bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on Seismic Data Interpolation and Reconstruction
Authors: Xie Xiaoguo , Pan Shuling , Luo Bing , Chen Cailing , Chen Kai 
Published: September 2020

Abstract
The seismic record samples are frequently larger than appropriate and the record data is often lost in survey, so we need to perform interpolation and reconstruct of seismic data. In this thesis, generalized F-K seismic trace interpolation method is applied to resolve the problem of the serious insufficiency of spatial sampling. τ-p transformation is applied to reconstruct seismic data technique to resume lost seismic data. Finally, models of theoretic data and practical seismic data can verify that the interpolation and reconstruction methods described in this thesis own high applicability and efficiency.
Criminal Punishment Prediction Based on Fuzzy Document Vector Model
Authors: Fu Taotao , Kong Dezhen , Jin Yilin , Jin Canghong , Zheng Min , Chen Bing , Zhu Danyan 
Published: September 2020

Abstract
The drug addict problem is more and more critical to the world during current periods, analysing the criminal process and predicting the final punishment from judgments report is an interesting and important task. Existing studies on text analysis and language model supply methods based on special feature selection and ontology models generation which need external knowledge by human experts. In this paper, however, we creatively leveraged such text data onto prediction in the public judgments without human business. We propose a combined framework to capture the prediction problem by considering both valued based rules and fuzzy document models. This framework contains the complete process as: information extraction, term fuzzy and document vector regression. We setup an experiment on a real-world dataset and compare our model with traditional classification and regression methods. The results show that our model outperforms than others by both RMSE and R squared measures.
Power Grid Fault Diagnosis Method Based on Stacked Sparse Denoising Auto-Encoder and GRU Network
Authors: Zheng Ling , Yan Panfei , Chen Fei 
Published: September 2020

Abstract
This research aims to solve the problems of feature extraction, data dimension reduction, gradient dissipation and low training efficiency in power grid fault classification. Aiming at PMU measurement data with high dimensionality and strong real-time characteristics, a fault diagnosis method based on Stacked Sparse Denoising Auto-Encoder and GRU network is proposed. The Stacked Sparse Denoising Auto-Encoder is used to reduce the sequence dimension to obtain the sparse feature expression of the data, and then the time-dependent features of the data extracted by the GRU are used to obtain the fault type. Simulations and experiments show that compared with the traditional neural network algorithm, the proposed method can effectively extract high-dimensional data features, reduce data dimensions, improve the efficiency of GRU network classification, accelerate the convergence speed and reduce the training time, and has better stability and higher accuracy.
Optimization of Tunnel Image Short-Distance Transmission Method Based on Visible Light Communication
Authors: Pei Jixue , Cao Shuhao , Wang Yutong , Liu Yun 
Published: September 2020

Abstract
Visible light communication (VLC) technique has a wide range of applications in the daily life, the penetration rate of it is further improved in recent years, and it provides a new possibility for information transmission without traditional communication signal coverage like the tunnel or the basement. Based on the analysis of the latest research by domestic and foreign scholars, and the research of the existing device for image transmission through visible light and the particularity of the tunnel environment, this paper studies the optimization of the light source distribution, the measurement of the illuminance at the receiving end, the signal modulation and demodulation, and the encoding and decoding technology. Through the simulation experiment and the selection of existing components and technologies, the optimization of the original device is realized, so that it can be used in the tunnel information transmission and can provide certain reference for future visible light communication technique research.
High Performance Online Loop Closure Detection for Topological Mapping
Authors: Shi Zhaowei , Luo Jingyu , Wang Yunfeng , Liu Jinfeng 
Published: September 2020

Abstract
In simultaneous localization and mapping (SLAM) system, loop closing is defined as the correct identification of a previously visited location. Loop closing is essential for the precise self-localisation of the robot; however, the performance of loop closure detection is seriously affected by dynamic objects and perceptual aliasing in the environment. In the traditional likelihood matching methods, the number of matching words and the difference between them are not considered. This paper proposes a method based on mixed similarity to calculate the similarity score, thereby improving the performance of closed-loop detection. Experiments are performed on datasets from dynamic environments and visual repetitive environments, and then this method can produce a higher recall rate with 100% accuracy compared to the latest methods.
Tank Behaviour Decision Based on Behaviour Tree and SA-QL
Authors: Liu Ruifeng , Tian Mengfan 
Published: September 2020

Abstract
With the wide application of artificial intelligence technology in the military field, unmanned intelligent equipment will play an increasingly important role in the future equipment combat system, and autonomous decision-making ability will directly affect the combat performance of equipment. Therefore, the research on decision-making ability of unmanned intelligent equipment has important practical significance. This paper introduces the principle of behavior tree and SA-QL algorithm, proposes a modeling method of autonomous behavior decision based on behavior tree and SA-QL, and applies it to the construction of tank behavior decision model. By constructing the tank behavior tree model, the autonomous behavior decision is realized, and the tank model has the ability of continuous learning and evolution by using Q learning. Simulation results show that the built tank model can learn new decision instructions according to environmental changes and make behavioral decisions autonomously, which also proves the effectiveness of the proposed modeling method.
An Identification Method of Question Subjects Based on Word Embedding and LSTM
Authors: Gao M X , Fu Z X 
Published: September 2020

Abstract
Using the subject of the question can locate the question area, narrow the scope of the query, and provide users with better answers. The question text is usually short text. Therefore, in view of its sparse features and irregular structure, this paper proposes an identification method of question subjects based on word embedding and LSTM (IQS-WE-L), and uses question set on the MadSci website for experimentation, which has three subjects. We firstly use the Word2vec to train the Wikipedia database to generate a dictionary. Then based on word vectors, we propose four feature extraction methods: W2V, W2V-TFIDF, W2V-c-TFIDF and W2V-c, which formalizes the text features into vectors through word embedding and other features. Finally, we build an LSTM network for classification training to identify the subject of the question and quantitative evaluate effect of four feature extraction methods we proposed. Experimental data shows that the method proposed in this paper can effectively identify the subject of the question. When classifying the subject of the question, the F1 value can reach a maximum of 0.9339.
A Comparative Study of Urban Built-Up Area Change Monitoring by Remote Sensing Images and POI Data—Taking Shenyang as an Example
Authors: Sun Lishuang , Wu Xinyu , Xie Zhiwei 
Published: September 2020

Abstract
The urban built-up area change monitoring is important for Urban planning. In this paper, 2016 and 2019 remote sensing images and POI data of Shenyang are selected for monitoring urban built-up area change. First, the remote sensing images and POI data are preprocessed. Second, the urban built-up change area of Shenyang is extracted from remote sensing images by neural network classification. Third, the built-up change area of Shenyang is extracted from POI data by kernel density surfaces. Finally, the similarities and differences are analyzed between the two data in urban built-up area change. Experimental results: the growth trends of built-up areas are same, but the change areas were quite different. The change area is about 404.8km2 with the remote sensing images, and the change area is about 63.3 km2 with POI data.
Tuina Force Acquisition System of Finger Pressing Therapy
Authors: Zhang Yu , Zhang Hao , Li Shanhong , Meng Fangfang , Ma Tingting , Chu Zhong 
Published: September 2020

Abstract
A Tuina force acquisition system of finger pressing therapy is discussed. Based on STC12C5A60S2, a display platform for Tuina force acquisition is designed. The hardware of the system is mainly composed of minimum system module, signal acquisition module, amplification and filtering circuit, sound and light alarm module and display module. The system is initialized at startup and adjusted to data acquisition state. When the doctor starts to massage, the measured data by sensor is transmitted to data acquisition system, then transmitted to host computer after processed by single-chip module. The data is displayed real-timely and dynamically on host computer and judged whether or not to generate alarm according to the preset threshold. When the sensor prober is placed on his upper arm, and mechanical stimulation is performed using finger-pressing therapy, the system can collect and display finger-pressing massage force accurately in real time. Experiments show that the system can collect massage force quickly and effectively in real time.
Research on the Technology of Ground Fault Location Based on Two Terminal Measuring Points
Authors: Chen Hongwen 
Published: September 2020

Abstract
At present, the power grid is particularly prone to small current grounding, it is difficult to detect the fault signal due to the large transition resistance, and it is very difficult to locate the fault accurately. Sometimes the fault is very difficult to find, because the line is longer and the wiring is more complex, At this time, we can only rely on the experience of on-site workers for line finding and investigation, and find out the problem in time Ground fault line, find fault point, Sometimes, obvious fault is easy to find, but hidden fault is difficult to find, and the fault is not continuous, so accurate fault finding is very useful for the State Grid, Therefore, this paper through advanced computer technology, the latest sensor technology, through the installation of appropriate detectors at both ends of the power grid, can real-time monitor the changes of the power grid and find fault points. Through theoretical derivation and practical verification, this method is proved to be very effective.
Learning Process Oriented Teaching Quality Improvement
Authors: Liu Yifan , Zhan Jing , Fan Xue 
Published: September 2020

Abstract
In order to get targeted improving strategies for better teaching quality, it is necessary to establish learning data model for specific courses covering the whole learning process, and accurate and stable student performance classification model accordingly. Drawing on existing learning data analysis of online teaching, this paper proposes process oriented learning feature model, student performance classification method, and corresponding improving strategies analysis. With learning data from “Computer Network Fundamentals” course, in which combined online and offline teaching methods are used, learning process oriented feature model are defined using correlation analysis and clustering methods; improved Support Vector Machine (SVM) classification method with grid search optimization are proposed to get optimized student performance classification model, which gains 8% improvement on the accuracy compared to classified SVM model. Based on experiments, the accuracy of optimized classification model is 90%, with 6% false positive rate and 11% false negatives rate, which are reasonable and shows the accuracy and stability of our model. The improving strategies analysis for different students classification are also given, which can provide strong support for teaching quality improvement.
A Resting-State fMRI Study of Static and Dynamic Graph Analysis for Seafarer’ Brain
Authors: Shi Yuhu 
Published: September 2020

Abstract
In order to explore the influence of the marine environment on the topological properties of seafarers’ brain functional connectivity networks and the specificity of the topological structure of seafarers’ brain compared with that of non-seafarers, the fMRI data of 33 seafarers before and after sailing as well as the data of 33 non-seafarers are used to explore the differences of topological structure between them from both static and dynamic brain functional connectivity networks among the 90 brain regions of AAL template in this study. The results show that the marine environment has a time-dependent influence on the topological structure of seafarers’ brain functional networks, especially for the dynamic brain functional networks, which means that some topological changes can be recovered in a relatively short time, while others may have long-term effects to form the unique topological characteristics of brain functions for seafarers. These results have an important reference value to further explore seafarers’ professional brain plasticity.
Equipment Operation Analysis and Application Research Based on Big Data Electric Power Distribution Network
Authors: Liu Shi , Ji Xin , Zhao Xiaolong , Wang Qian , Shang Zhongyi 
Published: September 2020

Abstract
With the rapid development of the current power supply enterprise, it is necessary to process the distribution network business according to the actual business requirements, and easily collect and transfer the archiving and operation data in four distribution network equipment types from the source business system to explore the potential correlation of power big data indicators. This paper proposes one method of constructing the distribution network operation monitoring index system based on two aspects of operation efficiency and power supply capacity, and proposes a corresponding data analysis model for detailed analysis, and finally carries out the model verification. The experimental tests of four types of equipment operation analysis in the internal big data distribution network show that, the proposed methods could locate the distribution network operation quickly and accurately.
Research on Single Grounding Location Technology of Power Grid Based on Wavelet Analysis
Authors: Song Xiaoyu 
Published: September 2020

Abstract
The research of single grounding location technology based on wavelet analysis aims at the current situation of complex distribution network structure, high failure rate, many branches of lines, large transition resistance, difficult to detect fault signals. This method analyzes the characteristics of fault traveling wave signal generated by single effective grounding, and filters the collected traveling wave signal by wavelet analysis, so as to improve the credibility of traveling wave signal characteristics. Through calculating the traveling wave line mode and zero mode component at the time of fault occurrence, the singular point of traveling wave waveform is obtained, and the fault point is determined by accurate traveling wave velocity. For the tree distribution system, the zero mode waveform of the reflected traveling wave is recorded by injecting the voltage ranging traveling wave into the system during normal operation, and the reflected traveling wave is injected into the fault phase during fault, and the zero mode voltage waveform of the reflected wave is recorded. The two waveforms are subtracted to eliminate the reflected wave head of the grid node, and the fault distance is calculated by wavelet analysis. The method needs to measure the current and voltage of each terminal of the line synchronously when the parameters of the line are known. Through the experiment of fault location for single-phase ground fault of multi branch distribution lines, the results show that this method is effective.
Performance Prediction of Marine Diesel Engine Based on Long Short-Term Memory Network
Authors: Shi Bingjie , Shi Haojian , Wang Haiyan 
Published: September 2020

Abstract
Performance prediction is one of the core technologies for the health management of the marine diesel engine. To predict the overall performance of diesel engine, a prediction method combining Mahalanobis Distance (MD) and Long Short-Term Memory (LSTM) neural network is put forward. The MD is used to measure the degree of performance degradation of the diesel engine. The Performance Index (PI) is proposed to normalize the MDs at different time into PI sequence that describe the performance degradation process of diesel engine. The three-layer LSTM network is established. The one-step method and the multi-step method are used to predict the PI sequence respectively so as to achieve the trend prediction of the overall performance of the diesel engine. The method is verified by the performance data of the MAN B&W 6S35ME-B9 marine diesel engine. The results show that this method can be used for short-term fluctuation prediction and long-term trend prediction of diesel engine performance.
Research on Automatic Preprocessing Method of Railway Catenary Geometric Inspection Data
Authors: Guo Jianfeng , Ke Zaitian , Liu Jinzhao , Zhang Wenxuan , Yang Zhipeng , Ning Liming 
Published: September 2020

Abstract
In order to solve the problems of low efficiency and variety of results in preprocessing massive railway catenary geometric inspection data by manual interaction, this paper proposes an automatic preprocessing method for railway catenary geometric inspection data. A mileage jump point identification method is proposed based on the modified threshold variation algorithm; a data outlier removal method is proposed based on the repeatability inspection algorithm; a mileage automatic calibration method is proposed based on the principle of maximizing data similarity; a calibration evaluation method is proposed based on the principle of minimizing relative error. The above mentioned automatic preprocessing methods are used to preprocess the field test data. The results show that the method can not only identify the abnormal jump points in the inspection data, but also achieve efficient mileage calibration and calibration evaluation functions, which improves the efficiency of preprocessing railway catenary geometric inspection data.
Analysis of Land Cover Change in Shenyang Based on Remote Sensing Image Supervised Classification Technology
Authors: Yang Dayong , Xie Zhiwei , Du Xishihui 
Published: September 2020

Abstract
Based on four satellite remote sensing images in different periods, this paper studies the change of land cover in Shenyang urban area. For the classification of land cover, the maximum likelihood method is adopted in the experiment. The land cover change is analyzed on the classification data. The analysis showed that these changes were significant. During the period of 2000-2010, the area of construction land increased greatly, the total area of cultivated land changed little, and the area of forest land and other land decreased. With the development of the city, the proportion of all kinds of land has changed. The research results can provide some meaningful scientific help for urban planning and land use in Shenyang.
Comparison of Time Series Forecasting Based on Statistical ARIMA Model and LSTM with Attention Mechanism
Authors: Zhou Kun , Wang Wen Yong , Hu Teng , Wu Chen Huang 
Published: September 2020

AbstractTime series Forecasting (TSF) has been a research hotspot and widely applied in many areas such as financial, bioinformatics, social sciences and engineering. This article aimed at comparing the forecasting performances using the traditional Auto-Regressive Integrated Moving Average (ARIMA) model with the deep neural network model of Long Short Term Memory (LSTM) with attention mechanism which achieved great success in sequence modelling. We first briefly introduced the basics of ARIMA and LSTM with attention models, summarized the general steps of constructing the ARIMA model for the TSF task. We obtained the dataset from Kaggle competition web traffic and modelled them as TSF problem. Then the LSTM with attention mechanism model was proposed to the TSF. Finally forecasting performance comparisons were conducted using the same dataset under different evaluation metrics. Both models achieved comparable results with the up-to-date methods and LSTM slightly outperformed the classical counterpart in TSF task.
Data Analysis and Research of Lithium-Ion Battery Based on Data Mining Technology
Authors: Yang Zhi , Wu Jianhua 
Published: September 2020

Abstract
With the development of consumer electronics and energy storage systems, the market demand of lithium-ion batteries is rapidly increasing. To improve the application efficiency of lithium-ion battery, it is necessary to investigate the influence of battery parameters on the available capacity of the battery. In this paper, the data mining technology is used to study and analyze the parameter data of lithium-ion battery, aiming at exploring relationships among multi-parameters and capacity in battery charge and discharge processes, and Python language is applied to realize this end. The proposed data mining technology for lithium-ion battery includes the cleaning and discretization of lithium-ion battery data, the correlation analysis of lithium battery parameters using association rule Apriori algorithm, and the visual processing of the relationship between charge and discharge time and battery capacity.
EEG Research Based on the Influence of Different Music Effects
Authors: Xu Yanjun , Xu Xin , Deng Lixin 
Published: September 2020

Abstract
Music has become one of the methods for regulating emotions in the daily life of most people. Different concerts bring different emotional changes to people, but the effect of music on the changes of human brain and EEG signals is little known. This paper uses the brain wave (EEG) experiment to study the effect of different musical stimuli on attention, and focuses on comparing the effects of brain wave music and heavy metal music that are widely studied on attention. In this paper, an experiment was designed to collect the EEG signals of 15 students in three states: brainwave music (slow 80BPM), heavy metal music (fast 140BPM) and resting EEG signals. Independent component analysis (ICA) preprocesses the EEG signal, and uses T, T, and T to analyze the characteristics of the α, β and θ wave signals obtained by wavelet transform. Through the experimental analysis of this article, it is found that brain wave music has a significant effect on the alpha wave (P<0.05), and heavy metal concerts have an effect on the power value of the beta wave. The experimental results show that brain wave music, which is the slow board music, can better pay attention. The concentration of force, and heavy metal music (clapper music) will make the attention more distracting.