bluuebunny/crossref_metadata_2025_split huggingface dataset:
Railway freight volume forecasting based on combined DWT-Bi-LSTM model
Authors: Qiu Yue-Ying , Lei Ming 
Published: August 2020

Abstract
Railway freight volume forecasting can provide information for National Railway Department to establish effective operation lines, and its research plays an important role in increasing the effective proportion of railway transport. In order to improve the prediction accuracy, this paper proposes a combined model based on wavelet transform (WT) and bidirectional long short-term memory (Bi-LSTM) for high-precision railway freight volume forecasting. In this method, experimental data are first denoised by wavelet transform to extract important signal features that can accurately express the data information, and then Bi-LSTM is utilized to learn from the historical denoised data and iteratively improve upon predictions. To verify the improved prediction performance of DWT-Bi-LSTM, results are compared with those of the single LSTM, GRU, Bi-LSTM model along with the LSTM, GRU model combined with wavelet transform. The experimental results show that the combined DWT-Bi-LSTM model proposed in this paper has higher accuracy in the prediction of railway freight volume than the other forecasting methods.
Acquisition and Research of PSF of L-R image reconstruction
Authors: Wang Qilong , Zheng Xiaohai , Teng Yuchao 
Published: August 2020

Abstract
In order to make the quality of image reconstruction more accurate, this paper adopts the method of PSF to image reconstruction, analyzes and studies how to obtain the PSF. Firstly, the principle of reconstructed image is modeled, the principle of reconstructed image is analyzed. Then the parameters of the PSF are simulated and the L-R algorithm is analyzed to observe and estimate the reconstructed image. Finally, multiple iterations of L-R algorithm can obtain the appropriate PSF to achieve and the best image reconstruction quality. The analysis can find a suitable PSF.
Research on Detection and Recognition of Abnormal Behavior in Video
Authors: Fu Yanli , Deng Rui , Xue Bei , Li Shuyao 
Published: August 2020

Abstract
In order to solve the problems of low detection efficiency and long working time in traditional video surveillance systems for abnormal behavior detection and recognition methods, a new method for abnormal behavior detection and recognition in video surveillance systems is proposed. This method first completes image preprocessing through four steps: video image noise filtering, image grayscale correction, binarization processing, and image edge detection; then, based on the characteristics of the image abnormal target, the key frame of the abnormal image is detected and the data is dissected to complete the abnormal behavior detection of the video monitoring system; finally analyze the video image rules through adaptive algorithms, and use the computer’s visual detection principle to change with the scene environment to identify the abnormal behavior of the video surveillance system. In order to detect the effect of the method, a comparative experiment was set up. The experimental results show that the new method can accurately detect abnormal behavior in a short time and has a strong working ability.
Identification of coherent generators based on clustering methods
Authors: Wu Peng , Xing Lin , Zhang Ji , Xi Peng 
Published: August 2020

Abstract
The identification of coherent generators is very important for the stability analysis and control of power system. So far, there are three main identification methods of coherent generators which have cetain disadvantages. In this paper, two new algorithms based on clustering are proposed for the identification of coherent generators. Algorithm 1 is the optimal classification of generators based on given swing curves. By introducing the validity index into the algorithm, the optimal classification of the given generator swing curves can be obtained. Algorithm 2 is identification of coherent generators under specific faults. This algorithm firstly calculates the critical clear time of the system. Since the swing curves obtained at the critical clear time(critical curves) contain the complete information reflecting the swing and oscillation between generators, they can be used as reliable samples for clustering. Examples show that the two algorithms are effective.
A hybrid feature selection algorithm combining information gain and genetic search for intrusion detection
Authors: Liu Yingshang , Liang Shouyu , Fang Wenchong , Zhou Zhifeng , Hu Rong , Zhou Huafeng , Hou Jian , Wang Yichang 
Published: August 2020

Abstract
Network attacks are one of the main threats to the stable operation of smart grid equipment. As a real-time monitoring system to prevent network attacks, intrusion detection is widely used in smart grid protection. However, the massive data in the network transmission process contains a large number of redundant and irrelevant features, which makes it difficult for the intrusion detection system to process in time and reduce the efficiency. Feature selection is a method to solve this kind of problem. It can improve the speed of intrusion detection by filtering the characteristics of massive data. Therefore, a hybrid feature selection algorithm which combines information gain and genetic search to improve the work efficiency of intrusion detection systems is proposed. The algorithm is mainly divided into three parts. Firstly, the information gain value of all features is calculated by using information gain, according to which all features are ordered, and the ordered features is ranked according to an exponential increase strategy; secondly, the ranked features is used to guide the genetic algorithm search process, and a new fitness function can be used to control the search direction of genetic algorithm; finally, a classification algorithm is used to test the dataset after feature selection. In experiments, by comparing with other feature selection algorithms on 5 sets of high-dimensional UCI datasets, it is concluded that the IGExpGA proposed in this paper significantly improves the detection rate and detection speed. More importantly, in the KDD1998 network data, the algorithm proposed improves the detection rate to 98.8%, which is significantly better than other algorithms.
Evaluation Scheme of Data-center-platform Service Based on Link Tracking Technology
Authors: Zhang Pan , Hong Jianguang , Wang Yuan , Ren Yingwen , Ding Xuewei 
Published: August 2020

Abstract
In the data-center-platform, all data is open to the outside world in the form of microservices. Due to lack of proper management, it is difficult to find the source of the problem from the intricate service call network when the service has a problem, thus missing the golden opportunity for stop loss. In addition, if an interface suddenly increases in time, it is no longer necessary to query the time-consuming situation by service, and the performance bottleneck of the service cannot be visually analyzed. In order to solve these problems, we propose a evaluation scheme of data-center-platform service based on link tracking technology. We use appropriate data tracking tools to collect data service operation data, analyze the operation data by designing a data service evaluation model, and find problematic data services in time.
Research on “Industrial Internet” to promote the development of Advanced Manufacturing Industry in Guangdong, China
Authors: Luo Ming , Yang Xi 
Published: August 2020

Abstract
As a major province in China’s manufacturing and Internet industries, Guangdong has taken the lead in promulgating local industrial Internet support policies and supporting enterprises to “build platforms, log in platforms, and use platforms.” Guangdong has taken the lead in promoting the development of the Industrial Internet. The policy system for the development of the Industrial Internet is becoming more and more comprehensive, Advanced Manufacturing Industry industries are developing in a coordinated and in-depth manner, and urban integration and application innovation are active. Guangdong Province further exerted the advantages of resource aggregation and industrial clusters, to boldly explore and innovate, and gave better play to the role of demonstration and leadership, making the Industrial Internet a new engine to promote the development of Advanced Manufacturing Industry and build a province strong with manufacturing.
Indoor Location Method of WiFi / PDR Fusion Based on Extended Kalman Filter Fusion
Authors: Zhan Mengqi , Xi Zhi Hong 
Published: August 2020

Abstract
This paper proposes an indoor positioning method based on extended Kalman filter WiFi-PDR fusion, which aims to improve the accuracy and stability of indoor positioning. In this method, the mobile terminal collects the WiFi fingerprint location information and establishes the fingerprint database. At the same time, using the accelerometer, gyroscope and magnetometer in the mobile terminal, the walking state of the pedestrian is judged by adjusting the dynamic threshold, and the direction detection is completed. After that, adaptive Kalman filter is used to integrate the WiFi location system and the PDR location system to update the user’s location. The results show that the scheme reduces the accumulated error of PDR positioning to a certain extent, and improves the continuity and stability of WiFi Positioning. Therefore, the scheme we propose can effectively improve the positioning accuracy.
Template Matching of Round Features of Aluminum Alloy Castings Based on Hu Constant Moment
Authors: Lin Jing , Zhang Wenbin , Liu Yongyue , Zhou Wendi , Dong Wenhang , Zhang Xuechang 
Published: August 2020

Abstract
Automobile structural parts, that is automobile frame, are used as load-bearing equipment with advanced mechanical properties so that it need to be strictly inspected. There are geometric characteristics, uneven distribution of positions, and large volume for automobile structural castings of aluminum alloy. Thus, it is necessary that the feature matching deals with the automobile structural casting by detection of visual image. However, the traditional visual image has exposed many essential problems, such as lower degree and lower field-of-view stitching accuracy. According to the above phenomenon, a template detection method is proposed based on Hu moment, combined with the target of round feature under the front sub-frame of a certain model of Zotye Automobile. The picture is converted into a binary image with the connected domain label. The feature values are obtained by the template image and the regional features is calculated. Secondly, the Euclidean distance is applied to judge the similarity before finishing the template matching. Finally, the matching accuracy can reach 100%, but also running time of the algorithm is less than 0.08s for the round holes of the front sub-frame under experimental data.
A Single Image Mosquito Streaks Removal Method of High-Speed Railway Traction Substation Using DCNN
Authors: Lu Xuemin , Zheng Danyang , Quan Wei , Liu Yueping , Guo Shaopeng , Chen Jim X. 
Published: August 2020

Abstract
With the proposal of intelligent high-speed railway, the research on remotely monitoring the intelligent traction substation becomes a key subject of high-speed railway safe operation. However, mosquitos adhered to the glass window or camera lens can severely hamper the visibility of a background scene, and degrade images considerably. Therefore, we propose a single image mosquito streaks removal method of high-speed railway traction substation based on the deep convolutional neural network. First, we employ guided filter to split input image into smoothing image and edge-preserving image. Then, the edge-preserving image is fed into our designed convolutional neural network to obtain learning map, which solves problems of background interference and focuses the model on the structure of rain streak in images, and the clean image is finally generated through adding the input image and learning map. The experiment results on Heishan traction substation real datasets show the effectiveness of our proposed method.
Study on friction and wear behavior of braided wire rope for traction
Authors: Han Xiaoran , Zhang Jianbiao , Wang Hui , Gu Weilin , Li Xiaoyang 
Published: August 2020

Abstract
In this paper, the friction and wear properties of braided wire rope for cable traction engineering are studied. Through the observation of the wear morphology of the rope at different contact locations by VHX-5000, it is found that the rope between strands is in point contact state, which leads to stress concentration, so the wear is the most serious. The second is the “rope-wheel” contact rope, while the rope in the strand is only slightly worn. On this basis, by means of the analysis of scanning electron microscope (COXEMEM-30), it is revealed that the main wear types of braided wire rope in actual working conditions are adhesive wear and composite wear formed by fatigue wear and corrosion wear.
Design of Grinding Tool for Isolating Switch with Multiple Operating Conditions in Substation
Authors: Li Yongsheng , Dong Xu , Liu Zhao , Lu Xianfei , Su Na , Li Jian 
Published: August 2020

Abstract
The isolating switch in the open substation is exposed to the outdoor environment for a long time, and it is easy to rust due to the erosion of the natural environment, resulting in contact heating and affecting the quality of power supply. The grinding tool with multi-working conditions consists of grinding section, cleaning section, clamping section, connection section, rotating section and control system. The grinding tool is used for isolation switch in the open-type substation with voltage level of 220kV and below. The operation mode of remote control is adopted to polish and clean the isolating switch under multiple working conditions to achieve the task of derusting.
Simulation study on anti-corrosion performance of a ferroalloy flange
Authors: Pan Xinglong , He Guo 
Published: August 2020

Abstract
The corrosion problem of seawater pipeline is an important problem that ships need to face. Installing anticorrosive zinc block in seawater pipeline is a simple and practical anticorrosion means. In this paper, the anti-corrosion performance of a ferroalloy flange is simulated and analysed. The results show that, for the copper pipeline, both cathode current and anode current of the seawater pipeline using the copper - ferroalloy anticorrosive mode are significantly lower than those of the seawater pipeline using the copper - zinc alloy anticorrosive mode under the same external environment. This means that the anticorrosion performance of copper - ferroalloy model is obviously better than that of copper - zinc alloy model.
Rolling Bearing Fault Diagnosis Based on Single Gated Unite Recurrent Neural Networks
Authors: Tan Wenwen , Sun Yuansheng , Qiu Dawei , An Yapeng , Ren Ping 
Published: August 2020

Abstract
In order to meet the real-time requirement of bearing fault diagnosis, a simplified strategy of the traditional long short term memory (LSTM) neural network structure is proposed. We designed a new single gated unite (SGU) recurrent neural network. In view of the non-stationary and non-linear characteristics of bearing fault vibration data, we use wavelet packet decomposition to extract the features as input signals of bi-directional single gated unite (Bi-SGU) to complete the diagnosis of 10 types of bearing data. Simulation results show that the proposed method can ensure the accuracy of bearing fault diagnosis, reduce the number of network parameters by 36%, and improve time efficiency by 41%.
Multi-agent Planing Based on Causal Graph
Authors: Lin Zeng , Chen GuangZhi 
Published: August 2020

Abstract
Multi-agent planning is a kind of difficult but not fully studied planning problem. In the cooperative assumption, previous approaches either find a globally consistent plan by the backtrack-based solution, or make the local plans first, and then merge them. However, local planning between agents is inconsistent and conflicting in most case, in this case finding a globally consistent plan is time-consuming and costly. In this paper, we propose a novel approach to get causal-links graph from causal constraints between agents in solving multi-agent planning problems to reduce the effort of repeatedly searching agents’ consistent local plans. Theoretical analysis show that our approach can efficiently solve multi-agent planning problems with tightly degree of coupling level.
Mode-I stress intensity factors for Periodic Cracked Stiffened Panels under tension
Authors: Yuan H. , Xie Y. J. , Wang W. 
Published: August 2020

Abstract
Stress intensity factor is the nuclear physical parameters in fracture mechanics. Stiffened panels structure is heavily used in actual engineering field, such as aviation, marine and other bearing structures. The SIF for periotic cracked stiffened panels under tension is a representative three-dimensional crack problem. In the present article, the solution of SIFs for stiffened panels periodic cracks are derived on the basis of materials mechanics and the conservation law J
2- integral.
Experimental Research On Static Strength of C-shaped Steel Structure in Photovoltaic Agricultural Greenhouse
Authors: Wen Zhimei , Zhao Yulei 
Published: August 2020

Abstract
Based on the research characteristics of the C-shaped steel structure of the photovoltaic agricultural greenhouse, the stress and strain under the design load of the solar cell module support are tested. Compared with the ANSYS simulation and test data, the test results are in good agreement with the simulation analysis results. The maximum stress measured by the main steel frame structure under the required load is far less than the steel stress value, the structural safety strength is high, there is a big reduction Heavy margin. The research results can provide a reference for the engineering design of the photovoltaic agricultural greenhouse steel frame structure.
Improving Session-to-session Transfer Performance of Emotion Recognition Using Adaptive Support Vector Machine
Authors: Yang Kai , Bao Guangcheng , Zeng Ying , Tong Li , Shu Jun , Yan Bin 
Published: August 2020

Abstract
The non-stationarity of electroencephalograph (EEG) signals has been a barrier in real-life application of EEG-based emotion recognition. The features extracted from emotional EEG vary from one session to another and a model trained on a temporally-limited EEG dataset may generalize poorly to data recorded at a different time for the same individual. In this study, the EEG features that stably characterize the differences between emotions are firstly explored. Then the classic progressive transductive support vector machine (PTSVM) is extended to three-classes classifications by a new region labelling rule, furthermore K-nearest neighbour algorithm and iterative process are utilized to improve the confidence of the predicted labels. Experimental results indicate that high gamma band local activation difference features and network features derived from prefrontal lobe, the temporal lobe on both sides, the left occipital-parietal lobe, the right occipital lobe and the posterior occipital lobe are temporally stable features for distinguishing different emotions (positive, neutral and negative). And the proposed session-to-session transfer model achieves an averaged classification accuracy 63.56% for three emotions classification on our dataset recorded from 23 subjects in three different days, which is 4% higher than the existing best adaptive classification model. Hence, the proposed classification model can effectively improve the performance of EEG-based session-to-session emotion recognition.
Research and Application on the Neutral Grounding Mode in Tianjin Distribution Network
Authors: Zhao Cong , Ma Hao , Zhang Chi , Xi Xiaoguang , Li Suya , Sun Zhao 
Published: August 2020

Abstract
The paper introduces the present situation of neutral grounding mode in 35kV and 10kV Tianjin distribution network. There are mainly four neutral grounding modes: neutral ungrounded, grounding via arc suppression coil, grounding via low resistance and grounding via intelligent reactor. The advantages and disadvantages of the four neutral grounding modes are compared. According to the development characteristic of Tianjin distribution network, the configuration principle of neutral grounding mode is formulated. In addition, the installation and configuration methods of low resistance and grounding transformer are both given, which provides experience and guidance for the application of low resistance grounding.
Measurement of periodic voltage waveforms on secondary side of high voltage measuring device using Arduino
Authors: Liu Jieyu , Li Shubin , Zhang Xin , Wang Guangfu , Zhao Yan , Wang Donglai 
Published: August 2020

Abstract
Data acquisition on the secondary side is an important part of high voltage signal measurement. Based on Arduino microcontroller, a data acquisition (DAQ) system is developed. The frequency, root-mean-square value and DC component of periodic voltage waveforms can be measured by the system. The relative errors of the DAQ system are tested by comparing with standard voltage waveforms. The measurement range of the DAQ system is further discussed. The DAQ system in this paper is helpful to measure the periodic voltage waveforms on secondary side of high voltage measuring device.
Research on online safety early warning technology for oil system of phase modifier
Authors: Shuai Yunfeng , Wu Yanyan , Zhang Enxian , Yu Guoqiang , Yu Dahai , Shen Yongjun 
Published: August 2020

Abstract
Lubricating oil is the blood of the machine, and health monitoring based on the quality of lubricating oil is an important means of condition based maintenance. The lubricating oil system is an important auxiliary system of the phase modifier. Metamorphic of the lubricating oil and contaminants carried by the lubricating oil will block the oil filter, scratch the seals and block or wear the components, which would cause the failure of the equipment. Particles and leakage generated when the equipment fails will also fall into the lubricating oil. Therefore, we can detect the indicators and pollutant content of the lubricating oil to speculate the equipment condition and make failure prediction. In view of the lack of operation and maintenance experience of rotating equipment in converter station staff, in order to ensure the safe and reliable operation of the converter station, this paper focuses on the research of online safety early warning technology, so as to improve the real-time and accuracy of lubricating oil diagnosis, reduce the probability of equipment failure, and improve enterprise benefits.
Research on key technology of infrared methane detector based on LED light source
Authors: Zhao Qing-Chuan 
Published: August 2020

Abstract
According to the characteristics of the infrared absorption spectrum of methane gas mainly distributed in the middle infrared band of 3.2 μm˜3.4 μm, an infrared methane sensor and detector based on nondispersive infrared spectrum technology are developed based on the research of three key technologies of sensor optical path structure design, LED current drive and temperature compensation. The optical path consists of a light emitting diode with a peak wavelength of 3.4 μm, a measuring photodiode with a cutoff wavelength of 3.6 μm and a spherical emitting surface. Through the research of LED current drive control logic, the power consumption of optical measuring device is reduced to 10 mW. The influence of temperature change on the measurement results of methane concentration is studied experimentally. Through data analysis and linear fitting, the temperature compensation algorithm formula is obtained. The experimental results of the compensated sensor and detector platform show that the average power consumption of the sensor is 19.68mW, the influence of temperature change in the temperature range of -20°C˜50°C on the measured value is not more than 3.5% of the true value, the measured concentration value is less affected by water vapor, the response time is less than 20s, and the battery working time is more than 7 days.