bluuebunny/crossref_metadata_2025 huggingface dataset:
Influence of high voltage cable on insulation coordination of Pumped Storage Power Station
Authors: Ren Hongtao
Published: August 2020

The insulation cost of electrical equipment of 330kV and above EHV power station accounts for a large proportion. This paper establishes a typical electromagnetic transient analysis model of pumped storage power station, and conducts lightning overvoltage simulation calculation based on the model. It studies the length of high-voltage cable and summarizes the amplitude law of lightning overvoltage of main electrical equipment: with the increase of cable length, the amplitude of lightning overvoltage decreases. It has guiding significance for insulation coordination of Pumped Storage Power Station.
Research and application of digital virtual maintenance system for steam turbine
Authors: Hua Yudong , Zang Xudong , Bai Rongge , He Lirong , Yang Yuanqin , Xi Qiuyan , Jin Hanmeng , Pan Xiaolei , Lei Liu
Published: August 2020

After a 430MW machine unit was started with C repair finished, the shafting vibration of it increase rapidly at the phase of on-load. Using the on-line vibration monitoring and fault diagnosis system (TDM system) matched with the unit to check the vibration historical data, and according to the vibration characteristics, oil film vortex fault is diagnosed. In view of this, the vacuum test was carried out on the lubricating oil temperature of the main transformer engine and the condenser of the unit. The results show that the low frequency variable oil temperature and the condenser can be restrained in a large certain load range. The distribution measures of bearing load are adjusted by unit’s adjustment to increase the load of No.3 bearing, so as to eliminate the unit’s low-frequency vibration fault and ensure the normal operation of the unit.
Population Cross Learning Algorithm Combining Greedy Search for Community Detection
Authors: Wang Xiaogang , Yan Guanghui , Li Shikui , Zhou Ning
Published: August 2020

In order to improve the precision of modularity optimization and community detection, this paper presented a complex network community detection algorithm based on cross learning among individuals of population combining greedy search. Individuals’ codes indicated community partition. Individuals comparatively studied with each other to spread good genes and optimize modularity fast. Besides, aiming at improving the algorithm, the best communities, where some randomly selected nodes will move in, would be found by using greedy search maximizing the local modularity increment. The algorithm was tested on artificial networks and some typical real networks, compared with some typical algorithms. The results show that algorithm can get convergence quickly, achieve better modularity value, and finely detect and identify community structures.
Research on Employment Prediction and Fine Guidance based on Decision Tree Algorithm under the Background of Big Data
Authors: Li H Y , Zhang Y
Published: August 2020

Refinement of college employment guidance is the demand of the current era. Big data thinking and technology have created the conditions for it. In this paper, the factors influencing college students’ employment are discussed and the method of applying decision tree algorithm to predict college students’ employment is studied. On this basis, the paper analyses the effective ways to improve the refinement of college employment guidance based on employment prediction, from the four aspects of improving the scientificity of decision-making, the pertinence of guidance, the accuracy of service, and the timelines of assistance.
Object Detection of Remote Sensing Airport Image Based on Improved Faster R-CNN
Authors: Han Yongsai , Ma Shiping , Zhang Fei , Li Chenghao
Published: August 2020

In order to effectively improve the detection accuracy of remote sensing images in airport areas, basing on the representative deep network Faster R-CNN as the object detection method, a deeper basic network ResNet and feature fusion component FPN are used to extract more robust deep distinguishing features, and add a new fully connected layer to the end detection network and combine the softmax classifier and 4 logistic regression classifiers for object detection according to the inter-class correlation of the object. Experiments show that the improvement of the original network brings a 7.7% mAP improvement to 76.6% of the mAP. Compared with other mainstream networks, it also has a better accuracy rate. At the same time, by appropriately reducing the input amount of the proposals, the speed can be increased 3 times to 0.169s under the premise of reducing the accuracy by 2.2%. According to the specific task, the accuracy and detection speed can be reasonably weighed, which reflects the effectiveness and practicability of the network.
Numbers of Thoughts on Promoting Standardization Management Work in Innovative and Efficient Manners
Authors: Liu Bin , Jiang Hong , Jigui Zheng
Published: August 2020

This essay introduces some existing problems in standardization management of research institute. The essay analyzes the key points of standardization management, discusses the thoughts on standardization process design. Meanwhile, this paper expounds methods of standardization management in many fields such as module division, template index, information release, quick motivation, human and standard connection and level upgrade. The research achievement of the essay has important guiding significance on standardization and technical base management.
Application and Analysis on Geological Hazard Monitoring and Early Warning System Based on Internet of Things
Authors: Xiao Yigai , Dai Jun
Published: August 2020

On the basis of reviewing the development of technology at home and abroad, this paper proposes the architecture, functions, and characteristics of a geological disaster monitoring and early warning system based on the Internet of Things (sensor network). The geological disaster monitoring and early warning system based on the Internet of Things (sensor network) includes on-site sensors and data acquisition processors, (wireless, wired) communication modules, and background data processing and information release system software. The system has the characteristics of intelligence, scale, standardization and real-time, reliability and security. Finally, combined with China’s national conditions, several key technologies and basic theoretical research directions of the geological disaster monitoring and early warning system based on the Internet of Things (sensor network) in China are proposed.
The influence of imprecise quantum measurement on quantum dense coding
Authors: Ma Zhiqiang , Luo Bingbing , Huang Chaowei , Wu Tianxiong , Wei Jiahua , Zhao Haiyan , Xing Wei
Published: August 2020

In this paper, the influence of imprecise quantum measurement on quantum dense coding would be analysed. The realization processes of dense coding under the condition of imprecise measurement are given in detail. The performance of dense coding is analysed from the successful probability and average classical information. The successful probability and the classical information are immune to the phase parameter error of quantum measurement. However, with the increase of amplitude factor error, both probability and classical information will decrease. The results of this paper would be useful in the field of quantum communication.
A Privacy protection scheme for carpooling service using fog computing
Authors: Kou Bangyan , Cao Suzhen , Lv Jia
Published: August 2020

As the traffic environment becomes more and more crowded, an increasing number of people are willing to carpool. Carpooling services provide convenience for drivers and passengers to share their journeys. But carpooling requests are uploaded to cloud server for processing and analysis in most existing schemes, resulting in communication delays and leakage of user privacy. To solve this problem, we propose a privacy protection scheme for carpooling service using fog computing. First, identity-based digital signature technology is used to achieve anonymous authentication. Second, location tags, bloom filters and fuzzy extractors are utilized to match passengers and drivers without disclosing the users’ location; Consortium blockchain technology is adopted to store carpooling records. Finally, security analysis and experiments results show that our scheme offers privacy preservation and security, while maintaining low computational cost.
An Automatic Online System for Detecting and Analyzing Quality Data of Products in Manufacturing Process
Authors: Bei Yijun , Zhou Yingang , Cheng Xuelin , Zhao Bin
Published: August 2020

As the business volume is getting bigger, more and more problems are exposed to product quality testing. For example, the application of the traditional statistical process control (SPC) is not flexible on different occasions due to the fixed testing rules and the manual data collection. These problems lead to large deviation and low efficiency. In order to fasten the testing process and optimize the results of testing, we design an online system that integrates the Drools rule engine and automatic collection mechanism based on some common communication protocols and devices into SPC to replace traditional manual quality testing. In this paper, we introduce the principle of the system design in detail. The experimental results show that we can utilize SPC in a more convenient and efficient way.
A Fusion Method for Satellite Multi-sensor Based on Improved Support Matrix
Authors: Li Ze , Li Yonghua
Published: August 2020

In this paper, a satellite multi-sensor data fusion method based on improved support matrix is proposed. The cross-correlation function is used to describe the support degree between multiple sensors, and the measured signals are weighted and fused optimally from the SNR perspective. Compared with the method of distance measurement, this method does not need to set threshold and the calculation process is simpler. Simulation results show that this method can objectively reflect the reliability of each sensor, effectively eliminate redundancy and error information, and select the optimal sensor combination.
Octave Residual Network for Image Motion Deblurring
Authors: An Qi , Jiang Lifen , Sun Huazhi , Liang Yan , Xu Tongtong , Xu Fan , Zhou Yongheng
Published: August 2020

Image motion deblurring technology is an essential topic. Traditional methods with a high algorithm complexity when estimating the blur kernel require a lot of calculations. To solve this problem, we proposed Octave convolution residual block, and proposed Octave Residual Network (ORN) based on this block. ORN is a Generative Adversarial Networks, where the generator is a multi-scale recurrent network, and the discriminator is a deep neural network. ORN restore sharp images in an end-to-end manner where blur is caused by various sources. We present multi-scale loss function that mimics conventional coarse-to-fine approaches. To verify the validity of ORN, we trained our model on GOPRO dataset, the peak signal-to-noise ratio (PSNR) of the model reached 30.04, and the operation time of each step is reduced by 0.04 seconds. Experimental results show that our method achieves the state-of-the-art performance of image motion deblurring not only in performance but also in training speed.
Rumor detection based on topic classification and multi-scale feature fusion
Authors: Tan Li , Ma Zihao , Cao Juan , Lv Xinyue
Published: August 2020

In recent years, with the rapid development of Internet technology, the spread of network rumors has become one of the important obstacles to maintain the stable development of social networks and ensure the public security. Most of the existing researches focus on the detection of rumors in general fields, ignoring the differences among different fields. According to the characteristics of rumor in the health field, this paper proposes a rumor detection method based on topic classification and multi-scale fusion. Different methods are used to extract features from different sub datasets of different scales, taking into account the overall, inter topic, and intra subject correlation and differences, and then judge after feature fusion. The experimental results show that this method is better than the general detection method in the data set of health field, and has some improvement compared with the algorithm in the same field.
Design of Agricultural Machinery Positioning and Monitoring System Based on Beidou/GPS
Authors: Li Yueyang , Li Zhen , Qin Zhenhua , Yang Shuolin , Li Chunlei , Zhao Jingkun
Published: August 2020

This paper designs an agricultural machinery positioning monitoring system, which can collect the positioning information of agricultural machinery operation, and is of great significance for precision agriculture. The system uses stm32f103rct6 as the controller, with Beidou + GPS dual positioning module and display screen and other related accessories, to achieve the accuracy and real-time positioning of agricultural machinery.
Development and Practice of Virtual Experiment Platform Based on Blender and HTML5--Taking Computer Assembly and Maintenance as An example*
Authors: Cai Lilu , Yang Gang
Published: August 2020

The use of virtual training, can solve the problems such as the shortage of training resources, slow update of equipment, lack of training supplies, risks and environmental pollution and other issues in pure physical equipment training. This paper applies Blender and Html5 technology to student’s experiment in colleges and universities, and designs an interactive virtual experiment platform which integrates learning and experiment and supports muti-terminal visualization. The design scheme, main function and implementation technology of the platform are introduced in detail, and applied to the course of computer assembly and maintenance. First, the 3D model is constructed by the free open source modeling software Blender and exported in a specific format recognized by the WebGL library. Then, this library is used to add interactive function for the application program, which can realize the real-time interaction between user’s Browser and 3D model. HTML5 is used to eliminate the problem of customizing special web pages for different devices, and adaptive page design is realized to support multi-terminal access. Based on this platform, the virtual reality learning system of computer assembly and maintenance was designed. After the completion of the study, knowledge acquisition, skill assessment, psychological feeling and cognitive load were measured. Practice shows that the application of this technology can enhance the immersion and intelligence of the experimental process, and improve the user’s interaction and experience. At the same time, the system has a cross platform, free plug-in, easy maintenance and other characteristics, can solve the problems of poor user experience and compatibility of traditional virtual experiment platform.
An improvement design of multi-sensor test bench
Authors: Fan J.H , He Y.Y , Tang F
Published: August 2020

Parallelism error is researched for a three-coordinate testing bench that simulates the automotive environment to test the performance of multi-sensor. The studies showed that the parallelism error between the sensor and the magnet and the signal wheel has an effect on the experimental data. A calibration device with multiple sets of force sensor probes was designed to improve measurement accuracy. And probe measurement results were used to compensate parallelism error in several directions. Results show that this method can reduce above-mentioned error and improve the accuracy of experimental data effectively.
Research on Cross-border E-commerce Platform Supplier Credit Evaluation Based on Big Data Interconnection
Authors: Zhang Yi-Wen
Published: August 2020

This paper first analyzes credit assessment of cross-border E-commerce platform suppliers based on big data from two aspects: credit evaluation index system and credit evaluation model. On this basis, this paper summarizes the recommendations of supplier credit assessment for cross-border E-commerce platform based on big data, in order to provide reference for the work of supplier credit assessment for cross-border E-commerce platform from the perspective of big data, hoping that this study can help promote the cross-border E-commerce platform construction and cross-border E-commerce industry development.
Recognition method of wide area measurement abnormal data based on GRA-IForest
Authors: Xing Zhou , Zhi Yong , Liu Wenfei , Li Jiawei , Qing Can , Ma Rui
Published: August 2020

Aiming at the anomaly recognition method of large area measurement system (WAMS), a method based on Grey Mrrelatian Aru and Isolation Forest algorithm is proposed, considering the attributes of power data and the big data characteristics of large area measurement system. In this method, the relational matrix of power data attribute sequence is obtained through grey relational analysis, and the dependent attributes are selected according to the relational matrix for attribute sequence merging. Then, the data set is partitioned by cascaded isolated forest model and abnormal data are detected. Taking the data of a province’s wide-area measurement system as an example for experimental simulation, compared with LOF, Kmeans and IForest, under the same inspection time, the GRA-IForest model has achieved good inspection results and achieved a higher overall detection accuracy and lower standard deviation.
Railway freight volume forecasting based on combined DWT-Bi-LSTM model
Authors: Qiu Yue-Ying , Lei Ming
Published: August 2020

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.
Research on the Application of Phased Array Detection Technology in Composite Insulator Interface Debonding Detection
Authors: Li Wenbo , Jiang Yue , Liu Lanlan , Zhu Jiran , Wang Jun , Xie Yi , Liu Zhenshan , Tian Xiangzhuang
Published: August 2020

The main reasons for the failure of composite insulator are the fracture of core rod and the breakdown of interface. However, due to the particularity of composite insulator, the traditional insulator detection method is not fully applicable to it. In this paper, phased array detection technology is used to detect the interface debonding defects of composite insulator sheath and core rod, and clear interface debonding signals are obtained. The research results can provide research basis for the application of phased array detection technology in the field of composite insulator defect detection.
Application Research on Ultrasonic Phased Array Technology in Weld Seam Inspection
Authors: Zhang Haixing
Published: August 2020

Welding seams are prone to safety accidents and failures during actual operation, which has an adverse effect on the economic benefits of the enterprise. When using traditional ultrasonic inspection methods to inspect the pipe butt welds, the staff needs to use the inspection probe to move as needed, which makes the inspection process difficult to operate and is greatly affected by human factors. The beam of conventional ultrasound cannot be deflected and focused. It is difficult to detect parts with complex geometric shapes and large anisotropic welds. Ultrasonic phased array is a new type of non-destructive testing method, which makes up for the limitations of conventional ultrasonic testing, and has great advantages compared with conventional ultrasound. Therefore, in order to reduce the misjudgment of defects caused by humans, this paper studies the application method of ultrasonic phased array technology in weld inspection, discusses the principles and characteristics of ultrasonic phased array inspection technology in nondestructive testing, and the defects were tested. The test results show that the phased array technology can effectively detect weld defects.
Research on green manufacturing technology
Authors: Lin Guomin , Hao Botao
Published: August 2020

The current situation, the contents and the features of green manufacturing technologies are introduced. The related technologies and the key technologies of green manufacturing technologies are analyzed. The contents, the targets and the carry out ways of green manufacturing technologies are researched. The development tendency and the application vistas of green manufacturing technologies are prospected.
The Design of Peanut Seedling Cutting Test Bench
Authors: Cheng Xiaofei , Zhou Fuping , Li Yueyang , Song Weihai , Li Zhen , Yan Yongzeng
Published: August 2020

In order to solve the problems of the peanut harvester, such as unclean fruit picking and serious crushing, a peanut seedling cutting test bench is built. This paper starts with the selection and design of the components of the test bench, and then completes the design of the whole test bench, and finally completes the design of the peanut seedling cutting test scheme.
Research on the financing dynamics of product crowdfunding: based on the perspective of emotion
Authors: Qi Zihao , Zheng Haichao , Li Liting
Published: August 2020

Product crowdfunding provides new financing channels for entrepreneurs. How to screen crowdfunding projects has become an important issue in this field, and the financing dynamics of projects is the starting point to study the filtering of crowdfunding projects. Based on the perspective of emotions, this paper uses text analysis technology to extract the emotions contained in the comments of crowdfunding projects, and analyzes the influence of emotions on the dynamics of project financing. The main conclusions are as follows: for financing dynamics, anticipation, trust and sadness can help to increase the number of subsequent investors, while joy and disgust have significant negative effects on the number of subsequent investors. The conclusion of this paper is useful to the protection of crowdfunding investors and the operation practice of crowdfunding platforms.
Research on Application of Artificial Intelligence Technology in Electrical Automation Control
Authors: Jiang Chao , Xiong Xiaorui , Zhu Tanqing , Cao Jiajia , Yu Jiahao
Published: August 2020

Among the evaluation indicators of national development level, the degree of power system development is one of the important indicators. Economic and social needs have gradually expanded the scale of the power grid, and many distributed renewable energy sources are continuously connected to the power grid system, making the power system more and more complex; the data generated during the application of the power system presents typical big data characteristics, such as presenting large, heterogeneous, and multi-source characteristics. Faced with the new characteristics of power data, the requirements for methods of analyzing and processing power data problems have increased. The current research on artificial intelligence (AI) in the computer field is getting deeper and deeper, and the application of AI in various industries has made greater research progress. It is the key to future scientific and technological progress. In my country’s development strategy, it is clearly required to promote the development of AI and regard the power system as one of the main application areas of AI. The rapid development and advancement of artificial intelligence can provide powerful tools in many aspects of the power system, including power system planning and design, coordinated control, simulation, prediction and estimation, diagnosis and identification. This article first analyzes the artificial intelligence technology, introduces the two mainstream artificial intelligence technologies in the current situation, and analyzes the power system, and then studies and analyzes the application of artificial intelligence technology in the electrical system.
MsrFace: Multi-Sphere Radius Loss for Deep Face Recognition
Authors: Du Xi , Han Xiaoxuan , Qin Yu , Wang Dong
Published: August 2020

Loss functions is one of the main challenges in face recognition problems. Recent works focus on designing loss functions that make learned features more discriminative by a larger angular or cosine distance. In this paper, in addition to the method based on additional angle margins, we propose a Multi-Sphere Radius Loss (MsrFace) to add radius constraints. MsrFace pushes learned features to hyperspheres with different spherical radii and the classes can be separated more strictly. We present experiments on several widely used benchmarks to show that MsrFace has a better performance in comparison with some recent state-of-the-art face recognition methods.