bluuebunny/crossref_metadata_2025_split huggingface dataset:
Wind Power Short-Term Forecasting Based on LSTM Neural Network With Dragonfly Algorithm
Authors: Liu Hui , Chen Dihuang , Lin Fang , Wan Zhouli 
Published: January 2021

Abstract
The volatility and randomness of wind energy limit its large-scale usage in power systems. Accurate short-term wind power prediction can provide effective criteria for wind energy parallel in the grid and provide favorable conditions for the commercial utilization of wind energy. Therefore, the paper proposes a short-term wind power prediction model based on the dragonfly algorithm optimize long-term and short-term neural networks. Firstly, the model preprocesses the collected data and divides the data into a training set and a testing set. Then, the DA used the training set to optimize the relevant hyperparameters in the long and short-term memory neural network. Finally, the DA-LSTM prediction model constructed with excellent hyperparameters will use the test set to obtain the prediction results. The simulation results of the examples show that, compared with the GWO-BP, ELM, and LSTM models, the DA-LSTM model can effectively use time series data for short-term forecasting of wind power and has higher prediction accuracy.
Improved IMM algorithm based on XGBoost
Authors: Li Da , Zhang Pei , Li Ruifang 
Published: January 2021

Abstract
Based on Markov hypothesis, IMM uses multiple motion models to match the moving states of the target, and assumes the transfer probability of each model according to the prior knowledge, which has strong adaptability to tracking maneuvering target. However, it is not direct enough to obtain prior knowledge from source data statistics and then make decision according to maximum likelihood, and the information of source data is not fully utilized. Therefore, we use XGBoost in machine learning algorithm to replace this process. We propose XGBoost-IMM model algorithm. XGBoost can fully learn the information of the source data and make decision on the target motion model, and then IMM can perform multi-filter filtering based on the decision. Experimental results show that our algorithm has good performance.
Research on Optimized Deployment of Virtual Network Functions in Network Function Virtualization Environment
Authors: Liu Mingyue , Liu Feng 
Published: January 2021

Abstract
This paper mainly studies the optimization of dynamically arrived SFCs deployment in the SDN scenarios, and optimizes the end-to-end delay and bandwidth consumption during the deployment. First we build the model of the optimization problem, it is expressed as a 0-1 planning problem. We use ILP to get the optimal solution, cause the problem is NP hard, its runtime increases as network scales increases, so we choose the heuristic algorithm instead to reduce algorithm runtime. During our heuristic algorithm, we design a new method to sort VNFs and network nodes, then use the greedy algorithm to select nodes literately to place VNFs, in order to avoid local optimality, further use the simulated annealing algorithm with the results of the greedy algorithm as its initial solution, and design two methods to generate new deployments, and still repeat iterating to find a better deployment until reaching iteration limits. This paper also considers the SFCs’ lifecycle and trade-off between the two parameters. The simulation proves that the algorithm proposed in this paper can significantly reduce the end-to-end delay and bandwidth consumption than the traditional method, and 80% of its results are very close to the optimal solution with less than 5% error, and the ratio of its runtime and optimal solution’s is at most 0.003, the algorithm also has certain applicability and can be used in other scenarios.
Research on Enterprise Employee Information System Based On Big Data Analysis
Authors: Wang Jicheng , Li Donghai , Wang Zhao , Wan Taifeng 
Published: January 2021

Abstract
The data integration time of traditional enterprise employee information system data integration method is long, and the data coverage rate is not high in the integration process. This paper designs a data integration method of enterprise employee information system based on big data technology. First of all, the unified enterprise information system data integration standards and data classification principles are set. According to the relationship between data, data types are defined, employee information system data are classified, and inconsistent and duplicate data are eliminated after classification, so as to reduce the interference of invalid data and improve the efficiency of data integration. Then, a unified coding standard is formulated to check and code the classified data to determine whether the classified data set matches the actual data. Based on the offline data, deep learning algorithm is used to integrate the data of employee information system, which can reduce the occupancy rate of resources in the system and improve the coverage of effective data. The experimental results show that the data integration method takes less time than the traditional method, and has high data coverage, so it has practical application significance.
PID Control of Vehicle Active Suspension Based on Particle Swarm Optimization
Authors: Zhao Lelong , Zeng Zhiyuan , Wang Zhenyu , Ji Chufan 
Published: January 2021

Abstract
In order to select the PID controller parameters, particle swarm optimization (PSO) is used to optimize the PID controller parameters. To evaluate the ride comfort of the vehicle, the objective function is established which integrates the vertical acceleration, suspension dynamic travel and tire dynamic load, and the controller parameters are optimized by particle swarm optimization. Simulate it in the software MATLAB. The result shows that the function of PID controller after particle swarm optimization is better than that before optimization.
Research on underwater target tracking based on Gaussian Hermitian Kalman Particle filter algorithm
Authors: Zhang Chengzhen , Yang Yang , Ding Yuanming 
Published: January 2021

Abstract
The underwater target tracking environment usually has the characteristics of strong nonlinearity and non-Gaussian. The target tracking problem usually uses the nonlinear filtering algorithm, which combines the nonlinear measurement model with the linear system dynamics. The primary goal of target tracking is to extract accurate information about the real-time state of the target from the noise nonlinear observation obtained by the sensor. A new Gaussian Hermitt Kalman particle filter algorithm (GHKF-PF) is used to improve the tracking accuracy of the particle filter algorithm (PF). GHKF-PF uses the GHKF sampling method to sample particles from the posterior distribution of the target, calculates the mean and covariance for each particle, and uses the mean and variance to guide sampling. In the simulation experiment, the uniform linear motion model of underwater 3D target in the environment of Gaussian mixture noise (GMN) is established, and the application of GHKF-PF algorithm in 3D space is realized, and the accuracy is higher than that of PF. In order to further verify the effectiveness of the algorithm, a six-dimensional uniform acceleration motion model is established and compared with the extended particle filter (EPF) algorithm and the unscented particle filter (UPF) algorithm. The simulation results show that the performance of the GHKF-PF algorithm is similar to that of the UPF algorithm and better than the EPF algorithm.
Learning Gaussian Hierarchy Embedding for Relation Prediction in Knowledge Graph
Authors: Huxue Ruobai , Huang Jie , Wang Jiantao , Li Yiming 
Published: January 2021

AbstractKnowledge graph embedding methods, which transform high-dimensional and complex graph contents into low-dimensional vectors (or matrices, tensors) representations. They are useful for a wide range of graph analysis tasks including link prediction, node classification, recommendation and visualization. Most existing approaches represent graph nodes as vectors in a low-dimensional embedding space, ignoring the semantic hierarchy and the uncertainty present in the real-world graphs. To address this challenge, in this paper, we propose GHAKE, Gaussian Hierarchy Knowledge Graph Embedding, a novel graph embedding method that preserves both semantic hierarchy and node uncertain effectively and efficiently in an end-to-end manner. GHAKE effectively models uncertainty through Gaussian embeddings, and models semantic hierarchy via mapping entities into the polar coordinate system. In our comprehensive experiments, we evaluate GHAKE on real-world graphs, and the results demonstrate that can effectively model the semantic hierarchies and the uncertain of entities in knowledge graphs, and significantly outperforms existing state-of-the-art methods on benchmark datasets for the link prediction task.
Research on Algorithms of Computing Offloading and Resource Allocation Based on DQN
Authors: Chen Guoqing , Cai Qiong , Fu Xingbao , Lai Yuanzhe 
Published: January 2021

Abstract
In the MEC unloading system with the weighted and minimized delay energy consumption as the optimization goal. Firstly, build a network model consisting of mobile devices, MEC servers and cloud centers. Secondly, considering the composition of the task, the amount of input data, the energy consumption threshold, the delay threshold and other factors, the task is divided into a non-unloadable part and an unloadable part, and then the unloadable part is divided into several subtasks. According to the AHP level analysis, this method Assign weights to subtasks to determine the allocation of computing resources. Finally, combined with the deep reinforcement learning method to determine the offloading decision, consider the frequency of the same task, and store the high-frequency tasks in the cloud center. In the simulation experiments of different system parameters, the results show that the combination of the model and the AHP-DQN method can better reduce the total system cost.
Research on Visual Data Mining Technology
Authors: Qu Kecheng , Wang Lina 
Published: January 2021

Abstract
As the rapid development of computer and network technology has produced massive amounts of data, it is becoming more and more difficult for various industries to process data, and the society’s demand for fast and effective data processing is also increasing. In this context, the use of traditional data processing methods is obviously insufficient. For this reason, using the advantages of visualization technology and combining it with data mining can realize the visualization of the entire process of data mining. This will not only help users understand the data mining process more clearly, increase the utilization of data mining results to ensure accurate and effective decision-making, but also improve the effectiveness and accuracy of data recognition and increase the credibility of the data mined. To this end, this article will analyze the relevant meaning and characteristics of visual data mining technology, and conduct analysis and research from several aspects such as technical design, implementation methods, application development status and future development.
Research on Semantic Feature Disambiguation Based on Improved Collaborative Filtering Algorithm
Authors: Yu Lin Lin , Su Li , Lin Qiang 
Published: January 2021

Abstract
Artificial intelligence is developing rapidly, and the amount of information data is also increasing. Human-computer interaction technology can use computers to understand and process massive amounts of large data texts and accurately locate useful information. Moreover, it is very difficult to achieve smooth human-computer interaction. The main reason for the difficulty is that there are various ambiguities in natural language. Therefore, a noun phrase referential disambiguation model is established by improving the convolutional collaborative filtering algorithm in the paper, where the hand-crafted features are applied to clean up data to remove redundancy, and the remaining correct data is used as the input of the model. After experiments, the MACSSR algorithm and the CSSR algorithm are used for comparative testing under different M directs. It proves that the research content of this paper is superior to the comparative test method, and can extract semantic dependencies more quickly and effectively, which contributes to the intelligent semantic disambiguation technology.
Research on Power Network Data Management Based on Convolutional Neural Network
Authors: Zhao Ruifeng , Li Bo , Guo Wenxin , Lu Jiangang , Li Shiming 
Published: January 2021

Abstract
Faced with huge business data, collecting and statistics of these data can be easily achieved through the system. However, during data analysis and result output, there is a large amount of data that cannot be intuitively accepted at a glance, resulting in disorder and inefficient work. Therefore, the grid load thermal map is scaled with cubic convolution interpolation in the paper, which requires the offset distance determination of the floating point in the horizontal and vertical directions. Moreover, based on the operating data of transformer and line, the analysis and verification of the load thermal map in power grid is realized. Besides, problem equipment can be clearly displayed on the map, which is convenient for staffs to identify and analyze.
GIS-based mine flood preventing and controlling assistance decision system
Authors: Xu Jiangtao , Yang Zhenwei , Wen Guangchao 
Published: January 2021

Abstract
The author has proposed the overall structure of mine flood preventing and controlling assistance decision system by analyzing the present situation of mine flood research survey, established the mine flood assistance decision platform. We have realized the mine flood preventing and controlling informatization and intelligent by using re-development on software.
An Unbalanced Security Protocol for IoT devices
Authors: Wang Huinan , Zhang Jianmin 
Published: January 2021

Abstract
Authenticated key exchange (AKE) protocol is a crucial process in Internet of Things. This paper aims to customize the Elliptic Curve Diffie-Hellman (ECDH)-based AKE according to the computational capability of devices. We propose a method to unbalance the computations in the ECDH key exchange scheme by configuring the more powerful device undertake one scalar multiplication on behalf of the computationally limited one. The analysis results show that the computationally unbalanced protocols are more friendly to computationally limited devices than the original one.
RFID Indoor Positioning Method Based on Received Signal Strength Indication
Authors: Wang Dong , Dong Enhong , Shi Yufeng , Wei Honghe 
Published: January 2021

Abstract
RFID indoor positioning technology has a good application prospect. This paper analyzes the RFID indoor positioning method based on received signal strength indication (RSSI).First, the indoor signal propagation model is constructed, the model parameters are estimated using the weighted least squares method, and then the received signal strength indication is used to estimate the distance between the target to be located and the reader at the known position. This method has certain practical value.
Research on the design of lake water intelligent monitor
Authors: Zeng Xi , Yi Mengdi 
Published: January 2021

Abstract
Recently, my country’s industrial production has caused great damage to wat er resources, especially the pollution degree of inland lakes is getting worse every year. Therefore, the monitoring of the water quality of inland lakes in China has become i mportant as one of the basic measures to protect the lake’s water resources and enviro nment. At present, most water quality monitoring methods in China are still in a relati vely backward stage. The water quality monitoring methods have the disadvantages of l ow efficiency and insufficient timeliness of results, which cannot meet the needs of cur rent water quality monitoring. The purpose of this study is to design a lake water intel ligent monitor with autonomous mobility and sustainable uninterrupted monitoring capab ilities. To optimize the design of water quality monitor, this paper analyze the existing water quality monitors, summarizes the deficiencies that exist in the existing water qual ity monitor products and draws the use requirements of the product, and Propose the d esign direction of the new intelligent water quality monitor; At the same time, accordin g to the technical principles required for the design of water quality monitoring instrum ents, the feasibility of product design is analyzed; finally, the overall design of the lake water intelligent monitor with independent mobility and sustainable uninterrupted monit oring capability is realized.
Application of a New-type Damping Structure for Vibration Control in Deployment Process of Satellite Antenna Component
Authors: Wang Min , Zhao Tingting , Liu Qinda , Liu Jiaxing , Zhang Yunfei 
Published: January 2021

Abstract
Vibration control of flexible deployable mechanism is seriously significant with the increasing application of it in aerospace field. Satellite antenna must be carried out in folded state due to small space of launch vehicle. In the process of deployment and attitude adjustment, vibration has a great influence on the contour accuracy. Based on transfer matrix method, the deployment structure of large flexible deployable satellite antenna is analysed to obtain its natural frequencies and modes. By solving the first three natural frequencies and modes in different deployment angles, the structure completely deployed is prone to be coupled and has the worst stability. Thus, damping structure to suppress vibration at all modal resonance points of fully deployed satellite antenna is necessarily to be installed. A new-type damping structure, multi-petal friction damping structure with the characteristics of simple structure and wide frequency-band, is proposed in previous paper. The dynamic analysis of deployment process at different angles for deployment component with damping structure is carried out to verify the damping effect and the feasibility of damping structure as well as the rationality of theoretical analysis.
Automatic disturbance rejection control of wind power connected T-type inverter based on filter function
Authors: Li Chunling 
Published: January 2021

Abstract
In order to solve the problems of large tracking error and poor ADRC effect of wind power grid connected T-type inverter, this paper studies the active disturbance rejection control method of wind power grid connected T-type inverter based on filter function. By establishing the mathematical model of wind power grid connected T-type inverter, the filter parameters are optimized by using the filter function, and the noise signal generated during the operation of the inverter is processed to reduce the control error. Taking the filtered bus voltage as the feedback input of the controller, the active disturbance rejection control of T-type inverter is realized. The results show that the control error of the filter function is reduced by about 4.57%, which has strong robustness.
Smart substation secondary equipment plug and play technology application
Authors: Tang Zhijun , Lin Guodong , Chao Wujie , Zhai Bolong , Liang Yi , Xu Jie 
Published: January 2021

Abstract
Is proposed in this paper, including the design of intelligent substation, construction and operational stages within the whole life cycle of technology innovation, further analysis the secondary equipment key problems and techniques of modular design, to “plug and play” technology system research and the application in the smart substation as the main line, the paper introduces the construction stage in prefabricated tank secondary equipment standardization of interface design, this paper discusses the operational phase process layer intelligent device plug and play, avoid setting of relay protection and automatic recognition of intelligent tripping and migration research such as technology protection function. The construction period of the substation is greatly shortened, and the intelligent control is realized to “plug and play”.
Development of Track Superelevation Simulation Testing System
Authors: Fu Huijun , Yang Jing , Liu Shihui 
Published: January 2021

Abstract
This paper first discusses the standard requirements and the existing test method for the coefficient of flexibility of the existing metro vehicle. The existing method has the disadvantages of manpower cost, insufficient accuracy of track lifting, different lifting speed of jacks under axle boxes. There are also safety hazards of the vehicle capsizing and the jack sliding. This paper introduces a set of simulation testing system based on hydraulic principle for these problems, measuring track superelevation accurately, reading and controlling the height of vehicle body and the track, thus realizing measuring the coefficient of flexibility. This system has the advantages of simple structure, easy installation, convenient operation and high measuring precision. The track distance, wheelbase and biaxial distance are all variable. It also saves manpower and effectively eliminate safety hazards.
Simulation Research on Cutterhead Hydraulic Drive System of Shield Machine
Authors: Wang Changlin , Chen Jiwen , Wang Rijun , Meng Decai 
Published: January 2021

Abstract
The core of the cutter head drive system of shield machine is the hydraulic system. Combined with a certain type of shield machine, the structure and working principle of 1.8 m hydraulic driving system for cutterhead of shield machine are studied, and the closed loop control mode of variable pump variable motor is adopted to design the 1.8 m hydraulic driving system for cutterhead of shield machine, the simulation model of the cutter head drive system is established in the hydraulic simulation environment, and the parameters of the sub-model are designed according to the actual situation.
Study on the Influence of Inlet Drag at Front and Rear Sides of Compressor on the Performance of Supercharged Engine
Authors: Wang Shangxue , Zhang Zhijun , Cao Jing , Chen Jinbing 
Published: January 2021

Abstract
In this paper, the influence of inlet drag at the front and rear sides of a compressor on the performance of a supercharged engine is tested. The test results show that the air flow into the engine, the combustion pressure and the engine output power decrease, the specific fuel consumption and the exhaust temperature behind the turbine increase with the increase of air intake drag. Especially when the engine at low speed operation, with the increase of inlet drag, engine performance will worsen sharply. Compared with the influence of the drag after compressor on the engine performance, the drag before compressor is more sensitive to the influence of the engine performance. When the engine speed is 1500r/min, 1900r/min and 2400r/min, the reduction in engine power caused by inlet drag in front of the compressor is 2.63 times, 4.1 times and 7.5 times that caused by the same inlet drag after the compressor. Also, the former results in an increase in specific fuel consumption 2.18 times, 3.1 times and 5.16 times of the latter. So optimizing the structure of the inlet system in front of the compressor can reduce the performance loss of the engine more effectively. The conclusions of this paper will provide guidance for the selection and matching of air filters, the spatial arrangement of multi-stage air filters in turbocharged engines, and the structural design of air intake system.