bluuebunny/crossref_metadata_2025_split huggingface dataset:
Convergence of IT and SCADA: Associated Security Threats and Vulnerabilities
Authors: Smurthwaite Michael , Bhattacharya Maumita 
Published: April 2020

Abstract
As many industries shift towards centralised controlled information systems for monitoring and control, more importance is being placed upon technologies such as Supervisory Control and Data Acquisitions industrial systems (SCADA). This focus on integration and interoperability presents numerous challenges for security personnel and organisational management alike. It becomes paramount therefore to reciprocate this new direction within an organisation with adequate plans and frameworks that ensure protection and security of its SCADA architecture. A clear understanding of the relevant threats and vulnerabilities is critical for adopting/developing appropriate policy and frameworks. To this end, in this research we identify and analyse relevant SCADA security threats and vulnerabilities and present a simple scheme to classify them for better understanding.
Research of E-map System Based on Baidu Map API for University Campus
Authors: Cai Yingjian , Hu Lishuan , Xiang Caihong 
Published: April 2020

Abstract
With the rapid growing of universities, the campus infrastructure continues to expand. Visitors are not clear the position of their destination. Traditional online mapping service updating not timely can only supports customers to browse and query incomplete description of the destination. In this paper, a spatial information inquiry system of campus is designed based on Baidu map API with JavaEE technologies. The system can provide the basic map function for almost all important things on campus. Based on this method, Beijing Union University campus E-map has been developed successfully, which demonstrates the feasibility of this solution.
Natural Scene Text Detection Based On Multi-level Fusion Proposal Network
Authors: Li Tong , Li Wanggen , Zhu Nannan , Gong Xuecheng , Chen Jiajia 
Published: April 2020

Abstract
Natural scene text detection is a challenging issues. In this work, a multi-level features fusion two-stage text detection network was developed to solve the problem of the insufficient use for feature map and the difference between text and common target. In order to obtain deep semantic feature, the network framework of VGG-16 was improved, and different levels of convolution feature map were confused, meanwhile the fusion approach was partially adjusted. Also, vertical proposal network was used to classify and regress bounding box. Results indicated the accuracy and recall rate of this method are 85.4% and 81.0%, respectively, by evaluating the net on ICDAR2013 dataset. The experimental results suggest that the multi-level features fusion method can improve the efficiency of feature map in natural scene text detection.
2D Extreme–Point Symmetric Mode Decomposition Method
Authors: Gao Feng , Chi Chunmei 
Published: April 2020

Abstract
This paper proposes a kind of 2D extension of Extreme–Point Symmetric Mode Decomposition Method. The 2D-sifting process is performed by 4 steps: 1 Extrema points detection (by neighboring window). 2 Delaunay triangulation is done over the grid of all extrema points. 3 Mid points of the triangulation are obtained. 4 Thin plate splines are used to interpolate the mid points. 5 Sifting processing is carried out. Finally, experiments are done with real images to show the efficiency of this 2D Extreme–Point Symmetric Mode Decomposition Method.
A Hybrid Collaborative Filtering Recommendation Algorithm Based on User Attributes and Matrix Completion
Authors: Yi Jie , Zhong Maosheng , Chen Yinfen , Jie Anquan 
Published: April 2020

Abstract
Collaborative filtering is a popular strategy in recommendation system. Traditional collaborative filtering relies on the user-item rating matrix that encodes the individual ratings of users for items to make recommendations. However, in the real-world, the rating matrix is highly sparse, and many new users do not have rating records, thus traditional collaborative filtering could not provide satisfactory recommendations. To alleviate this issue, we propose a hybrid algorithm that utilizes LMaFit to complete rating matrix, reducing the degree of sparsity, and provides a hybrid user-similarity to supply a good support for recommending to new users in the condition of cold start. Extensive experiment results on real-world datasets show the proposed algorithm has a better performance than other methods.
Distributed Cloud Platform Access Collaborative Selection Algorithm based on Multi-objective Optimization
Authors: Zhao Wei , Li Huiqin , Wang Chenfei , Wang Yajuan , Xu Liyang , Liu Juan 
Published: April 2020

Abstract
Decentralization is the mainstream trend of large-scale center construction. Based on the requirements of mass service access, big data processing and high reliability of State Grid 95598 cloud platform, a distributed cloud platform access collaboration selection algorithm based on multi-objective optimization is proposed. The algorithm considers load balance of distributed cloud platform and average user experience quality. In addition, it measures the comprehensive performance of the cloud platform from users and load balance of distributed cloud platform based on the synergy degree in the selection process in order to avoid “cask effect”. The simulation results show that the algorithm can adjust the access cloud centers of users in real time and balance loads of cloud centers, which can provide technical support for cloud platform construction in the era of big data.
Object Detection Algorithm for Improving Non-Maximum Suppression Using GIoU
Authors: Hou Zhiqiang , Liu Xiaoyi , Chen Lilin 
Published: April 2020

Abstract
To address the problem of object miss detection and object false detection in single threshold-Non-Maximum Suppression algorithm, this paper proposed a GDT-NMS (Generalized Intersection over Union Dual Threshold NMS, GDT-NMS)) algorithm which using GIoU(Generalized Intersection over Union). Using the GIoU indicator computing the similarity between objects, can better describe the relative position and overlap between the objects. And we proposed the dual-threshold NMS algorithm, which can balance the relationship between the object missed detection problem and the object false detection problem, reduce “false positive example” problem. By nonlinearly processing the weight function, the object is better distinguished. The algorithm uses Faster R-CNN as the detector. The experimental results show that the improved algorithm has outstanding performance.
Visual Tracking Algorithm Based on Color Name Histogram
Authors: Yue Chenchen , Yu Wangsheng , Hou Zhiqiang , Ma Sugang 
Published: April 2020

Abstract
Aimed at the problem that traditional histogram is sensitive to illumination changes in visual tracking, combined with the CN(Color Name) feature, we proposed a new feature(denotes CNH, Color Name Histogram) based on color name. Firstly, the method projected the original RGB image to CN space to obtain robust 11 feature layers. Then, we counted the each pixel numbers of feature layers. Finally, normalizing the amount of pixels in each layer. In addition, we adopted a feature adaptive fusion method to combine CNH and HOG(Histogram of Oriented Gradient). In order to prove validity of the proposed algorithm, we use Staple(Sum of Template And Pixel-wise Learners) algorithm frame to make a controlled trial. In contrast with the reference algorithms, the success of our algorithm increases by 1.5% and the precision increases by 1.7%. The results show that this method retains the advantages of traditional histogram which is insensitive to target deformation, but also enhances the robustness to illumination change.
Research on User Clustering Algorithm Based on Improved ROCK Algorithm
Authors: Wang Rong , Sun Aimin 
Published: April 2020

Abstract
This paper analyzes the merits and demerits of the ROCK algorithm firstly, then indicates that the merits of Rock is easy to cluster categorical database such as Mushroom, and the demerits is that the similarity formula sim of this algorithm depends on the intuition of domain experts. The improved ROCK gets the result of clustering by calculating the similarity using Jaccard coefficient based on the reason which is that the bigger figure of the similarity is, the more alike the object is. Then we can implement personality recommendation according to the result.
Scheduling Problems of Automated Guided Vehicles in Automated Container Terminals Using a Genetic Algorithm
Authors: Gan Yufan , Ji Shouwen , Zhao Qianru , Guo Dong 
Published: April 2020

Abstract
The automated guided vehicle (AGV) is adopted as horizontal transport equipment in automated container terminals. In order to improve the operation efficiency of the AGV, we constructed a mixed integer programming model for AGV scheduling optimization in unloading operations in an automated container terminal with a vertical layout. The model aimed at minimizing the end time of the last task and the time of AGV invalid operation. The constraints included the operation limit and the time window. We also designed a genetic algorithm to solve the model, and set three experiments to prove the superiority of the method. The experimental results show that the genetic algorithm can significantly improve the calculation efficiency in large-scale AGV scheduling problem, and the scheduling model we constructed can better improve the operation efficiency of the AGV. We also simply studied the problem of the optimal number of AGV configurations.
Research on Flight Time Optimization of Short-term Airport Group Based on Weather Conditions
Authors: Ji Shouwen , Zhang Dan , Liu Huaiyuan , Guo Dong 
Published: April 2020

Abstract
Flight schedule optimization is critical to improving air traffic flow management. This paper considered the impact of weather conditions on the capacity of the airport and airspace, and combined ground-holding and air-holding strategies to establish a multi-objective short-term airport group flight schedule optimization model. At the same time, this paper designed a non-dominated sorting genetic algorithm (NSGA-II algorithm) with an elite strategy, and verified the case with the Yangtze River Delta airport group. The results show that the model can reduce the delay time and delays of the airport group by nearly 25%. The innovation point of this paper is to further consider the impact of weather factors on flights in reality, and improve the traditional genetic algorithm for better solution, which has practical significance. The method can effectively improve the level of air traffic flow management.
A Novel Arc-length Numerical Method for Shock Interruption Problems
Authors: Xu Hui 
Published: April 2020

Abstract
Accurately tracking the propagation process of a wave front is important for studying the propagation law of shock waves. In this study, a novel arc-length numerical algorithm to effectively calculate the interruption of shock waves is proposed, and a systematic theoretical analysis and application of this research are conducted. First, the arc-length numerical method is proposed based on the hyperbolic conservation model equation, and the basic concept of the arc-length numerical method of hyperbolic problems is provided. Then, an introduction method for arc-length in multidimensional space is described from the perspective of tensor analysis, and the mathematical model of shock wave propagation problems in arc-length space is established. The discrete solution of the spatial mathematical model of arc length is given, and control and smoothing factors are added to ensure shock wave propagation without oscillation. Finally, the numerical calculation of the Lax and double Mach reflection problems shows that the arc-length numerical algorithm can be widely used as a new calculation method for shock interruption problems.
Time-window-based Scheduling Strategy and Optimization for Maximizing the Income of Drop and Pull Transport
Authors: Xu Linlong , Qiu Zheyong , Kang Zibo , Ma Xiaojuan , Xiong Huilin , Yang Lei 
Published: April 2020

Abstract
As an intensive and efficient way of transportation, drop and pull transport has become the mainstream transportation organization of foreign developed countries and regions. Based on the freight demand vector and aging of a given initial state, this paper established a goal planning and scheduling model based on the maximization of time window revenue to get the most efficient deployment plan under the condition of high customer satisfaction. The genetic algorithm based on task urgency was used to solve this model to obtain the optimal allocation scheme of total income. Finally, the number of algorithm iterations was analyzed, which proved the effectiveness of the algorithm performance. Through the sensitivity analysis of the number of tractors, this paper gave suggestions for more efficient tractor configuration.
A Case Study of Finite Element Check for the Strength and Rigidity of the Escalator Truss
Authors: Shu Wenhua , Ouyang Huiqing , Jin Mingdi , Kuang Lin , Wang Dongmei 
Published: April 2020

Abstract
As an important part of escalator, the truss must be checked for strength and rigidity after design. In this paper, the finite element method is used to check the strength and rigidity of an escalator truss under various working conditions. Compared with the traditional design method, the finite element analysis can obtain the mechanical properties of the object quickly and accurately, shorten the project development time, and thus reduce the design and test cost greatly.
An Adaptive High-Resolution and Low-Dissipation Hybrid Energy Consistent/WENOCU Scheme
Authors: Li Liang , Wang Hongbo , Xiong Dapeng , Sun Mingbo , Tang Tao , Zhao Guoyan , Wang Xinxin 
Published: April 2020

Abstract
We investigate the performance of the hybrid energy consistent/WENOCU4 scheme compared to the classical WENOCU4 scheme. It is borne out that the hybrid scheme preserves excellent shock-capturing ability without numerical oscillations. In the meantime, the hybrid scheme can achieve more accurate representations of smooth flow features which indicates its low-dissipation capacities. Moreover, hybrid scheme significantly outperforms WENOCU4 for its superior computational efficiency. Therefore, employing hybrid scheme is a much more cost-effective choice for the simulation of shock/turbulence interaction.
Fractal Effect of Random Disturbance on Reaction-diffusion Equation
Authors: Zhang Li 
Published: April 2020

Abstract
Fractal dimension is the main feature of many nonlinear phenomena like the coastline, stock indexes and surface growth. Reaction-diffusion equation which is used mainly in different fields such as physics and medicine has complicated characteristics as its components are nonlinear. In this paper, the fractal effect of the external disturbance on the reaction-diffusion equation which is a partial differential equation is studied. The relationship of the disturbance between the fractal dimension of the output variable has been obtained by finite difference method simutanously. Even there exists random term in the disturbance, the fractal dimension is also stable characteristics of the output variable of the reaction-diffusion equation.
Anomaly Location Method for QAR Data Based on Principal Component Analysis Hierarchical Clustering
Authors: Gao Xiaoxia , Cheng Zhen , Huo Weigang 
Published: April 2020

Abstract
The existing QAR (Quick Access Recorder) data anomaly detection algorithm can detect abnormal flights, but it cannot effectively locate the abnormal QAR data of abnormal flights. In order to address the issue, QAR data of abnormal flights are smoothed and pre-processed based on k-medoids algorithm. Pre-processed QAR data are transformed into a one-dimensional time series represented by angle cosine, and a subsequence feature matrix is generated from the angle cosine sequence by sliding window mechanism. Then, the dimension of the matrix is reduced based on principal component analysis, and the top-down hierarchical clustering is performed on row vectors of the reduced matrix according to the amount of information within each column. The anomaly nodes are detected according to the number of vectors contained in the clustering tree nodes. The abnormal data segments of QAR data of abnormal flights are located by the positions of vectors generated in the cosine angle sequence in the abnormal nodes. The experimental results on real flight data sets show that the proposed method can locate not only the known exceedance events, but also the abnormal fragments besides the monitoring items.
Dynamic Assessment of Water Resources Carrying Capacity in Heze Using Fuzzy Sets Methods
Authors: Xin Shuai , Li Wanwan , Jin Hua , Li Qingguo 
Published: April 2020

Abstract
It’s necessary to assessment the regional water resources carrying capacity for water resources long-term plan and projects. In this paper, the water supply and water utilization of Heze City in the past 10 years are analyzed firstly, and then figured out the current situation and existing problems of water resources in this study area. Thirdly, on the basis of constructing the comprehensive evaluation index system of water resources carrying capacity of Heze City, we introduced a fuzzy comprehensive evaluation model of water resources carrying capacity of Heze City. The purpose is tantamount to dynamically evaluate and analyze the water resources carrying capacity of the current level year and the planning level year of Heze City. The result showed that the water resources carrying capacity level of Heze City are changing to a positive direction, but the trend is weak and the prospect is not optimistic. At the same time, the validity and feasibility of the model and method are verified.
Study on the Calculation Method of Energy Saving Potential of the Whole Society during the 14th Five Year Plan Period
Authors: Tang Wei , Wu Peng , Duan Jinhui 
Published: April 2020

Abstract
This paper studies the calculation method of the energy-saving potential of key industries, then calculates the structural energy-saving potential, and finally puts forward a method to calculate the energy-saving potential of the whole society during the 14th Five Year Plan period. It is estimated that during the period of the 14th five year plan, the whole society will save about 300-400 million tons of standard coal.
Time-change Reliability Assessment and Analysis of Cryogenic Pneumatic Valve Based on Dynamic Bayesian Network Theory
Authors: Qin Yongtao , Sun Le , Tang Liping , Wang Jianfang 
Published: April 2020

Abstract
In order to fully identify the root cause of cryogenic pneumatic valve failure in the process of liquid rocket engine test, and to analyze the dynamic time change of the process of cryogenic pneumatic valve reliability analysis, this paper first analyzes the failure factors of low temperature pneumatic valve in the test process of liquid rocket engine according to the human-machine environment system engineering. By introducing the dynamic Bayesian network theory, and by means of analyzing the time-change failure failure mode of cryogenic pneumatic valve, constructing the time-change reliability model of cryogenic pneumatic valve was constructed in the test process of liquid rocket engine, and the reliability growth technology of cryogenic pneumatic valve was researched by the optimal failure search strategy. Finally, the validity and accuracy of this method are verified by the example. This method can provide a method guide for quantitative analysis and improvement of the reliability of cryogenic pneumatic valve, and provide support for the accurate and objective evaluation and development of liquid rocket engine performance.
Study on Enterprise Competitive Intelligence Evaluation Based on Mixed Uncertain Attribute Group Decision-making
Authors: Song Jiekun , Zhao Zhihao , Zhang Yemeng 
Published: April 2020

Abstract
Enterprise competitive intelligence evaluation has the characteristics of uncertainty and group decision. It is significant to construct the evaluation model based on the mixed uncertain attribute group decision-making. In this paper, we reviewed the algorithms, order relations and distance measures of the main uncertain parameters. Based on mixed uncertain attribute group decision-making, prospect theory and VIKOR theory, we constructed the evaluation model. We put forward the processes including standardization, group integration, value measurement and ranking. An example analysis shows that the evaluation model can integrate a variety of uncertain indicators, comprehensively reflect the evaluation opinions of each expert, and reflect the decision-maker’s risk psychology and preferences. The proposed model can provide more scientific and consistent conclusions for the evaluation and selection of competitive intelligence of enterprises.
Structure Oriented Local Image Feature Description
Authors: Yu Wangsheng , Qin Xianxiang , Wang Peng , Hou Zhiqiang , Ma Sugang 
Published: April 2020

Abstract
In this paper, we proposed a structure oriented descriptor (SOD) for local image feature description. Different from the traditional methods, we explored the structure information via hierarchical strategy and structure coding elements. Firstly, the support region is partitioned into hierarchical sub-regions according to the intensity orders. Secondly, a pre-designed structure coding image is explored to pooling the features according to the hierarchical sub-regions. The final descriptor is a conjunction of the feature of each sub-region. We evaluated the proposed descriptor on the public dataset and compared it with the state-of-the-art works. The experimental results indicate that the proposed descriptor is robust to image appearance change and outperform the state-of-the-art works in most cases.
Research of Stock Price Prediction Based on PCA-LSTM Model
Authors: Wen Yulian , Lin Peiguang , Nie Xiushan 
Published: April 2020

Abstract
At present, there are some problems in domestic stock market, such as difficulty in extracting effective features and inaccuracy in stock price forecast. This paper proposes a stock price prediction model based on Principal Component Analysis (PCA) and Long Short-Term Memory (LSTM). Firstly, PCA is used to extract the principal components of the technical indicators affecting stock prices, so as to reduce the data correlation and realize data dimensionality reduction. Then, LSTM is used to model and predict the stock price. According to the experimental results of Pingan Bank, compared with the traditional stock price prediction models, the stock price prediction model based on PCA and LSTM can accurately predict the stock price fluctuation trend.
Research on Digital Watermarking Technology Based on Discrete Wavelet Transform
Authors: Tan Dingying , Liu Huiling , Wang Huili , Zou Min , Chen Pingping 
Published: April 2020

Abstract
By using the advantages of discrete wavelet transform in digital watermark embedding, extraction and detection, a corresponding basic model was established. Using MATLAB as a tool for simulation experiments, the experimental results show that the discrete wavelet transform can extract the watermark signal under the common JPEG compression, noise, shear and rotation, filtering and other attacks, and has high robustness.