International Journal of Information and Communication Technology (63 papers in press)
Stargan Based Camera Style Transfer for Person Retrieval
by Yuanyuan Wang, Zhijian Wang, Mingxin Jiang
Abstract: Person retrieval is also known as person re-identification (ReID) aiming to match person among cross cameras. Although the results of the person ReID have performed well in small datasets, the issues of the large number of identities in real scenarios or with more cameras have not been fully investigated. Being an image retrieval task under cross multi-cameras of intelligent video security, person ReID is influenced by the image style change caused by different camera illumination and view angles. The number of cameras in the latest datasets is increasing and more camera transfer models need to be trained. Traditional methods of generative adversarial network (GAN) can only handle transfer of two domains. To facilitate the research towards solving these problems, we use star generative adversarial networks (StarGAN) to transfer the image from one camera to another camera in the latest large benchmark datasets. We train multiple transfer models simultaneously, minimizing the bias among different cameras. Label smooth regularization (LSR) algorithm is utilized to mitigate the effects of noise in the model. We learn part-based descriptors from pedestrian samples to generate robust feature representation. Our work is competitive compared to the state-of-the-art.
Keywords: StarGAN; Person retrieval; LSR.
Fuzzy judgment of edge features under dynamic constraints in pedestrian tracking
by Yaomin Hu
Abstract: Pedestrian tracking and recognition is influenced by the pedestrian environment and edge factors of dynamic features, which is easy to tracking errors, so in order to improve the pedestrian tracking and recognition ability, it is required to conduct fuzzy judgment to edge features. Therefore, a fuzzy judgment method of edge features under dynamic constraints in pedestrian tracking based on local motion planning and edge contour segmentation was proposed. In this method, a geometry mesh area model for pedestrian tracking and recognition was constructed, and the fuzzy dynamic feature segmentation method was adopted to reconstruct dynamic edge feature points in pedestrian tracking to extract the greyscale pixel set under dynamic constraints in pedestrian tracking; edge feature quantity was fused based on the distribution intensity of greyscale pixels to realise pedestrian tracking image fusion and information enhancement processing; the three-dimensional dynamic constraint method was adopted for local motion planning of pedestrian tracking, and then fuzzy judgment was carried out to edge features in pedestrian tracking based on the edge contour segmentation results. The simulation results show that in pedestrian tracking and recognition, this method has strong fuzzy judgment ability of edge features and can provide results with error below 10 mm and relatively stable fluctuation, so this method can provide relatively high recognition accuracy and good robustness.
Keywords: pedestrian tracking; dynamic constraint; edge feature; recognition; image fusion.
Design of Cloud Computing-based Foreign Language Teaching Management System Based on Parallel Computing
by Kanmanli Maimaiti
Abstract: In view of the long response time of the traditional foreign language teaching management system and the inability to guide students to improve their learning interest, a foreign language teaching management system based on parallel computing is proposed. In this method, the cloud architecture of the foreign language teaching system is given under the cloud computing environment, on which the parallel computing method is adopted to design the foreign language teaching management system hardware, which is scalable and flexible. The parallel algorithm is designed and the communication between the modules of the system is implemented with C# language. The experimental results show that response time for each of the four scores of Zhang is 1s in reference , the response time for each of them is only 0.6s in this paper. The system can shorten the response time for query and improve the development speed of web-based foreign language teaching, effectively promotes the development of web-based foreign language teaching and students interests in foreign language learning.
Keywords: parallel computing; cloud environment; foreign language teaching; management system.
Study on the Subway Transfer Recognition during Rush Hour Based on Big Data
by Shushen YAO, Xiaoxiong WENG
Abstract: With the development of the subway network, multipath coexistence becomes very common in big cities. Its followed that the tickets clearing problem is highly concerned by co-investors, which relies on accurate transfer paths identification. Different from the commonly used Logit models for subway transfer recognition problem, we adopted the Adaptive Gauss Cloud Transformation (A-GCT) model, which transformed the distribution of passengers trip time into multiple concepts of different granularity, and evaluated the maturity of the concept by the of parameter named Confusion Degree (CD). The case in this paper shows that, the A-GCT model has higher accuracy in dealing with uncertain problem such as subway transfer recognition.
Keywords: Gaussian Cloud Transformation(GTC); subway transfer recognition; big data.
Intelligent Monitoring System for Thermal Energy Consumption of Buildings under the IoT Technology
by Lu Wang, Difei Jiang
Abstract: In view of the poor accuracy of single value of heat energy consumption and the weak real-time monitoring of energy consumption in buildings, an intelligent monitoring system for thermal energy consumption of buildings under the Internet of Things (IoT) technology is designed. The overall structure of the intelligent monitoring system for thermal energy consumption of buildings is constructed. The DSP integrated signal processor is used for data acquisition and real-time information processing of thermal energy of buildings. A wireless intelligent gateway for building thermal energy consumption monitoring is designed by using Internet of things technology, a wireless sensor network model is constructed. The VME bus is used as information transmission channel to realize intelligent monitoring for thermal energy consumption. The test results show that the real-time monitoring accuracy of the system is better than that of the traditional method, and it has a good application prospect in all aspects.
Keywords: Internet of Things (IoT) technology; thermal energy of buildings; energy consumption monitoring; system design.
An Algorithm of Decomposition and Combinatorial Optimization Based on 3-Otsu
by Liejun Wang, Junhui Wu, Ji-Wei Qin
Abstract: Recently, the 3-Otsu(three-dimensional maximum between-class variance algorithm) have drawn great attention in image segmentation. However the time consumption and calculation amount of 3-Otsu is large, so this paper provide a compositing 3-Otsu decomposed algorithm. Firstly, the histogram of 3-Otsu is resolved into three two-dimensional histogram by projecting, and the projection plane is three coordinate plane of its own space. Secondly, the two-dimensional histogram formed after segmented by using 2-Otsu, then three segmentation results are obtained. Finally, three segmentation results are combined in linear manner, and combination result is the output of segmentation result, under the ideal noise-free, gaussian noise, salt noise, pepper noise and salt and pepper mixture noise, respectively. The results show that the proposed algorithm is nearly 30 times smaller in time consumption than 3-Otsu, although slightly more than 2-otsu,its value is still small. Meanwhile the anti-noise performance, especially for mixed noise, is better than two other algorithms.
Keywords: three-dimensional Otsu; decomposition and reduction; linear combination.
Video Encryption Based on Chaotic Array System: Working with Image Directly
by Hongyan Zang, Jing Yang, Guodong Li
Abstract: A new three-dimensional discrete chaotic system is proposed according to the Marotto theorem in this paper. On this basis, a coupled array chaotic system is given as the drive system. Moreover, with the help of the bidirectional generalized synchronization theorem, the response system is constructed and proved to be chaotic. According to the chaotic matrix generated by the above systems, a special video encryption scheme was proposed. While the experiment shows that the encryption scheme occupies a large key space and enjoys an approximately uniform distribution of the ciphertext entropy already, the main contribution of the new constructed system is the higher speed than the vector system since once the encryption starts, the chaotic matrix generated by the array systems and the image with the same size to the matrix can be operated directly.
Keywords: generalized synchronization; video encryption; coupled array chaotic system; computing simulation.
User Information Intrusion Prediction Method Based on Empirical Mode Decomposition and Spectrum Feature Detection
by Zheng Ma, Yan Ma, Xiaohong Huang, Manjun Zhang, Bo Su
Abstract: In distributed intelligent computing environment, user information is vulnerable to plaintext intrusion, resulting in information leakage. In order to ensure the security of user information, a user information intrusion prediction method based on empirical mode decomposition and spectrum feature detection in distributed intelligent computing is proposed in this paper. Firstly, a model of user information and intrusion signal in distributed intelligent computing is established; then an intrusion detection model is established with signal processing method; finally, time-frequency analysis and feature decomposition are conducted for intrusion information in distributed intelligent computing with empirical mode decomposition method, and accurate prediction of user intrusion information is achieved based on joint probability density distribution of spectrum feature, so as to improve the algorithm design. The simulation results show that when the signal to noise ratio is 12.4dB, the detection probability of the method proposed in this paper is 1, and then the false alarm probability can be 0, which indicates that this method can provide good intrusion detection probability and low false alarm probability even at relatively low signal to noise ratio. Therefore, the method proposed in this paper has good intrusion interception and prediction ability.
Keywords: distributed intelligent computing; user information; intrusion prediction; feature extraction; empirical mode decomposition.
Deep Forest-based Hypertension and OSAHS Patient Screening Model
by Pingping Wang, Lei Ma, Yun-Hui Lv, Yang Xiang, Dang-guo Shao, Xin Xiong
Abstract: Incidence of OSAHS is high in hypertension patients. To make the OSAHS diagnosis more precise and simple, an OSAHS screening model is built hereof by deep forest algorithm with the collected information of hypertension and OSHAS patients from the Sleep and Respiration Center of a hospital. Firstly, variation in index and dimensions and inter-class imbalance in sample dataset is resolved by normalization and SMOTE method; and OSAHS screening model is built by deep forest method (gcForest) after redundant information in features is removed with modified chi-square test single feature selection. The results show that with modified chi-square test single feature selection method, the redundant features can be effectively removed and performance of classifier can be improved; Deep forest-based OSAHS screening model is superior to other classification models in classification performance and can effectively improve the precision of OSAHS patient screening, reduce the incidence of OSAHS missed diagnosis.
Keywords: Hypertension; OSAHS; Unbalanced data; Feature selection; Deep forest; Screening model.
Design of Automatic Detection System for Vehicle networking Communication Abnormal Data based on CAN Bus
by Qun Le, Kun Jiang, Feng Zhang
Abstract: Aiming at the problems of low detection rate and accuracy rate, high false detection rate and missed detection rate caused by the proportion of abnormal data in the current design system when detecting abnormal data in the process of vehicle networking communication, an automatic detection system of abnormal data in vehicle networking communication based on CAN bus is proposed and designed. The system mainly includes two parts: vehicle networking communication subsystem and vehicle networking communication data anomaly detection subsystem. The vehicle networking communication subsystem includes vehicle networking communication data acquisition unit and C/S structure. The real-time interaction process between client and server is given. The experimental results and discussions prove that the design system can automatically detect abnormal data in vehicle networking communication, and it is less affected by the proportion of abnormal data, which has the advantages of higher detection rate and accuracy, lower false detection rate and missed detection rate. At the same time, the preliminary conclusion is drawn that the sampling ratio will hardly affect the detection results.
Keywords: CAN Bus; Vehicle networking; Communication; Abnormal Data; Automatic Detection; System Design;rnrn.
Slices Reconstructing Algorithm for Single Image Dedusting
by Haiyan Zhang, Shangbing Gao, Mingxin Jiang
Abstract: For solving the image degradation in the non-uniform dusting environment with multiple scattering lights, the slices reconstructing algorithm for single image was proposed for dust elimination in the paper. Firstly, the slices along the depth orientation were produced based on McCartney model in dust environment. Secondly, the union algorithm of dust detection was used to detect dust patches in the slices where non-dust areas were reserved while the dust zones were marked as the candidate detecting areas of the next slice image. Then, the image was reconstructed by combining these non-dust areas of each slice and the dust zone of the last slice. Finally, the faster guided filter was applied to the reconstructed area. The experimental results had proved that the reconstruction algorithm could get rid of dust in object image not only effectively but also fast. The papers work had laid the foundation for object detection and recognition work had based on computer vision in dusts environment.
Keywords: Image Restoration; Dust Detection; Image Reconstruction; Multiple Scattering; Single Image.
A Magnetic Resonance Imaging Denoising Technique Using Non-Local Means and Unsupervised Learning
by Tao Wu, Lei Xie
Abstract: We propose a new non-local mean (NLM) algorithm using unsupervised learning and k-means clustering for denoising magnetic resonance (MR) images. Our technique improves image pro-cessing speeds with enhanced denoising performance on multiple types of images. The calculation of similarity weights at the cluster level improves computational efficiency. We conducted experiments with brain MR images of various sizes, including three T1- and T2-weighted images. Three quality metrics show that our algorithm achieves moderate improvements in denoising accuracy with significant reductions in execution time. The proposed method processed the sample data in one-fifth of the time of the original NLM method. Compared to several state-of-the-art methods, our method offers improved peak signal-to-noise ratios (PSNRs) for samples with large amounts of noise.
Keywords: Magnetic resonance imaging; Image denoising; Non-local mean; k-means; Unsupervised Learning.
Research on Equilibrium scheduling of Airborne Network Resource based on Load Gini Coefficient
by Jin Guo, Shengbing Zhang
Abstract: Aiming at the problems of long running time and serious data loss in traditional equilibrium scheduling method for airborne network resource, a new equilibrium scheduling method for airborne network resource based on load Gini coefficient is proposed. According to the related principle of Gini coefficient of income distribution in the field of economics, the Gini coefficient of network load distribution is monitored. Through genetic algorithm, an optimal allocation scheme which can satisfy the load change constraints and effectively avoid dynamic migration is found. The experimental results show that the data loss rate of this method is between 0 and 2, and the average running time is 6 milliseconds and 12.5 milliseconds shorter than that of the other two methods, which can effectively reduce the data loss rate and running time of the system, and improve the overall efficiency of the system.
Keywords: load Gini coefficient; airborne network resources; equilibrium scheduling.
Fault Diagnosis of Fan Gearboxes Based on EEMD Energy Entropy and SOM Neural Networks
by Biao Ma, Gang Li, Guping ZHENG
Abstract: Aiming at the difficulty of feature extraction for gear fault diagnosis and the problem of traditional classification methods cannot diagnose the faults in wind turbine Gearboxes adaptively, a new fault diagnosis method based on Ensemble Empirical Mode Decomposition (EEMD) energy entropy and SOM Neural Networks (SOM-NN) is proposed. Firstly, the EEMD method is used to decompose the original vibration signal of the gear under all kinds of condition into several Intrinsic Mode Functions (IMF) and calculate the energy value of each IMF and the energy entropy of the signal. Then the IMF energy proportion and the signal energy entropy are selected to form a set of features which can reflect the fault vibration signal. The values of these features are inputted to SOM neural network for classification. The numerical simulation results show that the accuracy of the method is 100% in the fault diagnosis of wind turbine gearbox.
Keywords: ensemble empirical mode decomposition; energy entropy; self-organizing feature mapping (SOM); wind turbine; Gearboxes; fault diagnosis.
Design of Art Interactive Teaching System Based on Multiple Intelligence Theory
by Junliang Dong, Zhuomin Huang
Abstract: It is of great practical significance to study the interactive teaching system of art. The traditional art teaching system is used to analyze the information interaction model, which leads to the poor interaction performance of the system. Therefore, an art interactive teaching system based on multiple intelligence theory was proposed in this paper. The art interactive teaching system mainly consists of interactive teaching service information transmitter, student mark database and web page layering server. Based on hardware design, the entire art interactive teaching system was designed through the information interaction algorithm based on multiple intelligence theory. In order to verify the effectiveness of the designed system, a simulation experiment is performed, the results show that the system has high output signal-to-noise ratio (SNR) and the interaction efficiency is over 80%, which demonstrated the good information interaction performance and excellent application prospects of the system. The system has good application prospects.
Keywords: Multiple intelligence theory; art teaching; information interaction; teaching system.
Clustering based Word Segmentation from Off-line Handwritten Uyghur Text-line Images
by Askar Hamdulla, Aysadet Abliz, Abdusalam Dawut, Kamil Moydin, Palidan Tuerxun
Abstract: For the word segmentation of handwritten Uyghur text images, this paper proposes a segmentation method based on clustering algorithm. In this paper, firstly, the preprocessed text line images are projected to the vertical direction, which can get the initial probable segmentation points and record the blank spaces and text length between connected domains. By using clustering algorithm, the blank spaces are classified into two categories: within word gap and between words gap. Then the first mergence is completed according to the clustering results. For the existed phenomenon of over segmentation, one merging method based on threshold is proposed through the combination of text region length and blank space length so that the final segmentation points are obtained. And the experimental results show that this method can effectively solve the word segmentation problem in the handwritten text images.
Keywords: Uyghur handwritten text; Word segmentation; Clustering; Coloring process.
The Image Classification Algorithm Research using Class Information Loss and Joint Structural Similarity
by Shian Wang
Abstract: Aiming at the supervised training of Convolutional Neural Networks, the weighted joint structural similarity and class information supervised training method has been proposed. Firstly, for a small image, the Convolutional Neural Networks that can extract high-level information of images is designed. Secondly, a weighted joint structural similarity and class information loss function training convolutional neural network are established. Finally, handwritten numbers and Cifar10 images are obtained by Mnist dataset. The image classification experiments can validate the effectiveness of the proposed network. The experimental results can show that the image classification error rate of improved network on Mnist handwritten digits and Cifar10 dataset is 0.23% and 10% respectively. Under the premise that there is no dataset increase on the Mnist dataset, the performance of proposed network exceeds the performance. The performance of all single networks on the dataset, on the Cifar10 dataset, the proposed network can achieve higher image classification accuracy with less computational effort. At the same time, the supervision of joint structural similarity and class information loss can speed up the training process of proposed network.
Keywords: Convolutional Neural Network; Image Classification; Structure Similarity; Deep Learning; Metric Learning.
Color Image Encryption Algorithm Based on Hyperchaos and DNA Sequences
by Ji-xian Cui, Guodong Li, Le-le Wang, Cong Ma
Abstract: In order to overcome the shortcomings of the single chaotic encryption and the problems of simple structure and low security, the hyperchaotic color image encryption algorithm based on DNA sequences is proposed. Firstly, the color images are layered and encoded by DNA, then, the hyper-chaotic sequence is used to scramble the DNA matrix, the hyper-chaos is used to generate natural DNA matrix and DNA addition operations is dynamically selected to perform the DNA rule operations, Finally, the cipher-text image is obtained. The performance test of the ciphertext image shows that the gray sca le distribution of the histogram is relatively uniform; the test values of the related index parameters NPCR and UACI are 99.65% and 33.51%, respectively, which are close to the theoretical value. Through simulation experiments, The algorithm can effectively improve various anti-attack capabilities of the encryption system and has high security.
Keywords: Image encryption; Hyperchaotic Lorenz systems; DNA coding; Chaotic encryption.
Research on Image Denoising Algorithm Based on Non-local Block Matching
by Ying Yang, Dongrui LI, Xiaofeng Huang
Abstract: In order to study how to better suppress image noise, improve image resolution, and enhance the visual effect of images, three-dimensional joint filtering - block matching (BM3D) algorithm is used to explore image denoising and image detail retention, and in image denoising, sparse expression and low rank recovery theory are introduced. The results show that BM3D algorithm has a good effect in removing Gaussian white noise, but it is lacking in suppressing impulse noise and mixed noise. The non-local BM3D algorithm is superior to sparse expression and low rank recovery in removing Gaussian white noise, but sparse expression and low rank recovery has a good effect in removing impulse noise.
Keywords: non-local block matching; image denoising; BM3D; sparse expression; low rank recovery.
Optimization of Surrounding Layout of Enterprise Building using an Improved Genetic Algorithm
by Lin Cheng
Abstract: In order to achieve the optimal design of environment and overall layout of enterprise building, the improved genetic algorithm is applied in it. Firstly, the relationship between the environment and layout of enterprise building. Secondly, the optimal model of environment and overall layout of enterprise building is constructed, and the objective function and boundary conditions are confirmed. Thirdly, the procedure of improved genetic algorithm is designed, and the mathematical models of cross operation and niche computation are established. Finally, the simulation analysis of environment and overall layout of enterprise building is carried out, and simulation results show that the improved genetic algorithm can obtain the better environment and overall plan of enterprise building.
Keywords: Optimization; Environment; Overall layout; enterprise building; Improved genetic algorithm.
Research on Fault Diagnosis Technology based on FD-GT method
by Weijie Kang, Jiyang Xiao, Mingqing Xiao, Xilang Tang, Bin Hu
Abstract: Fault diagnosis can be divided into two main tasks: fault feature extraction and fault data classification. Firstly, aiming at the problem that the fault feature extraction method is not significant, this paper proposes a fault feature extraction method based on fuzzy distance. The concept of feature separation is proposed. The initial fuzzy distance calculation method is established by expert knowledge. The key parameters of fuzzy distance are optimized based on DE algorithm combined with historical test data. This method can effectively distinguish fault data from normal data and achieve more significant fault characteristics extract. Secondly, aiming at the problem of low accuracy when classifying fault data, this paper proposes a fault data classification method based on grey target decision. The concept of fault type grey scale is proposed, and the grey number decay matrix is updated with the current test data. The fault data classification is realized by calculating the target distance of each fault type with the current input signal set. The method can effectively improve the accuracy of fault data classification and has a certain time cost advantage. Finally, the FD-GT method is verified by an example. The results show that it can effectively improve the saliency of fault feature extraction and the accuracy of fault type classification, thus achieving more efficient and reliable fault diagnosis.
Keywords: Fuzzy Reasoning; Grey Target Decision; Fault Diagnosis; Differential Evolution Algorithm; Grey Number.
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.
Traffic incident detection based on the width characteristic of the moving object marker
by Shangbing Gao
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 analyzed, 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 preprocessing.
Design of orthogonal phase encoding for basis function based on genetic algorithm
by Kui Li, Ting Zhang, Hao Guo, Hua Wang
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 analyzing the requirements and the characteristics of the basis function in TDCS, the evaluation criterion of the 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 the 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 systems applicability is improved. By analyzing the simulation results, the feasibility and validity of the algorithm are verified.
Keywords: Basis Function; Phase Encoding; Genetic Algorithm; Multiple Access.
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 binarize the character verification code image, and the denoised character image was obtained. For the character verification code image after binarization, 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 recognize character verification codes.
Keywords: Artificial intelligence; Network; Character type; Verification code; Recognition.
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; Independence; Adaptive Key Frame; Video Sequence.
Radar Signal Sorting Method Based on Support Vector Clustering and Gray Relational Grade
by Shiqiang Wang, Caiyun Gao, Yanlong Zhang, Qin Zhang, Huiyong Zeng, Juan Bai
Abstract: Aiming at the issue of complicated feature distribution and undistinguishable boundary between clusters of radar signals, a support vector clustering (SVC) algorithm based on grey correlation degree (GCD) is proposed. In the algorithm, the SVC sorting model is constructed to obtain the cluster division firstly. Secondly, grey correlation degree is designed to measure the similarity between clusters, and to verify the clustering validity. Finally, the clustering parameters are adjusted adaptively to achieve the best clusters division. In this paper, 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; Gray Relational Analysis; Grey Correlation Degree; Verification; Cluster Validity Index.
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.
Study on Optimal Urban Land Classification Method Based on Remote Sensing Images
by Lede Niu, 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; modeling; K-Means method; NDBI index.
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.
Feature extraction algorithm for fast moving pedestrians with frame drop constraint based on deep learning
by Mei Ma, Yaomin Hu
Abstract: When the existing method extracts the information of the fast moving pedestrian, the frame dropping phenomenon may occur, resulting in low extraction precision. A fast moving pedestrian frame loss constrained feature extraction algorithm based on depth tilt is proposed. Block matching and denoising are performed on the pedestrian image. The contour feature extraction method is used to reconstruct the adjacent frames and the reconstructed image frame vector is sub-block fusion. The depth learning algorithm is used to extract the feature quantity of the gray pixel from the frame falling part of the image. Improved feature extraction algorithm for pedestrians with frame loss constraints. The simulation results show that the standard deviation of the frame loss of the extraction result is 8.235 and the standard deviation of the non-drop frame is 4.353. It proves that the algorithm has low frame loss rate and high extraction and recognition ability.
Keywords: pedestrian; frame drop; feature extraction; tracking and identification; deep learning.
Part-based pyramid loss for person re-identification
by Yuanyuan Wang, Zhijian Wang, Mingxin Jiang
Abstract: Person re-identification (ReID) is a challenging problem in computer vision, meanwhile attracted the attention of industry. Person ReID focuses on identifying person among multiple different cameras. A key under-addressed problem is to learn a good metric for measuring the similarity among images. Recently, deep learning networks with metric learning loss has become a common framework for person ReID, such as triplet loss and its variants. However, the previous method mainly uses the distance to measure the similarity and the distance measure is more sensitive when the scale changes. In this paper, we propose part-based pyramid loss to learn better similarity metric for the person ReID, in which batches of quadruplet samples as the input. Specifically, we simultaneously use the relationship of distance and angle among samples learn the local body-parts features of person images. Our approach uses the pyramid relationship in triangles as a measure of similarity, minimising the angle at the negative point of the triangle. Pyramid loss can learn better similarity metric and achieve a higher performance on the person ReID benchmark datasets. The experimental results show that, our method yields competitive accuracy with the state-of-the-art methods.
Keywords: person re-identification; ReiD; metric learning; pyramid loss; part-based.
Building energy consumption forecasting algorithm based on piecewise linear fusion and exponential spectrum analysis
by Dahui Li, Jianzhao Cui, Yunfei Bai, Chenqiang Zhan
Abstract: In order to solve the problem of large error in traditional statistical prediction methods, a large data prediction method based on pie chart is proposed. Linear fusion and exponential spectrum analysis methods are proposed. The method establishes the target model of building energy consumption prediction and carries out nonlinear exponential sequence analysis. Game analysis of building energy consumption, segmentation linear fusion method is used to decompose the characteristics of building energy consumption map, and statistical analysis is carried out. According to the evolution of feature decomposition and learning trends, the analysis and accurate prediction of building energy consumption big data is realised. The simulation results show that the method reduces energy consumption, is conducive to building energy-saving emission reduction and green building, and provides a new idea for building energy conservation. Provide scientific support for the development of building energy conservation and environmental protection.
Keywords: big data environment; building energy consumption; forecasting algorithm; map feature analysis.
An item recommendation model with content semantic
by Yunpeng Jiang, Liejun Wang, Jiwei Qin
Abstract: Current recommender service providers are offering interesting items for user-based user behaviour and ignoring the content semantic of items. The item semantic is should be taken into account as an accurate reflection of items. We present a recommender model that leverages content semantic and user rating. In this model, the item similarity is firstly calculated with content semantic by best Word2vec method, an item recommendation list is built by the similarities. Next, the user rating is used to model the user preference and build the other item list recommended by traditional recommendation method. Then, the two item lists is mixed together as final list for user. Comparing the above algorithm to traditional recommendation algorithms on MovieLens, FilmTrust and Online Retail datasets, we run experiments that show the presented algorithm has is greatly improved on accuracy and increase by an average of 25.32% to 31.41%, and present good scalability.
Keywords: recommender model; semantic feature; similarities; Word2vec; data sparsity.
Source code-based context-sensitive dynamic slicing of web applications
by Jagannath Singh, Durga Prashad Mohapatra
Abstract: Web applications are broadly utilised for spreading business around the globe. To meet the necessities of the huge numbers of users or customers, the web applications must have better quality and robustness than any other applications where the number of users is limited. Program slicing is found to be useful in improving program understanding, analysis, testing and maintenance. This paper presents a context-sensitive slicing technique for web applications. In this paper, a new intermediate representation called web dependence graph (WDG) is proposed for representing all dependencies that may present in a web application. We have proposed a context-sensitive web slicing (CSWS) algorithm for computation of slices of a given web application using the WDG. A tool is developed for automatic generation of the WDG for a given web application and computation of slices. During our literature survey, we noticed that majority of the automatic graph generation tools are mainly based on byte-code whereas our tool uses the dependency analysis from the source code of the given program. Using our tool WDG, we compared the performance of our proposed CSWS algorithm for slicing with other closely related slicing techniques.
Keywords: program slicing; JSP application; source code analysis; context sensitive; dynamic slicing.
CNN-based text multi-classifier using filters initialised by N-gram vector
by Yan Xiang, Ying Xu, Zhengtao Yu, Dangguo Shao, Hongbin Wang, Yantuan Xian
Abstract: Text classification based on convolutional neural networks (CNN) has got more attention recently. This paper presents an improved CNN-based text multi-classifier. First, word vector training is performed on the corpus to be classified. Then, the most important N-grams for a particular category are selected and clustered into different groups. Finally the centroid vectors of different groups are used to initialise the centre weights of filters. Initialisation weights enable CNN to extract N-gram features more effectively and ultimately improve text classification results. Multi-classification experiments using multiple advanced models were performed on different data sets. Experiments show that the proposed model is more accurate and stable than other baseline models.
Keywords: convolutional neural networks; text classification; N-gram; word embedding; clustering; filter; word vector.
Fast mining algorithm for multi-level association rule data under temporal constraints
by Mu Yicheng
Abstract: Redundant interference occurs between frames of multi-level association rule data under temporal constraints, which brings poor clustering and anti-interference performance to data mining. In order to improve the multi-level association rule data mining ability, this paper proposes a fast mining algorithm for multi-level association rule data based on temporal constraints. It constructs a fitting state model of multi-level association data distribution, and uses the reorganisation method of multi-level association rules to re-arrange data structure and extract the average mutual information feature; it constructs detection statistics to conduct multi-level linear programming design for association rules data, and uses the autocorrelation detection method to conduct de-interference processing and the fuzzy directional clustering method to conduct fuzzy clustering processing for multi-level association rule data, to realise fast mining of multi-level association rule data under temporal constraints. The simulation results show that compared with traditional methods, the proposed method reduces the execution time of multi-level association rule data mining by 12.77%, and the mining accuracy is improved by 23.34%. High mining accuracy and strong anti-interference ability make the data mining efficiency improved.
Keywords: temporal constraints; association rules; data mining; feature extraction.