International Journal of Information and Communication Technology (67 papers in press)
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.
A Recognition Method For Visual Image Of Sports Video Based On Fuzzy Clustering Algorithm
by Rongai Sun
Abstract: In order to overcome the problem of poor recognition accuracy of action visual image, a method of sports video action visual image recognition based on fuzzy clustering algorithm is proposed. This method uses the fuzzy clustering algorithm to segment the action visual image of sports video, which is divided into foreground and background. In the image foreground, the edge contour of the object is cut through the edge detection step, and the action representation feature quantity extraction method based on the joint point is used to extract the action feature of the human joint point in the foreground, so as to complete the action visual image recognition of sports video. The experimental results show that the segmentation accuracy is high, the noise iteration performance is good, and the recognition accuracy is higher than 0.96, which can achieve high-precision recognition of sports video action image.
Keywords: Fuzzy clustering algorithm; Sports video; Visual image; Recognition.
Achievement Management System For University Students Based On Cloud Storage Technology
by Mei Xu, Yi Liu
Abstract: In order to overcome the problems of long storage time, long query response time and low data aggregation accuracy of current methods for achievement data management of university student, this paper proposes an achievement data management system of university student based on cloud storage technology. Cloud storage technology is used to construct cloud storage system architecture, and design the overall architecture and software logical structure of the achievement management system. Combined with functional requirements, functional modules are built. E-R model is used to design database, to achieve the design of university students achievement data management system. The experimental results show that the data storage time is less than 0.5s, the query response time is less than 0.3us, and the accuracy of data aggregation is higher than 80%. It proves that the management system can meet the users experience requirements.
Keywords: Cloud storage technology; University students; Achievement data management system;.
Target Similarity Matching Algorithm Of Big Data In Remote Sensing Image Based On Henon Mapping
by Qing Sun, Quanyuan Wu
Abstract: In order to overcome the problem of low matching accuracy in traditional big data target similarity matching algorithm of remote sensing image, this paper proposes a new target similarity matching algorithm based on Henon mapping. The randomness of big data target in remote sensing image is analyzed by using the variation of Henon mapping invariant distribution. According to the randomness, the track of big data target in remote sensing image is selected to build a two-layer similarity matching model. The first layer of the model uses coarse granularity to reduce the dimension of big data, and the second layer uses the fine-grained representation of similar track set to output several tracks similar to the big data target of remote sensing image, so as to achieve the target similarity matching. The experimental results show that the proposed method has high matching accuracy, and the highest matching accuracy can reach 99.7%.
Keywords: Henon mapping; Remote sensing image; Big data target; Similarity matching.
Computer Threat Information Filtering Algorithm Based On Fusion Difference
by Liang Qian
Abstract: In order to overcome the problems of low information filtering coverage and long time-consuming in traditional computer threat information filtering methods, a new computer threat information filtering algorithm based on fusion difference is proposed. In this algorithm, the user feature model is established by integrating the difference method, and the objective function is introduced to protect the difference privacy data in the computer. Through Chinese word segmentation, feature extraction and weight calculation, user requirements and threshold initialization, filter matching, user feedback, five steps to achieve computer threat information filtering. The experimental results show that compared with the traditional algorithm, the proposed algorithm has a higher recall rate and a shorter filtering time, the shortest filtering time is only 0.4min, which shows that the proposed algorithm has a higher practical value.
Keywords: Fusion difference; Computer; Threat information; Filtering algorithm;rnrn.
The self-increasing expansion method for knowledge space based on deep learning algorithm
by Yuanhan Weng, Jingan Wang
Abstract: In order to overcome the problems of the traditional expansion method for knowledge space, such as small expansion range and low accuracy, this paper proposes an expansion method for knowledge space based on deep learning algorithm. Through deep learning algorithm, combined with multi-modal information fusion method, including the fusion and expansion of the current knowledge space, the knowledge space expansion framework is constructed. The framework is set as space organization knowledge, knowledge indexing, knowledge navigation, knowledge retrieval and other parts, and knowledge division is realized according to the continuous classification of knowledge sequence information. In the extended space, the multi-structure state of knowledge element is integrated by semantic description technology to realize the expansion of knowledge space. Experimental results show that the expansion method for knowledge space based on deep learning algorithm is better.
Keywords: deep learning; multi-modal information fusion; knowledge expansion; semantic description; knowledge of yuan.
Dynamic Fault Diagnosis means of the Power message System Based On Big Data
by Dong He, Tong Chen, Haichao Huang, Weihao Qiu, Yize Tang, Jinxia Jiang
Abstract: Aiming at the poor fault diagnosis ability of traditional power information system, a dynamic fault diagnosis method based on big data for power information system is proposed. Firstly, the original fault information of power information system is sampled, and the collected fault characteristic data are reconstructed by multi feature and information fitting. Then, the attribute distribution detection and big data mining are carried out for the fault dynamic characteristics of power information system. According to the high-order spectrum feature distribution of the extracted power information system fault signals, the dynamic fault diagnosis and fuzzy clustering analysis are carried out for the power information system, and the fault diagnosis is optimized according to the classification results. The simulation results show that the dynamic fault diagnosis accuracy of power information system is high, the fault sample detection results are accurate and reliable, and the dynamic fault detection ability is improved.
Keywords: Big data; electricity message system; fault; dynamic diagnosis; detection.
Automatic anti-interference control of intelligent mechanical communication terminal based on Neural Network
by Xiao-Xing Shi, Yanqin Zhang, Li-Ye Liu
Abstract: In order to overcome the disturbance of mechanical intelligent communication terminal caused by mechanical continuous movement, which affects the communication effect and quality, an active disturbance rejection control method for mechanical intelligent communication terminal based on neural network is proposed. This method analyzes the working principle of Cmnn and combines it with active disturbance rejection control technology. Aiming at the initial disturbance, the active disturbance rejection control technology is the dominant control, and the Cmnn enters the learning state and optimizes the weights according to the period; the active disturbance rejection control technology participates in the disturbance control, eliminates the interference, and ensures that the mechanical intelligent communication terminal is in a stable state. The experimental results show that the steady-state control time is about 0.06s, and the tracking error is controlled in the range of [+ 5 ?rad, - 5?rad], which has higher tracking accuracy and robustness.
Keywords: Cerebellar model; Neural network; Intelligent mechanical communication terminal; ADRC; Response speed.
Construction Of Integration Model For Regional Sub-Meter High-Resolution Remote Sensing Geographic Information
by Caijian Mo, Fengqiang Wu, Li Chen
Abstract: In order to overcome the problem of poor data coordination existing in the existing geographic information integration model, a new regional sub-meter high score remote sensing geographic information integration model construction method is proposed. The application of distributed integration technology to build a regional sub-meter level high score remote sensing geographic information integration model. Joint processing of images, in the integrated environment of GIS and RS, transform the independent variables in the data into a vector grid integrated structure, establish a joint index format based on B+ tree index and quadtree index, complete the RS and GIS model Build. Experimental results show that the model can apply street, city name, and place type keywords, with better integrated indexing effect and higher data coordination.
Keywords: Remote sensing geographic information; Integrated model; Grid; Joint index;.
Personalized Recommendation Method Of English Micro-Lectures Teaching Resources Based On Internet Of Things Platform
by Zhengui Zhang
Abstract: In order to overcome the problems of low accuracy and long time-consuming in traditional teaching resource recommendation methods, a personalized recommendation method of English micro course teaching resources based on Internet of things platform is proposed. Collect the Internet of things platform server agent, server and client for user interest resource data, preprocess user interest resource data. The user interest model is constructed by using the obtained user interest resource data, and the English micro class teaching resource model is constructed by vector space model. This paper combines the user interest model with the English micro class teaching resource model, and makes personalized recommendation of English micro class teaching resources. The experimental results show that: the accuracy rate of the proposed method is as high as 98%, and the recommendation time is less than 6 s, which can realize the personalized recommendation of English micro class teaching resources.
Keywords: Internet of things platform; English micro-lectures; Teaching resources; Personalized recommendation; Vector space model; Recommendation algorithm.
Feature extraction method of multi-frame image in cloud video
by Siyuan Cheng, Shouren Diao, Shuo Cai, Hongyan Liu
Abstract: In order to solve the problem of low detection and recognition ability of multi frame images in cloud video, a feature extraction method of multi frame images in cloud video is proposed in this paper. The image region information distribution structure model of multi frame images in cloud video is established, and the image edge gradient information detection method is used to detect the features of multi frame images and pixel information fusion in cloud video. According to the region information of the image, the edge contour feature detection model of multi frame image in cloud video is established, and the edge gradient information of multi frame video image is segmented and reconstructed by active contour detection method. Simulation results show that the method has high accuracy and high degree of information fusion, and improves the ability of video image detection and adaptive
Keywords: cloud video; multi-frame image; feature extraction; detection and recognition.
Feature Space Fusion Classification Of Remote Sensing Image Based On Ant Colony Optimization Algorithm
by Qing Sun, Quanyuan Wu
Abstract: In order to overcome the problems of low classification accuracy and poor application effect of traditional remote sensing image feature space fusion classification method, a new remote sensing image feature space fusion classification method based on ant colony optimization algorithm is proposed. According to the ant colony algorithm state transition rule, the global optimal path is updated. The spatial structure, edge and texture features of remote sensing image are extracted by feature extractor. The fusion weight coefficient of remote sensing image space and spectral feature vector is calculated. The extracted remote sensing image feature vector is replaced by the maximum likelihood method Image classification discriminant formula is used to realize remote sensing image feature space fusion classification. The experimental results show that the average classification accuracy is improved by 9.75%, and the classification speed is improved by 15.6%, which effectively improves the image recognition rate.
Keywords: Ant colony optimization algorithm; Remote sensing image; Image feature; Feature space fusion; Image classification;.
Design of real-time monitoring system for transmission channel energy consumption in wireless sensor networks
by Yong XIAO, Jingfeng YANG, Yun ZHAO, Xin JIN
Abstract: The collection of energy consumption data of the transmission channel of the wireless sensor network is neglected, which has the problem that the operation rate of the network is low and the time is long. The new real-time monitoring system of transmission channel energy consumption for wireless sensor network is proposed and designed. For the hardware part, the structure of the network master node, the child node and the transit node are analyzed respectively, employing the single chip program to design hardware part of the system;In order to complete the design of the software part of the system, a network transmission channel is designed to query historical data, display data in real time and collect energy consumption data in real time.The experimental results show that the proposed system has a higher network transmission rate and shorter monitoring time, which verifies the real-time and effectiveness of the system.
Keywords: wireless sensor network; transmission channel; energy consumption; real-time monitoring system;.
Fastvgg Network And Its Application In Automatic Identification Of Traffic Signs
by Xinyuan Li, Shangbing Gao, Zhonghe Lu, Kaige Gui
Abstract: Traffic sign recognition is an important part of driver assistance systems. Because the types of traffic signs are complex and diverse, they are difficult to identify. Traditional recognition methods require manual extraction of features, which is difficult and inaccurate. This paper proposes a FastVGG network based on VGG neural network to extract the features of the target image to realize the recognition of traffic signs under different angles and illumination. In the method of this paper, the connection layer parameters and the number of network layers are moderately reduced, the merging step is increased, and the recognition speed is improved. When the parameter value is less than zero, the Leaky ReLU is used to replace the activation function to solve the problem of neuron death. The experimental results of the German Traffic Sign Recognition Data Set (GTSRB) show that the algorithm can achieve accurate classification while increasing the speed.
Keywords: Traffic sign recognition; driving assistant system; deep learning; VGG Net.
Research on the Enhancement of Internet UI Interface Elements based on visual communication
by Can Zhou
Abstract: Aiming at the poor vision of traditional UI interface, this paper proposes a new method to enhance the communication of visual elements of multi-icon UI interface in mobile Internet. Design multi-icon user interface through user research, feature analysis and environment analysis. Principal component analysis (PCA) was used to obtain feature vectors and collect visual elements of mobile Internet multi-icon UI. The multi-resolution and multi-scale Retinex algorithm is applied to the communication of color enhancement visual elements in the multi-icon UI interface. Experimental results show that the mobile Internet multi-icon UI designed by the method in this paper has bright colors, and the average time for users to watch the mobile Internet multi-icon UI enhanced by communication of visual elements reaches 7.6 seconds, which verifies the effectiveness of the method in this paper.
Keywords: Mobile; Internet; Multi-icon; UI interface; Visual elements; Communication enhancement.
An Image Recognition Method For Speed Limit Plate Based On Deep Learning Algorithm
by Jian Gao
Abstract: In order to overcome the problems of large number of sample data collected from speed limit image and unclear image feature hierarchy, an recognition method for speed limit plate image based on deep learning algorithm is proposed. This method combines deep learning with SVM to build a multi-level classification model, and uses deep learning method to re represent the original data through unsupervised learning. The image features of speed limit plate are extracted in depth, and the image is preprocessed by color component compensation, image denoising and threshold segmentation. The similarity between the image features extracted layer by layer and the standard features is calculated, and the final recognition result of speed limit plate image is obtained through feature matching. The experimental results show that the average recognition rate is increased by 6.6%, which can effectively provide data reference for vehicle speed control in the actual driving process.
Keywords: deep learning algorithm; speed limit plate; image recognition; SVM;rnrn.
Research on the Selection of Interconnection Modes of Asymmetric Internet Backbone
by Qiming Tang, Meijuan Li, Qianbing Xiao
Abstract: At present, the interconnection mode between internet backbones in China is only peering, which makes the small and medium-sized backbone networks at a disadvantage in the interconnection. Optimizing the interconnection mode between Internet backbone in China has become an urgent problem to be solved by our government regulatory agencies. By constructing a dynamic game model, this paper analyzes the optimal selection of the interconnection modes of the asymmetric Internet backbone networks. The analysis results show that the selection of interconnection modes between the asymmetric backbone is related to the difference in network scale between the backbone networks. When the difference between the scale of the weak backbone network and the dominant backbone network is small, the paid peering mode could be chosen; when the scale of the weak backbone network and the dominant backbone network is large, the transit mode should be chosen. The introduction of transit mode has promoted competition between backbone networks. According to the conclusion of the study, specific suggestions are put forward to optimize the interconnection modes between internet backbone in order to promote the healthy and rapid development of the internet industry in China under the background of the triple play and national cyber development strategy.
Keywords: Internet Backbone; Interconnection; Paid Peering; Transit.
Research on multi-channel data acquisition system of production index information based on genetic algorithm
by Yilun Zhang
Abstract: The multi-channel data acquisition system is the key link of the industrial computer tomography (ICT) in image processing. How to realise the real-time data acquisition and storage of these huge data content has become an urgent problem for ICT. Based on this, a multi-channel data acquisition system of production index information based on genetic algorithm is proposed. This paper introduces the design of the basic structure of the multi-channel data acquisition system of production index information, expounds the hardware design and software design of the multi-channel data acquisition system, and gives the design and implementation scheme of the system. It is confirmed by experiments that the multi-channel data acquisition system based on genetic algorithm designed in this paper has high acquisition accuracy. It is particularly excellent, especially in the same sampling rate, compared with the traditional multi-channel data acquisition system of production index information. Through further analysis, it is found that the trigger mode of the multi-channel data acquisition system based on genetic algorithm can significantly reduce the loss rate of hardware resources compared with the traditional multi-channel data acquisition system.
Keywords: genetic algorithm; production index information; multi-channel; data acquisition.
Optimization of computer virtual image reconstruction method based on feature point matching
by Chiyu Pan
Abstract: Traditional computer virtual image reconstruction methods have the problems of slow reconstruction speed and insufficient image clarity. To solve this problem, an optimization method of computer virtual image reconstruction based on feature point matching is proposed. SURF operator is used to extract image feature points. After obtaining feature points, image feature points are matched according to TZNCC constraints. The virtual image is reconstructed by sparse method, and the high resolution depth image in virtual vision is represented by dictionary sparse linear combination. The sparse coefficient of the image is solved by alternating direction multiplier algorithm, and the problem of virtual image reconstruction is transformed into a problem of solving sparse signal, so that a better reconstruction effect can be obtained. The experimental results show that the proposed method has high speed and clarity of image reconstruction.
Keywords: Feature point matching; Computer; Virtual image; Reconstruction.
Image Segmentation Using Active Contour Model Driven by RSF and Difference of Gaussian Energy
by Qile Zhang, Xiaoliang Jiang
Abstract: As we all know, the region scalable fitting method is sensitive to initializations and suffers from bad results in images with complex scene. In our article, we put forward a new framework by integrating region scalable fitting (RSF) term and difference of Gaussian (DOG) term for segmenting images. We first propose a DOG function which can enhance the contrast at the edges of objects. Then, the RSF energy term is introduced to drive the curve closer to the edge. In the next step, the regularization term is established which can avoid of the process of re-initialization. Compared with traditional classical methods, the proposed technique is more flexibility with initialization and has better segmentation performance.
Keywords: Difference of Gaussian; Region scalable fitting; Active contour; Image segmentation.
Automatic assessment of adherence of terms and conditions of web service based process
by Lingaraj Panigrahy, Laxmi Narayan Padhy, Ajaya Kumar Tripathy
Abstract: In general, web service based processes (WSBP) are distributed and composed of one or more WSBP (called component web services (CWS)) which are developed and controlled by third parties. Hence, the functional and quality guarantee terms made with the WSBP provider and client are depends on the correctness of all CWSs involved. To enhance the trust and reliability on WSBP, it is essential to make functional and quality terms compliance assessment of adherence at run-time. Moreover, functional and quality need and priority constraint may change over a period of time. A run-time, dynamic and nonintrusive approach for compliance assessment can handle this situation. This article proposes a functional and quality guarantee terms specification mechanism between theWSBPprovider and client. The conformance verification of execution traces of WSBP against the guarantee terms specification is enabled by translating these specifications to automata.Acompiler is designed to automate the process of translation of these automatons into executableCprograms. These auto-generated C programs monitors the WSBP and CWS interactions to detect violation of functional guarantee terms or validate of quality related guarantee terms at runtime ofWSBP. This mechanism of conformance verificationworks independent of the WSBP design and implementation process. The use of the proposed approach is demonstrated by applying in an application system.
Keywords: Web Services; Web Service Composition; Service Monitoring; Formal Language.
A Multi-Attribute Recognition Method Of Vehicles Line-Pressing In Parking Lot Based On Multi-Task Convolution Neural Network
by Shaohui Zhong, Ting Hu
Abstract: In order to solve the problems of low recognition accuracy and long recognition time, a multi-attribute recognition method based on multi task convolution neural network is proposed. The structure principle of multitask convolution neural network is analyzed, and multitask is set in the bottom area of convolutional neural network. The Hough transform is used to extract the parking line in the parking lot, and the input layer of the multi-attribute label structure is established by multi-attribute classification convolution neural network. The loss function of vehicle line pressing attributes in different parking lots is obtained by combining the full connection layer and the connecting sub layer. The multi-attribute recognition of vehicle pressure line is realized by measuring and learning the line voltage attributes of vehicles. The experimental results show that the method can effectively identify the line pressing situation of vehicles in parking lot, and the recognition accuracy can reach 99%.
Keywords: Multi-task; convolution neural network; parking lot; line-pressing of vehicle; multi-attribute; recognition method.
Research on online-offline information resource joint regulation method of cross-border e-commerce model based on genetic algorithm
by Yajie Zhao
Abstract: In order to overcome the problems of large adjustment error, low utilization rate of information resources and low execution efficiency caused by traditional methods without considering task execution priority, this paper proposes a joint adjustment method of online and offline information resources based on genetic algorithm in cross-border e-commerce mode.Combined with grid technology, this method sets up an online-offline joint regulation model of information resources through multi-objective planning, and then USES multi-objective selection method to form information subgroups, and obtains new genetic population through crossover and mutation calculation, so as to obtain the optimal scheme of information resources joint regulation. The experimental results show that the maximum implementation efficiency of this method can reach 98.1%, the utilization rate of information resources is always above 95%, and the adjustment error is always below 6%, which proves that this method is effective.
Keywords: Cross border e-commerce mode; online-offline information; information resources; joint regulation; genetic algorithm.
Study On The Method Of Identifying Diseases With Abnormally High Signals Based On Machine Learning Technology
by Xiang SUN, Qianmu LI
Abstract: In order to improve the recognition level of abnormal high signal disease, a recognition method of abnormal high signal disease based on machine learning technology is proposed. The abnormal high signal feature extraction method based on machine learning is used to obtain the abnormal high signal. The wavelet threshold method is introduced to remove the noise signal and extract the features. The method based on BP neural network is used to identify disease types. The results show that the identification performance of this method is obviously better than that of similar methods, and the complexity of identification process is only 1.23%. The anti-interference ability of abnormal high signal in the identification of benign and malignant diseases is as high as 0.99, which can effectively eliminate the interference of noise signal, extract the characteristics of abnormal high signal, and complete the disease recognition.
Keywords: Machine learning technology; abnormally high signal; identification method; characteristic extraction; neural network.
Research On The Effect Of Mobile Multimedia Advertising Based On Deep Learning
by Bin Liu
Abstract: In order to solve the problems of inaccurate analysis and unsatisfactory evaluation effect of traditional methods, this paper proposes a mobile multimedia advertising delivery effect research based on deep learning. Taking the real data set of mobile multimedia advertising as the research object, the feature data of mobile multimedia advertising are extracted by convolution neural network, and the mobile multimedia advertising feature data are classified, and the classification decision model is constructed. The extracted feature data are standardized preprocessed, and principal component analysis is introduced to remove the redundancy of feature data; the evaluation index system of mobile multimedia advertising effect is constructed; and the support vector regression model is constructed to evaluate the effect of mobile multimedia advertising. The simulation results show that the proposed method can accurately evaluate the effect of mobile multimedia advertising, and the evaluation accuracy rate can reach 98%, and the evaluation time is short.
Keywords: Deep learning; Mobile multimedia; Delivery effect; Convolutional neural network; Principal component analysis method; Evaluation index.
Designing a cost-effective model leveraging serverless computing to provide weather forecasts to farmers in rural India.
by Ravi Prakash Varshney, Dilip Kumar Sharma
Abstract: Serverless computing has been widely adopted as a platform for the deployment of various event-driven applications and services. The features like autoscaling, no resource management, pay-as-you-go, less expensive are some of the key reasons for its wide adoption in software development. It allows the developers to define applications as a sequence of event-triggered functions. Weather forecast plays a vital role in Indian agriculture, but rural Indian farmers are devoid of any form of weather forecast information. The dearth of weather information accounts for the reduction in crop productivity and hence, financial loss. In this paper, we propose a cost-effective and scalable architecture and a prototype of a serverless application that provides the weather forecast to the farmers in the form of a mobile push notification or Short Message Service (SMS). With an operating cost of around $25 per month, the proposed model caters effectively to around 100,000 users'. We demonstrate how serverless computing, along with the other services offered by Amazon Web Services (AWS), can be glued to build a scalable and cost-effective solution and assist farmers in mitigating the losses incurred.
Keywords: Serverless computing; Function as a Service (FaaS); Weather forecast; Cloud Computing; AWS Lambda; Amazon web services (AWS).
Exact outage performance of two NOMA users in small-cell network over Nakagami-m fading under imperfect CSI
by Chi-Bao Le, Tu-Trinh Thi Nguyen, Dinh-Thuan Do
Abstract: We consider a two-user system in scenario of relay cooperative non-orthogonal multiple access (NOMA) network with imperfections of channel state information (CSI). Firstly, on the same allocated power source, the base station (BS) transmits the superposition signal to near user and far user, which is analysed via Nakagami-m fading channels. In this situation, the near user with good channel conditions employs NOMA scheme to serve the far user. Secondly, the far user combines signals from direct link and relay link. Then, the outage performance is examined in closed-form expressions to exhibit performance gap among two NOMA users.
Keywords: non-orthogonal multiple access; NOMA; decode-and-forward; Nakagami-m fading; outage probability.
Intelligent Diagnosis System for Jaundice Based on Dynamic Uncertain Causality Graph
by Nan Deng, Shichao Geng, Shaorui Hao, Qin Zhang, Lanjuan Li
Abstract: The healthcare system in China still has some defects such as the imbalance of medical resources. With the development of the computer science, medical diagnostics digitization has become possible. In this paper, a medical diagnosis system for jaundice based on Dynamic Uncertain Causality Graph (DUCG) is proposed. After a brief introduction to DUCG, a knowledge base of the DUCG for jaundice diagnosis is built. It can represent the experienced knowledge of human experts, explicitly with graphical symbols. During the construction of the knowledge base, how to classify the medical diagnosis data in a structural and standard minor is important. In this paper, we propose to classify these data as four categories: general symptoms, medical signs, results of laboratory tests and results of imaging examinations. The first two form the general clinical information and the last two are further information. Each category can be further classified as sub-categories, so that users are easy to find the right position to fill the clinical information. Based on such designed DUCG medical software, 203 randomly selected jaundice related cases out of 3985 case records of a hospital are tested. The final diagnostic accuracy of the system reaches 99.01%.
Keywords: DUCG; Jaundice; Intelligent Diagnosis System.
Research on intelligent recommendation of ecotourism path based on popularity of interest points
by Yong-mei Song, Yang Dong
Abstract: In order to overcome the problems of low accuracy, low recall rate and low popularity of current methods when recommending ecotourism paths for users, an intelligent recommendation method of ecotourism paths based on the popularity of interest points is proposed. This method uses distributed collaboration to crawl the data existing in the tourism website to obtain the data of tourist attractions and paths, takes the interest point and popularity as the optimization objectives to construct the interest point -transfer argot semantic model, and uses ant colony algorithm to solve the interest point-transfer argot model to realize the intelligent recommendation of ecotourism paths. The experimental results show that the precision rate of the proposed method is close to 80%, and the average recall rate is about 75%. It shows that the closer the actual interest is to the recommendation result, the popularity is close to 5.0, and the precision, recall rate and popularity are improved.
Keywords: Interest point; popularity; Ecotourism path; Interest point-transfer argot semantic model; Ant colony algorithm.
A hierarchical topic modeling approach for short text clustering
by Rahul Pradhan, Dilip Kumar Sharma
Abstract: Social networking websites such as Twitter, WeChat provide services for microblogging to its users; they post millions of short messages on it every day. Creating a dataset of these messages helps in solving many non-trivial tasks in the domain of computer science, natural language processing, opinion mining, and many more. Topic modelling is critical in understanding the tweets and segregate then into manageable sets. We are bringing the topic modeling approaches to cluster the tweets or short text messages to groups as conventional approaches fail to properly deal with noisy, high volume, dimensionality and sparseness of short text. The method we have proposed can deal with the issue of data sparsity of short text. Our method involves a hierarchical two-stage clustering method. We have analyzed the results on standard datasets, and we find that our method had better results as compared to other methods.
Keywords: short text clustering; STT; topic modeling; DMM; Twitter topic modeling.
Research on the Application of Data Fusion Technology in the Security of Wireless Sensor Networks
by Qiuxia Liu
Abstract: In the wake of the application and the development of sensors, wireless communication and other technologies, the technology of wireless sensor network has come into being, and it is widely used in fields of environment, medical treatment, transportation and so on. The problems and influencing factors of data security in wireless sensor networks are analyzed, and the data fusion technology is reasonably applied to wireless sensor networks. Shortcomings of the current data fusion technology are studied, basing on characteristics of the homomorphic encryption data fusion technology, we improve defects of the homomorphic encryption data fusion technology, such as high energy consumption, low network robustness and large communication loads, a sub-domain data security protection fusion algorithm which is based on the elliptic curve cryptography homomorphic encryption is put forward, a new model of networks is constructed, and perceived nodes of wireless sensor networks in different regions are processed. Through experimental simulation and comparison, we can get the sub-domain data security protection fusion algorithm based on elliptic curve cryptography homomorphic encryption, which can effectively heighten the robustness of the network, reduce energy loss and communication loads of networks. The new fusion algorithm achieves the desired goal and well protects the data of wireless sensor networks, thus it improves performances of wireless sensor networks.
Keywords: Data Fusion; Wireless Sensor Networks; Energy Loss; Elliptic Curve Cryptography; Homomorphic Encryption Technology.
A prototype network monitoring information system: modelling, design, implementation and evaluation
by Sarandis Mitropoulos, Vassilis Toulas, Christos Douligeris
Abstract: This paper presents the modelling, design, implementation, and evaluation of a prototype network monitoring () information system. First, a general overview of such management systems is provided highlighting the need for the visualisation of network elements in order reliable network monitoring to be achieved. Moreover, the importance of monitoring the network operation from the network administrators and other relevant users is underlined by presenting the main reasons for using NM tools. Then, the functional and architectural design and implementation of the proposed network monitoring tool are presented. This tool uses state-of-the-art implementation standards and technologies, like the TMF608 multi-technology network management (MTNM), the network management layer-element management layer (NML-EML) interface and the JBoss enterprise application platform. A major feature of this prototype NM tool is the graphical representation of the telecommunication elements/managed objects over a panel, which contains a map and representations of possible active alarms over these elements/objects. The proposed NM tool supports real-time alarms and allows the users to acknowledge them. Next, an operational demonstration of how the prototype NM information system works is provided along with some discussion on evaluation results. Finally, conclusions and future work are given.
Keywords: network monitoring; simple network management protocol; SNMP; management information base; MIB; event dissemination; management information systems; network operations.
Research on Control Strategy of Permanent Magnet Synchronous Motor Based on Improved MRAS Method
by Haigang Zhang, Xuan Chen, Piao Liu, Bulai Wang
Abstract: Aimed at improve the stability of the sensorless control method, it is particularly important to overcome the influence of the nonlinear factors of the motor on the control accuracy. The identification of the internal parameters of the motor has become a research hotspot in the PMSM control system and has received more and more attention. In the traditional MRAS, a limited memory least squares identification module is configured to identify the PMSM stator resistance R^*, stator inductance L_s^* and rotor flux linkage ?_f^* parameters, which improves the accuracy and speed of identification. The identified parameters are input into the MRAS adjustable model in real time, and the nonlinearity of the adjustable model is updated in real time, which improves the accuracy of the rotation speed estimation? ??_e^* and reduces the error in calculating the rotation angle and rotation speed. Finally, the proposed method is verified by Matlab. The results show that the improved MRAS control strategy based on the limited memory least squares identification algorithm reduces the steady-state error of the speed control system to a certain extent, and the reliability is high. Compared with the traditional MRAS control method, it has a certain improvement in dynamic performance and accuracy.
Keywords: Limited Memory Least Squares;Parameter Identification; PMSM;Model Reference Adaptation.
Random waypoint mobility and two-ray path loss model to analyse multiple routing protocols in mobile ad hoc networks
by Trilok Kumar Saini, Subhash.C. Sharma, Ram Kumar
Abstract: Mobile ad hoc networks face many challenges due to the wireless transmissions and mobility. The topology of the network changes rapidly due to the mobility of the nodes, and the wireless transmissions endure many effects like path loss, fading, interference, etc. The routing protocols in the mobile ad hoc networks are designed to provide the route under the dynamic topology and constraints environment. The purpose of this study is to analyse multiple routing protocols using random waypoint mobility of the nodes and by considering the effect of the two-ray path loss model for channel propagation. The simulation-based approach has been utilised to study the behaviour of the routing protocols in a dynamic environment. The flow of control messages and routing overheads has been analysed. The comparative performance of a set of six routing protocols has been analysed that assist in choosing the appropriate protocols for real deployments.
Keywords: mobile ad hoc network; path loss; mobility; control messages; routing protocol.
Research on the target recognition of marine surveillance radar based on ensemble learning
by Lingang Wu, Shengliang Hu, Jun Zhang, Xueman Fan
Abstract: The existence of corner reflector decoy makes the marine surveillance radar to be caught in severe challenges of target recognition. In order to enhance the accuracy of recognition, an ensemble classifier for marine targets is created based on the self-built dataset of high resolution range profile (HRRP). In addition, a confidence evaluation algorithm based on non-parametric estimation of probability density is proposed to reject unknown decoy target outside the database. With taking different interference conditions into consideration respectively, the comparison experiment between ensemble classifier and single classifier is carried out. The results show that the ensemble classifier is significantly better, which has strong robustness to noise as well as rejection ability to unknown target, and the classification accuracy can reach 92.63% under ideal conditions. This paper proves the feasibility of ensemble learning for maritime target recognition, and provides a reliable classification algorithm when the sample information is sufficient.
Keywords: corner reflector; target recognition; ensemble learning; high resolution range profile; HRRP; confidence evaluation.
Site-selection method of agricultural products logistics distribution center based on blockchain
by Xingui Liu, Ming Luo
Abstract: In order to overcome the problems of low on time delivery rate and high distribution cost existing in the existing location methods of agricultural products logistics distribution center, this paper proposes a new location method of agricultural products logistics distribution center based on blockchain. Based on the analysis of the basic problems affecting the location of agricultural products logistics distribution center, combined with the blockchain technology, the location model of agricultural products logistics distribution center based on input-output ratio was constructed. Combining the idea of mountain climbing algorithm and particle swarm optimization algorithm, the hybrid particle algorithm is used to solve the location model of agricultural products logistics distribution center, and the optimal location scheme is obtained. The experimental results show that the proposed method can effectively improve the on-time delivery rate and customer satisfaction, and reduce the logistics distribution cost. The maximum on time delivery rate is 97.4%.
Keywords: Blockchain; agricultural products; logistics distribution center; site-selection method; hybrid particle swarm optimization algorithm.
Research on self-driving tour path planning method based on Collaborative edge computing
by Zhongbin Wang
Abstract: In order to overcome the problem of inaccurate results of traditional self driving path planning methods, a new self driving path planning method based on Collaborative edge computing is designed and proposed. In this method, collaborative edge computing method is used to remove the abnormal data and improve the accuracy of path planning. From the point of view of optimizing network performance, the objective function of network channel capacity of candidate path and the multi-objective optimization model of path selection problem are established. Finally, the Nash negotiation axiom in game theory is combined to solve the multi-objective optimization model to realize the self driving travel path planning. Experimental results show that the proposed method can effectively remove abnormal data, the highest removal rate is 99.74%, and the planning efficiency is above 96%.
Keywords: collaborative edge computing; path planning method of self-driving tour; abnormal data; mapping relationship; objective function.
Nonlinear autoregressive neural network with exogenous input for an energy efficient non-cooperative target tracking in wireless sensor network
by Munjani Jayesh, Maulin Joshi
Abstract: The prediction algorithms have been studied as a part of target tracking applications for many years. The prediction algorithm helps to select appropriate nodes to achieve precise target locations while tracking. The only group of sensor nodes nearer the predicted location is activated to save network energy. The inaccurate prediction algorithm may hamper energy consumption by activating inappropriate nodes resulting in a target loss. We propose a nonlinear autoregressive neural network with exogenous input (NARX)-based target-tracking algorithm that improves tracking accuracy
and energy efficiency. The proposed algorithm uses vehicle location time series and exogenous vehicle velocity time series as inputs and exerts accurate prediction location for given non-cooperative manoeuvring targets. The proposed algorithm is evaluated in terms of average prediction error, total network energy used, and the count of a target loss with state of art. The experiment outcome proves that the proposed novel NARX-based tracking algorithm outperforms and saves up to 26% of network energy with up to 83% reduction in tracking error compared to existing target tracking algorithms.
Keywords: wireless sensor network; WSN; non-cooperative target tracking; energy-efficient target tracking; prediction algorithm; sensor node selection; nonlinear autoregressive neural network.
Finding and identifying expert team members in open source environments
by Hani Bani-Salameh, Muntaha Bnyan, Fatima Abu Hjeela
Abstract: Team members working in open-source development (OSD) environments, often are geographically distributed developing their software projects. For any software project to succeed, it is important to find the right experts who possess all the required skills and expertise. Searching for help and locating the right experts for each task are essential as well as extremely complicated tasks in OS environments. This article explores the problem of identifying experts in open-source environments. It focuses on mining developers mailing lists to understand the developers social structure and point out the experts involved in the development process. We identify the experts using the interaction factor (number of interactions), which means that the higher the number of interactions, the more active the team member. Results show that central members can be paramount experts
in the network. Also, it shows that there are groups that can be defined by similarity based on members connections. It concluded that the structure and location methods are very useful approaches in experts prediction, and the proposed methodology can serve as an effective approach that OS project coordinators may use to identify experts.
Keywords: interactions; experts; teams; centrality; degree; betweenness; closeness; open-source; OS; environment; knowledge.
The PSO optimisation SVM prediction model for the asphalt pavement environment and service fatigue life
by Yu Sun, Dongpo He, Jun Li
Abstract: In order to improve the accuracy of prediction by support vector machine (SVM), parameter optimisation of SVM is an important part of asphalt pavement life prediction. In this paper, a particle swarm optimisation support vector machine (PSO_SVM) method was proposed to predict the fatigue life of SBS modified asphalt mixture. This method combines SVM with particle swarm optimisation (PSO), makes full use of SVMs unique advantages in dealing with small sample regression problems and PSO global search optimisation, improves convergence speed, and achieves depth and breadth optimisation. Experimental results show that this method improves the
parameter selection efficiency of SVM, and the prediction results are more accurate than those of ANN and SVM.
Keywords: particle swarm optimisation; PSO; support vector machine; SVM; SBS; modified asphalt mixture; environmental impact; fatigue life.
Spatial texture feature classification algorithm for high resolution 3D images
by Ping Wang
Abstract: The existing feature classification algorithms have a lot of noise in the process of classification, which leads to the problems of low classification efficiency and unbalanced classification of image spatial texture features. Based on this, a texture feature classification algorithm based on RUSBoost is proposed. Wavelet coefficients, threshold processing and image reconstruction are used to denoise the image. On the basis of bdawpso algorithm, image segmentation is carried out by searching for the optimal threshold. Gabor transform and windowing are used to overcome the lack of local analysis ability and reduce the classification time. The original unbalanced image data is converted into new balanced data by using Rus boost algorithm. The experimental results show that the algorithm can improve the classification effect and display the texture information of the image better.
Keywords: high resolution; 3D image; spatial texture feature; classification algorithm.
RFID data cleaning method in heterogeneous space based on linear probabilistic motion state model
by Shuming Wang
Abstract: In order to overcome the problems of low efficiency, low precision and high load of heterogeneous spatial data cleaning methods, this paper proposes a heterogeneous spatial RFID data cleaning method based on linear probabilistic motion state model. Combining with the infinity of RFID data stream, this method uses sliding window technology to smooth the spatial tag data, and effectively removes the noise in the data. The Bernoulli binomial distribution is used to model the RFID data stream. At the same time, a probabilistic motion model for RFID tags is introduced. The transformation relationship between RFID initial data and tag motion state information is established. The vulnerability data of heterogeneous space is filled by tag motion state information, and heterogeneous spatial data cleaning is realised. The experimental results show that the operation efficiency is always above 95%, the highest data cleaning accuracy is 99.01%, and the lowest operating
load is 0.038%.
Keywords: RFID data; probabilistic motion model; heterogeneous space; data cleaning.
Research on target classification method for dense matching point cloud based on improved random forest algorithm
by Tiebo Sun, Jinhao Liu, Jiangming Kan, Tingting Sui
Abstract: Aiming at the problems of low accuracy and low efficiency of traditional point cloud target classification methods, this paper designs a new classification method based on improved random forest algorithm. Bagging is combined with random subspace to form a subset of feature training at random, so that the generalisation ability of random forest algorithm can be increased while the data processing speed can be accelerated to avoid overfitting phenomenon. On the basis of extracting geometric features of coloured point clouds, the optimal feature subset for classification is determined, and then the dense matching point clouds are classified using the improved random forest algorithm. Experimental results show that the classification error rate of this method is less than 1%, the average classification process takes only 83.995 s, and the VIM value is all over 0.1, indicating that this method can effectively improve the classification effect of dense matching point cloud targets.
Keywords: improved random forest algorithm; dense matching point cloud; target classification; optimal feature subset.
Design and implementation of smart power firefighting IoT based on massive heterogeneous terminals
by Zhili Ma, Feng Wei, Xi Song, Zhicheng Ma
Abstract: This paper is mainly aimed at a large number of old, unintelligent power system terminals, a set of intelligent IoT framework solutions with overall coordination, linkage, and perception have been researched and designed to solve the real-time monitoring of various terminal abnormalities and faults in the unattended situation. The specific methods here are as follows: First, a status monitor to the back end of the old equipment is added for grasping the operational health index of each terminal in real time. Secondly, the thermal imaging module, the fire water system module, and the fire alarm module are introduced to build a power fire monitoring network that can cover the entire area. Then, on the basis of setting thresholds for each monitoring module, advanced information network processing technology is used to realise fault warning for each monitoring object in the area. Finally, a statistical analysis module is embedded in the background to perform real-time statistics on various fire protection malfunctions and monitor the completion rate of each units failure rectification.
Keywords: the power systems; smart internet of things; real-time perception; fault warning.
Online distance music teaching platform based on internet plus
by Wanshu Luo
Abstract: In order to overcome the low response speed and security problems of traditional music teaching platform, a new online distance music teaching platform based on internet plus is proposed. Based on internet plus, online distance music teaching server and online distance music teaching client is designed, to complete the hardware design of the platform. Through the construction of students personalised learning evaluation model and online distance music teaching program, the software design of the platform is completed. Combined with the platforms hardware and software, the design of online distance music teaching platform is realised. The experimental results show that online music teaching platform based on internet plus can shorten response time, and the response time is only 290 ms, which greatly improves the security of the platform.
Keywords: internet plus; online education; music teaching platform; evaluation model.
Research on lossless restoration method of digital media image based on regularisation method
by Mi Tian
Abstract: In order to overcome the problems of high mean square error (MSE) and low peak signal-to-noise ratio (PSNR) in existing image lossless restoration methods, a new digital media image lossless restoration method based on regularisation method is proposed. The image is segmented by two-dimensional histogram and distance measure. According to the above processing results, a degradation model of digital media image is constructed. On the basis of the degradation model, a joint variation regularised image restoration model is constructed by regularisation method, and the pre-processed digital media image is restored losslessly. The experimental results show that compared with the traditional methods, the MSE of the proposed method is significantly reduced, the minimum MSE is only 0.17, the PSNR is high and the improved signal-to-noise ratio is significantly improved.
Keywords: regularisation method; digital media image; image degradation model; image lossless restoration; mean square error; MSE; peak signal-to-noise ratio; PSNR.
Traffic flow prediction model of urban traffic congestion period based on internet of vehicles technology
by Xiaofeng Shi, Yaohong Zhao
Abstract: There are some problems in the existing traffic flow forecasting models, such as low prediction accuracy and high time cost. RFID technology is used to transmit traffic flow data information during urban traffic congestion, and extract information to control the running state of vehicles on the road. The basic parameters of traffic flow are set, vehicle RFID data source is used to preprocess duplicate data and missing data of traffic flow, and differential stationarity and normalisation are processed; LSTM neural network is used to train traffic flow data iteratively and output estimate results. The comparison shows that the MAPE, RMSE and Mae of the proposed model are 12.34%, 23.18% and 15.87% respectively, which improves the prediction accuracy and the shortest prediction time is about 22 ms.
Keywords: internet of vehicles technology; traffic flow; radio frequency identification technology; LSTM neural network; normalisation processing.
Research on a feature fusion-based image recognition algorithm for facial expression
by Yilihamu Yaermaimaiti, Tusongjiang Kari
Abstract: In order to solve the problem that recognition rate of facial expression images is easily affected by non-uniform illumination factors, an improved face recognition algorithm is proposed in this paper. Firstly, a facial expression image with Log-Gabor feature vectors of multiple scales and directions is extracted from a face image, and then all the Log-Gabor feature vectors are blocked in a unified way. Secondly, gist algorithm is applied to extract gist feature blocks from Log-Gabor feature vector image, and then all those blocks are cascaded together as the feature vectors of a face expression sample. The fused feature vectors of the face expression sample are trained as the input feature of the stacked auto-encoder (SAE). Finally, the trained expression features are input into the classifier for recognition to obtain the final recognition result. Whether it is in the facial expression database JAFFE or the Uyghur facial expression database, its facial expression recognition rate is the highest, which verifies the superiority of the algorithm we put out in this paper.
Keywords: feature vectors; feature blocks; stacked auto-encoder; SAE; Uyghur facial expression database; UFED.
Research on interactive data packet storage algorithm for Hadoop cloud computing platform
by Yu-feng Ou, Yan-Xi Li, Wei-Guo Liu
Abstract: The accuracy of traditional interactive data packet storage algorithms for data packet storage is not high, the storage time is very large, and the integrity of the data cannot be guaranteed. Therefore, a new type of interactive data packet storage algorithm is proposed. First, Bayes theorem is used to calculate the frequency of grouping categories, so as to construct the interactive data classification model of the Hadoop cloud computing platform. By using the relationship between Smart-DIRPE codes and rules, the integrity of data packet storage is effectively ensured, and the domain conversion method is used to realise the range matching design of interactive data packets. Based on this, the design of the interactive data packet storage algorithm is completed. Experimental results show that the algorithm has higher grouping accuracy, better data integrity and shorter time consumption. When the data size is 90 MB, the time consumption is 2.3 s.
Keywords: Hadoop cloud computing platform; interactive data; packet storage.
Construction of short-term tourist volume prediction model based on Improved PSO algorithm
by Ya-nan Li, Shoujun Yan, Naipeng Bu
Abstract: In order to solve the problem of long time-consuming prediction of tourist volume, the traditional tourist volume forecasting method based on PSO is improved. Through the optimisation of particle motion distance, random influence factor and inertia weight, the PSO algorithm is improved; the actual value level of short-term passenger flow is counted according to the law of short-term passenger flow; the main physical factors influencing the short-term passenger flow are combined to determine the characteristics of the short-term passenger flow, and the main body of short-term passenger flow is accurately classified; the travel of improved PSO algorithm is constructed The short-term passenger flow forecasting model of scenic spots. The experimental results show that the minimum prediction error of the proposed method is about 2.5%, and the minimum root mean square error is 0.1, and the prediction time is always less than 10 s.
Keywords: PSO algorithm; tourist attractions; short-term passenger flow; prediction model; growth law; SVR parameters.
Research on wireless sensor privacy data measurement and classification model based on IoT technology
by Jianye Wang, Chunsheng Zhuang, Xin Liu, Shunli Wu, Ding Wang, Haoping An
Abstract: Traditional wireless sensor privacy data measurement and classification process has the problems of low accuracy and long time-consuming. A new wireless sensor privacy data measurement and classification model is proposed. The measurement module of the model establishes two levels of privacy measurement elements from three dimensions, calculates the weight of level privacy elements by using Shannon information entropy, and obtains the privacy quantity of each data privacy measurement element in the data set; With the help of Internet of things technology, the privacy data measurement and classification model of wireless sensor is designed. The experimental results show that the misjudgement rate of this model is very low, less than 5%, and the misjudgement error is kept below 10%. Using this model to protect the wireless sensor privacy data, the data running cost and time cost show a low level, has a certain application value.
Keywords: wireless sensor; privacy data; privacy elements; data measurement; data classification.
The walking stability control method of robots based on sensing quantitative fusion tracking
by Guan He
Abstract: In order to solve the problem of poor stability and large steady-state error in the process of robot walking, a method of robot walking stability control based on sensor quantitative fusion tracking is proposed. In this paper, several sensors are used to collect the robots attitude parameters and establish the robots kinematics and dynamics model. The integral term of the tracking error is introduced and the steady-state error of the robots walking is compensated by the backward sliding mode integral control. Under the external bounded interference, the conjugate gradient method is used to suppress the small interference and build the whole robots walking system. The control law of the robot realises the stability control of the robot. The simulation results show that this method improves the tracking ability of robot motion parameters fusion and the accuracy of attitude parameters estimation, and improves the robustness of robot walking control.
Keywords: sensor; quantitative fusion tracking; robot; walking; stability control.
Visual segmentation of diagnosis image of pulmonary nodules with vascular adhesion based on convolution neural network
by Yingying Zhao, Chunxia Zhao, XueKun Song
Abstract: Because of the low grey value of the background area in the diagnosis image of pulmonary nodules with vascular adhesions, the traditional visual segmentation method is weak for image feature recognition, which results in the unsatisfactory visual segmentation effect. A visual segmentation method based on convolution neural network is proposed for the diagnosis image of pulmonary nodules with vascular adhesions. Three modes are added to the original convolution neural network through the filter, and the convolution neural network is used to fuse the diagnosis image of pulmonary nodules with duct adhesion. The fusion results were processed by the fuzzy c-means method to complete the visual segmentation of the diagnosis image of
pulmonary nodules with vascular adhesion. The simulation results show that the proposed method can quickly and accurately complete the visual segmentation of the diagnosis image of pulmonary nodules with vascular adhesion, and has strong adaptability.
Keywords: convolution neural network; pulmonary nodules with vascular adhesion; visual segmentation of diagnosis image.
A collection method of motion video motion track based on fuzzy clustering algorithm
by Honglan Yang, Guan He
Abstract: In order to overcome the problems of low spatial accuracy, poor noise reduction ability and high spatial distortion rate of traditional methods, a motion video motion trajectory collection method based on fuzzy clustering algorithm is proposed. The fuzzy clustering algorithm is used to pre-process the motion video to improve the compression ratio of the motion video. The motion point accumulation method is used to extract the image sequences in the motion video, the motion targets are extracted, and the trajectory points of the motion targets are labeled. Establish a new motion trajectory range, and realise the collection of the motion trajectory within the marked range. The experimental results shows that the minimum noise value of the proposed method is only 0.02dB and 0.03dB, the spatial distortion rate of designed method is lower, it has better anti-noise effect, and more accurate motion
trajectory acquisition results.
Keywords: moving target extraction; track point annotation; motion trajectory; descriptor; integral invariant.
Facial expression recognition of aerobics athletes based on CNN and HOG dual channel feature fusion
by Shitao Wang, Jing Li
Abstract: The problem of low feature extraction accuracy and low recognition accuracy in facial expression recognition of aerobics athletes is presented. Propose a recognition method for fusion CNN and HOG dual channel features. The basic principle of the HOG is analysed, and the facial expression image of aerobics athletes is processed by grey level with the help of local binary mode. The pixel gradient intensity value in each small image is obtained, and all the intensity values are fused. Lagrange formula is used to transform high-dimensional features, support vector machine is used to classify facial expression images, and feature points are used as CNN, to process feature points according to network input and regularisation regression is used to realise facial expression recognition of aerobics athletes. The experimental results show that the accuracy of feature extraction is 97% and the recognition accuracy is always higher than 90%.
Keywords: CNN; HOG; local binary mode; pixel gradient intensity value; facial expression.
Feature extraction method of football fouls based on deep learning algorithm
by Weicheng Ma, Yanfei Lv
Abstract: In order to overcome the problems of abnormal detection and low accuracy in the process of football foul feature extraction, this paper proposes a football foul feature extraction method based on deep learning algorithm to accurately identify the fouls in the process of normal competition. In this method, the background is eliminated by the difference between the input image and the background image, so as to obtain the effective detection target. According to the characteristics of football competition, the human motion tracking algorithm is proposed. Through the template representation, candidate target representation, similarity measurement calculation and search strategy, the dynamic target is tracked in real-time, and its dynamic information is obtained. Finally, the star skeleton feature is used to extract the football foul action feature, and the image feature is transformed into available data to realise the data extraction of action feature. The experimental results show that the proposed method can detect the target with low accuracy.
Keywords: deep learning; human motion; action recognition; mean shift algorithm; background subtraction.
Research on dynamic parameter identification method of shallow reservoir based on kalman filter
by Shaowei Zhang, Rongwang Yin
Abstract: The identification method of reservoir parameters has the problems of low recognition accuracy and timeliness. A dynamic parameter identification method of shallow reservoir based on kalman filter is proposed. The history fitting method is used to establish and adjust the shallow reservoir model, and the parameters and range of the reservoir model are continuously adjusted according to the actual observation data of the shallow reservoir. Kalman filter is used to filter the data of shallow reservoir to filter out the noise and interference information. Then the dynamic parameters of shallow reservoir are identified by the method of water resistivity shale content discrimination, and the state of shallow reservoir is reflected by the shallow water resistivity. The comparison shows that the average recognition accuracy of the method can reach 95.2%, the recognition process takes only 22 seconds at most, and its recall precision value level is always high.
Keywords: historical fitting; least squares objective function; reservoir model; kalman filtering; parameter identification.
Research on reliability analysis of catenary model based on the fusion particle swarm least square support vector machine algorithm
by Haigang Zhang, Xuan Chen, Piao Liu, Decheng Zhao, Bulai Wang, Jinbai Zou, Minglai Yang
Abstract: The reliability of contact network is always an important part of the reliability analysis of traction power supply system. In this paper, combined with the failure rate data of the main parts of the contact network, the small sample data is expanded by Bootstrap non-parametric regeneration sampling method using the Fused Particle Swarm Least Squares Support Vector Machine (PSOLSSVM) algorithm to provide training set data for particle swarm optimisation. Mann and Schuer and Fertig fit and test the two-parameter Weibull distribution of the model based on MATLAB. Select the key components such as load cable and insulator, and combine the data to establish a model to characterise the overall reliability of the contact network. Based on the established model, the relevant parameters of each main part are estimated separately, which accords with the actual situation.
Keywords: Weibull distribution; least squares support vector machine; parameter fitting; bootstrap.
Energy consumption region sensing in sensor networks based on multi-objective greedy localisation algorithm
by Jinwen Liu
Abstract: The traditional energy consumption area sensing method has poor effect on energy consumption area division, and the perception delay is too long. Therefore, the energy consumption area sensing method of sensor network is optimised. The area of sensor network nodes is determined, and the energy consumption area division algorithm is designed. The MP algorithm is optimised, a multi-objective greedy positioning algorithm for energy consumption regions is designed, and the position function of energy consumption nodes is obtained; the multilateral measurement method is used to realise energy consumption region perception of sensor networks. The experimental results show that the perceived delay of the paper method is only 0.0039 min, the average execution efficiency can reach 99.63%, and the accuracy of energy consumption region division is as high as 96.8%, which shows that the practical application performance of the method proposed in the paper is better.
Keywords: multi-objective greedy localisation algorithm; sensor network; energy consumption region sensing; location function of energy consumption node.
Intelligent traffic assignment method of urban traffic network based on deep reinforcement learning
by Zhiyong Jing, Huanlong Zhang
Abstract: In order to overcome the problems of high relative error and long response time of traditional methods, a new intelligent traffic flow allocation method based on deep reinforcement learning is proposed. In this method, deep reinforcement learning is introduced, and experience pool technology is used to obtain and retain samples in a certain stage to train urban traffic network. The complete track is divided into several independent state action pairs, and the sample database is established. In a certain range, the vehicle congestion density is simplified to the degree of congestion. When the starting point and the end point are known, all traffic demands between the two points are calculated allocation, intelligent traffic network traffic assignment is realised. Experimental results show that the average relative error of passenger travel time is 12.34%, the traffic flow prediction indexes are the lowest, the allocation time is the highest, which is 0.878 s.
Keywords: Deep reinforcement learning; urban traffic; network traffic; intelligent distribution.
Image edge extraction algorithm based on adaptive wiener filtering
by Jialong Sun, Jinlei Liu, Zhengyang Zhang, Jiangtao Qin, Yonghao Yan, Lize Wang
Abstract: In this paper, we propose an improved edge extraction algorithm based on adaptive Wiener filtering, aiming at the disadvantage of extracting easily lost edge information from the edge of Canny operator. In this method, the image processed by Wiener filtering is convoluted. Whereas, the classical Wiener filter cannot meet the requirements of noising. Accordingly, a new adaptive Wiener filter is proposed. To begin with, the noise variance of the whole frame image is estimated by a new method, then the image is denoised with four templates. Finally, according to the average value of the difference between all pixel points before and after processing, we can judge which template is used to process the best result. Then the results are applied to the new edge extraction algorithm. The simulation results show that this method effectively preserves the edge information of image processing and suppresses generation of the edge.
Keywords: classical Wiener filter; adaptive Wiener filter; Gaussian noise; noise variance; edge detection.
An identification method of malicious nodes in wireless communication based on dynamic reputation algorithm
by Jia Chen
Abstract: Due to the complex internal structure of wireless communication network, the traditional methods for malicious node identification are relatively single, which leads to a large number of security risks in the network environment. This paper proposes a method of identifying malicious nodes in wireless communication based on dynamic reputation algorithm. A model of WSN wireless communication malicious node identification based on routing protocol reputation mechanism is established. The network is divided into clusters to determine the transmission path of network packets. Send the packet to the sink node and analyse it, calculate the node number and reputation value in the packet and compare with the threshold value to realise the identification of malicious nodes in wireless communication. The simulation results show that the proposed method can complete the identification of malicious nodes in wireless communication with high accuracy, and it takes less time and has better recognition performance.
Keywords: dynamic reputation algorithm; malicious nodes in wireless communication; identification.
Research on user experience evaluation of man-machine interaction interface based on virtual reality technology
by Hong Song, Zhi Yue
Abstract: Due to the complexity of user experience evaluation information data of human-computer interaction interface, the traditional evaluation method has a single information channel, resulting in the low quality of human-computer interaction interface. This paper proposes a user experience evaluation method based on virtual reality technology. Using virtual reality technology to restore the quality of human-computer interaction interface, build the fuzzy analytic hierarchy process of samples, and obtain the corresponding membership function. According to the superposition of the membership degree, the membership degree evaluation matrix of the first level index is established, and the user experience evaluation of human-computer interaction interface based on virtual reality technology is completed. The simulation results show that the proposed method can effectively improve the operation efficiency and accuracy, and has good stability, and has good application value.
Keywords: virtual reality technology; man-machine interaction interface; user experience; evaluation.
3D reconstruction of UAV remote sensing sequence image based on iterative constraint weighting
by Tiebo Sun, Meng Li, Weibing Wang, Chunyue Liu
Abstract: Aiming at the problem that the traditional 3D reconstruction method of UAV remote sensing sequence images takes time and affects the reconstruction accuracy, a 3D reconstruction method of UAV remote sensing sequence images based on iterative constraint weighting is proposed. Construct a UAV remote sensing platform, and process the images of UAV remote sensing sequences through image enhancement, uniform light processing and stitching. An iterative constraint weighting method is introduced to solve the global rotation matrix problem as a rotation vector in algebra. Through the iterative constraint weighting method, the second programming obtains the optimal solution of the global position and optimises the global position and attitude. According to the position and attitude parameters of the acquired UAV remote sensing sequence image and the reconstruction point cloud, the 3D reconstruction of the image is realised. Experimental results show that the method is short, accurate, effective and reliable.
Keywords: iterative constraint weighting; remote sensing sequence image; 3D reconstruction.
R2DCLT: retrieving relevant documents using cosine similarity and LDA in text mining
by R.S. Ramya, Ganesh Singh, Santosh Nimbhorkar Sejal, K.R. Venugopal, S.S. Iyengar, L.M. Patnaik
Abstract: The availability of digital documents over web has increased exponentially and hence there is a need for effective methods to retrieve and organise. Since data is dispersed globally and unorganised, a number of algorithms have been proposed based on relevance calculations. However, it is found that there is a gap between user's search intention and retrieved results. In this paper, we propose a framework for retrieving relevant documents using cosine similarity (CS) and LDA in text mining (R2DCLT). The uniqueness of this approach is that LDA is applied for the documents and extracted patterns like unigram, bigram and trigram. Documents are ranked based on the CS score. Experiments are conducted on Reuters Corpus volume and custom news dataset. It is observed that R2DCLT outperforms pattern taxonomy and relevance feature discovery models by providing high quality relevant documents with improved response time and dynamically updated document set.
Keywords: pattern mining; query search; query expansion; text feature extraction; text mining.
A novel protocol of RFID tag identification using a single mobile reader
by Xiaowu Li, Runxin Li, Lianyin Jia, Jiaman Ding, Jinguo You, Hongbo Fan
Abstract: Classic RFID tag anti-collision protocols mainly aim at the identification of tags in small area. That is, all unidentified tags are located in the reader's read-write region. When there is one or more tag outside the reader coverage area, multi-reader RFID system is an effective method of identifying all tags. However, the cost of multi-reader RFID system is much higher than that of a single reader system. There are many other tag identification scenes where tag distribution area is only slightly larger than a reader coverage area. For the scene, in the paper, we let the all tags, including outside the reader coverage area, have an opportunity to enter the reader coverage area by moving reader and be identified. The scene is called single mobile reader systems (SMRS) at the paper. However, SMRS cause the tag reappearance phenomenon (TRP), which leads to the multiple identification problem of tag (MIPT). TRP and MIPT make low the effective tag identification efficiency happen. In the paper, we propose an improved EPC C1G2 protocol, which can decrease MIPT effectively and increase tag identification efficiency.
Keywords: radio frequency identification; RFID; anti-collision; single mobile RFID systems; tag identification; effective tag identification efficiency; ETIE.
Community structure detection algorithm based on link prediction
by Gang Dai, Quanxin Wang, Baomin Xu, Lijun Sun
Abstract: Community structure identification has received a great effort among computer scientists who are focusing on the properties of complex networks. The label propagation algorithm is a near linear time algorithm to find a good community structure. Despite various subsequent advances, an important issue of this algorithm is the efficiency and accuracy of the identified community structure. In this paper, we propose a novel community detection algorithm by using link prediction algorithm based on label propagation. The method is the first to introduce the idea of link prediction into community detection. The experimental results show that the proposed method is less resolution limited than modularity optimising methods, and it can be more effective in detecting communities.
Keywords: complex networks; link prediction; label propagation; community detection.