bluuebunny/crossref_metadata_2025 huggingface dataset:
DB-Net: Dual Attention Network with Bilinear Pooling for Fire-Smoke Image Classification
Authors: You Chen , Li Zhen , Li Meng , Gao Zongliang , Li Wei
Published: September 2020

In recent years, with the help of deep learning, image classification has been significantly improved. More and more researchers are seeking fire alarm solutions based on these artificial intelligence algorithms. However, this raises new challenges in an effective method of classification for fire-smoke images. In this paper, we propose a Dual Attention Network with Bilinear Pooling of three steps to accomplish the task of fire-smoke classification. We first extract features of fire pictures or smoke ones by using ResNet-50 as a basic net. Then we attach two kinds of attention modules, channel attention and spatial attention, which are called dual attention, to extract key information ‘what’ and ‘where’ from pictures. Finally, we merge them by using the bilinear pooling module which has been shown to be effective at improving our classification rate. Results show that our most accurate model can reach 90.11% per-image accuracy, which is improved by 4.81% compared to the traditional ResNet-50.
Research on Main Steam Temperature Prediction Model Based on Improved LSTM Algorithm
Authors: Zheng Ling , Ye Xin , Chen Fei
Published: September 2020

In order to improve the reliability and accuracy of the main steam temperature trend prediction, a main steam temperature prediction model based on improved LSTM is proposed. Firstly, uses the grey correlation analysis method to select the important influencing factors. Then, a linear structure is introduced into the LSTM structure to construct a main steam temperature prediction model. Finally, based on the historical operating data of the thermal power unit, a simulation experiment is performed to compare the prediction error of the output of the RNN model, the LSTM model, and the improved LSTM model. The results show that the method has higher prediction accuracy for the main steam temperature. At the same time, compared with other traditional methods, this method has better fitting effect, which can be well applied in practical engineering.
Human Activity Recognition Using Gaussian Naïve Bayes Algorithm in Smart Home
Authors: Shen Jiajie , Fang Hongqing
Published: September 2020

Human activity recognition (HAR), as an important research issue, aims to identify human activities in smart homes. In this paper, we apply Gaussian Naive Bayes (GNB) algorithm to HAR and evaluate the model based on smart environment sensor data. Experimental results show that the effective selection and processing of features are helpful to improve the accuracy of activity recognition of the model. Compared with NB whose accuracy rate is 82.7%, GNB has a better accuracy rate of 89.5% and even has a higher recognition accuracy in almost every category of activities. Selecting the feature variables as good and useful as possible to get a better model in the process of activity recognition is conducive to the correct classification of samples by machine learning algorithm and improves the classification performance of the model.
A Novel Spectral Clustering Algorithm Based on Randomly State Changed Particle Swarm Optimization
Authors: Chen Hao , Guo Dechun , Yang Shengzhi , Hou Xiaochen
Published: September 2020

Spectral clustering algorithm is a method of clustering which allows one piece of data to belong to two or more clusters. In this paper, a novel spectral clustering algorithm based on randomly state changed particle swarm optimization is proposed. The initial population was classified by considering global and local optimal functions, the evolutionary state of each particle is considered through the comparison of cost functions, and the evolutionary state of the particles was subdivided. The Weight mode added the previously optimal local particles and global particles. According to the rule that newer particles have greater weights, particles speed was updated to reduce the possibility of falling into a local optimal state and to expand the search range of particles. The classification accuracy of the clustering algorithm was presented. Finally, by using the UCI datasets for comparison, it was found that the algorithm proposed in this paper increase the performances by 3% to 28% for different datasets, comparing with the known clustering algorithms such as particle swarm optimization algorithm, Fuzzy C-Means algorithm and conventional Spectral Clustering.
Research on a Novel Influence Maximization Algorithm Based on Community Structure
Authors: Chen Jing , Liu Jiangchuan
Published: September 2020

With the research of influence maximization algorithm, many researchers have found that the existing algorithm has the problem of overlapping influence of seed nodes. In order to solve the problem of overlapping influence of seed nodes, this paper proposes an IMCS algorithm based on community structure. Firstly, we divide the community through the central node, and the quality of community division is ensured by defining community fitness and node contribution. Then through the analysis of the community division results, the seed node selects the one with the largest degree. Since most of the nodes activated by seed nodes of different communities also belong to different communities, this method solves the problem of overlapping influence to a certain extent. The experimental results show that the effectiveness of the IMCS algorithm is verified under the real network, cooperative network and artificial network, and the IMCS algorithm has a better effect than IEIR and Degree algorithms in most networks under the IC model.
Application of Practical Astronomical Timing Algorithm in the Lighting Control of Residential Street
Authors: Dong Jie
Published: September 2020

With the development of this society, the street lighting system has become a part of the community infrastructure construction. Most of the traditional street lighting methods use simple area control and timing switches, it can’t change the switch time of street lighting intelligently according to the change of actual application scene. In order to overcome this deficiency, this paper has designed an intelligent astronomical timing algorithm and software control process, which can change the street lighting switch time manually or automatically, also which is programmed in the controller and monitored through the TD200 LCD display panel. In addition, the automatic switching on/off on holidays is also added in the street lighting. Practice of using this system has proven that this intelligent timing algorithm can effectively improve the efficiency of the lighting control system and save energy.
An Improved A* Algorithm Applying to Path Planning of Games
Authors: Zhang Cheng , Ao Lei , Yang Junsheng , Xie Wenchuan
Published: September 2020

Pathfinding is the core issues in the artificial intelligence field of games, and how to establish an effective method of pathfinding is still focused on. A* algorithm is the most widespread type of game pathfinding at present but the problems still exist such as the curve path and the time-consuming. Based on the analysis and research of traditional A* algorithm, this paper proposes an A* algorithm based on region search. Firstly, the algorithm divides the game map into several connected regions where the role can move freely and without barriers. Then the algorithm uses region search instead of node search. It reduces the search path and the search time. According to the portal between regions, the optimal feasible path is extracted from the region paths. It can effectively solve the zigzag path problem. Experimental simulation results show that the algorithm has shorter search time and can generate smoother path and meet the real-time and real experience requirements of pathfinding in the game.
Wheat Powdery Mildew Spore Images Segmentation Based on U-Net
Authors: Liang Xinshen , Wang Botao
Published: September 2020

In recent years, there have been significant development in deep learning technology, which has also promoted the development of image segmentation. Wheat powdery mildew is a common crop disease, which is very harmful to crops. The segmentation task of wheat powdery mildew spore images is important for the target spores identification and spores counting. In this paper, an improved framework based on U-Net is proposed, comparing with original U-Net, we add the pyramid pooling module after the 1024-channel feature map in the down-sampling part to extract different sizes pooling feature and fuse into a global feature map, and adjust some skip connections in original U-Net to obtain more features that are effective for spore images segmentation, experiment shows that the improved U-Net structure has better segmentation performance than U-Net, and the segmentation miou (mean intersection over union) has reached 91.4% in wheat powdery mildew spore image dataset, which proves that our proposed architecture is effective and competitive in the wheat powdery mildew spore segmentation task.
Fixed Power Allocation Algorithm Based on Time-Shift Pilot in Massive MIMO System
Authors: Guo Zijiao , Xiong Rujing , Deng Honggui
Published: September 2020

Massive Multiple-Input-Multiple-Output (MIMO) technology can greatly improve the spectrum utilization rate and system anti-interference ability as the key of technologies in modern wireless communication. However, pilot pollution has restricted the further development of large-scale MIMO technology. In this paper, a fixed allocation algorithm based on time-shift pilot to control pilot pollution in massive MIMO systems is proposed. Firstly, signal-to-interference-plus-noise-ratio (SINR) expression of the edge terminal based on time-shift pilot is derived, and then we calculate the proportional power allocation factor through the number of terminals. Finally, the uplink data power is reallocated by combining the proposed algorithm and time-shift pilot strategy. The simulation results show that the proposed algorithm can improve the communication ability of the edge terminal effectively.
The Use of LSTM-Based RNN and SVM Models to Detect Ludian Coseismic Landslides in Time Series Images
Authors: Zhou Zeng-Guang , Chen Binbin , Li Ziyang , Li Chuanrong
Published: September 2020

With the increase of temporal frequency of satellite images, time series analysis based on Artificial Neural Network has become a tendency to detect land cover changes in images. We briefly introduces a case study that uses Long-Short Term Memory (LSTM), a specific Recurrent Neural Network (RNN) for time series modelling and forecasting, and Support Vector Machine (SVM) to detect landslides triggered by Ms.6.5 earthquake in Ludian, China in 2014. The study uses 72 available images with 16/30m spatial resolution from Landsat-7/8, GaoFen-1 and HJ-1A/1B satellites. Firstly, per-pixel LSTM models are trained by Normalized Difference Vegetation Index (NDVI) images before the earthquake. Secondly, the trained LSTM models are used to predict NDVI images after the earthquake. Then, anomalies or changes are detected by comparing predicted and observed NDVI images. Finally, anomalies related to landslides are separated from other changes with a SVM model which was trained by multi-spectral images in the study area. Experiment demonstrates that the recall rate and precision rate of landslides detection are 82.09% and 76.21%, respectively. The study shows a potential that the combination of LSTM and SVM models can be used to detect landslides in Landsat-like time series images with 30m resolution.
Analysis on the Vibration of Cracked Cantilever Beams with Application on Crack Detection
Authors: Zhao Di
Published: September 2020

In this research, the transfer matrix of a cracked beam which includes the vibration modes of both longitudinal and transverse deformation is derived based on Timoshenko beam model. The expression of the natural frequencies and mode shapes of vibration using transfer matrix method are proposed which can be effectively used for real-time crack detection. Manifold types of cracks in the cantilever beam which have been simulated by finite element method, and the solutions are compared with those from the transfer matrix method. The difference between results could be used for detecting both the size and location of the crack. Therefore, the transfer matrix method can be successful used for analysis of vibration in the cracked beam, and the errors are within the tolerant range.
Design of Pump Station Operation Condition Monitoring System Based on MATLAB Simulation
Authors: Zhang Yitong
Published: September 2020

In this paper, through analysis of pump station operation status monitoring system, on the basis of applying wavelet packet analysis to signals processing, MATLAB was used to simulate the design of pump station operation status monitoring system. In the simulation link, through the wavelet packet decomposition and reconstruction of the collected original signal and fault signal of the pump station rotor, fault signal can be judged by comparing and analysing the characteristic vector, power spectrum and energy spectrum of the wavelet packet node. Real-time monitoring of running status of pump station unit rotor was realized, and a simple human-computer interaction interface was designed using MATLAB/GUI (Graphical User Interface), which is simple and convenient to operate. In the human-computer interaction system, the user can observe some characteristic values and spectrum of the rotor signal of the pump station through the drop-down menu, so as to quickly find the fault signal and realize real-time monitoring.
Constraint Inversion Model of Core Science Complex Network
Authors: Jia Yuzhe , Yi Junkai , Yan Shuan
Published: September 2020

In order to reduce the research costs, increase the efficiency of scientific researches, and improve the inversion abilities of core technologies, in the paper, a constrained inversion model aiming at tracking the source of core technology was proposed. Firstly, a complex network was built and it treated the most important product or technology as the core node vector. In the network, the social and technical attributes of the product were assigned as the elements of the core node vector. Therefore, the relationships among the core node and other nodes represented connective strength which were fairly important for calculating the path weights. Essentially, the weights were integrated attributes derive from a called score matrix. Then a constrained optimization criterion was employed to evaluate the scores of paths so that the best optimal path was determined. For reducing the number of searching and the times of calculating, all the constraints relationships of node vectors were classified into several types. The experimental results demonstrate that the proposed approach has a superior performance against the current state-of-the art methods over a real case.
Research on Fault Line Selection Method Based on Convolutional Neural Networks
Authors: Gao Jinfeng , Wang Yufeng
Published: September 2020

In order to improve the reliability and accuracy of Single-Phase-to-Ground Fault line selection for small current grounding systems, this paper proposes a method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN). The proposed method uses the original current signal as input and uses a wide kernel in the first convolutional layer to extract features and suppress high-frequency noise. The following convolutional layers with small convolution kernels are used for multilayer nonlinear mapping. Batch normalization layers and dropout layers are carried out for improving the generalization ability of the model. The simulation results show that the recognition rate of the proposed WDCNN can reach 99%, and it is not affected by factors such as grounding impedance, fault location and fault time.
UAV Altitude Measurement Method Based on Data Fusion and Kalman Filter
Authors: Yang Junjia , Qian Bing , Zhang Erwei , Qu Kun
Published: September 2020

Accurate altitude measurement of UAV is very important for its flight mission. Based on the analysis of the measurement performance of different altitude measurement sensors, this paper designs a multi-altitude measurement sensor fusion filtering structure, and then describes the fusion filtering method. This method mainly provides weighted fusion of data from various altitude measurement sensors to obtain altitude information with higher accuracy than any sensor. Through Kalman filtering of the fusion data, more accurate flight altitude data of UAV can be obtained. The method is validated by a typical example. The results show that the proposed method can solve the problem of precise measurement of altitude data in UAV flight process, and has certain theoretical significance and good application value.
Collaborative Filtering-Based Film Recommendation Technique Utilizing Time and Film Genres
Authors: Wang Huilin , Wu Jianhua
Published: September 2020

Due to the uncertainty of users’ preferences for different movies at different times, movie recommendation poses a huge challenge. We present an improved collaborative filtering algorithm for film recommendation whose design was motivated by a need to extract meaningful information for further recommending from film viewing applications. To this end, the method we propose utilises an improved method for analyzing user interest in film genres, which can then be subsequently used to predict users’ ratings of unwatched movies in combination with the current viewing time. In our new algorithm, we extract genre information considering time decay factor and combine it with matrix factorization collaborative filtering algorithm, and propose a novel algorithm structure, the MFTGICF algorithm. Compared with general traditional collaborative filtering algorithms and some other methods, this algorithm has more precision and higher stability. Both the theoretical analysis and extensive experiments show the improvement of the effectiveness and efficiency of the proposed method.
Research Progress of Network Public Opinion Based on Fuzzy Set from the Perspective of Big Data
Authors: Tang Jianguo , Wang Jianghua , Li Fang
Published: September 2020

Granular computing is a new method of intelligent information processing. It describes knowledge and simulates human thinking mode in a granular way. It can realize the rapid transformation between different granular knowledge and is suitable for the analysis and modeling of large-scale and complex data. Fuzzy set is an important model in granular computing, which describes uncertain problems through fuzzy membership function and has been widely used in many fields such as network public opinion analysis. This paper combs the application research of fuzzy sets in network public opinion analysis from the perspective of big data, and discusses the different roles of fuzzy sets in network public opinion analysis from four aspects: fuzzy comprehensive evaluation, fuzzy reasoning, fuzzy granularity and generalized fuzzy set. The characteristics of different methods are compared and the unique features of fuzzy sets in these four aspects are pointed out. The unique advantages and some problems are discussed as well, and the future trend of this field is discussed.
Research on Seismic Data Interpolation and Reconstruction
Authors: Xie Xiaoguo , Pan Shuling , Luo Bing , Chen Cailing , Chen Kai
Published: September 2020

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Time 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

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

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.
sEMG Motion Intention Recognition Based on Wavelet Time-Frequency Spectrum and ConvLSTM
Authors: Liu Yixin , Xin Daxin , Hua Jin , Liu Mengzhao
Published: September 2020

The electromyographic signal (EMG) is a kind of bioelectrical signal, which can predict human motion intention through signal analysis. Multiple classification models are used to predict the motion intention. It has been found that the classification accuracy is closely related to the feature information extracted from the signal. Traditionally, features are designed manually through prior knowledge. In this paper, a kind of EMG signal classification method based on convolutional neural network and convolutional long-term memory network (CNN-ConvLSTM) is proposed. ConvLSTM, with its global feature extraction capability, is designed to extract the sEMG features of each channel, and due to its strong local feature extraction capability, CNN was designed to further extract the fused feature information and realize end-to-end classification. The experimental results show that this algorithm has better classification performance than the existing classification methods. The electromyographic signal (EMG) is a kind of bioelectrical signal to recognize human movement intention.
The Design and Implementation of Campus Network Streaming Media Live Video On-Demand System Based on Nginx and FFmpeg
Authors: She Bo , Wang Qiang , Zhong Xiaoge , Zhang Zhe , Qin Zunying , Li Guodong
Published: September 2020

For the current demand for live broadcast and live video in colleges and universities, relying on the advantages of campus network, this paper designed and implemented a video live broadcast and video on-demand system suitable for colleges and universities. The system used the streaming media server built by the open source Nginx to capture high-definition video streams output by cameras, etc. through the RTMP protocol, and can provide video live broadcast and video on-demand services using multiple carrier lines. Video live broadcast used the open source Flash player JW Player, stored video in high-performance HDFS distributed storage at a certain time interval and format by a storage algorithm, and supported on-demand playback of MP4 format video stored in HDFS. The paper tests the various functional modules of the system and the overall performance of the system to verify the effectiveness of the system. The system is currently deployed in the campus network of Xi’an Jiaotong University, providing normal recording and broadcasting of video of 293 intelligent classrooms in Xi’an Jiaotong University, video on-demand for after-school courses, video live broadcast and video on-demand for various large-scale events held on campus.
How to Apply Statistical Software in Doing Multilevel Modeling: A Comparative Study Perspective
Authors: Lin Liming , Zhang Xiaosan
Published: September 2020

Multilevel model methodology is widely used in social science. There are many textbooks and h papers introducing the principles and methods of multilevel model. However, most of them are simply a brief instruction how to apply statistical software to run multilevel model, which brings some obstacles for beginners in empirical research. To address this gap, this paper aims to help beginners and common users to grasp the operation skills of multilevel model in these statistical software. Firstly, the operation procedure of multilevel model in eight statistical software are introduced. Secondly, this paper systematically compares the output results of multilevel models of these statistical software. Finally, some operational guidelines and suggestions are put forward for the empirical application of multilevel model.
Research on Password Cracking Technology Based on Improved Transformer
Authors: He Shen , Fu Jun , Chen Cancan , Guo Zhihui
Published: September 2020

Password plays a vital role in identity authentication. However, password security is facing great challenges. In this paper, we research the password cracking technology based on artificial intelligence, aiming to study the probability of password cracking in common password setting methods, and provide references for the setting of password. First of all, we collected a large amount of user’s personal information and passwords, and analysed the correlation between the personal information and passwords. And then, we implemented a password guessing model based on improved Transformer in which information weights were introduced into the data pre-processing and the modified beam search algorithm was used in the model to quickly search the top ranked output results. The percentage of password cracked was 68.63%, and the average guess time was 51.99 seconds. The experiment result shows that artificial intelligence brings great challenges to user password security, and this paper puts forward suggestions on user setting passwords.