International Journal of Information and Communication Technology (64 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.
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
by Haigang Zhang, Xuan Chen, Piao Liu, Bulai Wang
Abstract: The identification of internal parameters of PMSM has become a research hotspot in PMSM control system. In this paper, a limited memory least squares identification module is configured inside the traditional model reference adaptive system to identify the parameters of the stator resistance R*, the stator inductance Ls* and the rotor flux chain of* of the permanent magnet synchronous motor. This method improves the accuracy and speed of identification. The real-time update of the nonlinear variables of the adjustable model improves the accuracy of the speed estimation we* and reduces the error of calculating the rotation angle and speed. Finally, the proposed method is validated by using MATLAB/Simulink. The results show that the improved model reference adaptive control strategy based on the limited memory least squares identification algorithm reduces the steady-state error. The dynamic performance and control accuracy are improved to some extent compared with the traditional model reference adaptive control method.
Keywords: limited memory least square method; parameter identification; PMSM; model reference adaptation system.
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.
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.
Potato late blight disease detection using convolutional neural network
by Mominul Islam, Md. Ashraful Islam, Ahsan Habib
Abstract: This paper proposes a convolutional neural network-based deep learning model to classify and detect the infectious potato leaves suffering from late blight disease. The proposed model has two classifiers - the potato leaf classifier and the late blight disease classifier. Both healthy and diseased plant leaf images taken from the plantVillage dataset and real-time images are used to train, validate and test the classifiers. A total of 4,680 and 1,470 plant leaf images are used for the two classifiers, respectively. The potato leaf classification accuracy of the proposed model is 97.12%. The proposed CNN model also provides an accuracy of 98.62% while identifying late blight disease. The ten-fold cross-validation technique is used to observe the performance of the proposed late blight classifier and then compared with other cutting-edge approaches. In observation, it has been shown that the proposed technique outperformed many other existing techniques.
Keywords: late blight; convolutional neural network; deep learning; image processing; image augmentation.
Classification of existing mobile cross-platform approaches and proposal of decision support criteria
by Ayoub Korchi, Mohamed Karim Khachouch, Younes Lakhrissi, Nisrine El Marzouki, Aniss Moumen, Mohammed El Mohajir
Abstract: The smartphone market has known an exponential growth since 2007, with the apparition of the first Apple phone. Nowadays, developing an application that targets all existing mobile platforms, becomes a tedious task for developers, due to the diversity of mobile platforms, their tools. For that, cross-platform approach with its various sub-approaches has shown its strength in reducing projects cost and time respecting the slogan develop once and deploy everywhere. This paper aims to compare the mobile app developments approaches and suggests a decisional framework to choose the adequate one to get an application respecting the clients need with a low cost and time. This frameworks criteria have a huge impact on the eventual cost, time, and success of an application building. If developers fail to match an app demands to the right development approach, it can turn their project into a certain failure.
Keywords: OS; development; cross-platform approaches; MDA.
Converged communication method of multi-source data about underground equipment based on Internet of things
by Shunli Wu, Haoping An, Yan Gao, Jianye Wang, Zhan Su
Abstract: The difference and incompatibility of multi-source data in complex environment lead to high bit error rate and low efficiency in communication of downhole equipment. Therefore, a multi-source data fusion communication method for underground equipment based on internet of things is proposed. Using multi-dimensional information acquisition system, the feature recognition model of multi-source data of underground equipment is established. According to the distribution of boundary mesh objects among clusters, the boundary object set of data distribution is obtained, and the data features are extracted. The balance degree of multi-source data is calculated, and the data is represented in grid form to realise fusion. The communication process is optimised according to the convergence result. Experimental results show that the proposed method has high efficiency (the highest efficiency is 97%) and low bit error rate (the lowest bit error rate is 0), which improves the efficiency of multi-source data fusion of downhole equipment.
Keywords: internet of things; underground equipment; multi-source data; convergence; channel configuration.
Research on fault signal detection method of mechanical vibration based on Kalman filtering algorithm
by Yaozeng Jia, Ye Hu
Abstract: There are some problems in traditional mechanical fault signal detection, such as large fault detection error and long time-consuming. A mechanical vibration fault signal detection method based on Kalman filter algorithm is proposed. The time-varying nonlinear oil film force of mechanical equipment under different working conditions is analysed. The piezoelectric acceleration sensor is set in the mechanical equipment to obtain the mechanical vibration fault signal. The energy difference between the normal signal and the fault signal is obtained by arranging them according to the acquisition time sequence. The mechanical vibration fault signal is pre-processed by Kalman filtering algorithm to obtain the state prediction of the fault signal. The frequency and amplitude characteristics of mechanical vibration fault signal are obtained to complete the detection of mechanical vibration fault signal. The results show that the error of the proposed method is small and the detection time is 2 s.
Keywords: Kalman filtering algorithm; Mechanical vibration; Vibration fault signal; Signal detection.
Information technologies of the Russian-Cuban GNSS service
by Ilia Bezrukov, Vladislav Yakovlev, Dmitry Marshalov, Yuri Bondarenko, Alexander Salnikov, Omar Pons Rodriguez
Abstract: We present the hardware and software implementation of the information technologies for the GNSS service of the Russian-Cuban co-located geodetic station. The service is equipped with geodetic and meteorological instruments with data acquisition and transmission systems and allows to conduct high-precision observations of the GPS and GLONASS global navigation satellite systems (GNSS) as well as meteorological measurements. The coordinates of the station are refined while processing GNSS observations and according to the change in coordinates, the movement of Cuban tectonic plates is estimated. Information technologies of the GNSS service is one of the key elements required for conducting regular automated geodetic and meteorological measurements. Information technologies is a group of computing and networking equipment that provides preliminary processing and transfer of observations to the data processing centre at the Institute of Applied Astronomy in St. Petersburg. It also allows the remote monitoring, control, and maintenance of GNSS service scientific instruments.
Keywords: remote control and monitoring; virtual private network; VPN; informational security; GNSS service; geodesy; applied geophysics.
A PRI estimation and signal deinterleaving method based on density-based clustering
by Lei Wang, Zhiyong Zhang, Tianyu Li, Tianhe Zhang
Abstract: In the existing statistics-based PRI estimation method, it is difficult to improve the PRI estimation accuracy due to the contradiction between the width of the statistical interval and the PRI extraction accuracy. In order to improve the accuracy of PRI estimation, a radar signal PRI estimation and deinterleaving method based on the density-based clustering is proposed in this paper. The dense area of the time of arrival (TOA) difference sequence near the true PRI value is extracted out by density-based clustering, take the intra-class mean value as the PRI estimation value and the intra-class point dispersion interval length as the PRI jitter amplitude. Combined with the sequence searching method with dynamic tolerance, the pulse sequence with a large number of pulses and small PRI jitter is preferentially extracted, which can improve the accuracy of signal deinterleaving. The simulation results show that the proposed method can significantly improve the accuracy of PRI estimation and the success rate of signal deinterleaving in the case of PRI jitter and false pulse interference.
Keywords: radar emitters; radar signals; pulse repetition interval; PRI; PRI estimation; signal deinterleaving; density-based clustering; DBSCAN; time of arrival; TOA; PRI jitter.
Research on financial information disclosure risk management method based on internet of things
by Ximeng Li
Abstract: In order to overcome the problem that the traditional risk management method is difficult to achieve the expected effect due to the careless classification of risk levels, this paper designs the financial information disclosure risk management method based on the internet of things. Based on the network financial information disclosure traceability model, the risk assessment index system is built, and the risk events are classified by ranking the risk events. The evaluation result is divided into five categories and the risk warning level is set. Then, the wireless sensor technology in the internet of things is used to design data fusion algorithm and distributed routing algorithm to achieve risk management. Simulation results show that the recall rate of the risk assessment index is above 94%, and the time to issue risk warning is always less than 6 min, which fully proves the effectiveness of the method.
Keywords: internet of things; financial information disclosure; wireless sensor technology; disclosure traceability mode; risk perception; risk assessment; risk warning level; fine division of grades.
Intelligent recommendation method for personalised tourist attractions based on cloud computing technology
by Changchun Guan, Jinhua Luo
Abstract: In order to overcome the problems of poor recommendation results and long travel time of traditional personalised tourist attractions recommendation methods, this paper proposes an intelligent personalised tourist attractions recommendation method based on cloud computing technology. The method constructs user interest model based on knowledge map vectorisation and user interest vectorisation. In the algorithm recommendation module, based on Hadoop cloud platform, Maple-Duce is parallelised, and the Bayesian network is used to predict the users preference for items. The probability is presented to show the possibility of the users preference for items, and the user is recommended according to the probability, so as to complete the personalised intelligent recommendation design for tourist attractions. The experimental results show that the personalised tourist attractions intelligent recommendation method has the highest recommendation accuracy up to 99%, and reduces the travel time, the minimum time is 430 min, which is feasible to some extent.
Keywords: cloud computing technology; individualisation; tourist attractions; intelligent recommendation.
Research on operational effectiveness of air and missile defence in maritime stronghold based on queuing theory
by Wenfei Zhao, Kenan Teng, Jian Chen, Yan Wang
Abstract: For the complexity and uncertainty of the enemy situation faced by the defence decision-making in the maritime stronghold, this study adopts the queuing theory to study the process of air and missile defence operations in the maritime stronghold, quantitatively analyses the state transfer probability of the stochastic service system for air and missile defence operations in the maritime stronghold, constructs a mixed system queuing model of multiple service stations arriving in batches, and gives the evaluation model of operational effectiveness for air and missile defence. Finally, the validity of the model and algorithm is verified by arithmetic simulation.
Keywords: maritime stronghold; air and missile defence; queuing theory; operational effectiveness.
Group popular travel route recommendation method based on dynamic clustering
by Yanhua Guo
Abstract: This paper proposes a new group popular travel route recommendation method based on dynamic clustering. Based on the recommended pattern graph, the decreasing function of tourist interest and the score matrix of interest preference are calculated. And the dynamic clustering method is used to construct the dynamic mining model of group passenger preference data to obtain the tourist preference data. Based on the preference data, the hot areas of tourist routes are divided, and the Markov model is used to calculate the transfer probability of tourist routes, and the final result of tourist route recommendation is obtained. Experimental results show that, compared with traditional recommendation methods, the proposed method has higher recommendation accuracy and efficiency, and the highest recommendation accuracy and efficiency can reach 97% and 98%. Therefore, the proposed method is more effective.
Keywords: Dynamic clustering; popular group; tourist route; recommendation method.
Algorithm for interference filtering of Wi-Fi gesture recognition
by Wei Han, Jing Yu
Abstract: Nowadays, On the one hand, with the continuous improvement of computer technology, human-computer interaction becomes more and more important in people's life. Among them, gesture, as an intuitive human language, has become an important way of human-computer interaction. On the other hand, as an important branch of mobile network, WLAN has gradually developed into an irreplaceable technology in indoor communication. This paper based on the relevant knowledge of the wireless channel state information (CSI), put forward under the environment of Wi-Fi, with the help of fertility carrier amplitude information provided by the CSI for fine-grained gesture recognition. Because of interference, a gesture recognition system based on Wi-Fi accuracy and robustness to ascend, therefore this paper proposes a Wi-Fi gesture recognition interference Filter algorithm, using Butterworth low-pass Filter and principal component analysis (PCA), combining to the CSI of raw data denoising processing, filtering CSI noise in the raw data. On the basis of the recognition and machine learning the results verify the robustness and accuracy of the algorithm.
Keywords: channel state information; CSI; noise and interference filtering; gesture; recognition; machine learning.
Colossal pattern extraction using optimised length constraints based on differential evolutionary arithmetic optimisation algorithm
by T. Sreenivasula Reddy, R. Sathya, Mallikharjunarao Nuka
Abstract: Extracting large amounts of information and knowledge from a large database is a trivial task. Existing bulk item mining algorithms for an extensive database are systematic and mathematically expensive and cannot be used for large-scale mining with interruptions. In this paper, the problem of mining the colossal patterns (CPs) is solved by using optimised length constraints (LCs). First, we describe the minimum LC and maximum LC problems and connect them to the optimal LC by identifying the optimal threshold values. Here, the differential evolutionary arithmetic optimisation algorithm (DAOA) is used to find the optimal threshold values of the constraints and extract the colossal patterns. The effectiveness of the proposed algorithm is proven by various experiments using different biological datasets.
Keywords: biological databases; colossal patterns; differential evolutionary arithmetic optimisation algorithm; DAOA; massive data itemset; optimised length constraints.
Design of a new type of logistics handling robot based on STM32
by Xinliang Cheng, Jinyun Jiang, WanXin Fu, Shiyi Ying, Xiaoliang Jiang
Abstract: In order to improve the stability of the logistics handling robot and the accuracy of grasping logistics materials, this paper proposes a new type of logistics handling robot, which is designed by using the combination of PID closed-loop control algorithm and visual recognition technology. In the part of robot software, we use the PID closed-loop control algorithm to make real-time control of the robot, so that it can accurately patrol the line, and use the raspberry Pi call recognition algorithm (opencv) to complete the identification of two-dimensional code, bar code and logistics materials. In the structural part, we improved the five-degree-of-freedom articulated manipulator to realise three-dimensional grasping and ensure fast and accurate grasping and placing of logistics materials. In order to verify the performance of the robot, we design a mini logistics warehouse environment to simulate the real experimental scene. The experimental results show that the designed logistics handling robot can accurately locate and place logistics materials, and complete the handling task according to the task requirements.
Keywords: logistics handling robot; PID; recognition; articulated manipulator; STM32; image.
Assessment and insurance of cyber risks as tools for ensuring information security of an organisation (on the example of Russia)
by Dmitry R. Sergeev, Oksana N. Suslyakova, Gulnaz F. Galieva, Elena E. Kukina, Olga Yu. Frantsisko
Abstract: The purpose of the study is to reveal the specifics of cyber risks as a source of reducing the information security of organisation, as well as to develop a methodological approach to the formation of tools for managing cyber risks based on their assessment and insurance. The authors analysed the dynamics of the growth of cyber risks in relation to small, medium and large organisations, and also assessed the possible scale of damage from their occurrence in the global economy. The article offers a methodological approach to the analysis of risk factors that affect the amount of possible damage and the likelihood of cyber risk. The authors formed an algorithm for managing the organisations cyber risks based on their assessment. The analysis of advanced foreign experience allowed the authors to determine the directions of its adaptation in the process of modernisation and improvement of the mechanism of cyber risk insurance.
Keywords: cyber risk; cyber incident; digital economy; cyber risk insurance; information security.
Securing BYOD environment from social and mobility related threats: the case of Nigerian banking sector
by Lizzy Oluwatoyin Ofusori, Prabhakar Rontala Subramaniam
Abstract: Globally, bring your own device (BYOD) is gradually gaining popularity in work environments and businesses. The benefits of BYOD, which include increased productivity, flexibility, and efficiency, have necessitated all sectors to adopt this trend to maximise the benefits. The banking sector, in particular, has been at the forefront of the adoption of BYOD, and employees are now enjoying the benefits. However, despite BYOD benefits, security threats and privacy invasion have been a major concern for individuals and organisations. Ofusori et al. (2018) have classified these threats into technical, social and mobility threats. Thus, this paper investigates the influence of social and mobility threats as it relates to BYOD phenomenon in the banking sector. Data was collected from Nigerian bank employees, and the study found that there are overlapping threats due to the influence of social threats over mobility threats. This study addresses the overlap security threats.
Keywords: security vulnerabilities; security measures; security practices; bring your own device; BYOD; mobile devices.
Online education big data mining method based on association rules
by Na Zhang
Abstract: In order to solve the problems of slow mining speed, high noise and poor data correlation in the existing online education big data mining methods, an online education big data mining method based on association rules is designed. Firstly, the recursive distance of the big data centre is determined, and the online education big data is extracted according to the calculation of fuzzy membership. Secondly, the covariance matrix is used to remove the noise in online education big data and reduce the dimension. Finally, calculate the confidence and support of online education big data association rules, determine the association strength between online education big data in the set, and complete data mining. The experimental results show that the mining speed of this method is significantly improved, the longest time is no more than 4 s, and the data mining is highly correlated.
Keywords: association rules; online education; big data; Euclidean distance; uniformity.
Analysing the Algerian social movement through Twitter
by Meriem Laifa, Djamila Mohdeb, Mouhoub Belazzoug
Abstract: Technology has altered collective actions guidance resulting in a new regulatory frame for action. For the sake of being successful in a social movement, people plan and advertise in advance to encourage and gather greater participation to strengthen the influence of crowds. For this, social media offers exceptional opportunities to organise masses of people into actions with lower participation expenses, and to foster new repositories of information and actions that go beyond communities offline. While most contemporary social movements have been studied from different perspectives, the Algerian social movement (i.e., Hirak) was overlooked in the literature. This paper presents a distinctive foundation for understanding the Algerian Hirak through analysing Twitter data. The used approach is established mainly at the intersection of sociology and data analysis, with the intention to generate an improved discernment of this movement. Promising future research directions are also discussed in this paper.
Keywords: social movements; social media; Algerian Hirak; natural language processing; Twitter; Algerian Social Movement.
Received signal strength-based power map generation in a 2-D obstructed wireless sensor network
by Mrinmoy Sen, Indrajit Banerjee, Tuhina Samanta
Abstract: This paper analyses the effect of received signal strength (RSS) in efficient deployment, in presence of obstacles. We consider RSS based power values, so that x-y plane represents the spatial coordinates within a target field and z coordinates denote power values over the field. We plot the power values on the x-y coordinates, addressed as power map, having some peaks and falls: the peaks represent strong signals and the falls represent weak signals at the co-ordinates. It is intuitive that locations with strong signals are more suitable for communications. The falls in the power strength indicates that more sensor nodes are to be put for successful communications. We validate the proposed scheme via simulations as well as small-scale indoor and outdoor experiments with XBee sensor motes. We propose an algorithm to estimate the received power and analyse the estimated results with the results generated through the hardware test-bed.
Keywords: noisy channel; node deployment; power map; channel frequency; obstructed network.
Research on dynamic bidirectional security authentication of user identity in wireless sensor network based on improved des algorithm
by Xiaodong Mao, Haiyan Li, Shixin Sun
Abstract: In order to overcome the problems of time-consuming and weak attack defence of traditional authentication methods, this paper proposes a new dynamic bidirectional authentication method for user identity in wireless sensor network based on DES algorithm. This method uses DES algorithm to complete the user identity information encryption at the client-side through three steps: initial transposition, encryption operation and final transposition of identity information. In view of the server transmission to obtain the challenge value, combined with the one-time password DES to distinguish the authenticity of the server, the user identity authenticity verification is completed. Through the client-side and server-side legitimacy verification, the dynamic bidirectional security authentication of user identity is realised. The experimental results show that the proposed method is efficient, and the security of identity authentication is stronger, which provides theoretical support for the research of related fields.
Keywords: wireless sensor network; DES; user identity; bidirectional; authentication.
A multi-objective optimisation algorithm for rural tourism route recommendation
by Yuping Lu
Abstract: In order to solve the problems of low accuracy and long time-consuming in traditional rural tourism route recommendation methods, a recommendation method for rural characteristic tourism route based on multi-objective optimisation algorithm is proposed. The specific opening time, ticket price and GPS coordinates of different scenic spots are extracted to obtain the comprehensive evaluation of rural scenic spots; the optimal recommendation list is obtained by multi-objective optimisation algorithm, and the candidate set of rural characteristic tourism routes is formed through the recommendation algorithm to realise the recommendation of rural characteristic tourism routes. The experimental results show that the accuracy of the proposed method is up to 99%, the recommendation time is always less than 5.5 min, and the recommended route cost is low, which verifies the feasibility of the proposed method.
Keywords: multi-objective optimisation algorithm; rural characteristic tourism; route recommendation.
Which people are loyal followers of influencers? An exploratory study
by Javier A. Sánchez-Torres, Juan Sebastían Roldan-Gallego, Francisco-Javier Arroyo-Cañada, Ana María Argila-Irurita
Abstract: Influencers are tools implemented in digital marketing as a communication mechanism between the brand and its target; however, there are few studies that observe the relationship between the personality of the follower and their attitude towards the influencer. The objective of this study is to explore whether personality traits influence positive attitudes towards influencers. An empirical study was carried out in Spain and Colombia with a sample of 381 individuals and cause-effect relationships were analysed using the partial least squares methodology. The results show that extroversion and disordered personality traits are related to positive attitudes towards influencers and there could be some differences between genders, specifically men with a calm personality and women with a sympathetic personality
Keywords: influencers; personality; followers; social network analysis; internet marketing; digital marketing; partial least squares methodology; extroversion; disordered personality; calm personality; sympathetic personality.
An automatic correction system of singing intonation based on deep learning
by Hui Tang
Abstract: In order to solve the problems of low accuracy and slow correction speed in traditional singing intonation correction system, an automatic singing intonation correction system based on deep learning is proposed. In the hardware, floating-point DSP and TDSP-TF984 chip are selected as the core chips of automatic correction processor of singing intonation. The data input module and parameter calculation module of singing intonation are designed to improve the singing intonation data collector. In the software, the group delay estimation method is used to collect the singing intonation signal, and the deep learning algorithm is used to decompose the false component of the singing intonation signal, and the autocorrelation function and characteristic distribution operator of the singing intonation signal are obtained to realise the singing intonation signal correction. The experimental results show that the highest accuracy of the proposed system is about 97.8%, and the shortest correction time is about 1 s.
Keywords: deep learning; singing intonation; automatic correction; signal extraction; autocorrelation function.
A wireless sensor network node redeployment method based on improved leapfrog algorithm
by Bin Zhang, Xinhua Wang
Abstract: In order to overcome the problems of large errors and low average coverage of nodes in traditional node redeployment methods, a node redeployment method based on improved frog jump algorithm is designed in this paper. The output node path of wireless sensor network is determined by constructing the distribution node deployment model, then the leapfrog algorithm is improved by introducing virtual force algorithm, and the physical model of node deployment area is established, so as to optimise the node deployment process by using gravity and repulsion force, and redeploy static and dynamic nodes. Experiments show that the minimum node redeployment error of this method is only 0.01. When the energy of some nodes is exhausted, it still has relatively good coverage quality performance, and the average coverage rate reaches 75%, which proves that it not only ensures the network coverage quality, but also reduces the number of working nodes.
Keywords: improved leapfrog algorithm; wireless sensor network; node redeployment; node path.
Study on a method for capturing basketball player's layup motion based on grey level co-occurrence matrix
by Luojing Wang
Abstract: In the process of basketball players layup motion capture, the image blur leads to high layup motion capture error. Therefore, a basketball player layup motion capture method based on grey level co-occurrence matrix is proposed. The hue, saturation and brightness components of basketball players layup action image are unified greyed, and the fuzzy information in the image is transformed into different greyscale information; The Pearson correlation coefficient is used to analyse the correlation between each component after greying, and the grey information of fuzzy image is filtered by establishing grey co-occurrence matrix; By analysing the change of positioning coordinates of basketball players layup action in three-dimensional space, the core area of action capture is determined, and the key point position capture results are aggregated to realise the capture of basketball players layup action. The results show that the accuracy of the proposed method can reach 97.14%.
Keywords: grey level co-occurrence matrix; motion capture; grey processing; Pearson correlation coefficient; blur image; positioning coordinates.
A fast radar target recognition based on single Gaussian background model
by Yue Fan, Zhiyuan Ma
Abstract: In order to overcome the problems of low quality factor, low recognition rate and long recognition time of radar target recognition results, a new fast recognition method of radar target based on single Gaussian background model is proposed. In this method, the radar target image is obtained by scattering point model, and the background of the target image is modelled based on the single Gaussian model to distinguish the background from the target area. Principal component analysis (PCA) and linear discriminant analysis (LDA) are used to extract the features of the target area, and the feature vectors are obtained. The SVM classifier is established. The obtained feature vectors are input into SVM classifier to realise the rapid recognition of radar targets. Experimental results show that the quality factor of the proposed method is higher than 8, the recognition rate is higher than 80%, and the recognition time is less than 0.5 s.
Keywords: single Gaussian model; background modelling; feature extraction; SVM classifier; radar target recognition.
Research on deduplication method of multiple relations based on hierarchical clustering algorithm
by Ying Wang, Weiwei Cheng, Chang Liu
Abstract: In order to overcome the problems of low efficiency and large error in traditional data deduplication methods, a multi relational data deduplication method based on hierarchical clustering algorithm is proposed. According to the inter class relationship information of duplicate data, different types of closely related class clusters are merged. Through hierarchical clustering algorithm, all the duplicate data are clustered according to the data similarity. After finding the similar class in the first level index, the super eigenvalue is used to complete the detection of multi relationship duplicate data. According to the specific situation at that time, the detected duplicate data is deleted by automatic, semi-automatic or manual methods. Experimental results show that the method has low error rate and good deletion effect, and improves the efficiency of multi relational data deduplication, with the highest deletion rate of 99%.
Keywords: hierarchical clustering; multi relational data repetition; super eigenvalue; inter class relationship.
Special Issue on: Human Computer Interaction for Speech and Augmentative Communication
Brain computer interface with EEG signals to improve feedback system in higher education based on augmentative and alternative communication
by Hua Sun, Hongxia Hou, Wenxia Song, Hongjuan Hu, Na Liu, Achyut Shankar
Abstract: A predictive brain-computer interface is a way to monitor EEG signals in humans currently under the experiment to understand the capability and get proper feedback about the digital education systems. The use of dynamically processed and collected data in a feedback system is unfeasible. Substantial processing delays are caused by a large volume of data utilised by the modern higher educational ideas. An artificial intelligence assisted brain-computer interface feedback system (AI-BCIFS) for ugmentative and alternative communication is proposed to improve feedback analysis based on the EEG signals. AI-BCIFS method is proposed to avoid an unwanted and improper understanding of feedback in the higher education systems. An expanded optimisation methodology is introduced based on the feedback analysis, and the enhanced seeking feedback protocol (ESFP) has been developed to describe automatic recognition and storage. The experimental studies show that AI-BCIFAC is preferable to the existing approaches in terms of accuracy.
Keywords: brain-computer interface; BCI; higher education; signal; augmentation.
A deep learning approach in brain-computer interaction for augmentative and alternative communication
by Yubin Liu
Abstract: The electroencephalography classification is the primary aspect of brain-computer systems. The changes there are two main concerns. Firstly, conventional approaches do not use multimodal knowledge to their fullest degree. Second, it is almost unlikely to obtain the rule-based EEG repositories as genetic information processing is complex, and metadata accuracy is expensive. In this sense, researchers suggest a new approach named deep learning-based brain-computer interaction (DLBCI) for augmentative and alternative communication to profound transfer learning to address these issues. Initially, these model perceptual activities based on EEG signals, using the EEG input images characterisation, which is intended to retain a standardised description of multimodal ECG signals. Secondly, this model develops a deep-scope transmission of knowledge through joint operations, including an opponents infrastructure and an incredibly unique transfer function. The proposed framework for EEG classification problems, such as strength and precision, has many economic benefits in experimentation.
Keywords: deep learning; brain-computer interaction; augmentative and alternative communication; multimodal signal.