bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mechanical equipment fault detection applying data mining technology
Authors: Liu Yiqun , Wang Xiaogang , Gong Xiaoyuan , Mu Hua 
Published: December 2020

Abstract
Mechanical equipment fault detection in manufacturing enterprises has always been an important link in the manufacturing process. Along with the computer technology, artificial intelligence technology and various sensors are widely used in manufacturing industry, The amount of data produced by manufacturing machinery and equipment at all stages of the production process is also increasing rapidly, It is particularly important to analyze the data generated by these devices for fault detection and even prediction, Data mining technology provides advanced analysis methods for this purpose. This paper first introduces the basic concepts of Data Mining, Data Mining process and the key technology of Data Mining, and then focuses on the application of Data Mining in machinery fault detection, finally through the analysis of Data Mining in the field of top class meeting KDD2019, explored the could be applied to machinery fault detection of new technology, forecast the Data Mining technology in the fault detection of possible development trend.
Research on Fintech Methods Based on Artificial Intelligence
Authors: Hu Zhongjian 
Published: December 2020

Abstract
The financial industry is one of the earliest and most comprehensive industries that integrate with artificial intelligence. The application of a series of artificial intelligence technologies such as data mining, accurate profiling, machine learning, neural networks, will provide financial products, service channels, service methods, risk management, Credit financing and investment decision-making have brought about a new round of changes. Firstly, the rapid development of artificial intelligence has a profound impact on the high-end finance of the deep service value chain; Secondly, the integrated development of artificial intelligence and finance is irreversible, risks and opportunities coexist, and the financial industry must accelerate the pace of adjustment and development; Finally, the financial supervision department further clarifies the supervision responsibility, applies artificial intelligence technology to supervision methods and means, and improves the degree of supervision automation and intelligence.
The New Type of Reducts in Intuitionistic Fuzzy β-covering Approximation Spaces
Authors: Chang Zaibin , Wei Junchao , Dai Xuezhen 
Published: December 2020

Abstract
A type of reducts in intuitionistic fuzzy (IF) β-coverings has been presented based on the union operation. But for some problems, there is no reducts in IF β-coverings according to this definition. That is to say, this notion has its boundedness. Therefore, we present the new type of reducts in IF β-coverings in this paper, and we call it type-2 reduct. Moreover, the type-2 reducts in IF β-covering approximation spaces are investigated while adding and removing some objects of the universe. Firstly, the notion of the type-2 reduct in an IF β-covering approximation space is presented. Then, some properties of type-2 reducts of IF β-coverings are investigated while adding and removing some objects.
A dynamic instrumentation tool for obtaining software logs
Authors: Zhou Hongchen , Li Jing 
Published: December 2020

Abstract
Compared with the software analysis based on source code, log based software analysis technology has gradually emerged in recent years, but it is not easy to obtain the log of software. To automatically obtain enough information on the software log, this paper proposes a dynamic instrumentation tool, it can dynamically instrument a software implemented in Java language, and get the log finally. This tool does not require users to be familiar with the software, and the instrumentation process will not cause an intrusive impact on the source code. It is a general-purpose automatic dynamic instrumentation tool.
STL_Siam: Real-time Visual Tracking based on reinforcement guided network
Authors: Shijia Huang , Luping Wang 
Published: December 2020

Abstract
In recent years, deep visual tracking algorithms based on Siamese has made great breakthrough in both speed and accuracy. However, due to the dependence of Siamese network on the target template, these trackers are prone to drift or even fail to track in a complex tracking environment. In this work, we navigate the Siamese network with a STLNet model and propose the STL_Siam method. The STLNet, trained offline with the dataset enhanced by ROMIX method, is introduced into the SiamGrad algorithm to infer the movement of the target during online tracking, and guides the SiamGrad network to track. In order to evaluate the reliability of the proposed algorithm, we conducted experiments on the OTB2013 and VOT2016 two benchmarks. The algorithm achieved excellent performance, and the performance of the sequences with complex obstruction interference has been slightly improved. Experiments suggests that the proposed architecture reaches 40+fps and gets 0.865 precision on OTB2013, which is higher than results of the ACT. Meanwhile, compared with the baseline algorithm ACT, the A, R, and EAO of the proposed approach is increased by 5.6%, 1%, and 0.3% respectively on VOT2016 respectively.
Exploration-Exploitation Strategies in Deep Q-Networks Applied to Route-Finding Problems
Authors: Wei Pengyuan 
Published: December 2020

Abstract
Reinforcement learning is a class of algorithm that allows computers to learn how to accumulate rewards effectively in the environment and ultimately get excellent results. Among them, the exploration-exploitation tradeoff is a very important concept, since a good strategy can improve learning speed and final total reward. In this work, we applied DQN algorithm with different exploration-exploitation strategies to solve traditional route-finding problems. The experimental results show that the epsilon greedy strategy with a parabolic drop in epsilon value over reward improvement is the best, while it is not satisfactory after incorporating the softmax function. We hypothesized that the simplicity of the maze we use in this work in which the agent attempts to find the shortest path leads to the inadequacy of applying softmax to further encourage exploration. Future work thus involves experimenting with mazes at different scales and complexities and observing which exploration-exploitation strategies work best in each condition.
Intrusion detection using a combination of one-dimensional convolution and GRU
Authors: Wang Xiaojuan , Xiao Bo 
Published: December 2020

Abstract
Intrusion detection plays an important role in ensuring network information security. Traditional machine learning technology does not work well enough with massive data and various intrusion classes, and detection accuracy turns unsatisfied with unknown intrusions. This paper proposes a new network intrusion detection model (Conv1d-GRU) that combines one-dimensional convolution and GRU for multi-class intrusion detection scenarios, and improves data imbalance by weighting the samples of each category. NSL-KDD as an improved version of the KDD CUP99 dataset is selected for our intrusion detection system. Experimental results on this dataset show that the proposed deep learning method is superior to present intrusion detection methods based on machine learning and deep learning, and has better feature representation learning and classification capabilities.
An EHW - based Anti - power Consumption Detection Hardware Trojan Implantation Method
Authors: Liu Lijun , Ding Qiao , He Tianyu 
Published: December 2020

Abstract
Hardware Trojan (HT) is developing towards miniaturization and concealment, but the detection of this kind of HT is still in the research state. The main reason is that the real HT has some problems such as difficult sampling and few kinds, so it is necessary to design the circuit with this kind of HT artificially. Therefore, the EHW technology is introduced to implant this kind of HT, and the original circuit is redesigned to make the power consumption change small and increase the HT function, so as to achieve the effect of concealment.
WCET Aware Cache Locking and Task Scheduling in Single Core Embedded Systems
Authors: Zhang Tingxu , Zheng Wenguang , Xiao Yingyuan 
Published: December 2020

Abstract
Task scheduling in embedded system needs the WCET (Worst-Case Execution Time) of the task to ensure that tasks can be completed before their deadline. The application of cache reduces the average case execution time of tasks but increases the difficulty of scheduling because WCET becomes difficult to determine. Cache locking is a technique to help determine the WCET of a task. In addition, applying suitable cache locking selection can reduce the WCET of a task, bring easier task scheduling and further reduce the total WCET of all tasks. In this paper, we propose an approach integrated dynamic cache locking and task scheduling in a single core embedded system. We choose the most valuable locking content for each task in limited cache capacity to determine the WCET, schedule the tasks so that all the tasks can be finished before the deadline and make the total WCET minimized. We have implemented this approach and compared it to no-cache task scheduling and non-preemption task scheduling model with LRU cache by using a set of tasks. The experimental results show that our approach achieves successfully task scheduling for all test data. Compared to no-cache task scheduling, our approach achieves the average improvements of 3.22%, 12.68%,18.96% and 19.12% for the 256B, 512B, 1KB and 2KB caches, respectively. Compared to the non-preemption task scheduling model, our approach achieves the average improvements of 1.32%, 7.27%, 10.2% and 9.13% for the 256B, 512B, 1KB and 2KB caches, respectively.
On normalized generating sets for generalized quasi-twisted codes over finite fields
Authors: Lv Jingjie , Li Ruihu , Zheng Youliang , Yao Yu 
Published: December 2020

Abstract
In this short correspondence, we firstly determine the normalized generating set for generalized quasi-twisted codes over finite fields. Then we present an algorithm to construct it. As an application, some optimal or the best-known linear codes are derived from generalized quasi-twisted codes by applying our method.
Application of DSP Technology in Radar Signal Processing
Authors: Aichun Zhu , Wenhui Sun , Kejin Wang 
Published: December 2020

Abstract
Aiming at the problems existing in applying DSP technology to radar signal processing, the article analyzes the current use of DSP technology from a practical view, and proposes methods and strategies for optimal control of future technologies. The results show that only under the premise of the operational control requirements of the radar signal processing system can the application advantages and reliable effects be brought into full play, thereby promoting the healthy and stable development of the industries involved.
The application of virtual reality and augmented reality technology in the field of Education
Authors: Liyuan Liu 
Published: December 2020

Abstract
With the continuous development of modern information technology and the increasing level of science and technology, virtual reality and augmented reality as a new technology have been widely used in many industries, and play a positive role. This paper focuses on the differences and educational characteristics between virtual reality and augmented reality technology, analyzes the application of virtual reality and augmented reality technology in the field of education, and puts forward its own views for reference.
Heterogeneous remote-sensing image matching method based on deep learning
Authors: Hou Huitai , Xu Qing , Lan Chaozhen , Cui Zhixiang , Qin Jianqi , Wang Lianxia 
Published: December 2020

Abstract
In this paper, a deep learning matching method is proposed to address the difficulty in matching heterogeneous remote sensing images, which is caused by their differences in imaging modes, time phases, and resolutions. A heterogeneous image is inputted into the convolutional neural network (CNN) to extract deep features and, then, a graph neural network (GNN) is used for matching. Finally, correct matching points are retained while ensuring the effective elimination of mismatches. The algorithm adopted in this study was tested using multiple sets of heterogeneous remote-sensing images and compared with D2-Net+NN+RANSAC and Superpoint+SuperGlue algorithms. The results show that the algorithm used in this study possesses strong adaptability and robustness and is an optimal algorithm for the robust matching of remote-sensing images with different sources and large differences.
A User Mobility-Based Rate Adaptation Approach for Dynamic HTTP Streaming
Authors: Chen Renkang , Li Zeping , Yang Huawei , Xue Yangshang 
Published: December 2020

Abstract
It is common for users to use smartphones to watch streaming videos when they are walking, taking a bus, or taking a subway. However, the current streaming rate adaptation algorithms are mostly based on the available network throughput or the player buffer occupancy, and the user mobility state is less considered. In order to improve the user experience quality under the mobile streaming media, we propose a user mobility-based rate adaptation approach, which classifies the user mobility state using the random forests algorithm and adopts different rate decision under different mobility states. The experimental results show that compared with popular approaches, our approach reduces the re-buffer events by 24% on average, and reduces the data traffic consumption by 19% on average.
Analysis Nonlinear mechanical characteristics analysis of a UAV landing gear shock absorber
Authors: Lu Jianmin , Nie Wenzhong , Ma Yajian , Li Xiaoxuan 
Published: December 2020

Abstract
Shock absorber of landing gear can reduce landing overload and vibration. Under different working conditions, the shock absorber structure is easy to produce stress and strain changes. This paper takes a kind of spring shock absorber as the research object. The single factor method is used to analyze the mechanical characteristics of the shock absorber under different compression stroke and landing deflection angle. The simulation calculation is carried out by ANSYS Workbench software. The simulation results show that: with the increase of the compression stroke of the shock absorber, the contact pressure and stress between the piston and the outer cylinder as well as between the piston rod and the lower cover will also increase. With the increase of the land deflection angle, the contact pressure between the structures will also increase. When the contact pressure is too high, the shock absorber will be stuck. It is easy to cause shock absorber failure. The results of this study can provide guidance for structural optimization design of shock absorber.
Real-time tracing FPGA circuit system of the virtual signals from thermal motion simulations in optical tweezers
Authors: Hu Zhanming , Han Xiang , Chen Zhijie , Xiong Wei , Chen Xinlin , Xiao Guangzong , Yang Kaiyong , Luo Hui 
Published: December 2020

Abstract
Optically trapped nanospheres are demonstrated capable of atto-Newton force sensing, where its experiment need to control the modulation voltages of the laser power according to the positions of the trapped nanosphere in high speed. In this paper, position fluctuations of the trapping nanospheres due to thermal motions are simulated using Monte-Carlo method and finite difference method. Equal-scale amplified transformations of those position sequences are generated as the discrete voltage signal of the virtual position detector. A high-speed digital incremental PID(Proportion-Integration-Differentiation) control system is developed by a low-cost FPGA circuit system, which can generate feedback voltage signals correspondingly. The responsive signal frequency is up to 1MHz with a time delay of 0.3μs and quite high amplitude stability. It is validated to integrate the virtual position detector and the PID feedback system into a low-cost semi-physical system, which can test various feedback cooling mechanisms for the complex systems of optical tweezers in vacuum. It will be a further step relative to the pure simulations in digital computers and provide references for the development of optical tweezers in vacuum.
Experimental study on curing of bisphenol A epoxy resin
Authors: Chun-wen Wang , Qin Zhang , Xin-long Chang , Kuan Hu , You-hong Zhang , Renli Ma 
Published: December 2020

Abstract
Taking the bisphenol A epoxy resin system as the research object, the thermal curing and microwave curing experiments were carried out. The relationship between the curing degree and the hardness of the product after thermal curing and microwave curing was analyzed. The thermally cured product and microwave cured product were tested for tensile properties and infrared spectroscopy respectively. The results show that there is a positive correlation between the degree of curing of the product and the surface hardness. Compared with thermal curing, microwave curing can save 50% of the time, and the microwave cured product does not generate a new structure. The tensile strength of the microwave cured product is about that of thermal curing. 88% of the tensile strength of the product.
Design of Decoupling Fuzzy Logic Controller for Quadrotor UAV
Authors: Huang Chao 
Published: December 2020

Abstract
This article first briefly introduces the quadrotor UAV model based on the Newton-Euler method, including kinematics model, dynamic model and force and moment model, and then proposes a decoupling fuzzy controller, which can effectively decouple the position and the orientation of the quadrotor UAV, i.e., the inner loop uses the input and output linearization method to control orientation, and the outer loop uses a fuzzy logic controller to control the position of the quadrotor UAV. Finally, we use Matlab to model the quadrotor UAV and design the decoupling fuzzy controller to control the quadrotor UAV. The results show that the tracking performance is good, which proves the feasibility of the controller algorithm.
Multi-objective Optimization Design for Battery Pack of Electric Vehicle Based on Neural Network of Radial Basis Function (RBF)
Authors: Li Yuefag 
Published: December 2020

Abstract
As a carrier of EV batteries, battery pack is a key component that ensures stability, safety, and reliability of energy system for power batteries. In order to complete the requirement of light weight for EV, the battery pack shall have light weight as much as possible while meeting structural strength. This paper uses an Surrogate Models algorithm based on RBF neural network to solve the problem of multi-objective optimization for battery pack. It can be observed from LsDyna simulation results that the battery pack has declined 17.62% in mass and 30.78% in maximum deformation. Therefore, the proposed structure optimization model of battery pack hereof provides an effective design and optimization method for battery pack.
Mathematical-physical modeling for analytical calculation of multilevel pulse-width modulations
Authors: Pabón Fernandez L D , Caicedo Peñaranda E A , Diaz Rodríguez J L , Pardo Garcia A 
Published: December 2020

Abstract
Currently there is a great interest from the electronic physical point of view in reducing losses and optimizing the harmonic content of power converters. Especially of the multilevel ones that have great advantages since they can work with low commutations. That is why, this paper proposes and verifies a methodology for the development of equations for the analytical calculation of the effective value and the total harmonic distortion of step and Pulse width modulations type multilevel. The equations are obtained by analysis in the frequency domain using Fourier theory. A generalized mathematical expression is obtained as a function of the switching angles, which is theoretically verified by comparing the calculated with the expression and the MATLAB fast Fourier transform algorithm. These equations allow the modification of the upper limit of calculation of the series, a characteristic that can be used to determine a vector of commutation angles with the minimum value of harmonic distortion for a range of harmonics.
Cold production from a metallic salt and low temperature sources
Authors: Noriega Sánchez C J , Afanador García N , Guerrero Gómez G 
Published: December 2020

Abstract
Chemisorption cooling is a type of technology that facilitates energy saving and produces less environment impact than that produced by vapor compression cooling systems. The advantages or disadvantages of these systems can be explored from mathematical models through which the impact of different working pairs as well as construction and operating parameters on these cooling systems can be described. In this sense, the impact of design variables on performance coefficient of a chemisorption refrigeration system that uses sodium bromide and ammonia as working pairs are evaluated from a mathematical model that describes the heat transfer into the system. The results indicate that the cycle time affects favorably the coefficient of performance as well as the apparent specific density of the sorbent and the reactor radius. Although the coefficient of performance values is low when compared to vapor compression cooling systems, the system can be operated by low temperature thermal sources, found in thermal solar collectors or flue gases, and it can increase the energy efficiency of industrial or residential applications.
Data processing from electrical signals acquired by an E-nose system used for quality control of cocoa
Authors: Florez A , Durán C , Carrillo J 
Published: December 2020

Abstract
This research consists of the implementation of different pattern recognition methods applied to discriminate and classify the physical signals (electrical) acquired by an artificial electronic nose system composed of a gas sensor array of 10 units coupled with a data acquisition board that were used to perform a sensor chamber that receives the electrical information from volatile organic compounds generated by cacao beans. A concentration chamber for samples conditioning of fermented beans was used and the samples were fermented around 72 and 144 hours while the cacao samples infected with monilia were over-fermented. For obtaining the temperature inside of the sensor chamber, a digital temperature sensor was implemented by using a Peltier Cell mechanism which was controlled through a classical algorithm. At the design stage, a data acquisition system composed of an Arduino card and a graphical interface made in LabView was developed for data storing, controlling, and signals monitoring. For the electrical signals treatment and data analysis, two pattern recognition models were applied by using Python software where two signals pre-processing methods such as Euclidean normalization and Roboust Scaling were used afterward with data processing techniques as principal component analysis and clusters analysis, obtaining a 96.51% of variance in the two first components.