bluuebunny/crossref_metadata_2025_split huggingface dataset:
A predicton model for the seismic intensity in meizoseismal area based on Genetic Neural Network
Authors: Shao Dan , Tian Qinhu , Zhang Weichao , Ren Hao 
Published: June 2020

Abstract
In this paper, a novel model for seismic intensity prediction in meizoseismal area is proposed based on BP neural network. 322 earthquake events of MS
5.0 or more occurred in the mainland of China from 1966 to 2017 with detailed intensity records. From the collected sata set, seven factors related to the seismic intensity are selected. Principal component analysis is performed on these factors to generate the network input, while the intensity of the epicenter is the output of the network. The BP neural network is optimized by using genetic algorithm. Simulation results show that, compared to the traditonal prediction methods, the proposed prediction model based on BP neural network has obvious advantages in terms of prediction accuracy and correct prediction probability.
Using Topological Data Analysis to Process Time-series Data: A Persistent Homology Way
Authors: Ma Gang 
Published: June 2020

Abstract
Topological Data Analysis (TDA) is a novel new and strong-growing method to deal with various data in most areas. And Persistent Homology is one of the most pivotal tools in Topological Data Analysis to acquire topological properties of the data. This article is based on the main mathematics behind Topological and Topological Data Analysis. And it describes how to use the above theories and methods to do the analysis job for time-series data. Moreover, it discusses the further applications of TDA to other domains and the combination of machine learning with Topological Data Analysis. The article outlines the TDA model and principle behind the data set and provides insights into the function of TDA for time-series analysis as well as opportunities for future work.
An Extended Position Based Dynamics Method for Cloth Simulation
Authors: Quan Wei , Teng Dexin , Li Hua , Han Cheng 
Published: June 2020

Abstract
Position based dynamics is an effective method in real time deformable body simulation, which is widely used. But, as we all know, it is not suitable for greater accuracy applications. As to cloth simulation, the over-stretching problem especially affects its accuracy. Therefore, an extended simulation method based on position dynamics is proposed in this paper. The time step is subdivided effectively, and the number of iterations is controlled when cloth is deforming; We also optimize the Gauss-Seidel iterative solver in parallel using an improved grouping method which ensures the efficiency of the algorithm. This method is simple and easy to implement. Compared with the traditional method, the position error of deformation is significantly reduced while cloth is stretched, and the simulation efficiency is enhanced. Besides, it can be easily integrated into the existing position based dynamic framework.
A New Type of Marine Solar-energy Sail System
Authors: Chen Yan , Liu Xuetao , Tang Xujing , Zhang Yan 
Published: June 2020

Abstract
In this paper, a new type of marine intelligent solar-energy sail system is designed. According to the state and environmental parameters of the ship, it can increase the illuminated area of the sail by changing the sail attitude, realize the intelligent control of the sail, realize the efficient utilization of wind energy and light energy, and effectively increase the endurance capacity and application range of the clean energy ship. The promotion of the research results can greatly save the operation cost of cruise ships, have greater economic and environmental benefits in the long run, and conform to the concept of sustainable development.
Research on Similar Medical Records Recommend Model based on Natural Language Processing
Authors: Shuai Yong , Zhou Chunjiang , Pang Qiaoyue , Yuan Can , Xie Zheng 
Published: June 2020

Abstract
Aiming at the problem of low efficiency and poor credibility in medical record text data mining, this paper proposes a similar medical record recommend model based on natural language processing. Firstly, extract the samples by structured data in the medical record data and narrow down the extraction of similar medical records, then use the text keyword extraction algorithm to obtain the keyword text data about chief complaint, medical history and physical examination in the medical record text, use these data as input data, get the similarity between the input data and the patient data by text similarity algorithm, and use collaborative filtering algorithm to calculate the most similar medical records and obtain the most similar medical records diagnosis conclusions, take the chief complaint, medical history, physical examination data and diagnosis conclusions as input data to calculate the text similarity and collaborative filtering values and obtain the final recommend treatment plan. The case analysis proves that the model proposed in this paper has high credibility and practicality.
Difference Attention Based Error Correction LSTM Model for Time Series Prediction
Authors: Liu Yuxuan , Duan Jiangyong , Meng Juan 
Published: June 2020

Abstract
In this paper, we propose a novel model for time series prediction in which difference-attention LSTM model and error-correction LSTM model are respectively employed and combined in a cascade way. While difference-attention LSTM model introduces a difference feature to perform attention in traditional LSTM to focus on the obvious changes in time series. Error-correction LSTM model refines the prediction error of difference-attention LSTM model to further improve the prediction accuracy. Finally, we design a training strategy to jointly train the both models simultaneously. With additional difference features and new principle learning framework, our model can improve the prediction accuracy in time series. Experiments on various time series are conducted to demonstrate the effectiveness of our method.
Design and Research on Integrated Platform of EMC Laboratory
Authors: Shichao Guo , Dandan Guo , Qiongyu Zhang , Nankai Wu , Jiaxin Deng 
Published: June 2020

Abstract
The electromagnetic compatibility (EMC) laboratory plans to implement a proprietary EMC testing platform, which is customized according to the professional perspective of EMC testing. The integrated platform will fully utilize the overall advantages of knowledge resources from various angles to realize the automatic operation of various functions. The simple and tedious work of the engineers during the inspection process will be completed automatically, reducing the burden and devoting more time to creative and high-value work. At the same time, the platform will integrate higher-level related management systems and all EMC testing equipment to achieve overall data interaction among the testing department, design department and higher-level management agencies during the EMC testing process.
Unmanned Sorting Site Combined with Path Planning and Barcode Identification
Authors: Tan Haipeng , Zheng Xinrui , Chen Yiying , Zheng Weibin , Ni Weichuan 
Published: June 2020

Abstract
At present, the existing express sorting in the market has low accuracy and cannot be solved from manual handling. In addition, the management of express delivery sites is confusing, and the phenomenon of lost shipments is also endless. Regarding the issues above, we have proposed unmanned sorting site combining path planning and barcode identification. The site uses STM32 as the main control chip to realize barcode recognition and robotic arm control. Among them, barcode recognition uses calibration, image pre-processing and other related OpenMV technologies to realize fast identification, classification of express barcodes and obtain the location information of express shipments. The control of the robotic arm is achieved by using the D-H parameter method to locate each node of the robotic arm. And we are using introducing fuzzy adaptive PID algorithm to improve the control precision after path planning. After a large amount of experimental data analysis and comparison, the final conclusion is that the system can maintain high bar code recognition and fast picking accuracy under different conditions of different objects. And it can make up for many shortcomings of traditional express delivery sites and has certain market value.et value.
Study on the intelligent evaluation model after stoke based on random forest
Authors: Ge Yaru 
Published: June 2020

Abstract
According to the summary of “China cardiovascular report 2018”, in recent years, there are 2 million new stroke patients in China every year, and nearly 70% of them have lost hand function due to stroke. Therefore, the rehabilitation evaluation of postoperative hand function of stroke patients is complicated with low intelligence. In this paper, data collection methods are introduced firstly, and the original data are artificially classified. Then, starting from the decision tree method, the motion data of stroke patients are intelligently evaluated by establishing a random forest, and the evaluation results are returned to the patients for review in the form of floating point number based on their probability proportion. Finally, the results of the model experiment and its advantages and disadvantages are analyzed, and the future development direction and prospect are put forward.
Denoising Method for Underwater Acoustic Signals Based on Sparse Decomposition
Authors: Huang Guanqun , Xiao Yewei , Yin Zhe 
Published: June 2020

Abstract
In order to improve the problem of relatively low signal-to-noise in the extraction of underwater acoustic signals under the background of strong interference, a sparse decomposition-based underwater acoustic signal denoising method is proposed. The main work is as follows: First, the signal is decomposed into a complete dictionary that can reflect the characteristics of the signal environment through the singular value decomposition method. Then, a cyclic shift is used to construct a signal matrix and an initial dictionary. A newly generated super-complete dictionary is obtained through training and updating. The atoms in the dictionary matrix are correlated and orthogonalized by an adaptive orthogonal matching pursuit method. Finally, the linear combination of the atoms that can best reflect the characteristic information of the underwater acoustic signal is used to reconstruct the underwater acoustic signal to achieve the purpose of denoising. The noise filtering underwater noise signal is simulated and compared with the traditional filtering method. The simulation results show that this method improves the signal-to-noise ratio of the signal after sparse decomposition and reconstruction of the original underwater acoustic signal, and has a certain denoising ability under strong noise and various types of reverberation interference.
Media communication strategy in international disputes based on information Cybernetics
Authors: Li Kan 
Published: June 2020

Abstract
Media communication strategy is a key to controlling the initiative of public opinion in international disputes. In view of the problem that the public opinion strategy of safeguarding maritime rights in China is not systematic enough, this paper uses the cybernetic thinking of information dissemination, on the premise of analyzing the law of public opinion information movement, the expression of dispute elements and the positioning of communication perspective, This paper puts forward a strategic focus on the direction and efficiency of information dissemination, and promotes legal identity and public opinion strategies. Under the condition of information asymmetry, we can achieve the same effect as information symmetry through system, mechanism and scheme design, so as to lead the trend of social ideology in international disputes.
Void Aware Routing Protocols in Underwater Wireless Sensor Networks: Variants and challenges
Authors: Khasawneh Ahmad M. , Abualigah Laith , Al Shinwan Mohammad 
Published: June 2020

Abstract
Recently, Underwater Wireless Sensor Network (UWSN) become a wide range technology for gathering important events from underwater world. UWSNs is consisting of underwater sensors with limited energy and using acoustic link as a communication medium. Routing methods is the major part that need to be designed in order to transmit the data with minimum cost such as high delivery rate and low energy consumption. Communication void is one of the major perspectives that effects the delivery ratio and energy consumption in the whole network. Therefore, this issue attracts researchers to follow up and design techniques that helps in improving the overall performance of the networks. This article reviews and compares different void aware algorithms proposed for UWSNs. We analyze and discuss representative routing protocols, and discover the requirements considered by the different routing algorithms, as well as limitations and the design requirements under different operations. Finally, we highlight some important future perspectives for UWSNs belonging to routing protocols.
Interpretable Neural Network Construction: From Neural Network to Interpretable Neural Tree
Authors: Ouyang Xuming , Feng Cunguang 
Published: June 2020

Abstract
The neural network has made outstanding achievements in many fields, while comparing with traditional machine learning models, the neural network has poor interpretability, which brings great limitation to its practical application. Therefore, many researchers try to combine neural networks with traditional models to improve the interpretability of the neural network. Their methods either result in performance depreciation or lead to computation-intensive. In this paper, we propose to transform a neural network into the interpretable neural tree. In the interpretable neural tree, each node contains transformation function and routing function. Each transformation function corresponds to a layer in the neural network, using for controlling data transformation. The routing function is utilized to control the direction of data flow in the tree structure. Our experiments have indicated that the interpretable neural tree makes the neural network interpretable to some extent while maintaining the performance.
Ensemble of online sequential extreme learning machine based on cross-validation
Authors: Wei Liyun , Wang Lidan , Li Yunfei , Duan Shukai 
Published: June 2020

Abstract
The online sequential extreme learning machine (OS-ELM) was proposed as an evolution of the Extreme learning machine (ELM) in 2006. The learning algorithm can learn data by fixing or changing the size of the chunk (a block of data). Compared with other online algorithms, it has faster training speed, and the generalization performance is better. The proposed ensemble of neural networks further demonstrates that the use of the neural network sets with multiple consensus schemes has a greater improvement over the stability of a single network. The basic idea of cross-validation is to group the original data sets, part of which is to train as the training set and the other part of which is to verify as the test set, so the algorithm can reduce the over-fitting problem of the data to a certain extent, and extract more valid information in the limited data set. In this paper, a new ensemble of online sequential extreme learning machine based on cross-validation (ENOS-ELM) is proposed. Through application of cross-validation and integration to the training stage, the accuracy of the algorithm in classification is further improved.
From Viewpoint of 3C Model to Use Supply Chain Integrating to Create Competitive Advantage
Authors: Liu Jia , Chang Shih-Feng , Lee Xin-Ru , Tan Yong-Lin 
Published: June 2020

Abstract
While global environment is proceeding globalization and internationalization, traditional logistics management can not be coped with the change. Therefore, to be the winner, supply chain integrating is necessary. Domestic car manufacturers take supply chain integrating to cope with the pressure of the market in now and future, and they hope to keep the competitive advantage. In view of this, from viewpoint of 3C model, we use case study to discuss and analysis supply chain integrating on company C which is the benchmark of the car industry, and then we offer a good suggestion to car manufacturers and future study direction.
Development and verification of wireless torque sensor node for rotating shaft
Authors: Wang Dajian , Wang Zhinan , Chen Demin , Li Zhimin , Yang Sujun 
Published: June 2020

Abstract
There are many torque measurement technologies at present, because the rotating shafting torque monitoring is very important. However, compared with the cost-effective strain measurement technology, there are signal transmission and power supply technology bottlenecks in the long-term monitoring of large shafting torque. In view of these technical bottlenecks, this paper designs a strain torque monitoring system based on wireless signal transmission technology and wireless inductive power supply technology. In this paper, the current torque measurement technology is analyzed. The design principle and technical characteristics of wireless torque sensor and wireless induction power supply device are developed. This paper is a complete long-term monitoring solution for shafting torque, which has a good referent value for relevant engineers and technicians.
Torque allocation strategy for two axles four wheel drive electric vehicle with improvement of economy and stability
Authors: Ou Yang , Wang Peng , Xu Lei , Fan Jie , Zhou Zhou , Li Zhe , Bai Qin , Zhang Yuanwei , Gao Zihao 
Published: June 2020

Abstract
Two axles four wheel drive electric vehicle is an important development tendency of electric vehicle. However, how to coordinate the torque distribution between front and rear axles thus ensuring vehicle stability while maximizing motor’s efficiency still confronts great challenges. In this paper, we propose a three-layer hierarchical control method, which not only incorporates prior engineering knowledge into the control policy so as to relieve computational burden for real-time usage, but also properly deals with the priority between safety and system efficiency. Simulations are conducted to verify the effectiveness of the proposed method. Results show that the derived control policy can ensure high system efficiency when safety is guarenteed. Simultaneously, it can actively decrease the motor torque to improve vehicle stability when wheel slip occurs.
Research on automatic aerodynamic optimization for a SUV based on RBF model
Authors: Wu L L , Fu Y , Bu X B , Li X R , Ma X L 
Published: June 2020

Abstract
Considering the continuous shortening of automobile R & D cycle, mathematical methods have been more used in automotive aerodynamic optimization design for their capability to fit the law and calculate the optimal results. In this paper, the deformation software, fluid analysis software and optimization software are connected to build an automatic aerodynamics optimization platform of automotive shape. Based on the RBF model, the automatic aerodynamic optimization for a SUV is completed with the front windshield angle, rear windshield angle, engine cover angle, approach angle and departure angle as design parameters and the drag coefficient as design objective. The drag coefficient of the optimal model is 9.40% lower than that of the initial model, and remarkable drag reduction effect is achieved.
Experimental Study on Determining the Elastic Modulus of Metal Materials by Deflection Method
Authors: Wei Liuchuang , Wei Ping , Yang Yabiao 
Published: June 2020

Abstract
This paper studies the method of measuring the elastic modulus of metal materials by the deflection method. The relationship between the deflection and the load and the elastic modulus of the metal cantilever beam is established, so that the elastic modulus of the cantilever beam is indirectly measured. The experiment requires that the deformation of the material is within the elastic range. Furthermore, the deflection method is adopted. Not only the method is simple and does not require expensive equipment, but also the average value of multiple sets of experimental results is used as the modulus of elasticity of the material during measurement, which ensures a certain accuracy.
Detection of Rotor Position with MR Sensor TLE5309D
Authors: Cai Bingkun , Li Jianqiu , Hu Jiayi , Liu Shucheng , Xu Liangfei , Ouyang Minggao 
Published: June 2020

Abstract
The rotor position is a key state in the closed-loop control system of the permanent magnet synchronous motor (PMSM). Its measurement accuracy has a significant effect on steady accuracy, dynamic response, and torque ripple. The magnetoresistance (MR) sensor is widely used in the rotor position detection due to its distinct advantages, such as simple structure, high sensitivity, and strong robustness. In this paper, a solution using the giant magnetoresistance (GMR) sensor in TLE5309D chip for angle measurement is introduced, and its feasibility is verified by finite element analysis (FEA) simulation and bench test. In the simulation, two schemes different in the sensor location and the number of permanent magnet poles, are evaluated according to the higher harmonic content and field direction stability near the sensor. The experiment from 1 rpm to 1000 rpm is implemented, and the measurement results after calibration based on discrete Fourier series (DFS) show that the GMR sensor has a good performance in the test.
Research and design of scanning servo device for airborne microwave radiometer
Authors: Hu Yong , Liu Xin , Hu Ye , Wang Xianli , Xu Mingxu , Li Bin 
Published: June 2020

Abstract
The Microwave Radiometer can obtain various characteristics of the measured target. The remote sensing aircraft has several outstanding advantages such as stronger mobility and larger capacity, which makes the Airborne Microwave Radiometer become the most important part of Microwave Remote Sensing technology. The mechanical structure was designed in this paper, which included airborne microwave radiometer scanning servo device structure and servo control system according to the requirements of the project. The statics and dynamics simulation of the scanning servo device were analyzed, the maximum value is 46.48MPa at the connection between the left support pedestal and the left drive shaft in the statics analysis and the first-order natural frequency of the scanning servo device is 50.516Hz in the modal analysis. The results showed that the mechanical structure meets the requirements of airborne working environment. The current - speed double closed loop control system was used to achieve the high precision speed control. The current - speed double closed loop control system was used to meet the high precision speed control requirements of high precision airborne microwave remote sensing technology.
Driving Behavior Recognition Based on EEG Data From a Driver Taking Over Experiment on a Simulated Autonomous Vehicle
Authors: Zou Bin , Xiao Zhenming , Liu Mutian 
Published: June 2020

Abstract
Driving behavior recognition is a critical part of the driver safety system. Most of the popular researches have utilized the driver’s questionnaire data and driving data to recognize driving behavior. But few studies have used physiological data to recognize different driving behaviors, such as EEG data. In this paper, a new method was presented to recognize different driving behaviors based on EEG data. The driving data and EEG data were collected when participants took over the autonomous vehicle. Through K-means, the driving data were classified into two groups, and the classification results were utilized as inputs to generate a k-Nearest-Neighbor model to recognize driving behavior groups via EEG data. The results showed that the average recognition accuracy was 80.6%, and the highest recognition accuracy was 84.2%, indicating that there is an obvious relation between EEG data and driver taking over behavior.