed. The modified algorithm detects the target maneuvering according to the residual information of the filter, while the model structure can be adjusted under different maneuvering conditions. Monte Carlo simulation shows that the AGIMM algorithm based on maneuvering feature correction can improve the calculation accuracy by 35% and reduce the calculation time by 18%.
Keywords: tracking filter; maneuvering feature; turn model; adaptive grid interaction multiple model; simulation.
Behavior Pattern Recognition of Video Image based on Computer Vision Technology
by Fan Jiang, Yi Yu
Abstract: In order to overcome the problems of low target tracking accuracy, low recognition accuracy and low recognition efficiency existing in traditional behavior pattern recognition methods for video image, this paper proposes a behavior pattern recognition analysis method for video image based on computer vision technology. This method obtains video image information through computer vision technology, and processes the collected video image. Combining with automatic selection tracking region algorithm and particle filter algorithm, the tracking state equation is constructed on the basis of the target color distribution model to track the target in the video image. The feature of video image is extracted, to construct multi-instance learning model. Feature instances are selected, and positive instances are constructed and input into multi-instance SVM classifier to complete the recognition and analysis of video image behavior. The experimental results show that the proposed method takes less than 2 seconds to identify the target, and has high target tracking accuracy, recognition accuracy and recognition efficiency.
Keywords: Computer vision technology; Video image; Human behavior pattern; Recognition method;.
Fault Diagnosis Model Based on Adaptive Generalized Morphological Filtering and LLTSA-ELM
by Jie Xiao, Jingtao Li
Abstract: It is difficult for single feature to contain all the information needed to describe the running state of the equipment, while multi-features can contain more information about running state, but the redundancy between high-dimension features can easily reduce the accuracy of the classifier. Aimed at that,a fault diagnosis method for rolling bearings combining adaptive generalized morphological filter, Linear Local Tangent Space Alignment and Extreme Learning Machine (LLTSA-ELM) is proposed. Firstly, the rolling bearing vibration signals are filtered by adaptive generalized morphological filter. Secondly, the multi-domain features are extracted from filtered signal to construct high-dimensional features set of bearing.Thirdly, the dimension of high-dimensional features is reduced by maximum likelihood estimation (MLE) and LLTSA.Finally, the bearing condition monitoring model based on ELM is constructed by the reduced dimension features and then use it to analyze and diagnose the running state of bearing.
Keywords: Adaptive Generalized Morphological filter; LLTSA; Dimension Reduction; ELM; Fault Diagnosis.
The Color Block Registration Method Of Fuzzy Image Under Multi-Layer P-Spline Geometric Transformation
by Xiaofeng GUO, Jie XU, Wenquan LU
Abstract: In order to overcome the problem that the traditional similarity measure is easy to be affected by the gray-scale migration, this paper proposes a color block registration method of fuzzy image under the multi-layer P-spline geometric transformation. This method mainly sets sparse coding as similarity measure, and divides two images that are not registered into image blocks. K-SVD(K-singular value decomposition) algorithm is used to train image blocks, acquire analysis dictionary and find sparse coefficients. The multi-layer P-spline geometric transformation is used to simulate the fuzzy image, and the gradient descent method is used to optimize the objective function to complete the color block registration of the fuzzy image under the multi-layer P-spline geometric transformation. The experimental results show that the registration time is the lowest, the root mean square error is kept below 0.04, and the registration accuracy is up to 100%.
Keywords: Multi-layer P-spline; Geometric transformation; color block of fuzzy image; Registration method.
Storage Method For Customer Preference Information In E-Commerce Platform Based On Similarity Matching Algorithm
by Chengliang Lin, Lihua Jiang
Abstract: In order to overcome the problems of small IOPs parameters, long response time and inaccuracy of customer access key information similarity in existing e-commerce platform information storage, this paper proposes a new method of customer preference information storage in e-commerce platform. In this method, crawler algorithm is used to collect customer access information, and key customer access information is extracted by setting indicators. Libsvm classifier is used to classify customer access key information, similarity matching algorithm is used to calculate the similarity of customer access key information, and collaborative filtering recommendation algorithm is used to recommend relevant information for customers, so as to realize the storage of customer preference information in e-commerce platform under the background of artificial intelligence. Experiments show that the proposed storage method greatly improves the IOPs parameters, and the minimum storage response time is 21ms, which fully shows that the proposed method has better storage performance.
Keywords: Artificial intelligence; E-commerce platform; Customer preferences; Storage; Information;.
Visual Information Enhancement Method Of Multimedia Human-Computer Interaction Interface Based On Virtual Reality (Vr) Technology
by Li Li
Abstract: The traditional enhancement method of multimedia human-computer interface has the problems of poor user experience and long running time.A visual information enhancement method of multimedia human-computer interface based on virtual reality technology is proposed.The relationship among virtual environment, users and real environment is constructed by using the modeling technology and three-dimensional technology in virtual reality technology to generate multimedia objects; Under the virtual reality technology, the image transformation technology is used to transform the local part of the analog image; The gray level histogram is constructed according to the human visual characteristics. The idea of equalization is to adjust the dynamic range of gray level and enhance the color contrast of image. The simulation results show that the proposed method can solve the problems of blurring, whitening, blocking and ghosting in the interface, and the enhanced visual effect is more in line with the characteristics of human eyes.
Keywords: Virtual reality technology; Multimedia; Human computer interaction; Interface; Visual information; Enhancement.
Research On Fast De Duplication Of Text Backup Information In Library Database Based On Big Data
by Ling Ji
Abstract: In order to overcome the problems of poor effect and low efficiency of traditional information de duplication methods, this paper proposes a fast de duplication method of text backup information in library database based on big data. Firstly, this paper carries out parallel mining of text information features in library database, uses the features with strong classification ability to determine the parameter value of the repeated feature function, obtains the entries with the parameter value higher than the threshold value, determines the number of text repeated backup information and the group weight, sets the difference between the two as the remaining digits, and stops de duplication when the remaining digits are lower than the threshold value. The experimental results show that the average accuracy of this method is 96.95%, the weight removal efficiency is always above 98%, and the weight removal effect is good.
Keywords: Big data; Library; Database; Text Backup Information; De Duplication; Simhash Algorithm.
Research on Traffic Flow Detection Algorithm Based on Road Video
by Jianfeng Han, Yan Zhang, Hongyang Zhang
Abstract: When using the road monitoring video to detect the traffic flow, the vehicle may be treated as one due to the close distance in the video, or the vehicle may be miss detected or missed due to noise interference, resulting in inaccurate traffic data. In order to solve this problem, this paper proposes to generate a foreground for road vehicles by LK local differential optical flow method, calculates the Euclidean distance between the vehicle and the background by using the base watershed algorithm, screens the vehicle position, and segment the adhesion vehicles. At the same time, according to the best position of the vehicle in the video, the virtual detection line is set. The Kalman filter is used to optimize the monitoring data, the traffic flow data statistics error problem is solved, and the accurate traffic flow data is obtained. The experimental result shows that the method mentioned in the paper can obtain accurate traffic data.
Keywords: Vehicle Detection; ,Vehicle Segmentation; Watershed Algorithm; Optical Flow Method.
Framework for concealing medical data in images using modified Hill Cipher, Multi-bit EF and ECDSA
by R. Sreejith, S. Senthil
Abstract: Digitization spread to the core area of the medical field and one of the results is telemedicine. Telemedicine provides the transfer of medical information of patients such as medical results, reports and images which closely deals privacy. The real essence of telemedicine application relays on the secure storage and transfer of medical data over unsecured platform or network where the patient is remotely located from the service provider. The proposed work focused on combining security aspects into a single framework providing the CIA triad of security i.e., Confidentiality, Integrity and Authentication To achieve the above criteria, we use Modified Hill Cipher and multi-bit Encoding Function (EF) for Confidentiality, ROI based Hashing for Integrity and Elliptic Curve Digital Signature Algorithm (ECDSA) for Authentication. The experimental results prove the framework have a better embedding capacity with a comparatively higher PSNR value.
Keywords: Telemedicine; Security; Data Hiding; Steganography; Hill Cipher; Differential Expansion (DE); Multi-bit Embedding; Elliptic Curve; ECDSA.
Optimization of Massive MIMO Data Classification Algorithm Based on Fuzzy C-Means and Differential Evolution Method
by Jia Chen
Abstract: In the environment of massive data multi input multi output (MIMO), it leads to poor data recognizability, clustering ability and overall data processing ability. Therefore, a massive MIMO data classification algorithm based on fuzzy c-means and differential evolution method is proposed. The information flow model of time series of massive data in data flow is established, and the time series features of massive data are extracted by using autocorrelation feature analysis method. Through fuzzy C-means, the features of massive data are cross fused and clustered. The global convergence and stability of data classification are adjusted and controlled by differential evaluation method, and then the delay of data classification is modified to optimize the data classification algorithm. The simulation results show that the data classification recall rate of this method is higher than 95%, and it has the advantages of strong clustering ability and low misclassification rate.
Keywords: MIMO; Massive data classification; Clustering; Information fusion.
3D Scene Reconstruction Method Based on Image Optical Flow Feedback
by Yulin Su
Abstract: When the traditional method is used for 3D reconstruction of a 3D scene, the noise in the scene image cannot be effectively removed, the reconstruction efficiency is low, and the reconstruction accuracy is low. Therefore, a 3D reconstruction method based on optical-flow feedback is proposed to build the camera mathematical model. The weighted least square method is used to denoise the scene image based on the camera mathematical model. Based on the relationship between the optical-flow and the scene flow, the optical-flow is used to calculate the scene flow vector through the grid adjustment algorithm, and the rough grid is adjusted according to the calculation results to realize the 3D reconstruction of the three-dimensional scene. The experimental results show that the proposed 3D scene reconstruction method has good image denoising effect, high reconstruction efficiency and high reconstruction accuracy.
Keywords: Image denoising; Optical-flow feedback; 3D scene; 3D reconstruction.
Enhanced View-independent Representation Method for Skeleton-Based Human Action Recognition
by Yingguo Jiang, Lu Lu, Jun Xu
Abstract: Human action recognition is an important branch of computer vision science. It is a challenging task based on skeletal data because joints have complex spatiotemporal information. In this work, we propose a method for action recognition, which consists of three parts: view-independent representation, combination with cumulative Euclidean distance, and combined model. First, the action sequence becomes view-independent representations independent of the view. Second, these representations are further combined with cumulative Euclidean distances, so the joints more closely associated with the action are emphasized. Then, a combined model is adopted to extract these representation features and classify actions. It consists of two parts, a regular three-layer BLSTM network, and a temporal attention module. Experimental results on two multi-view benchmark datasets Northwestern-UCLA and NTU RGB + D demonstrate the effectiveness of our complete method. Despite its simple architecture and the use of only one type of action feature, it can still significantly improve recognition performance and has strong robustness.
Keywords: Action recognition; enhanced spatiotemporal representation; attention model; Euler angle; cumulative Euclidean distance.
Traffic incident detection based on the width characteristic of the moving object marker
by Shangbing Gao, Wenting Li, Dajing Hao, Hui Wang, Jian Zhou
Abstract: Aiming at the current situation that the existing traffic event detection algorithm is complex and cumbersome, a traffic incident detection method based on the width characteristics of the moving object was proposed. Firstly, the foreground object is extracted by ViBe (visual background extractor) and the foreground target is subjected to hole removal and smoothing processing. Then, the moving target is marked with a label, the width change characteristic of the moving target mark frame is analysed and the traffic event detection model is introduced, thereby achieving detection of traffic events. The experimental results show that the method can effectively detect the rear-end event, the crossing event and the collision-fixing event in the traffic incident.
Keywords: ViBe; visual background extractor; classification of vehicle types; traffic incident detection; image pre-processing.
Design of orthogonal phase encoding for basis function based on genetic algorithm
by Li Kui, Zhang Ting, Guo Hao, Wang Hua
Abstract: A new basis function generation algorithm based on genetic algorithm is proposed to overcome the drawbacks of the traditional basis function phase encoding algorithm in transform domain communication system (TDCS). Firstly, by analysing the requirements and the characteristics of basis function in TDCS, the evaluation criterion of basis function is presented, and the corresponding genetic algorithm is designed according to the criterion. Then, the algorithm is used to generate the orthogonal basis function set. The proposed algorithm not only improves the correlation performance and randomness of basis function, but also improves the system multiple access performance and security. Furthermore, the length and quantity of the basis functions can be flexibly selected and the system's applicability is improved. By analysing the simulation results, the feasibility and validity of the algorithm are verified.
Keywords: basis function; phase encoding; genetic algorithm; multiple access.
The adaptive key frames extraction method based on representativeness and independence features
by Wenbin Xie, Zhen Zhang, Yuefei Wang, Yuanyuan Zhang, Liucun Zhu
Abstract: For the existing traditional method to extract key frames from the video sequence with gradual change of content, there are problems of having too much redundancy or too much loss in the extraction results and other issues, a new key frames extracting method for adaptively extracting the video key frames has proposed in the paper. The experimental results have shown that the proposed method can effectively extract video key frames, especially for the video with gradual change of content, which can express more succinctly and comprehensively. The experiments on a large number of videos containing various scenes have shown that the key frames extracted by the proposed algorithm can more fully express the main content of video. When dealing with content gradient video, the effect is better. The content adaptively determines the number of key frames based on video.
Keywords: representativeness feature; independence feature; adaptive key frame; video sequence; SURF feature.
A method of character verification code recognition in network based on artificial intelligence technology
by Yongzhuo Wu
Abstract: In order to solve the problem of low recognition rate of network character captcha, a method of network character captcha recognition based on artificial intelligence is proposed. Firstly, the image preprocessing of character verification code is carried out. The weighted average method and the iterative optimal threshold method were used to process and binarise the character verification code image, and the denoised character image was obtained. For the character verification code image after binarisation, Laplace operator is used to segment the image, and the characters in the image are divided into blocks. Finally, according to the feature vector, the artificial neural network model is used for sample training and adaptive learning. Simulation results show that this method has higher recognition rate, better adaptability and robustness than the existing methods. This method can effectively recognise character verification codes.
Keywords: artificial intelligence; network; character type; verification code; recognition.
Ontology expansion based on UWN reusability
by Hankiz Yilahun, Sayyare Imam, Askar Hamdulla
Abstract: In the present era of big data the demand for developing efficient knowledge representation techniques for different applications is expanding steadily. Knowledge representation is largely based on ontology in the Semantic Web. However, one of the major difficulties to construct a new ontology require users the high cost and long time. Collecting complete knowledge about the domain of opening is time consuming and cannot guarantee expected results too. Therefore, reuse of existing resources can offer a cost and time benefit than building a new one from scratch. Hence, an approach is proposed to reuse the existing ontologies available on the web or online libraries then enrich it. This paper proposed Uyghur domain ontology constructing method based on UWN reusability, which can extract domain ontology factors from UWN and get the initial ontology in which the concepts and relations are already relative standard, then expand it automatically via Word2Vec models, at last construct the final higher quality version after revising and improving. The method is simple, but effective on ontology enrichment. After evaluating the accuracy, the experimental results show that our method is feasible and effective.
Keywords: Uyghur WordNet; domain ontology; reusability; Word2Vec; expand ontology.
Radar signal sorting method based on support vector clustering and grey correlation degree index
by Shiqiang Wang, Caiyun Gao, Yanlong Zhang, Qin Zhang, Huiyong Zeng, Juan Bai
Abstract: Radar signal sorting method based on traditional clustering algorithm takes on a high time complexity and has poor accuracy. Aimed at the problem, a new sorting method is researched based on improved cone cluster labelling (ICCL) method for SVC algorithm and grey correlation degree (GCD) index. The ICCL method relies on approximate coverings both in feature space and data space. By using ICCL the calculation of adjacency matrix is avoided. This method is interpreted in theory and is modified for lower complexity and high accuracy by handling the outliers. And a new cluster validity index, grey correlation degree (GCD) index, is proposed which assesses the compactness and separation of clusters using average grey relational degree. In this paper, the SVC sorting model is constructed to obtain the cluster division firstly with ICCL method. Secondly, grey correlation degree is designed to measure the similarity between clusters, and to verify the clustering validity. Thirdly, the clustering parameters are adjusted adaptively to achieve the best clusters division. Finally, clustering validity sorting experiments of interleaved pulse streams is implemented. The results show that this method can obtain better clustering results.
Keywords: radar signal sorting; support vector clustering; grey relational analysis; grey correlation degree; GCD; verification; cluster validity index.
Study on optimal urban land classification method based on remote sensing images
by Lede Niu, Mei Pan, Yan Zhou, Liran Xiong
Abstract: Traditional urban land classification methods are relatively complicated in information collection, resulting in large time consumption and low classification accuracy. Therefore, an optimal urban land classification method based on remote sensing images is proposed in this paper. In this paper, four influencing factors of urban land classification are clarified based on the actual situation, on which the information about urban land in urban areas is extracted through the NDBI index method, and the ETM remote sensing image information of urban land is obtained. The genetic algorithm based on K-means mutation operator is adopted to classify the ETM remote sensing image information of urban land and finally the optimal classification results of urban land are obtained. In order to test the superiority of the method, a simulation comparison experiment was carried out. The simulation results show that the proposed method can provide overall accuracy of 93.75% and classification accuracy of 96.18% in urban land information collection, has the overall Kappa coefficient of 0.8967 and cost 10 s averagely, indicating that this method is superior in time consumption during urban information collection, collection accuracy and land classification accuracy, so it has high application value.
Keywords: remote sensing image; urban land; modelling; K-means method; NDBI index.