bluuebunny/crossref_metadata_2025_split huggingface dataset:
Design and Implementation of Intelligent Robot Walking Control System Based on DSP
Authors: Junsong Chen 
Published: August 2019

Abstract
In recent years, the aging of the population and the acceleration of urbanization have led to a decrease in labor force, an increase in production costs and a decrease in labor productivity. In order to solve these problems, the degree of mechanical automation is bound to increase. This paper has completed the design and development of the automatic walking robot control system, mainly to realize the functions of visual navigation and walking control of the control system. The experiments prove that the vision navigation system of the automatic walking robot can provide the walking control system with direction identification and identification information of the ridge and furrow extension paths between fields. The designed walking control system can call the motor driver to realize the forward, backward, left forward, right forward, left backward and right backward rotation of the control object according to the path information. After experimental debugging, the automatic walking robot can move according to the direction of the identification image, which greatly improves the real-time and intelligent level of the automatic walking robot.
Research on Security Problems and Defense Strategies of Power Communication Networks
Authors: Feng Chi , Chen Qingping , Cai Xiang , Yang Xianjie , Li Zhou , Chen Shan , Wu Lisha , Hu Zhongkun 
Published: August 2019

Abstract
The development direction of power systems is automation, networking and intelligence. The intranet and extranet dual network operation modes have been applied in the power system, but there are still many problems. Due to the continuous expansion of the power communication network, massive communication data is continuously generated in daily operation. At the same time, the means of attack on the power system communication network has evolved, seriously threatening the security of the power communication network. Therefore, it is necessary to further increase the research on the security and protection of power communication networks. This paper discusses the current status of China’s power communication network, analyzes the main factors affecting the security of power communication networks and the security problems of power communication networks. Finally, corresponding protective measures are given to provide reference for improving the security and stability of power communication networks.
Indoor formaldehyde monitoring system based on fog computing
Authors: Chen Hao , Li Sixuan , Zhang Jie , Peng Xiaoxiang 
Published: August 2019

Abstract
With the enhancement of people’s awareness of environmental protection, environmental monitoring technology has gradually entered into all aspects of people’s life and work. However, due to the high demand of real-time monitoring for data transmission delay, the traditional cloud computing architecture cannot meet the demand, and the fog computing architecture emerges at the historic moment. In this paper, the traditional cloud computing architecture is improved by using the fog computing architecture combined with the function as a service mode of the serverless architecture, and an environment monitoring architecture based on fog computing is proposed. Taking indoor formaldehyde monitoring as an example, the delay of the system is significantly improved, and the resource utilization efficiency is higher and the energy consumption is lower. It solves the deficiency of traditional cloud computing architecture and better meets the demand of real-time environmental monitoring.
An original design of fresh agricultural products e-commerce platform based on Mobile Internet
Authors: Shen Hongmin 
Published: August 2019

Abstract
With the development of Mobile Internet, it provides a good technological environment for the development of agricultural e-commerce. The fresh agricultural products e-commerce is to design, planning and control of the information flow, the trade flow, the fund flow and the material flow of the fresh agricultural products with a view to strengthening competitiveness. Because of the previous view, the paper design the functions of the e-commerce platform suitable for fresh agricultural products. According to the function design, we design the app prototype of the fresh agricultural products e-commerce platform, which can more accurately express the functions of the e-commerce platform.
Movement authority security modeling and verification based on fault statechart
Authors: Yu Fan , Tang Minan , Hao Binbin , Wang Qianqian 
Published: August 2019

Abstract
Communication Based Train Control System (CBTC) has become the development trend of train control system, and generating safe and reasonable movement authority is the key to ensure the normal operation of the whole system. Combined with the security of the system, a security analysis method based on fault tree information to construct fault Statechart is proposed. The fault tree that generates the abnormality of the movement authority is analyzed, and it is represented as a form that the Statechart can describe, thereby establishing a fault statechart of the movement authority generating abnormality. Finally, the formal modeling of the fault statechart is carried out by using the time automaton theory, and the fault state unreachable is taken as the attribute of the specification for inspection. The results show that the method of combining fault Statechart and time automata proposed in the paper is feasible and suitable for the analysis and verification of safety critical systems.
A Multiple LRU List Buffer Management Algorithm
Authors: Wu Xiang , Cai Delin , Guan Shujie 
Published: August 2019

Abstract
Paper[1] proposes LLRU algorithm. The buffer is divided into four categories in LLRU: cold clean LRU list, cold dirty LRU list, hot clean LRU list and hot dirty LRU list. But the cold clean LRU list may be empty when the algorithm runs for a while. Then a new page just reading in buffer will be victim when the LLRU selects the victim page, leading to a clean page hard to turn into a hot page. The AM-LRU proposed in this paper ensures that the cold clean page has the chance to turn into a hot page by setting the minimum length of the cold clean LRU list and adopting secondary opportunity replacement strategy for the victim page from the hot area. As a result, compared with LLRU, the hit rate of AM-LRU can be improved and the flash reads counts, writes counts and erases counts can be reduced.
Research on Multi-objective Project Scheduling Model Based on Multi-skilled Workers
Authors: Zhaoyue Zhang , Zhenqiang Bao , Yun Kan , Yuqing Zhu 
Published: August 2019

Abstract
There are many kinds of skills for the workers in the project group, but the present scheduling methods do not consider that the workers have different proficiency in different skills. In this paper, a scheduling model of human resources project is established, which takes the worker’s skill proficiency into account and the project duration and labor cost are considered as the optimization objectives. The improved non-dominated sorting genetic algorithm (NSGAII) is used to solve the model. The simulation results show that the model can significantly improve the scheduling efficiency of the project team, and managers can make scheduling decisions according to preference in a series of solutions.
Short-term Forecast of Shanghai Composite Index in China Using BP Neural Network
Authors: Guo Qiushi 
Published: August 2019

Abstract
This article analyses the historical statistics of the Shanghai Composite Index over the last 25 years, namely the characteristics and influencing factors. The article also predicts the trend of a 60-day time sequence of Shanghai composite index after 30th, Nov, 2018, using BP neural network model. The BP model fits the historical statistics well after consecutive training. The prediction result of the BP neural model indicates three characteristics of the trend in next 60 days: (1) the price will go up; (2) fluctuation frequency will reduce; (3) fluctuation range of the price will remain low.
By analysing the relevant daily data of Shanghai Composite Index from 1993 to 2018, it is found that the forecast for Stock Market is very challengeable due to the intrinsic indeterminacy of the stock market and the complexity of the variables. To train and test the BP neural network model, this research adopts the trading record of 4465 transaction records as the training group, the next 935 trading days as the testing group and 935 trading days as verification group. The input variable in this BP neural network is Shanghai Composite Index (SZCindex), and the expected Shanghai Composite Index (EXSZCindex) is output variable. The BP neural network model is constructed as a three-layer neural network model.
In the analysis of the daily closing price of the Shanghai Stock Exchange Index, this paper uses the goodness of fit to evaluate the BP neural network model and to continuously adjust the weight of the parameters. The results show that the training goodness of fit is 0.99898, and the correction goodness is 0.99897. The test goodness is 0.99904, and the overall goodness of fit is 0.99899, which indicates that the BP neural network has a rather good fitting effect. It is predicted that the price of the next 60 days from November 30, 2018 will rise to the highest point of 2623.874.
In the end, the rationality of the predicted results is analysed in this paper. Via the analysis of the obtained data, it is found that there is no over-fit. The images of predicted values and real values are drawn by using the data from subjective and objective analysis.
Speed estimation of PMSM using SRUKF algorithm
Authors: Li Jingnan , Gao Yuan , Hong Shuai , Zhang Yin 
Published: August 2019

Abstract
Speed estimation is a key technology to realize sensorless control for the PMSM. Based on the unscented Kalman filter (UKF) method without linearization of nonlinear system equations, this letter provides square root unscented Kalman filter (SRUKF) algorithm that operates through iterating the square roots of the covariance matrixes obtained by QR decomposition and Cholesky decomposition. The presented method can further improve speed estimation performance through decreasing the effect of truncation error and enhancing the convergence and stability of algorithm. Simulation results of sensorless control system demonstrate the feasibility and effectiveness of the proposed algorithm.
Bayesian Network Parameter Learning Algorithm for Target Damage Assessment
Authors: Du Xiuli , Fan Guichuan , Lv Yana , Qiu Shaoming 
Published: August 2019

Abstract
Aiming at the problem that the existing methods of target damage assessment based on Bayesian network mainly determine the structural parameters of Bayesian network by giving conditional probability tables based on expert experience, which results in too subjective and having large errors in the evaluation results, an improved learning algorithm of conditional probability tables of Bayesian network is proposed in this paper. We divided the E step of the EM algorithm (Expectation Maximization Algorithm) into three steps. Firstly, the range of the missing variable is determined by expert experience, then the Gibbs sampling algorithm is used to complete the sample set, and finally the sample is weighted. The proposed algorithm is compared with EM algorithm, Gibbs algorithm, EM and Gibbs algorithm. The experimental results show that the proposed algorithm has good stability and high precision.
A Novel Algorithm to Differential Scanning Calorimeter Temperature Control Based on Liquid Nitrogen Feedforward Technique
Authors: Yang YU , Jingtao HU , Yang Jia , Laixing Wei , Wei Sun 
Published: August 2019

Abstract
Differential scanning calorimeter (DSC) is an important instrument in the thermal analysis which could be pervasively applied to property analysis of inorganic compounds, organic compounds and drugs. An effective temperature control technique could ensure DSC thermal experimental accuracy. Unlike the traditional temperature control methods which have limited temperature control ranges and change rates, this paper presents a novel algorithm which exploits the liquid nitrogen feedforward technique to control the temperature of DSC. To be specific, the proposed approach fully takes advantage of the characteristic of the liquid nitrogen that has the ability to rapidly decrease the objective temperature. Furthermore, a cold-heat synergistic feedforward-feedback control is utilized. On one hand, the feedforward controller is able to avoid the singularities for the amount of liquid nitrogen. On the other hand, the feedback controller is able to compensate for the temperature errors due to external disturbances. The proposed algorithm has been applied to the DSC. The experimental results and analysis prove that the control method is able to change the temperature accurately within a wider range.
Image Stitching Algorithm for Drones Based on SURF-GHT
Authors: Duan Haohua , Liu Yangyang , Huang Hexiang , Wang Zhengtao , Zhao Hongwei 
Published: August 2019

Abstract
Aiming at the problem that the amount of single remote sensing picture information is limited due to the limited angular range of single drone, this paper proposes SURF-GHT algorithm based on SURF algorithm and generalized Hough transform based on the existing image stitching algorithm. After the feature points are extracted by the SURF algorithm for the pictures to be spliced, the decision tree forest in the generalized Hough transform is used to filter the set of feature points, and then the fading image fusion algorithm is used for image splicing. The experimental results show that the SURF-GHT algorithm improves the quality of image stitching on the basis of higher stitching efficiency, and better satisfies the subsequent identification of remote sensing images of drones.
Discretization method based on binary ant colony and variable precision rough set
Authors: Song Jian , Jiang Yu , Li Dong , Bao Yangwanying 
Published: August 2019

Abstract
Discretization plays an important role in data pre-processing and has been used in fields such as artificial intelligence and data mining. A new discretization algorithm based on binary ant colony and variable precision rough set is proposed in this paper. Binary ant network is first built using candidate breakpoints, then global optimal breakpoints is searched in the network. The fitness function is established by the number of breakpoints and approximation classification accuracy of variable precision rough set. This method is compared with other algorithms using C4.5 classifier on the WEKA (Waikato Environment for Knowledge Analysis) platform and seven UCI data sets. The results indicate that the proposed method performs well.
Implementation of Vehicle Traffic Statistics Algorithm Based on ViBe Image Processing
Authors: Lu Mingyu , Tang Zhihua 
Published: August 2019

Abstract
This project uses all kinds of main roads and intersections of UAV aerial photography cities to obtain information on traffic flow and real-time traffic conditions of various main roads, and feeds back information to unmanned airborne image processing devices through big data and clouds. The extracted image is processed and analyzed, and then a traffic flow safety prediction and evaluation model database based on road vehicle conflict is established.Through the analysis of the data, relevant departments can better plan the layout of traffic facilities on the road, so that the traffic facilities can be fully utilized, relieve traffic pressure, and reduce the incidence of traffic accidents.
Experimental study on crop disease detection based on deep learning
Authors: Xia Xiaoling , Wu Yongbo , Lu Qinyang , Fan Changqi 
Published: August 2019

Abstract
At present, deep learning has been widely used in our daily life, and the recognition of crop disease degree based on deep learning has gradually entered the public’s vision. The degree of crop diseases can usually be judged by the characteristics of leaf colour, shape and spot. According to the data set provided by AI Challenge Competition, the degree of crop diseases can be divided into many situations. According to the current open source data, the recognition accuracy rate on this data set has not been able to exceed 90%. In order to achieve better recognition results, on the basis of the convolutional neural network CNN, the transfer learning method is used to load the residual network ResNet pre-training model and SeNet + ResNet to carry out deeper network training to improve the efficiency and accuracy of recognition. This paper carries out many experiments under the condition of controlling variables. The experimental results show that Resnet can play a good role in this data set with training parameters and achieve 83% accuracy, while SeNet + ResNet can achieve 90% accuracy without using pre-training parameters, and 87% accuracy of test set recognition. It can be seen that the SeNet + ResNet method has better effects in image classification and recognition tasks in this data set.
Manipulator Meta-Imitation Learning Algorithm with Memory Weight Integration
Authors: Yin Mingjun , Zeng Qingshan 
Published: August 2019

Abstract
Versatility is one of the key characteristics of general agent. In order to enable the manipulator to quickly and effectively acquire the ability to perform multiple tasks in an unknown environment, a large capacity model is essential. In this paper, the memory weight integration term adapted to meta-learning algorithm is proposed. By adjusting the plasticity of neurons, the manipulator can learn to learn more effectively in the process of learning multi-task and improve the forgetting problem of multi-task learning. Then, this paper combines the memory weight integration with meta-imitation learning, so that the manipulator can acquire new skills from a single demonstration task. Finally, a 7-DoF manipulator in PusherEnv experiment is used to explore the influence of different integration coefficients on the algorithm. The results show that the memory weight integration can effectively improve the success rate of tasks.
Research on Library Information Visualization Retrieval Technology Based on Readers’ Needs
Authors: Hu Jianfei , Wang Di , Zhang Bin 
Published: August 2019

Abstract
With the advancement of social informatization and the widespread application of network, the resources accumulated by libraries are becoming larger and larger. In addition to the need to store, transmit, retrieve and classify massive data, there is an urgent need to understand the relationship between data and its development trend. With the continuous development of modern information technology, digital libraries have also made great progress. And to introduce visual information retrieval technology into digital libraries is an inevitable trend in the future development. This paper first analyses the characteristics of readers’ information retrieval needs in the new period, and then introduce library information visual presentation technologies to design the library information resource visualization retrieval system. The system can visually display the collection structure, popular books, readers’ interests and so on, to truly reflect the structure, coverage, utilization and other indicators of books resource, and provide convenience for the visual sharing of library information resources.
Research on Damage Evaluation of Radar Target Based on Deep Learning
Authors: Xu Pengcheng , Shu Jiansheng 
Published: August 2019

Abstract
In view of the evaluation of the damage effect of a radar on a damaged object, considering the nonlinearity and complexity of the target damage assessment, deep learning can be used to evaluate the radar target damage effect. Determine the network parameters and network node structure characteristics according to the characteristics of damage information; use the appropriate software and tools to build a modular network model; train the damage assessment network to make the network have strong experts Experience has led to a neural network with expert experience.
A scale transform and deformation target tracking algorithm based on correlation filtering
Authors: Hu Cong , Xia Wei , Chen Peng , Huang Shun , Guo Hui , Jin Xing 
Published: August 2019

Abstract
In order to improve the robustness of the correlation filtering (CF) tracking algorithm and overcome the problem that the traditional correlation filtering method can not deal with the target’s scale and deformation change, a feature-adaptive scale adaptive correlation filter tracking algorithm is proposed. Firstly, the target is characterized; then the output filter is calculated by using the correlation filter; finally, the image blocks of different scales are intercepted from the target position of the current frame, and the optimal estimation of the target scale is obtained by the adaptive scale pool model. The experiment selects multiple video sequences for testing and compares the proposed algorithm with other target tracking methods. The experimental results show that the average performance is better than the comparison method.
Construction of Artificial Intelligence-Based Interactive Oral English Teaching Platform based on application problems of present intelligent products
Authors: Zhou Junping 
Published: August 2019

Abstract
This paper investigated the application status of current intelligent oral English learning products, summarized the most favourable products applied by college English learners and analysed their common feature, advantages as well as problems. The survey result shows that the present intelligent oral English teaching products have the merits such as rich teaching resources, correct voice scoring system and individualized assessment and interesting game bonus etc. However, there are also some demerits like lack of words choice judgement, real-time scoring and indicating system as well as interactive teaching platform required by both teachers and learners etc. Based on the above problems, the paper put forward an Artificial Intelligence-Based Interactive Oral English Teaching Platform, which includes all-round spoken corpus that can be updated by teachers, students and AI system all together, an On-line Teaching Assistant System aimed for monitoring, controlling and interacting with students in learning process and a modified scoring system offering real-time assessment for students’ speaking performance, with a view to improving students’ oral English proficiency in a better and more effective way.
Prediction of Data Stream Based on Gaussian Process Regression with Online Free Variational Inference Approximation
Authors: Wang Xin , Duan Jiangyong , Yan Zhen 
Published: August 2019

Abstract
Online prediction of data stream is currently being used in various fields. The prediction method based on Gaussian Process Regression has obvious advantages since its outputs have a probabilistic significance, which is suitable for dealing with nonlinear and complex regression problem. However, the characteristics of the data stream are real-time and online. If only using the historical data of the previous time as the training sample to construct the prediction model, the model will not be accurately predicted once the distribution of the new data changes. To this end, we employ the Online Free Variational Inference approximation to build the prediction model. The key idea is to introduce a variational distribution and maximizing the Kullback-Leibler Divergence between the variational distribution and the true value of the output’s posterior distribution. Further, we use telemetry data to verify the validity of the Online Free Variational Inference approximation and the experiment shows that the online data stream can be predicted well by the method employed in the paper.