bluuebunny/crossref_metadata_2025_split huggingface dataset:
Efficient Texture Mask Prefiltering in Global Volume Rendering
Authors: Lin Chengyu , Zhu Meizheng , Wang Jiarun 
Published: September 2021

Abstract
In the application of ray-casted volume rendering on digital earth, the data in the region of interest (ROI) need to be pre filtered: culling or highlighting. At present, there are few researches on this problem in the field of volume rendering. Referring to the image mask in the field of image processing, an efficient pre filtering transformation technology based on texture mask is proposed: the pre filtering region is transformed into a 0-1 mask array, which is uniformly described as a two-dimensional texture. The texture is transferred into GPU Shader, and the pre filtering operation is transformed into an efficient texture query based on GPU hardware to realize filtering. And high performance rendering is based on GPU. Based on the global meteorological data, the experiments of land culling and administrative region highlighting are carried out, and the experimental results verify the effectiveness of the technology. The overall rendering effect is good, reaching the level of smooth display: the rendering frame rate is not less than 24 frames / s.
Hybrid Data Mining Method of Telecom Customer Based on Improved Kmeans and XGBoost
Authors: Shi Yongge , Yan Shaoyun , He Meibin , Li Xiangjun 
Published: September 2021

Abstract
In order to mine specified telecom customers with special behaviours from vast voice communication records of Telecom company, a novel Hybrid Data Mining method of Telecom Customers (HDMTC) with special behaviours is proposed in this paper, which integrates Kmeans and XGBoost into one framework. First, AHP model helped to model the features of customers with special behaviours. Then, Due to semi-supervised methodology, Kmeans approach was improved by small amounts of tagged initial cluster centroids and weighted Euclidean distance. Improved Kmeans was utilized to tag the decision attribute of data to construct training dataset. After that, XGBoost model was trained via the training dataset. Finally, the efficiency of HDMTC was validated via real records from telecom company.
Some effective tricks are used to improve Soft Actor Critic
Authors: Qin Chenjie , Zhang Lijun , Yin Dawei , Peng Dezhong , Zhuang Yongzhong 
Published: September 2021

Abstract
The development of artificial intelligence is becoming more intelligent, and Reinforcement learning (RL) plays an important role. RL has been widely used in recommendation systems, smart cars, game AI, and investment transactions. Since the emergence of DQN, DRL improvement methods have been proposed continuously. Soft Actor-Critic (SAC) is the current state-of-the-art RL algorithm. In this paper, some tricks are used to improve SAC. Firstly, the baseline is added to the policy gradient algorithm to reduce variance and speed up training. This paper adopts a new method to implement it in SAC. Because the temperature coefficient is a super parameter, which is difficult to adjust, so I propose a novel method to set it. These tricks proved to be effective on the MUJOCO.
Determining which physical parameters are significant for heart disease
Authors: Li Zhuoning , Tao Dingxin , Zheng Jiaao , Zhu Chenhao 
Published: September 2021

Abstract
Heart disease currently, as one of the most important causes of death in the world which can take one’s life immediately, should be taken seriously. In 2019, there were almost 9 millions people died from this disease, and the mortality was still increasing. This study aims to warn people be more focus on their heart condition, and provide a further suggestion about whether they can speculate their heart disease at home. In order to test the hypothesis that the cardiac parameters examined at home can also demonstrates the correct heart condition, many different machine learning algorithms were used to predict the final heart disease accuracy by different data sets (from home and from hospital). Also, there would be spot diagram and Pierson correlation diagram to show the correlation between features and condition, and the correlation among the features. In the end, chi-square method was used to determine the influential degree of those factors which can cause the heart disease. The results of these illustrated that even though several important factors which could only be examined in the hospital rather than at home played the leading role of predicting heart disease, people could still try to predict or observe the heart condition at home, because accuracy difference of the predicting results were only 2% to 4%. Therefore, the result from home could be the reference of whether people should go to the hospital to do more advanced examinations.
Take full advantage of demonstration data in deep reinforcement learning
Authors: Zhang Yong-Xu 
Published: September 2021

Abstract
Deep reinforcement learning (DRL) algorithms have achieve a great breakthrough in many tasks, even though, they still suffer from the problems, such as random exploration and sparse reward. Recently, some reinforcement learning from demonstration (RLfD) methods have shown to be promising in overcoming these problems. However, they usually require considerable demonstrations and the demonstration data may not be optimal which means the absent of exploitation. To deal with it, we propose a novel algorithm in which we use a behaver cloning network BC to learning the demonstration data, and then use it to guide the reinforcement learning agent, when the agent learn and explore a better trajectory, the BC then learning the trajectory to make itself better, which will lead the BC network and the agent become better at the same time.
Object classification based on RGB-D features fusion and LLC coding
Authors: Hong Zhang 
Published: September 2021

Abstract
An object classification method based on RGB-D features fusion is proposed in this paper. Firstly, image features in terms of texture, size, shape, from the RGB image and the depth image are extracted; secondly, the improved K-means clustering method is used to cluster the different extracted features to obtain seven different visual dictionaries respectively; then, the LLC coding model is used to perform feature coding on the extracted visual features to obtain seven different image feature expressions; meanwhile, above seven features expression are fused using the linear cascade mode to obtain the fusion feature; finally, linear SVM performs classifier is used to realize the object classification based on fusion feature. Experimental results show that compared with some existing algorithm based on RGB-D, the accuracy of our proposed method is increased to 87.3%. The accuracy of object classification is significantly improved.
An improved IP scheme for compressible Navier-Stokes equation
Authors: Zhao Hui , Ma Mingsheng , Liu Fan 
Published: September 2021

Abstract
Based on the internal penalty discontinuous Galerkin method (IP-DG), an improved discretization method is proposed to solve the viscosity term of Navier-Stokes equation. The improved method can eliminate the calculation process of intermediate variables and variable gradients, and effectively improve the calculation efficiency. Because the calculation process of convection flux, viscous flux, and lifting operators can be performed at the current integration point, there is no need to calculate the derivative of the viscosity term with respect to the variable gradient, the improved method has very good local characteristics, and is easy to extend to other PDEs. The Couette flow verifies the design accuracy of the improved method, and the cavity flow problem proves the effectiveness of the method for viscous flow simulation. The example of the flow around a sphere shows that this method also has a good performance for three-dimensional problems.
Multi-dimensional data analysis technology of business application system based on Spark framework
Authors: Dong Changchao , Jiao Yanbin , Chen Youyong , Feng Lanxian 
Published: September 2021

Abstract
The current multidimensional data analysis technology uses the data approximation to obtain deep data information in the data cube, which requires repeated traversal of the full link data, resulting in low processing efficiency and unable to analyze a large amount of data in parallel. Therefore, a multi-dimensional data analysis technology based on Spark framework for business application system is proposed to optimize the defects of traditional technology. Spark technology is used to build a full-link data analysis framework, and the data warehouse logic is designed to build a full-link data warehouse. Genetic algorithm is used to mine the full-link data of the business system, and the data are stored in the data warehouse, and the data warehouse and the sequential structure neural network are established. In the sequential structure neural network, multi-dimensional data analysis is realized from multiple angles and aspects. The technical feasibility verification results show that the research technology shortens about 14.7% of the time on average, significantly improves the analysis efficiency and can conduct concurrent analysis on a large number of data, which is better than the application effect of traditional analysis technology.
Verifiable Access Technology of Hybrid Database in Distributed System Under Big Data
Authors: Jia Jun 
Published: September 2021

Abstract
Targeting at the problem that the existing hybrid database solutions can not simultaneously realize the client’s verification and access function to the encrypted data in the public cloud database and the cross database query function in the heterogeneous database environment, this paper designs an access and integration middleware to realize the client’s verification and access function to the heterogeneous hybrid database in the public cloud and the cross database query function. Access and integration middleware is composed of verification access component and heterogeneous database data integration method. The verification and access component reconstructs the protocol interaction process based on MySQL protocol, and encrypts and decrypts the encrypted data in the public cloud database through the existing hybrid database scheme, so as to realize the client’s verification and access to the encrypted data in the cloud database. The heterogeneous database data integration method unifies the data from different types of databases, and then integrates them into the same database, and uses this database for query operation to realize cross database query. This paper first analyzes the hybrid database system model, then designs from the aspects of MySQL protocol analysis, verification access, data integration, and finally analyzes the access and integration middleware from the aspects of functionality and security.
Formal Verification of ST Programs using CSP
Authors: Zhou Qibin , Cai Fangda , Yang Yang , Wu Changshun 
Published: September 2021

Abstract
Programmable Logic Controllers (PLCs) are embedded devices that are widely used in industrial control systems. PLC programs are written in special languages that are defined in the IEC 61131-3 standard, which includes two textual and three graphical programming languages. Structured text (ST) language is one of IEC 61131-3 standards, which is a high-level text language similar to PASCAL language. ST language features the following advantages: freely programming style, compact structure and strong data processing capabilities. Using ST language can be more convenient to realize the complex control programs. In this paper, we research and discuss formal verification of PLC program based on ST language, and show the conversion rules from ST language to the formal modeling language named CSP# (short for Communicating Sequential Programs). Finally, a case study is presented to demonstrate the procedure that PLC programs written in ST language are translated into CSP#, consequently, and the result of verification is given using the Process Analysis Toolkit (PAT).
Research on Early Warning of Hoist Failure based on Big Data and Parallel Simulation
Authors: Zhang Yuyan , Zhao Sihai , Li Dan 
Published: September 2021

Abstract
Aiming at the problems of easy fault diagnosis and difficult early warning of mine hoist, a parallel system architecture of hoist fault early warning based on big data is proposed, the structure of each subsystem of hoist is analyzed, and a parallel simulation system of hoist fault early warning is established; secondly, the Hadoop ecosystem of hoist is established on the virtual machine, and the massive data is mined by using clustering algorithm and association rule algorithm, so as to speed up the calculation speed and improve the reliability of early warning; finally, the safety state evaluation rules of hoist are proposed, and the system decision is made according to the fault early warning results. The experimental results show that it can achieve the purpose of fault prediction.
Numerical analysis and improvement of longitudinal moment pitch-up characteristics for civil aircraft
Authors: Cui Xingda , Zhou Zhu , Yu Yonggang , Yu Lei , Li Yalin , Zhou Tao 
Published: September 2021

Abstract
The longitudinal moment pitch-up ahead of the stall angle can decrease the available range of maximum lift coefficient. It has a major influence on take-off and landing performance, and safety of a civil aircraft. Numerical simulation by CFD (Computational Fluid Dynamics) is used to study the longitudinal moment characteristics of a civil aircraft with landing configuration in this paper. Combined with the flowfield post-processing interpolation technology, the reason of longitudinal moment pitch-up is analysed and discussed. According to the research results, the characteristics of longitudinal moment pitch-up are further improved by modification. The relevant research methods and results have some guiding significance for improving the moment characteristics of a civil aircraft.
An estimation method of underwater target spectrum line parameters based on convolutional neural network
Authors: Luo Zhaorui , Du Shuanping , Chen Yuechao 
Published: September 2021

Abstract
The spectrum line parameters of underwater target radiated noise are important basis for underwater target recognition. In this paper, the convolution neural network (CNN) is applied to estimate the spectrum line parameters. The low frequency analysis record (LOFAR) spectrum of radiated noise is used as input data, and a suitable convolution neural network is constructed to estimate the spectrum line parameters. The processing results of simulation data show that the estimation sensitivity of convolution neural network reaches 91.6% under the condition of low SNR. It proves the effectiveness of CNN in estimating spectrum line parameters of underwater targets.
Hardware implementation of the DFP algorithm using inexact line search
Authors: Su Kaikai , Guo Anhua 
Published: September 2021

Abstract
The Quasi-Newton method (QN) is widely used in solving large-scale optimization problems due to its high efficiency. However, this algorithm requires a large number of iterative calculations, which is more time-consuming to compute in software. The Davidon-Fletcher-Powell (DFP) algorithm has been implemented on field-programmable gate array (FPGA) in order to speed up the computation. The number of iterations in the online search part of the algorithm is excessive, which affects the speed of the calculation. In order to solve this problem, this paper proposes a hardware implementation of DFP algorithm using inexact linear search. The results show that the calculation speed of the DFP algorithm implemented in this paper on the software side and FPGA platform is increased by 7.59 times and 3.08 times, respectively. And resource consumption and accuracy have little effect on the results.
Design and implementation strategy of data migration system based on Hadoop platform
Authors: Wang Xiaoshu 
Published: September 2021

Abstract
This paper analyzes the Hadoop technical system related content, and combines the design points of the data migration system, including interface migration design, data synchronization design, data transmission process design, data consistency verification design, data storage scheme design. Through the research of data cleaning function, data conversion function, data loading function, data return function, unstructured data migration and other implementation strategies, the purpose is to improve the application value of data migration system, improve the application value of the collected data.
Design of Network Information Management System Based on C/S Architecture
Authors: Wang Ruibin 
Published: September 2021

Abstract
Due to the incomplete data preservation of traditional systems, the success rate of network information data update is reduced. Based on C/S architecture, a network information management system is designed. In terms of hardware design, the C/S architecture allows for the optimisation of the RFID circuitry to speed up the rapid conversion of data. In software design, information management model is established based on C/S architecture, protocol identification method deletes abnormal data in the transmission process, fuzzy method sets up data storage mode, centralizes control of network information, and completes the design of network information management system. The experimental results show that the new system has an average success rate of 98.96%, which is 9.16% and 10.46% higher than the traditional system, respectively, and can increase the update effect of network information.
Proximal Policy Optimization with Future rewards
Authors: Yu Chengcheng , Zhang Lijun , Yin Dawei , Peng Dezhong , Huang Haixiao 
Published: September 2021

AbstractAmong the current reinforcement learning algorithms, the Policy Gradient algorithm (PG)[7] is one of the traditional and most widely used algorithms, but it has the disadvantage of unstable gradient estimation, and the newly Proximal Policy Optimization algorithm (PPO) [8]solves the problem. It solves the stability problem, but the update policy is slow, and it is easy to produce over-fitting when the training times are too many. In this article, a new method is proposed, referring to Asynchronous Advantage Actor-Critic (A3C)[9], the basic PPO algorithm is trained in parallel, and a method that considers future rewards is introduced, and the future rewards are also calculated to the current. In the reward, through the OPEN GYM platform and the experimental results of capturing at any position of the robotic arm, our actions can ensure a faster training speed, while also avoiding overfitting during long-term training.
Computational simulation and theoretical comparative study for the complexes of heme-iron and heme-zinc
Authors: Wang Yashi , Lei Zhiming , Lan Wenbo , Wang Xiaofeng , Li Qianru , Zeng Huiwen , Meng Yanbin 
Published: September 2021

Abstract
By using computer technology, molecular simulation and analysis of the coordination structure of heme iron and complex formed by the heme ligand and Zn are carried out under the same level of calculation. Studies have found that the ability of heme ligands to capture Fe is significantly stronger than that of Zn. However, the binding energy and the stability of the complex of heme ligand and Zn are stronger than those of the complex of heme ligand and Fe, and at the same concentration, the complex absorbs light in the near-ultraviolet region to a greater degree.
The security function dynamic calling scheme for software-defined security
Authors: Ma Yuanyuan , Guo Zhimin , Yang Wen 
Published: September 2021

Abstract
With the rapid development of cloud computing, various network attack methods are becoming more advanced, and the attack speed is getting faster. Traditional security protection solutions can no longer adapt to new network attacks, bringing significant challenges to security protection in virtualized network environments such as cloud computing. Based on these problems, this paper proposes a dynamic calling mechanism of security functions for software-defined security based on software-defined network technology. This mechanism defined the strategies of dynamically scheduling of virtual security service nodes in the virtualized network environment. The strategies describe the network’s data flow and virtual security devices by extending the attribute-based access control policy model. It can dynamically construct the mapping relationship between network flow and virtual security devices according to users’ security requirements, thus forming a personalized security service chain for specific network flows. This mechanism can protect the “north-south” network flow at the IoT edge agent according to the user security business requirements.
Design of system-level hot standby redundancy-based signal system
Authors: Zhang Yu , Wang Xiaoyong , Yu Yi , Chu Pengzi , Xu Ye 
Published: September 2021

Abstract
Focusing on the improvement of the availability for signal systems, a system-level heterogeneous hot standby redundancy-based signal system is discussed. The system includes a communication-based train control system (CBTC), a track circuit-based train control system (TBTC), and a switching unit. The CBTC and TBTC are respectively connected to the communication system via the switching unit. Under normal condition, the CBTC is connected to the communication system via the switching unit, and the function commands received by the train come from the CBTC. When the CBTC fails, the TBTC is connected to the communication system via the switching unit, and the function commands received by the train come from the TBTC. The switch between the CBTC and the TBTC is automatically executed by the system. Simulation results showed that the proposed design in this paper improved the availability of the signal system and reduced the impact of failures on operations.
Analysis of Lower Limb Muscle Fatigue Based on Surface Electromyographic Signal
Authors: Huang Shuping , Jang Haiyan , Chen Yan , Shi Shaoyun 
Published: September 2021

Abstract
Surface electromyographic (sEMG) signal contains abundant information such as joint torque and joint motion, which is widely used in human-computer interactive intelligent rehabilitation equipment. In this work, the ankle torque of lower limb is taken as the research object, and the feature parameters of sEMG which represent the fatigue state are analysed. Advance prediction of fatigue features for specific time periods was performed using a normalized minimum average square (NLMS) filter. While the modified cerebellar model neural network (WFCMNN) is used to classify fatigue, which can be divided into three states, namely no fatigue, transition to fatigue, and fatigue. The results show that the accuracy of classification is 96.429%, which is better than other advanced models. At the same time, sEMG signal is used to predict fatigue in advance, which can solve the problem of differences between different individuals. Such strategy is helpful for doctors and physiotherapists to carry out rehabilitation treatment for patients, as a pre judgment and diagnosis index.
l-extra edge connectivity of graphs
Authors: Li Yalan , Zhang Shumin , Ye Chengfu 
Published: September 2021

Abstract
When we design an multiprocessor systems of interconnection network, one important question is considering the reliability evaluation of network. For a positive integer l, the l‐extra edge connectivity λl(G) of a connected graph G, is the minimum number of edges so that when removal the edges from G, then we get a disconnected graph and each component has more than l vertices. In this paper, we get the exact values of l‐extra edge connectivity. Upper and lower bounds of l‐extra edge connectivity. Get the extremal results of this connectivity.