bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on Visual Perception of Intelligent Robots Based on ADMS
Authors: Wu Changzhong , Ge Fan , Shang Guangchao , Guo Hengshuai , Wu Liang , Zhao Mingpeng , Wang Guitao 
Published: January 2021

Abstract
In application environments such as logistics, home services, and venue welcome, reliable and efficient autonomous environment modeling has become an urgent research need and technical challenge in the field of service robots. This paper introduces the three-dimensional simultaneous positioning and map creation (SLAM) of today’s highly potential (ADMS) technology research service robots, and uses cross-disciplinary research methods to carry out innovative theoretical research, principle derivation and experimental verification. The application of ADMS intelligent robots provides strong technical support.
Student Performance Protection Based On Blockchain Technology
Authors: Liang Xiao , Xu Shiyuan 
Published: January 2021

Abstract
In recent years, performance management has become more and more significant in terms of management and credibility of universities. They have to review student performance when issuing diplomas. Therefore, the reliability of student performance management is extremely important. Nowadays, many universities adopt a data-centralized management model. However, centralized database often brings about problems such as information leakage and easy tampering. With the development of blockchain technology, its decentralized and unforgeability characteristics have gradually attracted people’s attention. In order to prevent students’ scores from being maliciously tampered with, we utilize blockchain to design and implement a decentralized student score sharing management system. Moreover, by using the Ethereum smart contract to manage on-chain and off-chain data storage as well as on-chain query and verification, we ensure the traceability, tamper-proof, distributed storage, and the security of query process. Finally, this article explores the construction of prototype system based on the Ethereum environment. The experimental results show that the anti-tampering and traceability scheme proposed in this article is feasible. The application of blockchain can fully satisfy the needs of various participants in student performance management. Each subject’s information has a separate decryption private key, which perfectly protects student’s score information.
Research on Simulation Rendering Technology of Watercolor Painting Based on Canny Edge Darkening
Authors: Mai Xin 
Published: January 2021

Abstract
Watercolor painting is a branch of traditional painting art, which has a history of more than years of development. Watercolor painting has been widely used and popularized because of its bright colors, transparency, rich expression techniques, strong expressiveness, and simple production tools and materials. The artistic aesthetics of watercolor painting has a wide audience base, which is the premise of the research of watercolor simulation drawing. Edge detection technology is the basis of image measurement, image segmentation, image compression and pattern recognition and other image processing technologies, as well as the technical basis of watercolor digital rendering. Based on the Canny algorithm, this paper proposes a watercolor simulation drawing method based on Canny edge detection. From the experimental results, this method has a good simulation rendering effect.
State Evaluation of Circuit Breakers Based on Improved Fuzzy Comprehensive Evaluation Method
Authors: Wu Yuefei , Cao Na , Yu Qun 
Published: January 2021

Abstract
In order to quickly and effectively distinguish the operating state of the circuit breaker and improve the accuracy of the state evaluation, an improved fuzzy comprehensive evaluation model is proposed. The evaluation index is weighted based on the combined weighting method, which avoids the dependence of the traditional analytic hierarchy process on the subjective scoring of the weights. The gray correlation theory is introduced to modify the evaluation results, which effectively overcomes the inherent shortcomings of the principle of maximum membership. The improved fuzzy comprehensive evaluation method is applied to the state evaluation of circuit breakers, and the evaluation indexes are established through time parameters, speed parameters, circuit parameters and electrical characteristic parameters, and then a multi-layer fuzzy comprehensive evaluation model is established. Finally, the evaluation model is used to analyze the laboratory circuit breaker, which verifies the feasibility and effectiveness of the model.
Path planning using deep reinforcement learning based on potential field in complex environment
Authors: Jia Qingxuan , Yang Maonan , Miao Yu , Li Xulong 
Published: January 2021

Abstract
This paper introduces a deep reinforcement learning path planning method based on potential field for complex environment. Based on the potential field model in the artificial potential field method, we define states, actions, rewards in reinforcement learning, and use Deep Deterministic Policy Gradient (DDPG) reinforcement learning algorithm for optimization. By training robots in the environment, our method can effectively plan the path in a complex environment with massive obstacles, and avoid trapping in the local minimum region of the potential field.
Natural Thermal Convection Field Measurement Technology Based on Background Oriented Schlieren Technology
Authors: Chen Xi , Wu Min , Zhang Ming-Yuan , Yang Kai-Di , Liu Peng , Zhu Hai-Dong 
Published: January 2021

Abstract
Background oriented schlieren technology is developed from the combination of schlieren technology and PIV technology. It is an emerging non-contact flow field quantitative measurement method. The development and principle of background oriented schlieren technology are introduced in detail. A set of background oriented schlieren imaging system suitable for natural thermal convection field is built. The quantitative measurement research is carried out on the natural thermal convection field above the candle flame, and the density field is calculated. Distorted vector displacement map. The results show that the background oriented schlieren technology can be better applied to the quantitative measurement of natural thermal convection field, and it has broad development prospects.
A Method of Robot Grinding Force Control Based on Internal Model Control Principle
Authors: Wang Ting , Miao Hongbin , Shi Shangyao , Chen Zhenya , Zhang Huang 
Published: January 2021

Abstract
The application of robots in the grinding industry can solve the problems in traditional artificial grinding, such as high labor intensity, low work efficiency, unguaranteed processing quality and serious physical injury to workers. In order to ensure the grinding quality, it is necessary to control the grinding force. The aims of this study is use PID-IMC controller to control the grinding force. Carry on the robot dynamics analysis to establish the dynamics model. Based on the principle of internal model control (IMC), PID controller is optimized and PID-IMC controller is obtained, which is used to realize the constant force control of robot grinding. Simulation and experiments results show that, the dynamics model and control method proposed in this paper have a better effect in the control of robot grinding force.
Analysis of the Influence of the Number of Torque Converter Blades on Working Performance Based on the Response Surface Method
Authors: Xiong Pan , Sun Hongjun , Zhong Jiping , Zhen Chenping , Chen Xinyuan , Gao Huichao 
Published: January 2021

Abstract
To understand the effect of the number of torque converter blades on the performance of torque converters, in this paper, we employed computational fluid dynamics and the response surface method to optimize the design of the torque converter. The number of pump blades, stator blades and turbine blades were used as design variables, and the torque transmission ratio and pump capacity factor were used as objective functions for multi-factor optimization design. The results show that the number of turbine blades had no significant impact on the converter’s performance, while the number of pump blades and stator blades had significant impact and showed strong correlation. Compared with the original torque converter, the optimized converter increased the starting torque ratio by 0.12 and the efficiency by 4%. It shows that the computational fluid dynamics and the response surface method can be combined to determine the optimal number of blades in the design of torque converters to reduce the cost and experiment time.
Research on Machine Vision Technology of High Speed Robot Sorting System based on Deep Learning
Authors: Mou Hai Rong , Lu Rui , An Juan 
Published: January 2021

Abstract
Workpiece sorting is an important link in industrial production and transportation, which will directly affect the accuracy and speed of the workpiece. In the traditional manual sorting, we will be affected by the environment and subjective factors, which will cause visual fatigue error. With the rapid development of computer technology, the intelligence of robot is higher and higher, which has been applied to many fields. Robot sorting has many advantages, such as high speed, small inertia, high positioning accuracy, which has become an important part of industrial manufacturing and express transportation. For sorting system, we need to focus on machine vision technology and deep learning algorithm. Through image recognition and target tracking, we can provide accurate basic data. Through deep learning algorithm, we can intelligent sorting, which needs our in-depth study.
Knowledge Graph Construction of High-Performance Computing Learning Platform
Authors: Dong Tianyue , Tang Lei , Peng Jinye , Zhong Sheng , Luo Hangzai 
Published: January 2021

Abstract
With the development of intelligent education, it has become one of the more efficient learning schemes to construct the knowledge graph which can excavate the knowledge base. People generally use RDF triples and use languages such as OWL to construct knowledge graphs, but this method has problems such as limited expression ability and too much manual annotation. In this paper, we propose a framework that combines statistical language models, neural network language models, and clustering and clipping algorithms. When processing unstructured text data, unsupervised extraction of representative key words as features of the structured graphs’ entities, so that the processed entity information has more accurate semantics and human learning relevance, and the method of clustering and calculating the learning rate is used to further clarify the learning order and mastery of knowledge points. We have conducted extensive experiments to collect data from the two major modules of HPC high-performance computing courses and domains as datasets, and used this framework to build many knowledge graphs which can provide practical learning. The knowledge graph of this paper can be obtained from: https://v2.easyhpc.net:10000/knowledge Knowledge Graph Module.
Three different SVM classification models in Tea Oil FTIR Application Research in Adulteration Detection
Authors: Zhang Yu , Li Tinghui 
Published: January 2021

Abstract
Fourier transform infrared spectroscopy (FTIR), as a new type of rapid environmental detection technology, has attracted extensive attention from researchers. In this study, FTIR combined with stoichiometry was used to establish and study the qualitative detection model of tea oil-doped soybean oil and corn oil. Different spectrum preprocessing and feature value extraction were performed on the infrared spectra of 105 samples of tea oil and adulterated oil in the spectral band of 600-4000cm-1. The KS sample selection method was used to divide the training set and the test set, and the training set was used to construct Support Vector Machine (SVM) Classification model, select the best model construction method based on the accuracy of the test set. The results show that the SVM tea oil adulteration detection model with convolution smoothing ( SG ) spectral preprocessing and random forest ( RF ) feature extraction achieves the best accuracy of 0.93, which is simple, fast, and pollution-free for the market to detect adulterated tea oil. Provide technical reference.
Slam and navigation of indoor robot based on ROS and lidar
Authors: Yang Xiaozhuo 
Published: January 2021

Abstract
For indoor service robot, this paper designs an indoor robot based on ROS (robot operating system) operating system. The robot uses two-dimensional lidar as the core sensor to collect the depth information of the environment and uses SLAM algorithm based on particle filter to realize the robot mapping; Based on the framework of ROS, A* algorithm and DWA algorithm are used to realize the navigation function of the robot; The map and navigation are simulated in the gazebo simulation environment to verify the function of path planning, local obstacle avoidance and navigation.
Guided Policy Search Methods: A Review
Authors: Du Jinyu , Fu Jian , Li Cong 
Published: January 2021

AbstractGuided policy search methods (GPSs) have become important methods in the field of reinforcement learning in recent years. GPSs are a kind of policy search methods that utilize trajectory optimization methods to generate training data, guiding supervised learning. In theoretical research, GPSs combine convex optimization and deep learning, and have achieved fruitful results. In practical applications, they have achieved good results in complex control fields such as robots learning, especially manipulator operations. This paper mainly elaborates the development process and improvement route of GPSs. Firstly, the theoretical knowledge related to the GPSs is introduced. Secondly, the framework and basic methods of GPSs are analyzed; Thirdly, various improved GPSs based on the basic methods are generalized. Finally, the development and future improvement directions of GPSs are summarized, and the problems and future development trends are discussed.
Research on Optimal Cooperative Attack Strategy of Multiple UAVs
Authors: Wang Wenhua , Wang Xiaoping , Liu Bo , Zhou Wen 
Published: January 2021

Abstract
Aiming at the problem of the optimal coordinated attack strategy of multi-UAV system against multiple moving targets in a known environment, a PMMAXQ layered reinforcement learning algorithm applied to multi-UAV cooperative attack is proposed. Firstly, the problem of multi-UAV coordinated strike, the successful strike conditions and the movement strategy of moving targets are described. Then the moving targets are allocated rationally through the 0-1 planning method. Finally, the MAXQ algorithm is improved by the Bayesian probability statistical formula. The simulation results show that, compared with the MAXQ algorithm, the PMMAXQ algorithm converges faster and the average cumulative reward value that the UAV can obtain is higher.
Research on the Performance of Character Convolutional Neural Network in Different Text Encoding Formats
Authors: Bao Zhiyao 
Published: January 2021

Abstract
With the continuous progress and development of society, text classification has become an important task in the field of text data mining and text value exploration. Compared with existing text classification technology, deep learning technology has many advantages, such as high accuracy and effective feature extraction. This paper is mainly based on the character convolutional neural network to study the classification performance of different texts, hoping to provide experience for related research work. Through research, it is found that character-level convolutional neural network deep learning technology can achieve Chinese text classification more effectively.
A Kind of new parallax algorithm based on symmetric continuous optimization
Authors: Jiang Kaiwen , Zhang Degan , Xu Haixia 
Published: January 2021

Abstract
With the fast development of 3D imaginations becomes more and more fascination, multi-view stereo based 3D reconstruction is a significant technique for those application. To facilitate the subsequent processing of 3D reconstruction and reduce the possibility of other algorithms falling into local optimal solutions, attempting to get better and faster performance, a new parallax calculation based on symmetric continuous optimization is proposed in this paper. The algorithm is proposed here will be tested respectively in the same data, in order to certificate the algorithm applied is better than traditional minimize El
algorithm.
A high-precision time-to-digital converter applied to CDC
Authors: Xin Xiaoning , Hu Yifan , Jian REN , Li Yuxuan 
Published: January 2021

Abstract
This paper presents a high-precision CMOS time-to-digital converter (TDC) applied to a capacitance-to-digital converter (CDC). The time-to-digital converter in the capacitance-to-digital converter is used to measure the time interval between the charging time of the capacitor under test and the charging time of the internal reference capacitor. The time resolution of the TDC directly determines the capacitance resolution of the CDC. In order to improve the measurement resolution of TDC, a TDC structure based on phase-locked loop (PLL) is proposed. This paper first gives a capacitance-to-digital converter scheme, and then introduces the time-to-digital converter used in the scheme. The frequency multiplication technology of the phase-locked loop optimizes the integral nonlinearity (INL) of the interpolator and greatly improves the interpolation of the interpolation ratio. The TDC was realized in a 0.13-μm CMOS process, and the measurable time interval is 21.14μs. When the external reference clock is 200MHz, the resolution is 40.3ps and only 31 delay units are needed.
Decision making method of University Asset Allocation Based on Deng’s grey relational analysis
Authors: Wang Hongrui , Jia Guangtian , Wei Ran 
Published: January 2021

Abstract
At present, most of the domestic colleges and universities have been using management information system which records the history and status of all assets in detail to achieve asset management. Mining and analyzing the huge asset data of colleges and universities can better provide scientific basis for university management and decision-making. This paper proposes to use Deng’s grey correlation analysis method to analyze the situation of assets replacement in colleges and universities, so as to provide decision support for asset allocation.
Improvement of Virtual Network Mapping Based on OpenVirteX
Authors: Zhang Yuan , Qin Jian , Li Xiaoping , Zhang Shiling 
Published: January 2021

Abstract
Network virtualization allows the flexible sharing of physical networking resources by multiple users (tenants). Each tenant runs its own applications over its virtual network. OpenVirteX, a network virtualization platform, provides virtual Software Defined Networks (vSDNs). Each vSDN is customizable in terms of topology as well as addressing scheme. However, the virtual network mapping process of OpenVirteX does not take into account resource constraints and deployment cost, just to complete the virtual network mapping. To solve this problem, this paper proposes to deploy the classic mapping algorithm named Improved-vnmFlib on the OpenVirteX platform and designs the overall process of implementation. Finally, we build a complete simulation environment to test the new platform, the test results show that the new platform can take a virtual network mapping combined with the deployment cost under the premise of resource constraints, and can create the virtual network automatically according to the mapping result.
Optimization of cable vibration response signal in cable force analysis of cable-stayed bridge
Authors: He Bozhou , Zou Lanlin 
Published: January 2021

Abstract
In the cable force detection of the cable bridge, the frequency method is the most suitable and most commonly used method. And in the application of the frequency method, the core issue is the pickup of the natural frequency. In the practice of the general frequency method, the natural frequency is often picked up by collecting the vibration response signal of the bridge cable, and then analyzing the spectrum to pick up the natural frequency. This method can be applied to most cables. However, for some cables with unsatisfactory acquisition signals, the periodicity of the signal on the spectrogram will not be obvious. At this time, further cepstrum analysis of the cable signal can better analyze the spectral period of the cable vibration response signal in order to obtain a more accurate natural frequency and calculate a more accurate cable force value.
Solving Approximate Nearest Neighbor by Hash Aggregate and Clustering in High Dimensional Euclidean Space
Authors: Hu Hangfei , Wang Hongya , Tan Zongyuan 
Published: January 2021

Abstract
Approximate Nearest Neighbor search has attracted great attention in the field of information search. The method based on Locality-Sensitive Hashing has good performance with probability guarantee, which needs m hash functions for collision detection. In the hash table corresponding to each hash function, collision counts are performed respectively during querying, which will result in additional collision counts and longer response time. To avoid the shortcoming, we aggregate the m hash values as the representation of the original data object. Then the aggregate values of all data objects in the dataset are clustered. In the process of querying, it is needed to find the class of the aggregate value of the query for searching. Experiments show the effectiveness of our method.
Wau: A User Authentication System Based on Channel State Information and Deep Neural Networks
Authors: Wang Zhengjie , Dou Wenwen , Huang Zehua , Zhang Chengming , Chen Da 
Published: January 2021

Abstract
Recently, user authentication techniques have attracted more attention and played a more important role in human-computer interaction applications. In this paper, we propose a user authentication system based on CSI (channel state information) called Wau. This system works in a device-free way and has no privacy violation because it facilitates the crucial characteristics of CSI that the unique CSI variation from different user activity can be measured and recognized. We adopt deep neural networks to realize the authentication of 8 people in three environments, including a through-the-wall scenario. The highest recognition accuracy reaches up to 98%. These results validate the feasibility and effectiveness of the user authentication system using CSI and deep neural networks.
Review on Quantum Walk Algorithm
Authors: Zhou Wenda 
Published: January 2021

Abstract
Quantum walk is the quantum version of classical random walk, which has shown great advantage over classical algorithms. In this article, main discoveries and breakthroughs along with several applications in the last two decades are reviewed and discussed. Reducing hitting time and diminishing mixing time, as two hot research fields, are addressed. Possibility of universal computation with more generic case of quantum walk is also mentioned. In the future, we can expect new methods and models being feasible ways to approach the theoretical lower bounds of related problems, as well as more efficient applications on specific scene.