bluuebunny/crossref_metadata_2025_split huggingface dataset:
SOC Estimation Based on Time Series Neural Network and Its Performance Evaluation
Authors: Li Jianhua , Liu Mingsheng , Jiang Yanmei 
Published: October 2020

Abstract
Aimed at the disadvantage of state of charge (SOC) estimation by using traditional feed forward neural network, a new method proposed to soleve the problem. The time series neural network is introduced to estimate the SOC. The experiments results show that the time series neural can estimate the SOC more accurate. In addition, the different structures and the key parameter are discussed to achieve the best performance.
Decision theory analysis of airport driver and management based on linear regression analysis
Authors: Yao Jingmin , Lu Zhenbo , Wang Zicheng 
Published: October 2020

Abstract
In this paper, we turn the driver decision problem into: the game theory between the revenue generated by the driver after waiting for the waiting in the storage pool and the return to the urban area. When the driver chooses to wait for the yield greater than the return to the urban area, he chooses to wait. On the contrary, we return directly to the urban area. Meanwhile, we collect, analyze and process the information about the taxi in Shanghai Pudong Airport and Shanghai, and calculate the proportion of passengers choosing taxi after arriving at the airport through the Nested Logit model. And multiple linear regression equation model is used to deal with airport passenger throughput, forecast the take-off and landing of flights, speed analysis of taxis and the driving range of taxis under different weather conditions, and ultimately determine the drivers’ different decisions.
Analysis of Environment Accounting in the Context of Big Data
Authors: Yao Qian , Gao Yuanyuan 
Published: October 2020

Abstract
The production and operation of enterprises create wealth for themselves, but the consumption of resources in the operation of enterprises also brings pressure to the natural environment. It is an inevitable choice for enterprises to improve their core competitiveness and adapt to the future economic environment more quickly by integrating environmental protection into their strategic planning, management activities and accounting system. The implementation of environment accounting has become the trend of economic development in the future. This paper analyzes environment accounting under the background of big data, by using the method of SWOT analysis. The article holds the opinion that the strengthening of environmental awareness of enterprises and the public and the increasingly perfect laws and regulations have laid a good foundation for the development of environment accounting. Under the rapid development of information technology, some problems of environment accounting, disclosure of environmental information and the increase of operating cost in the short term are also exposed. Finally, this paper puts forward some suggestions on the development of environment accounting at the present stage.
Analysis of loading imaging based on time reversal theory
Authors: Feng Hao , Shi Fengjiao , Qin Lei 
Published: October 2020

Abstract
Based on the simulation results of the finite element software, setting up an experimental platform, and the echo signals reflected from the hole interface are collected on the concrete test block, and the positioning imaging is realized through the time inversion model. According to the simulation imaging research, in this paper, the peak-time contrast imaging method will be used to analyze and compare the displacement field, stress field, and energy field respectively. Finally, damage imaging will be performed through the energy field.
Research on Attack and Defense Game Model for Improving the Efficiency of Network Intrusion Detection System
Authors: Zhou Guohui 
Published: October 2020

Abstract
The widespread use of wireless networks has expanded people’s ability to obtain information, but its inherent network characteristics make it more vulnerable to cyber-attacks. Existing intrusion detection systems usually only target specific attack methods, but are incapable of other attacks. Based on this research background, taking game theory as the theoretical basis, it analyses the offensive and defensive process in wireless sensor networks, and demonstrates the necessity of implementing an intrusion detection system by analysing model equilibrium solutions. In view of the diversity of attack methods of network intruders, the game model is further improved, and a non-cooperative complete information static game model is established. By analysing the mixed Nash equilibrium solution of the model, the best defines strategy of the intrusion detection system is obtained and the system is balanced. Energy consumption and detection efficiency. Simulation experiment results show that the wireless sensor network intrusion detection system based on offensive and defensive game can not only effectively resist multiple network attacks, but also reduce the energy consumption caused by the intrusion detection system and prolong the service life of the network.
Research on Image Processing Technology of Dynamic Face Video Recognition Based on Computer Vision Monitoring
Authors: Liu Min 
Published: October 2020

Abstract
At present, face recognition methods are mainly used to identify static images. In surveillance video, faces in different video frames are correlated and only part of the face can effectively reflect face information; according to the characteristics of face image changes in surveillance videos, A face recognition method based on surveillance video is proposed; firstly, a video face sequence is obtained by combining face detection and tracking technology, and then a partial face image recognition result in the video face sequence is taken as a guide to select all face sequence images The representative face image in the image is recognized, and finally the face information is comprehensively reflected according to the recognition results of all the selected face images; the experimental results show that this method can effectively improve the face in the surveillance video while ensuring the recognition rate and the false recognition rate. Real-time recognition.
Real-time Detection System of Gait Event for Disabled People
Authors: Gong Sijia , Wang Zhiming , Ye Wangwei , Hua Qi 
Published: October 2020

Abstract
The gait detection technique provides objective and quantitative kinematic and dynamic evidence for lower limb amputee, which plays an important role in supporting gait analysis. Whether a prosthesis orthotic is reasonable and can achieve the best therapeutic effect depends on the doctor, therapist and orthotist to conduct a complete physical examination, walking ability test, gait analysis assessment and reassessment after completion. This paper mainly studies and designs a portable gait detection system for lower limb amputees based on the inertial measurement unit, including the hardware and software design of the data acquisition module, the software and hardware design of the data receiving module and the real-time display of the gait detection data of the upper computer software.
The Reliability Design of Switch Chip Based on THENA Process Stimulation System
Authors: Zhao Zhihuan , Zhao Zhibin , Jiang Mingming , Chen Chuanzhong , Song Weihai , Zhang Li , Liu Lili , Yu Huijun 
Published: October 2020

Abstract
This paper mainly focuses on the 2CK6642 silicon switch diode chip, which is mainly used for the surface-mounted packaging device of CLCC-3 metal ceramic. Different from that of ordinary diode, the internal structure of 2CK6642 silicon switch diode has a high resistance region I in the middle of Type P and Type N silicon materials, forming a P-I-N structure. Silvaco TCAD interactive tools, i.e. ATHENA process stimulation system and ATLAS device stimulation system, are used to conduct the design and stimulation of 2CK6642 silicon switch diode chip, so that alternative plugging with external devices could be achieved finally.
Research on Logistics Text Information Abstract Generation Method
Authors: Ma Yao , Zhuang Yufeng , Hu Yanzhu , Tian Wenjia , Wang Song 
Published: October 2020

Abstract
The sensational headline news has seriously affected the news browsing experience of netizens, and has caused an increasingly bad impact on the entire network. Due to this problem, the paper proposed a summary generation method based on Graph2Seq model. This method integrates keywords obtained from graph neural network training with contextual information, and integrates them into the Graph2Seq model as an attention mechanism. It plays a central role in guiding the abstract generation process and improves the accuracy of abstract generation.
Real-Time Panoramic Multi-Target Detection Based on Mobile Machine Vision and Deep Learning
Authors: Wen Anzhou 
Published: October 2020

Abstract
Research on computer vision technology based on deep learning has become an important research direction in the field of artificial intelligence. Among them, the target detection task of images and videos has occupied a pivotal position in many intelligent vision research and applications. The purpose of this article is to learn from the existing object recognition and target detection technology foundation, to conduct in-depth research on the current traffic patrol deficiencies, to achieve real-time patrol and monitoring of key roads. This article chooses to use the target detection algorithm of Faster R-CNN in deep learning, applies drones and mobile machine vision to traffic patrol inspection, and detects various targets in the PASCAL VOC data set. The AP of the bus is 68.20%; The AP of the car is 75.50%; The moving object recognition and tracking method based on Faster R-CNN and Hungarian matching method is verified, and traffic flow data can be calculated to realize real-time monitoring of road traffic. The research in this paper helps to overcome the defects of human eye recognition in the traditional monitoring system and improve the efficiency of traffic inspection.
The Research of the control system of the intelligent vacuum robot
Authors: Zhang Guang Cai , Gao Xiuqian , Guo Lei , Liu Yuting , Wang Zhihao , Wu Xiaoguang 
Published: October 2020

Abstract
In this paper, a household intelligent vacuum robot based on STC89C52 is researched. The hardware composition and working principle of the system are introduced, and the program and flow chart are given. After continuous study and discussion, the general research, circuit research, physical welding and system debugging of the whole system are completed. In this paper, the home intelligent dust collection robot is researched to detect the obstacles along the way through e18.d80nk infrared obstacle avoidance sensor, and transmit the detected signals to the single-chip microcomputer for discrimination. The single-chip microcomputer then controls the motor steering through the L298N driving module to achieve the obstacle avoidance function, and carries out the dust collection through the bd6081 fan circuit when traveling. After the completion of the research, it can basically achieve the functions of quick response, obstacle avoidance and dust collection of household intelligent cleaner.
Convergence Analysis of Multilayer BP Neural Network with Momentum Term
Authors: Xu Xiang , Xie Gang 
Published: October 2020

Abstract
In this paper, we analyze the convergence of a back-propagation (BP) neural network with momentum term containing multiple hidden layers. When the learning rate is constant and the momentum coefficient is adaptively changed under certain conditions, we give both the weak and strong convergence results of the algorithm, and give corresponding theoretical proofs for both convergence results.
Construction of Fault Diagnosis Model of Metro Wheel Speed Box System Based on Convolution Neural Network
Authors: Ren Xiangyi , Wu Jingyuan 
Published: October 2020

Abstract
This research focuses on a fault detection method apply on the subway wheelsets. This fault diagnosis method is mainly aimed at the vibration signal to distinguish the fault and abnormal situation. At the same time, the vibration signal incorporated with the deep learning method to establish the diagnosis mechanism. Reliable online data of subway in a city of China and CNN (Convolutional Neural Network) are applied in the web training process. The degradation of vibration signal of rolling functional unit (wheelset) was summarized, and the difference between normal and fault signals of rolling functional unit (wheelset) was studied. The feedback learning mechanism makes it possible to update the neural network in real time.
Research on computer aided diagnosis based on artificial intelligence
Authors: He Ziqi , Qin Chunkai , Zeng Zhaobo 
Published: October 2020

Abstract
Magnetic Resonance Imaging (MRI) is a clinically important means of examination. Different pulse sequences can be used to obtain information about the structure, function, metabolism and other aspects of human tissues. Magnetic resonance imaging is also one of the most complex medical imaging methods. There are a lot of scientific and technical problems to be studied in various stages of magnetic resonance imaging, such as data acquisition, image reconstruction, image post-processing and image analysis. In this paper, the methods of data-driven and artificial intelligence are used to study the MRI image reconstruction, image processing and image analysis.
The Computational Model of Firing Error and Transfer Range During Naval Gun Implementing Transfer Fire at Sea Target
Authors: Sun Dongyan , Xie Weihe , Hu Jiang 
Published: October 2020

Abstract
According to the characteristic for naval gun to implement transfer fire at sea target, the causes of transfer fire errors are analysed, and the computational model of transfer fire errors is put forward, then the calculating methods of transfer value and transfer range are proposed. This paper provides strong technical support for naval gun to implement transfer fire.
Multimodal Haptic Interaction Technology and its Application
Authors: Meng Xiangrui , Song Yan 
Published: October 2020

Abstract
Haptic human-computer interaction technology is an important part of human-computer interaction in virtual reality. It simulates the process of human’s haptic perception of real objects and feeds back the haptic information of virtual environment to people, greatly improving the interactivity and telepresence of virtual environment. Starting from the differences between virtual objects and haptic expressions, this paper summarizes the research status of haptic modeling technology and haptic rendering equipment, and analyzes the characteristics of haptic interaction technology in various modes. The framework, research progress and application of haptic human-computer interaction information sensing system based on cloud are proposed. Finally, the development trend of multimodal haptic interaction technology is prospected.
Pointer instrument image recognition system for veterinary drug production based on RFID and deep learning
Authors: Qiu Qin , Cao Shanshan , Wu Jianzhai , Kong Fantao , Sun Wei 
Published: October 2020

Abstract
Based on RFID technology, the tag data can be read and identified dynamically, real-time, contactless, long-distance and batch, and the image information of pointer instrument can be collected. Using Faster-RCNN to detect and locate the dial area, and combining with Le Net-5 method to identify the dial number, the system has the advantages of fast reading, high accuracy, strong adaptability and intelligent management. It provides a solution for the automatic recognition and reading of pointer instrument in the complex environment of veterinary medicine production workshop.
Arbitrary Finite Dimensional Generalization of Quantum Coin-Flipping Game
Authors: Cao Fasheng 
Published: October 2020

Abstract
Two players of coin-flipping game randomly choose one of two strategies, flipping over or not. Meyer quantized states of the game to be described instead by density matrices of the two-dimensional Hilbert space. In order to obtain and unify generation of the two players coin-flipping game with more than two strategies, we renew the need for equivalently describing Meyer’s PQ coin-flipping game by representing player’s strategies as elements of permutation groups. Expanding the dimension of quantum vector to arbitrary N-dimension in the coin-flipping game model, we generalize Meyer’s PQ coin-flipping game to PQ arbitrary finite strategies game. Owing to the quantum Fourier transform, quantum contrivance of Q has a winning strategy in PQ coin-flipping game with arbitrary finite strategies. Even though the player Q has a small probability of winning his opponent in the classical PQ coin-flipping game with arbitrary finite strategies, he can win his opponent who uses classical mixed strategies by his quantum strategies. Furthermore, we realize the winning quantum strategies of quantum player Q of PQ coin-flipping game with arbitrary finite strategies in macro world.
Simulation and Analysis of Noise from Acoustic Hood of Micro Electric Vehicle Range Extender*
Authors: Yuan Chun , Yuan Haowen , Su Hongchun , Li Long 
Published: October 2020

Abstract
Aiming at the noise control requirements of micro electric vehicle(EV) range extender, the author designed an acoustic hood and base it to build a 3D model and make a modal analysis and acoustic simulation to analyze the effect of noise control. Verifying the design of acoustic hood is reasonable. At the same time, it’s one of the important reasons the natural frequency of the hood results in the difficulty of noise control which can supply reference for improving design of acoustic hood of range extend.
Logistic big data prediction of gastric cancer
Authors: Wang Yuxuan , Zhong Caiwei , Zheng Yuxiang 
Published: October 2020

Abstract
In the era of big data, precision medicine for gastric cancer refers to the high throughput analysis of clinical data of patients with gastric cancer. The proposal of personalized diagnosis and treatment for each patient’s molecular biology and pathological characteristics, so as to achieve personalized precision medicine. In this paper, the prediction method is significance analysis and the statistical method of prediction model is logical analysis and discriminant analysis, and the significance test standard is p < 0.05. Logistic regression was used to identify and predict the prevalence of cancer patients, and the data used for prediction analysis were all from MS SQL database. The results showed that blood routine, blood biochemical and urine routine data can be used to distinguish cancer patients from healthy people. The cancer risk prediction model based on blood routine, blood biochemical and urine routine data can accurately target high-risk cancer people with an accuracy of 95.5%. In the era of big data, the accurate diagnosis and treatment strategy of gastric cancer can promote the aggregation, integration and sharing of gastric cancer related data.
An Improved K-means Algorithm for Supplier Evaluation and Recommendation of Purchase and Supply Platform
Authors: Li Junting , Qiu Wenwen , Li Weigang 
Published: October 2020

Abstract
Aiming at the problem of supplier evaluation and selection in B2B e-commerce, a supplier evaluation and recommendation method based on improved k-means algorithm is proposed. Firstly, this paper analyzes the supplier evaluation and recommendation ideas based on the purchase and supply platform, and proposes the data mining algorithm ideas of clustering analysis and AHP evaluation; secondly, K-means algorithm is proposed based on the data mining model, and the algorithm is optimized according to the data characteristics of the purchase and supply platform; finally, taking the business data of the industrial product purchase platform of the volume purchase network as an example, not only the effectiveness of the algorithm is verified, but also the clustering effect of the algorithm is good and the calculation speed is fast, which provides a practical and effective supplier evaluation and recommendation method for B2B trading website.
Research on Optimization Model of Dynamic Distribution Path Based on Intelligent Logistics
Authors: Gao Yiwen , Wang Xifu 
Published: October 2020

Abstract
Compared with traditional logistics distribution, today’s express delivery is more dynamic, increasing the difficulty of distribution path optimization. The application of intelligent logistics technology in dynamic distribution path optimization is a deep application of intelligent logistics technology and a major breakthrough in logistics distribution. This paper, aiming at minimizing the total cost, establishes the DVRP-TW model of distribution path optimization model based on dynamic traffic and uses improved Genetic Algorithm to solve it. A delivery network in Beijing is selected as an example, the optimization results of distribution path under dynamic traffic are obtained by MATLAB programming. Finally, by comparing and analyzing the results of distribution path optimization under static condition, it is concluded that it is very necessary to consider the influence of dynamic traffic on distribution path optimization, and the more accurate the road information processing is under intelligent logistics, the more scientific the design of distribution path is.
On the Discussion of Computer Mass Data Access and Its Characteristics in Time Limit
Authors: Wu Xingping , Liu Xusheng , Li Ziqian , Deng Zhidong , Zhu Qing , Zhang Li 
Published: October 2020

Abstract
With the rapid development of the Internet era, the Internet of Things, big data, mobile payments, and the digital age of AI, information technology is gradually covering people’s lives and learning. People come into contact with tens of thousands of information every day. Getting useful data can not only shorten the time effectively, but also meet specific needs. For example, the State Grid will conduct big data analysis on our daily electricity consumption according to our daily electricity consumption, analyzing the peak and trough of electricity consumption, and the abnormal electricity consumption in January. In all the consumption data analysis, the results of the analysis are used in the power transmission and transformation business planning of the power sector, carry out targeted preparation plan in the specific transmission of power, and further improve the quality of power consumption. Based on this, the paper discusses and analyzes the massive data access, massive data analysis and data effectiveness.
Otsu Image Threshold Segmentation Method Based on Seagull Optimization Algorithm
Authors: Wang Yuyin 
Published: October 2020

Abstract
In this paper, a two-dimensional Otsu image threshold segmentation method based on Seagull optimization is proposed. The Seagull algorithm is used to calculate the threshold points of two-dimensional Otsu image segmentation and segment the image. The algorithm makes full use of the seagull migration operator of global search and the seagull attack operator of simulated local search. Simulation results show that the algorithm not only improves the speed, but also improves the segmentation accuracy.