bluuebunny/crossref_metadata_2025_split huggingface dataset:
Simulation Research on Damage Identification of Plate Weld Based on Correlation Vector Amplitude Vector
Authors: Lin Lizong , Zhang Xin , Chang Haoyuan 
Published: November 2019

Abstract
A method for detecting the quality of the plate weld based on the cross-correlation function magnitude vector is proposed. In the large-scale plate welding process, the weld is the most vulnerable part of the damage. This paper proposes a method to accurately determine the location of the weld defect. In this paper, the finite element software ABAQUS is used for numerical simulation to establish a plate weld model, and the weld area is divided into 19 units. In order to avoid noise interference, a 10 second 1000N stationary random excitation is used, and 10 excitation reference points are arranged around the random excitation point, and the best reference point is determined by the simulation result. The damage of each weld unit is characterized as the decrease of Young’s modulus, the acceleration response is collected at the joint of each weld unit, and the single defect, double defects and multi-defects conditions are simulated respectively. The numerical results show that the cross-correlation amplitude vector method can always accurately locate the defects under various damage conditions.
Parameter optimization of governor for hydropower turbines to improve damping characteristics and regulation rate
Authors: Liu Donghao 
Published: November 2019

Abstract
Due to its characteristics, hydropower turbines may provide negative damping in the process of ultra-low frequency oscillation. It is necessary to improve the response of hydropower units in the process of ultra-low frequency oscillation by adjusting the PID parameters reasonably. Although the adjustment of PID parameters can significantly improve the damping characteristics of hydropower turbines, it will sacrifice a certain speed of regulation, which is not conducive to the primary frequency regulation of the system. Through the establishment of the hydroelectric turbines model, the damp and regulating speed are optimized as the control objectives, so that better parameters can be obtained to suppress the ultra-low frequency oscillation of the system.
Improved FAHP Evaluation of Comprehensive Performance of Mechanical Products
Authors: Jin Qichun , Yang Xiaojian , Hu Yuan , Lu Shourong , Cheng Yanhai 
Published: November 2019

Abstract
In order to overcome the characteristics of multi-dimensionality, fuzziness and uncertainty in the evaluation of comprehensive performance of mechanical products, the Improved Fuzzy Analytic Hierarchy Process is used to perform the evaluate. An evaluation index system is established, the weight of index in consolidated layer is determined by the Analytic Hierarchy Process, and the weight of index in the item layer is determined by Valuation Method. The variance is used to test the volatility. The degree matrix of membership is constructed using the Fuzzy Comprehensive Evaluation, and the vector of the target layer is obtained by fuzzy calculation. The result is obtained according to the principle of maximum membership degree and by calculating the comprehensive evaluation value. An case study has been given to illustrate the application of Improved FAHP, which effectively solved the problem.
Research on Single-Phase Grounding Fault Location Technology
Authors: Xiping Zhang , Zhiqiang Wang , Zehui Dai , Yutao Hu , Songshan Hui , Danfeng Si , Suzhen Mei 
Published: November 2019

Abstract
In view of the complex structure, large scale and high failure rate of distribution network, as well as many branch lines, large transition resistance, difficult to detect fault signals and difficult to locate accurately., A single-phase grounding fault location method based on single-terminal measurement for distribution network with multi-dominant transmission lines is proposed. This method can solve the problem of reliable monitoring of small current grounding fault through data processing and analysis of measuring units installed at substation equipment and line end, find grounding fault line in time, find fault point, and adopt corresponding treatment measures to block the impact scope of user branch line accident, restore the power supply of non-fault line as soon as possible, and reduce the outage time. Providing further reliable power supply fundamentally solves the problems of reliability and safety of power supply in distribution network. This method only needs to synchronously measure the voltage and current at each end of the line when the parameters of the line are known. The method is simple and the measurement accuracy is high. By locating the single-phase-to-ground fault of multi-dominant power lines, the results show that the method is correct and accurate.
A coaxial thickness measuring device for full-width banknote based on hall effect
Authors: weisong Yang , shukun wang , wei song 
Published: November 2019

Abstract
In this paper, a multi-channel banknote thickness measurement mechanism based on hall effect is designed to meet the demand of banknote identification for the cash circulation module of financial instruments. It uses coaxial type rocker mechanism to mechanical amplification of the thickness of the banknote, translates the magnetic steel with hall sensor location change into money thickness signal, and the signal is amplified, filtered and calibrated to form a banknote thickness image. Finally, the detection results of the whole paper currency are obtained through calculation, so as to realize the detection and identification of the whole paper currency tape, folding and partial damage.
Sales Forecast of CNC Machine Tool Suppliers Based on Ensemble Learning
Authors: Zhong Kai , Wen Jiwen , Zheng Jia 
Published: November 2019

Abstract
CNC machine tools, whose product sales forecast is a complicated dynamic process, are the basic equipments for manufacturing industry. It is difficult for a single prediction model to achieve the expected effects because product sales can be affected by multiple factors with features like time-varying, non-linear and random. This paper, which takes the sales data of a CNC machine tool supplier as an example, designs an ensemble learning model based on XGboost and random forest, obtaining lower prediction error through data cleaning, data conversion and parameter optimization. The experimental results show that the generalization ability of this model is excellent, so it can provide decision supports for CNC machine tool suppliers in purchase and sales plans.
An Automatic Grabbing Method of Manipulator Based on Monocular Ranging
Authors: Zhang Chenyu , Huang Mengxing , Wu Di , Ding Yiyin , Ding Yiyin , Chen Weizhe , Xiao Jin , Chen Rong 
Published: November 2019

Abstract
Most existing studies on manipulator focus on the field of teaching and research. Other manipulators used for industrial production also have several disadvantages, including high cost, complicated control and low flexibility. Accordingly, in this paper, a simple automatic grabbing method of manipulator is proposed based on monocular ranging. The method is developed via the openmv vision platform. First, the target object is identified in the lens, the linear distance of the lens from the target object is calculated through monocular ranging, and then the inverse kinematics of the manipulator is exploited to calculate the rotation angle of each steering engine. Next, the contradiction between system speed and impact is balanced using the five-time curve trajectory optimization algorithm. The results of tests suggest that the method can achieve accurate capture of the target object in the grab range. The method, compared with the teaching and even the engineering manipulator system, is characterized by lower cost, simple and flexible control. Besides, it can be used to implement simple tasks in industry.
Design of Train Carriage Abnormal Noise Alarm System Based on Zigbee
Authors: Zhang Yongsheng , Liu Miaomiao 
Published: November 2019

Abstract
In order to realize the timely and accurate alarm for abnormal conditions in train carriages, a scheme of abnormal noise alarm system based on zigbee is proposed. The overall system architecture as well as the hardware and software structure of each node is designed. Meanwhile, the workflow and implementation process of each module are elaborated in detail. The system includes five parts: sensor node, zigbee router, coordinator, video module and intelligent terminal. Firstly, the decibels collected by the sound sensor are processed through threshold judgment and sent to the coordinator through the zigbee router module. Secondly, the video module recognizes and analyzes the video of the alarming carriage by means of artificial intelligence. Finally, the detailed alarm information is sent to the intelligent terminal. The development cost of this system is low while its installation and maintenance is convenient. It improves the monitoring facilities of train carriages, which can not only guarantee the accuracy and real time of the warning, but also set the threshold value and delete alarm mark manually. It has strong expansibility and portability, which is of practical significance for improving the safety of train operation.
A Method Reconstruct Ballistic Target Based on Compression Sensing
Authors: Han Li-xun , Tian Bo , Feng Cun-qian , Xie Jia-hao 
Published: November 2019

Abstract
In order to solve the problem of large data volume and complex processing in the processing of ballistic target echo signal, compressing sensing is applied to the processing of ballistic target echo signal. Firstly, the mathematical model of the cone-cylinder ballistic target’s precession is constructed, the signal processing when the radar radiates sparse Orthogonal Frequency Division Multiplexing-Linear Frequency (OFDM-LFM) signal is analyzed, and then match this process with compressed sensing decision branch; a new denoising recovery algorithm for signal-to-noise ratio(SNR) based on (SAMP) algorithm was proposed, and finally verify the effectiveness of the algorithm through simulation experiments.
Visualization of the Frontier Evolution of Strategic Management Based on Knowledge Map
Authors: Fanyoujun  
Published: November 2019

Abstract
Over the past 20 years, western strategic management has developed rapidly. In the new century, strategic management is becoming more and more contentious. It not only pays attention to solving the problems of traditional strategic management, but also explores the Enlightenment of the “new scientific group” theory and the importance of strategic decision-making. It is only sooner or later that a new breakthrough will be made. CiteSpace software is used to visualize the relevant literature data since 2000, and the frontier and evolution of strategic management are discussed. Through the analysis of the key words of relevant domestic research, the author puts forward the research focus and the most concerned keywords and the authors and literature contents with high frequency of reference in each stage of domestic research, so as to present the research of domestic strategic emerging industries. Several features. It is necessary to provide necessary reference for scientific research planning and management in the same field in China.
A Literature Review of Sentiment Analysis on Chinese Social Media
Authors: Yuan Zhang , Yuwei Gu , Feng Zhao , Reimei Wang 
Published: November 2019

Abstract
Weibo (The mainstream social media of China) has grown to a huge scale since founded in 2014 and has become a platform for most people today to access information and express opinions. By analysing comments and forwarded posts in Weibo, we can dig deeper into the users’ perceptions and opinions about products, which is of a great commercial value. This paper summarizes the related researches on the sentiment analysis of Weibo in China and abroad since 2013, and analyses the analysis of Weibo sentiment analysis based on different methods.
RETRACTED: Parametric Design of Quasi-Natural Waterscape Gardens Based on Big Data Technology
Authors: Naize Wang , Chongyu Wang , Zhangke  
Published: November 2019

Abstract
In order to better design the water scenes of garden, the natural water scenes of garden parameters are designed by combining the big data technology, the water scenes of garden generation rules are collected by the pseudo-natural principle, and the garden landscape parameters are designed by combining the regular production characteristics with the big data technology. In order to ensure the rationality of the principle landscape design, the pseudo-natural water scenes of garden parameters are standardized, and the standard parameters are input into the Grasshopper auxiliary simulation software, so that the optimal design scheme of the pseudo-natural dynamic correlation water scenes of garden view is automatically generated. Finally, through experiments, it is proved that the landscape simulation effect of pseudo-natural water scenes of garden parametric design based on big data technology is more satisfactory than that of traditional design simulation results.
Research on Ontology Perception Technology in Recommendation of Professional Books
Authors: Li Liu 
Published: November 2019

Abstract
A book recommendation method in the paper is proposed by using the professional book ontology perception method. By establishing a model framework based on ontology technology, the ontology triggering mechanism of professional books is combined to obtain relevant information parameters and improve the higher recommendation accuracy, so as to more accurately calculate the regional weights and preferences of professional books, and complete the professional books and the recommendation process. Through the evaluation and testing of the theoretical methods, the feasibility and accuracy of the research content in the paper is proved, which contributes to the improvement of book recommendation service.
Application of Three-dimensional Digital Model in Digital Archive Ubiquitous Intelligent Service
Authors: Ling-ping Ren , Li Dou , Li Li 
Published: November 2019

Abstract
With the rapid development of information technology and mobile Internet technology as well as the rapid spread of smart phones, the way of the information obtained by people is more dependent on mobile devices. The traditional archive service model can no longer meet the needs of users. The way of archival information services should also keep up with the times. Based on the fully analysis of the current archival information service methods, the main modes of the current archive information service mode are analyzed in this paper. A three-dimensional digital model is put forward, The application of the three-dimensional digital model in the ubiquitous intelligent service of digital archives is studied. Its purpose is to organically combine archive management ideas, systems, organizational methods, management measures, and modernization of management talents, to further realize the sharing of archive information resources. It will provide an effective guarantee for the archives ubiquitous intelligent service.
A Comparison of Traditional Machine Learning and Deep Learning in Image Recognition
Authors: Lai Yunfei 
Published: November 2019

AbstractThe growth of the mobile Internet, smartphones and social networks has brought in huge amounts of picture information, and traditional manual identification is not able to meet the demand well enough. Therefore, the automatical image recognition [1] has been proposed which can help us recognize the image efficiently and get the corresponding information. Although traditional machine learning methods [2] have already been widely used in the field of image recognition, most of these methods are designed to handle one-dimensional vector information. Thus, we should first stretch image matrix to one-dimensional vector or extract features from images to employ traditional image recognition methods, which would lose the adjacent information in images and miss some important features. With the development of computer technology, deep learning [3] is gradually applied to the field of image recognition. It can deal with two-dimensional image data naturally and extract features automatically. Compared with the traditional machine learning methods, deep learning is popular for its good learning ability and low generalization error. In this paper, we compare the differences between SVM [4] and deep learning on image recognition, with an application to handwritten digital images recognition. The results show that the deep learning method is more accurate and more stable in image recognition.
A Method of Making Video Trailer Based on Artificial Intelligence
Authors: Na Li , Lanyou Li 
Published: November 2019

Abstract
Based on the flownet2 optical flow algorithm, cascade CNN face detection and facenet face recognition algorithm, the method of automatically generating video trailers by using the sum of the optical flow vector sum per second and the absolute value of the audio loudness amplitude per second and the character duration information is proposed. It can effectively edit the visual essence clip, the video essence clip corresponding to the audio, the main character clip and the story outline to generate a trailer.
Research on Cluster Mining Algorithms for Personal Privacy Protection in the Background of Big Data
Authors: Huang Tianming 
Published: November 2019

Abstract
With the rapid development of cloud computing and mobile Internet technology, huge amounts of data are generated on the network at every moment. In the era of big data, most of the data are designed in distributed storage, stored on many independent sites. The typical data mining algorithm can not meet the requirement of privacy data security in the big data background. In the big data background, distributed data mining is faced with the need to solve data security, privacy disclosure and other technical problems. Therefore, this paper applies homomorphic encryption technology to typical K-means clustering mining algorithm, and designs a distributed privacy protection PP-kmeans algorithm based on big data. Experiments show that this algorithm can realize data privacy security protection. And more accurate big data clustering mining effect is achieved.
The Application of orthogonality cross correlation algorithm in weak signal detection
Authors: Hu Pengfei , Liu Liping , Shen Li 
Published: November 2019

Abstract
In this paper, the Xilinx Artix-7 FPGA chip was used as the processor to submit an orthogonality cross correlation algorithm with strong anti-interference capacity that constructed two reference signals have a large amplitude and same frequency with the reception signal from transducer to search the weak signal in the transducer. It can prominently improve the Signal to Noise Ratio (SNR) of transducer information and exact the feature information of reception transducer. The processing time of this algorithm is short and able to ensure the real-time performance of data transmission in the measurement.
Application Research of Cuckoo Neural Network Algorithm in Surface Reconstruction
Authors: Wang Hai-jun , Jin Tao 
Published: November 2019

Abstract
Aiming at the current BP neural network model for surface reconstruction, it is sensitive to the initial weight and threshold, easy to fall into the local minimum value, and the reconstruction accuracy is not high. The CS-BP surface reconstruction algorithm is proposed. Firstly, the cuckoo search algorithm is used to optimize the weight and threshold of BP model for the first time, and then the improved BP algorithm is used to further optimize, finally, the surface reconstruction model based on CS-BP neural network is established. Compared with the reconstruction results based on BP algorithm, quadratic fitting algorithm and Wolf neural network algorithm model, it is proved that the idea of using cuckoo algorithm to optimize BP model to construct surface reconstruction model is feasible, and cuckoo algorithm, like other swarm intelligence algorithms, can effectively improve the efficiency of BP surface reconstruction model.
Research on computing verification algorithm on body sensing data in cloud computing environment
Authors: Gao Yanfang , Yao Lan , Yu Jinyue 
Published: November 2019

Abstract
Cloud computing enables users with limited resources to accomplish complex computation. Meanwhile, the massive human body sensing data generated by Wireless Body Area Network (WBAN) can be stored in cloud servers. However, cloud storage of human body sensing data is challenged by some issues. How can users ensure that cloud servers can return computing results and at the same time confirm that the results returned are correct? To address this problem, based on the argument system of commitment scheme, this paper designs a DMP verification algorithm. By compiling the hash of the header node into a constraint set and verifying the accuracy of the data of the intermediate node through a hash list, the algorithm reduces the compiling time of the intermediate node and improves the verification efficiency.
Statistical Research Based on Machine Vision: A Review
Authors: Xiong Xin , Huang Li , Cheng Yangwei 
Published: November 2019

Abstract
Machine vision technology has been widely studied and applied in the development of science and technology, and has achieved remarkable achievements in real-time monitoring. Aiming at the upper limit of construction workers’ personnel, there are safety hazards in the unclosed elevators. The development of the machine vision number statistics is discussed in detail, mainly from the status quo of the population statistics research, the direct method of the population statistics and the application of the indirect method. According to different algorithm research, the problems existing in the current research direction are analyzed, and the number of people in the future elevators is forecasted.
Low power consumption Internet of Things and its core sensor package technology in underground pipe gallery
Authors: Zou Qiang , Lei Zhiming , Meng Zhen , Ai Xinchen 
Published: November 2019

Abstract
Underground pipeline corridor is a part of the construction of Smart City. There are many kinds of pipelines in it. Because of the complex air composition and special space structure, it is impossible to carry out high-frequency manual maintenance. Therefore, in order to ensure the safe operation of pipeline corridor, it is necessary to monitor its working environment in real time. In this paper, a low-power Internet of Things (IOT) system is designed for the special environment of underground pipeline gallery. The system’s network is built by low-power ZigBee module. Cooperated with micro-sensors, it can monitor the environment of pipeline gallery in real time, and the low-power of the system can make up for the long-term unattended situation of pipeline gallery. This paper also designs a sensor packaging technology for this environment, which protects the sensor from corrosion in the corridor environment without affecting the smooth network. Finally, the material is tested.
Matching Algorithms between Investment Projects and Grid Weak Links
Authors: Liu Lixia , Ma Chao , Zhang Dong , Li Na , Xiao Fei , Yang Jie , Ye Yutong 
Published: November 2019

Abstract
This paper analyzes the weak links of the power grid from the perspectives of grid operation and future investment management of power grids. Based on the research theory, the matching algorithm of investment project and weak link of power grid is constructed based on 0-1 planning and empirical analysis is carried out. This paper construct a model based on constraints such as the maximum objective value of the net present value rate and the investment ratio, calculate and analyze the case, get the investment results, and optimize the resource allocation.