bluuebunny/crossref_metadata_2025_split huggingface dataset:
Application of an Improved Hough Transform and Image Correction Algorithm in ACC
Authors: Chen Biao , Ding Bangfeng , Wang Jiangtao 
Published: September 2020

Abstract
Numerous studies have shown that ACC (Augmentative and Alternative Communication), as a tool in language and communication training for children with autism, has effectively improved and played a positive role. ACC’s Picture Exchange System (PECS) provides an important support for the correction and feature recognition of language and training teaching cards. When designing the picture exchange function of ACC, this paper proposes a modified Hough transform and image correction algorithm suitable for teaching cards to solve the problems of large data calculation, time-consuming calculation, large space occupation and false peaks caused by traditional Hough transform algorithm in detecting straight lines, which also optimizes the interactive experience of the teaching scene. Finally, we design two experiments to compare and analyse the classic Hough transform, random Hough transform and improved Hough transform. Experimental results show that the improved Hough transform algorithm has superior performance in terms of execution efficiency and accuracy of line detection.
Research on Sound Source Localization and Real-Time Facial Expression Recognition for Security Robot
Authors: Han Zhonghua , Li Tingting 
Published: September 2020

Abstract
Security robots are intelligent equipment used to perform security tasks, which can play a role in reducing the labor intensity and duty risk of police. In view of the shortcomings of existing police security robots in human-computer interaction and cooperation, the sound source localization and real-time facial expression recognition for the security robot is introduced in this paper. The auditory system of the robot obtains external sound analog signals through a microphone array, and performs corresponding digital signal processing on the collected sound signals. In order to strengthen the information interaction capability between the security robot and operators, the Kinect with deep data processing capabilities is introduced in this paper. The experimental results show that the system can analyze and recognize the position and status of the operators.
Quaternion Mahalanobis Non-local Means for Color Image Denoising
Authors: Xu Xudong , Zhao Jinling 
Published: September 2020

Abstract
By improving the quaternion non-local means (QNLM) image denoising algorithm, the quaternion Mahalanobis non-local means (QMNLM) method is proposed. This method combines quaternion with non-local similarity priors and introduces quaternion Mahalanobis distance. Firstly, the Mahalanobis distance between the image patches is calculated in the eigenspace by singular value decomposition of quaternion since the Mahalanobis distance is not robust in the sample space. Secondly, the image data is analyzed with the principal component analysis method, thus the Mahalanobis distance equation is simplified. The image with noise affected by high noise was first processed with a Gaussian low pass filter (LPF). Experimental results show that the noise reduction effect of this method is better than the non-local means (NLM), generalized non-local Means (GNLM) and QNLM methods. The proposed algorithm can effectively eliminate image noise and efficiently improve the image visual effect.
Using Graph Convolutional Networks Skeleton-Based Pedestrian Intention Estimation Models for Trajectory Prediction
Authors: Cao Dong , Fu Yunbin 
Published: September 2020

Abstract
In autonomous driving scenarios, pedestrian trajectory prediction is an important research direction. Based on the spatio-temporal graph convolutional neural network, we propose a new pedestrian trajectory prediction algorithm. The new algorithm constructs a series of new models around pedestrian intention estimation. The construction of the estimation algorithm considers the following aspects: the contextual information of pedestrians and the surrounding environment, the “pedestrian ego-vehicle” interaction combined with the vehicle speed estimation, the pedestrian’s own skeletal structure information and body language estimation, which includes head joints and the relative structural relationship of the torso joints, including whether it is out of the same plane, is rotated, and so on. Skeleton information feature extraction and construction adopts the method of graph convolutional neural network to structure pedestrians into joints in the form of graphs in non-Euclidean space, and further adopts spatial temporal graph convolutional network for feature extraction and learning. The new method is named a “head-torso”-based spatial temporal graph convolutional network (HT-STGCN). On the dataset PID, the novel method achieves substantial improvements over mainstream methods. Experimental results show that combining HT-STGCN with observed action can improve trajectory prediction.
Research on Vehicle Tracking Algorithm Based on Deep Learning
Authors: Zhou Yan , Zhou Jun , Liao Fangli 
Published: September 2020

Abstract
Comparing with the advantages and disadvantages of the existing target tracking algorithms based on deep learning, a vehicle tracking algorithm based on Yolov2 and GOTURN algorithm is proposed, which is called YOLOv2-tracker vehicle tracking algorithm. The Algorithm is trained and tested by using the collected training set and test set. The results show that the YOLOv2-tracker vehicle tracking algorithm can achieve higher tracking accuracy and faster tracking speed, and can effectively overcome environmental interference. Further analysis of the test results, the algorithm found that there is “errof” phenomenon, the paper discusses and analyzes the causes of this phenomenon, and put forward a reasonable solution. In addition, a “dynamic save” method is proposed to solve the “lost track” problem.
Design and Implementation of English Text Recognition System under Robot Vision
Authors: Wang Gang 
Published: September 2020

Abstract
The traditional methods of English letter visual recognition are machine learning and contour feature extraction. However, limited by the hardware resources such as camera and processor, it is difficult for the robot to realize fast and accurate text extraction and recognition. By comparing the results of domain average method, median filtering method and KNNF filtering method in text image preprocessing of robot, this paper proposes to use median filtering method for image preprocessing and corner detection method for text area extraction, which improves the efficiency of text image processing for robot. Based on the writing characteristics of English letters, a concentric rectangle tangent based on equal difference area is proposed The overall structure of the algorithm solves the problem of fast recognition of multi-directional mixed text, effectively reduces the influence of the angle between robot vision and pre recognition target on text recognition, and improves the speed and accuracy of text recognition by optimizing SOFM network. The system model has been verified based on Nao robot platform.
An Empirical Study on Utilizing Neural Network for Event Information Retrieval
Authors: Li Minglei , Zhu Yu , Wang Ren 
Published: September 2020

Abstract
Event information retrieval (EIR) is the task of retrieving news articles in response to the event-oriented query, which tailors for dealing with a large scale of news articles on breaking news (e.g., natural disaster). However, the challenges caused by the dynamic and complex natures of the event make EIR less studied than traditional information retrieval (IR). The existing studies usually employed either traditional IR models or machine learning approaches over hand-crafted features to retrieve events, which fails to capture more-complex structures of events with event evolution well. To address this issue, we exploit neural models incorporating the characteristics of events to EIR task. Experimental results demonstrate the effectiveness of our method over two datasets, which can regard as the simple but strong baseline for further research in EIR.
Tracking Control for a Rehabilitative Training Walker Considering Human-Robot Interaction
Authors: Sun Ping , Sun Yao 
Published: September 2020

Abstract
A new extended state observer combined with a Backstepping tracking control method were proposed in this paper for the omnidirectional rehabilitative training walker. The aim of this study is to obtain a stable tracking controller, and it considered the interaction forces of the user and walker to ensure that the omnidirectional walker can track accurately specified trajectory. The extended state observer was designed to estimate the unknown interaction forces of user and the walker using output position and speed states. Further, a Backstepping controller based on the observed value was constructed to resist the adverse effects of interaction forces. The asymptotic stability of the trajectory tracking error, the velocity tracking error and the state observation error were guaranteed. Through programming, simulation results show the effectiveness of the proposed design method.
Research on Improved Triangular Mesh Surface Subdivision Algorithm
Authors: Ma Hua , Zhu Yanan , Niu Xinya , Wang Wenshuai 
Published: September 2020

Abstract
Subdivision surfaces have been widely used in computer graphics. They are also commonly used to subdivide surfaces in 3D geological modeling. The classic triangular mesh subdivision algorithms are Loop subdivision algorithm, Sqrt3 subdivision algorithm and Butterfly subdivision algorithm. Based on the realization of the three algorithms, the paper displays that the improved Butterfly subdivision algorithm, compared with the Sqrt3 subdivision algorithm and the Loop subdivision algorithm, can alleviate the contradiction between data volume and smoothness. It can also achieve a better fittness, and can deal with the smooth subdivision of the model well in 3D geological modeling and other applications.
Study on BP Neural Network Optimization by Improved Decay Parameter Genetic Algorithm
Authors: Jiang Yi , Zhang Fan 
Published: September 2020

Abstract
BP neural network optimized by standard genetic algorithm is hard to meet the balance between global search capability with local search capability. Therefore, the decay parameters genetic algorithms were presented to improve the convergence rate and accuracy. The results of this approach were compared with that of the standard genetic algorithm as well as the improved genetic algorithm by MATLAB simulation software. The simulation results show that the decay parameter genetic algorithm has better accuracy and robustness than the standard genetic algorithm, and greatly gives consideration to both the global search ability and the ability of local search. As a result, the precision and speed on the system fault diagnosis are dramatically improved.
Deep Metric Learning Based Histopathological Image Classification
Authors: Zhang Gang , Xu Hui 
Published: September 2020

AbstractWith the rapid development of deep learning, histopathological image classification models have made remarkable progress. Recent deep learning-based methods have been applied to raw histopathological images to construct end-to-end models, which avoid hand-craft feature engineering. To construct a model that can capture the intrinsic pattern of the histopathological image dataset, we design a model based on deep metric learning which embeds data points into a Euclidean space. The proposed model trains a deep neural network, which embeds an input image into a Euclidean space where dissimilar images are located far away to each other and vice versa. We adopt a BN-Inception network pretrained on ImageNet as the embedding model. Then it is retrained on target datasets with some triplet loss function. A weighted distance-based triplet sampling strategy is designed to generate hard triplets for the training procedure. Evaluations on benchmark datasets indicate that our deep metric learning-based method outperforms recent successful deep learning models.
Construction and Application of Field Investigation Support Platform for Land Spatial Planning Based on GeoServer
Authors: Dai Yao , Duan Zengqiang , Ai Dong 
Published: September 2020

Abstract
Basic data and satellite images, such as topography, soil, cover type, social economy and so on, are very important for planning, and many of the data are scattered among multiple Internet resources and researchers. At present, geographic information system and Internet technology are developing rapidly. Many institutions have published a large number of thematic maps related to resources and environment on the Internet, and can realize the aggregation of these Internet maps in GIS software through WMF, WMTS and other protocols. Relying on the rapid development of cloud virtual server in recent years, the technology and economic feasibility of deploying WebGIS server to distribute proprietary data thematic map are greatly improved. With the help of cloud server, this paper constructed WebGIS service with the assistance of GeoServer and published thematic map information on OpenLayers as the client, so as to provide basic information related to natural environment and social economy for territorial space planning.
Research on Mechanical Deviation Correction Method for Industrial CT
Authors: Liu Jiayin , Yang Long , Wu Yuan 
Published: September 2020

Abstract
Industrial CT is the abbreviation of industrial computed tomography (CT) technology, which adopts X-ray imaging principle to realize the non-destructive three-dimensional imaging to obtain high-precision three-dimensional tomographic data and volume data inside the product, clear, accurate and intuitive. In addition, it can show the internal structure, composition, material and defective condition of the inspected object. Factors such as mechanical accuracy and mounting deviations can adversely affect CT imaging and, in severe cases, lead to CT imaging failure. It is important to achieve effective correction based on the existing CT mechanical accuracy and mounting deviations. In this paper the authors collect data and performed experiments in a real CT system, they use standard test blocks to correct the deviations while keeping the existing CT system unchanged, the effectiveness can be validated by the experimental data.
Dynamic Chain Analysis by Bipartite Network for Medicine Selection
Authors: Yin Xinming , Guo Yi , Cao Zhiwei , Xiong Min 
Published: September 2020

Abstract
The rapid development of society has brought about the uncertainty of social relations, and the structure of social networks is constantly changing. Chain forecast, as an effective method, plays an increasingly important role in walks of life in understanding the dynamic nature of the network and determining future relationships, combining the structural characteristics of the present status of the network to foresee the possible existence of future network nodes, this paper proposes a Chain forecast method for Disease treatment and a Chain forecast method based on bipartite networks (such as treatment correspondent diagrams). In order to verify the forecast effect of the method, we selected several Chain forecast algorithms for check. The results prove that our proposed method is better than other methods based on Chain forecast.
Sonar Image Translation Using End to End Network for Underwater Recognition
Authors: Hua Jin , Liu Mengzhao , Xin Daxin , Liu Yixin 
Published: September 2020

Abstract
With the rapid development of sonar technology, the classification of underwater sonar images is of great significance. In order to recognize underwater sonar images more effectively, side scan sonar is used for data acquisition. After pre-processing, six types of sonar images are selected. Combining with the characteristics of underwater sonar images, relevant theories and methods of convolutional neural network are studied to establish an underwater sonar CNN recognition model, which achieves 98.5% recognition success rate. Experiments show that the recognition model can improve the accuracy, speed and robustness of underwater sonar image classification, and can meet the practical application requirements.
Frequency Diverse Array with Cubic Sinusoidal Frequency Offset
Authors: Qin Xinxin , Li Changli 
Published: September 2020

Abstract
Frequency diversity array radar (FDA) has range-angle dependent beampattern, providing enormous potential for the application of this new type of radar. However, the beampattern of conventional FDA (CFDA) has two problems: range-angle coupling and range periodicity, limiting the application of FDA. How to solve the problems is the premise of realizing beam accurate control and target accurate positioning. In this paper, we propose a new FDA transmitter structure with cubic sinusoidal frequency offset (Cubic sin-FDA). Simulation results show that this FDA beampattern eliminates coupling and forms a spot-shaped beampattern. Compared with the existing nonlinear frequency offset FDA beampattern, this method has a narrower half power bandwidth and lower side lobe level.
Invulnerability Analysis of Electric Power Optical Transmission Network in Smart Grid Based on Complex Networks
Authors: Shi Jianqiang , Sun Na , Xia Zhengyun 
Published: September 2020

Abstract
In order to improve the invulnerability of electric power optical transmission network in the smart grid, based on complex network theory, experimental simulation of random attacks and selective attacks on electric power optical transmission network in Jiangbei District of Nanjing City is carried out, and the method of improving the invulnerability of the network is given according to the simulation results. In the experiment of random attacks, we investigate the impact of single node failure on network connectivity and screen the hub nodes, and get the hub nodes that need to be protected. In selective attacks, two kinds of attack strategies are proposed, namely, network efficiency relative loss rate first attacks and real-time betweenness first attacks. From three aspects that include connectivity, network efficiency and network efficiency relative loss rate to evaluate the invulnerability of the network. The experimental results show that the electric power optical transmission network has the worst invulnerability to real-time betweenness first attacks. On this basis, an important node evaluation and protection method is proposed to improve the invulnerability of electric power optical transmission network, and the feasibility and effectiveness of the proposed method is verified by the example simulation.
Low-Latency Ultra-Wideband High-Speed Transmission Protocol Based on FPGA
Authors: Hu Jinxian , Wang Jinfeng , Li Rengang 
Published: September 2020

Abstract
Aiming at the shortcomings of current high-speed serial transmission protocol including high latency and low effective transmission bandwidth, this paper designs a low-latency, ultra-wideband high-speed serial transmission protocol based on FPGA. Based on homologous system clock, data scrambling and synchronous frame synthesis are used to complete the data integration, so as to increase the effective bandwidth of data transmission. The receiving end reduces the data transmission latency by using the internal logic of FPGA and the buffer pool built by the register group together with status machine. The protocol is simple and universal, which can be applied to the FPGA platform of any OEM. Experiments show that the protocol can effectively reduce data transmission latency, improve the effective bandwidth of data transmission, and monitor the health and error code status of each data transmission channel in real time.
Position and Speed Measuring Method of Maglev Train Based on Federal Kalman Filter and Information Fusion
Authors: Liu Chang , Cheng Shu , Li Kaidi 
Published: September 2020

Abstract
The position and speed measuring system is an important part of the High-speed maglev operation control system. The speed and position information accuracy and reliability directly affect the train operation safety. Aiming at the shortcomings and defects of the existing position and speed measuring methods, based on the Federated Kalman filter principle, a multi-source information fusion algorithm is designed to effectively integrate speed and position information from Inertial Navigation System, global satellite navigation system and Doppler radar sensor into the train position and speed measurement. On this basis, a combined position and speed measuring method for maglev train is proposed, it not only has high measurement accuracy, but also has strong robustness and autonomous operation ability. The effectiveness and reliability of the proposed method are experimentally verified based on the MATLAB software simulation platform. The experimental results show that the proposed method can be applied to the real-time position and speed measuring of high-speed maglev.
A Container Service Chain Placement Greedy Algorithm Based on Heuristic Information
Authors: Yue Qiang , Liu Xiangtao , Fang Lijia , Wang Xiaoxiao , Hu Wenbin 
Published: September 2020

Abstract
Container technology is a light weight back-end virtualization solution which copes with the growing demand for Internet service concurrency. Network services can be provided by a series of containers between which network requests are sent and responded. We call these cooperating containers a container service chain. On a certain physical cluster, there are various available container placement of a service chain. Different container placement causes different service response time. In order to reduce the response time of container service chain, a greedy algorithm for container service chain placement is proposed based on heuristic information. Through quantitative analyzing resources provided by hosts and consumed by containers, we build a mathematic model of container service chain. We use this model to decide where to place container when service is expanded, and where to remove container when service is reduced. Our model reduced the response time of container service chains by 40% compared to average placement.
Research on Monitoring Method of Impulse Vibration in Large Transformer Transportation
Authors: Zhang Zhengxiang , Zhou Haibin , Zhou Zhengchao 
Published: September 2020

Abstract
The safety of long-distance transportation of large power transformers has become an important constraint factor for the EHV projects. In view of the problems existing in the Three-dimensional impact recorders and condition monitoring system of the long-distance transportation of large power transformers, this paper analyzes the amplitude frequency characteristics and influencing factors of the impact vibration during the transportation of transformer, adopts the fast Fourier transform to deal with the interference in the vibration signal, designs a monitoring terminal of the impact vibration based on the Internet of things which can accurately measure the vibration impact of the transformer, and develops a remote real-time online monitoring system to monitor the displacement and impact of transformers at anytime and anywhere. The monitoring terminal and monitoring system are applied in the actual transportation of converter transformer in an UHVDC project. Three monitoring terminals are installed in different positions of the transformer to monitor the transformer status and send the monitored information to the monitoring system. The reliability of the terminal and the superiority of the monitoring system are verified.