bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dynamic simulation of robot milling process based on ADAMS
Authors: Linxin Wang , Hao Luo , Shujun Li 
Published: January 2021

Abstract
In this paper, a stability model for milling chatter based on instantaneous cutting thickness was established, and the flap diagram of the stability region was drawn accordingly. The finite element simulation of dynamic milling process was carried out by using AdvantEdge, and the dynamic simulation was carried out by using ADAMS, and the torque of each joint was obtained. The conclusions show that: 1) The torque on all joints of the robot reaches the maximum value and fluctuates greatly at the initial cutting, and then tends to be stable; 2) the linear velocity jitter of the end-effector is less than 2.5·10−5m/s, and the angular velocity jitter is less than 3.3·10−5s−1, meeting the processing requirements.
An Aesthetic Reasoning System of Artistic Creation Based on Factor Space of Memory-Perception
Authors: Cheng Shuolei , He Yumeng 
Published: January 2021

Abstract
In order to build an intelligent art design system with human-computer interaction, this paper proposes an aesthetic learning model based on memory mapping and perception inversion. Through the relationship construction of practical aesthetic model and on-the-spot model, it sets up a couple of mapping models aesthetic information acquisition. The study shows that the premise of aesthetic reasoning is to set up fuzzy perception of human aesthetic, on the basis of the fuzzy perception, we can analyses the aesthetic relation among memory and object, and discuss the fuzzy perception characteristic of aesthetic process, and then obtain some conclusions and results. A simple example on how to create and apply the model is give. The presented model can be applied conveniently by selecting suitable ACDS in accordance with the give aesthetic judge and computing the best decision from the rules in those ACDS.
Harmonic compensation of APF based on unconstrained CCS-MPC
Authors: Meng Ning Ning , Yang Jian Feng , Liu Yang , Qi Rende 
Published: January 2021

Abstract
Active power filter (APF) is a device that can suppress harmonics and compensate reactive power. In order to improve the ability of three-phase active power filter (APF) to track and compensate harmonics in real time, an unconstrained CCS-MPC method based on state space model is proposed. Compared with variable constraint, it has faster response speed and less computation. Firstly, the time domain model of APF is established, and the discrete space state model is changed. The system state and output prediction equations are established iteratively to solve the objective function. The closed-loop solution of APF following harmonic is solved by using the open-loop objective function to achieve the purpose of feedforward and feedback. Finally, the simulation model is established, and the results show that the method has better dynamic response, steady-state characteristics and robustness.
Seizure Detection using Deep Multiset Canonical Correlation Analysis and Bayesian Optimization
Authors: Bai Xuefeng , Yan Lijun 
Published: January 2021

Abstract
As one of the most common neurological diseases in the world, epilepsy seizure is difficult to ignore. Seizure detection is receiving more and more attention from researchers. Feature extraction is one of the key steps in automatic seizure detection. Lots of features have been proposed to detect seizure using EEG signal. However, few works focus on feature fusion. In this paper, deep multi set CCA is explored for seizure detection. Since deep neural network architecture has a great impact on performance of deep multiset CCA, bayesian optimization is employed to search architecture parameters automatically. Preliminary experiments show it is effective for seizure detection using deep multiset CCA and bayesian optimization. Satisfactory seizure classification results are achieved with little manual intervention.
Research on the purchase decision of family cars
Authors: Wu Xinlin , Chen Congli 
Published: January 2021

Abstract
Faced with a wide variety of cars in the automobile market, many consumers often find it difficult to choose. In order to solve the decision-making problem in the purchase of private cars, this paper takes several compact family cars as the research object. The price, power, fuel consumption, configuration and space of the car are selected as the research indicators. The relevant mathematical model is established based on analytic hierarchy process combined with relevant data information of automobile market. The results of this paper can provide consumers with some decision references and suggestions in reality.
Research on Finite Control Set Model Predictive Control Strategy of Three-level APF
Authors: Shi Hang , Gong Xue Jiao , Chi Gong Sang Pu , Zhang Juan Juan 
Published: January 2021

Abstract
Active power filters (APF) can compensate for harmonics and reactive power. The traditional APF PI control has difficulty in parameter setting, and has the characteristics of control lag, and the control accuracy of current is low. The control of three-level APF needs to consider multiple objectives. Finite Control Set Model Predictive Control (FCS-MPC) is a newly proposed method that can increase constraints and multi-objective control. According to the limited switching state, it tracks the reference current and has dynamic Fast response and good compensation results. In this paper, the finite set model predictive control algorithm of three-level APF is studied, and the results show that the harmonic content of the power grid is low, which meets the relevant requirements.
Accuracy Improvement of Age Prediction Model Based on AutoML
Authors: Xu Shusong , Zhou Zixuan , Tao Make 
Published: January 2021

Abstract
Recommendations from websites are sometimes not suitable enough to fit the needs of different groups of people. One element in considering the recommended algorithm is the age range of users. To approach the goal of improving recommended algorithms in general, age prediction based on machine learning became the target of this research. We started by using AutoML from Google Cloud as the training tool and proportionally choosing 4,908 facial images of Asian people, which were categorized into six groups: images of the age below 10; images of the age from 10 to 20; images of the age from 20 to 30; images of the age from 30 to 40; images of the age from 40 to 50; images of the age from 50 to 60; and images of the age above 60. From the first training, we demonstrated 64.89 percentage of precision with the confidence threshold value of 0.5. After changing the threshold to 0.71, we optimized the result to 69.83 percentage of precision. By observing the result given by the deployment from Cloud training to an Android device, we found that the model differentiated Asian people aged from 10 to 40 most precisely, while it was relatively weak to distinguish people of the age from 50 to 60 and people of the age above 60. These results show an intelligent work of the prediction model on sifting people by ages and the strong relationship between the precision and the quantity of corresponding data. Its focus on distinguishing young people is suitable for our goal as young people are the main force of Internet users. By adjusting the composition of the dataset, we are looking for broader usage in other fields.
Economic dispatching Optimization of power grid based on IGWO Algorithm
Authors: Yu Han 
Published: January 2021

Abstract
An improved grey Wolf optimization algorithm (IGWO) is proposed to solve economic dispatch (ED) of power systems is proposed in this paper. The column vector composed of the active power of each generator in the power grid system is taken as the decision variable, and the total power generation cost of the power grid is taken as the objective function. The improved grey Wolf optimization algorithm is used to search for the optimal value by introducing the inertial characteristic constant and Gaussian mutation operator, and is compared with particle swarm optimization (PSO). The simulation results show that the proposed method has the characteristics of fast convergence speed, high solution accuracy and easy to jump out of the local optimal solution, and can be used as an effective means to solve ED problems.
Planning of VHF Radio Networking Based on Tabu Search
Authors: Shang Shuai , Zhang Bin , Pei Yunxiang 
Published: January 2021

Abstract
In order to solve the problem of frequency planning of VHF radio network, the constraint relation of VHF radio network is analyzed, the inter-network interference optimization model including interference matrix and cost function is established, and a frequency planning method based on tabu search is proposed. Finally, the model and algorithm are analyzed and verified with examples.
An initial clustering center optimization method based on neighbourhood density for K-means
Authors: Luo Mingxue , Yuan Yingchun , Wang Kejian 
Published: January 2021

Abstract
The selection of initial clustering centers of traditional K-means algorithm is random and sensitive to outliers, which leads to unstable clustering results and low accuracy. To solve the above problems, the NDK-means algorithm based on neighbourhood density is proposed. Firstly, the grid distribution characteristics of samples are obtained by multi-dimensional grid division. Then, by defining the grid density and the grid neighbourhood density, several local high-density grids are determined. At the same time, the iteration factor is introduced to merge adjacent high-density grids to obtain a candidate set of initial clustering centers. Finally, combined with grid density and distance, K initial clustering centers are obtained by using Max-Min-distance algorithm. Experiments on UCI dataset show that compared with k-means algorithm and literature algorithm, the accuracy of NDK-means algorithm is improved by nearly 11% and 4%, and the iteration speed is improved by 70% and 60% respectively. The algorithm improves the accuracy of clustering and the results have good stability.
A Convolution Operation Method Based on Domain Transformation
Authors: Wang Zhichen , Hao Ziyu , Li Hongliang 
Published: January 2021

AbstractThe convolution operation takes up a large proportion in the neural network, which is a kind of computation resource consuming operation. In this paper, domain transformation is introduced to participate in convolution, which reduces the computation of convolution operation. The acceleration is obvious in the inference operation of convolutional neural network.
Intra-Domain Heuristic Traffic Scheduling Algorithm for Time-Sensitive Networks
Authors: Zang Yu Hang , Wang Zhi Li 
Published: January 2021

Abstract
Time-Sensitive Network (TSN) is a new network transmission technology, which has the advantages of high data transmission and priority setting. Researching and improving the performance of TSN is of great significance to the development of future industrial communication and automation control fields. However, in practical applications, due to complex scenarios and difficulty in TSN traffic scheduling, traditional network traffic scheduling algorithms are not fully applicable to TSN, which may lead to timeout and inefficiency of traffic scheduling, and it is difficult to guarantee the quality of scheduling. In response to this situation, a routing and scheduling algorithm suitable for TSN is proposed. Firstly, TSN is modeled and analyzed, and the balanced routing algorithm based on bi-directional A* algorithm (BR-BidA*) and the modified greedy randomized adaptive search procedures (MGRASP) are proposed to solve the TSN routing and scheduling respectively. Compared with the contrast algorithm, the simulation results show that the proposed method can improve the performance of the algorithm, and greatly improve the solution quality and scheduling success rate.
A Text classification algorithm based on topic model and convolutional neural network
Authors: Ge Junwei , Lin Songce , Fang Yiqiu 
Published: January 2021

Abstract
Based on the neural topic model ProdLDA and convolutional neural network, this paper proposes a text classification algorithm based on topic model and convolutional neural network. Firstly, the text information is modeled on the word vector model, then the convolutional neural network is used to extract the granularity features of high-dimensional text, and the neural topic model ProdLDA is used to extract the potential topic features. Then, the connection layer is established to connect the text features, and finally the classification layer is processed. At the same time, a new topic feature introduction method is used in the process of extracting topic features. Experimental results show that this algorithm can effectively improve the performance of text classification.
MLNet: Multi-level Classification Network
Authors: Xue Doudou , Cheng Yinglei , Wen Pei , Zhang Liyin 
Published: January 2021

Abstract
The point cloud classification network only uses point cloud structure features or edge features to construct the feature vector, so that the classification accuracy is low. To solve this problem, this paper proposes a multi-level classification network. First, the original point cloud is divided into small samples using the preprocessing algorithm to obtain batch input and improve training efficiency; then construct structural features and edge features for point cloud feature extraction, and obtain local fine-grained descriptions through multi-level expressions within and between points; then design a convolutional neural network for feature learning. With the deepening of the network level, the degree of feature abstraction level is higher and higher, and the degree of distinction increases, so as to effectively improve the accuracy. Using the Vaihingen dataset to test the MLMS-Net network, the accuracy rate reached 85.4%.
Resource Scheduling Algorithm for Power Wireless Service
Authors: Yuan Bingyu , Qiu Yinghui 
Published: January 2021

Abstract
Based on the application of LTE technology in smart grid construction, this paper studies the technical background and demand of LTE technology and carrier aggregation technology in smart grid communication network construction, and analyzes the technical principle and technical background of LTE and carrier aggregation technology used in smart grid. Aiming at the problem of insufficient spectrum resources in smart grid construction, this paper focuses on the combination of carrier allocation and wireless resource allocation faced by carrier aggregation technology in smart grid environment. Carrier aggregation technology is used to improve spectrum utilization and speed up transmission speed. This paper studies a joint resource scheduling algorithm based on service priority, evaluates the service priority by studying the characteristics of wireless private network, and designs different scheduling methods for different services according to the characteristics of wireless resource allocation. The ns-3 simulation results show that the algorithm can provide sufficient resource allocation for high QoS services in the case of traffic congestion, balance the relationship between fairness and throughput, and improve the service transmission performance.
Collaborative filtering recommendation algorithm based on KNN and Xgboost hybrid
Authors: Li Yingxian , Xu Junwu , Yang Min 
Published: January 2021

AbstractIn the traditional collaborative filtering recommendation algorithm, it is easy to fall into the dilemma of local optimization due to single classification, which affects the recommendation effect of the algorithm. A hybrid collaborative filtering recommendation algorithm based on KNN-Xgboost is proposed. The algorithm uses KNN to fill in the user’s predictive evaluation with less project evaluation, reducing the sparseness of the matrix. Then the Xgboost algorithm is used to implement multi-classifiers to predict the data, and the multi-class calculation results are calculated to form the recommended results. The experimental results show that the sparseness of the matrix is solved to a certain extent, and the recommendation accuracy is improved.
Research on Emotion Classification based on Complex Network and Ensemble Learning
Authors: Cao Qianqian , Chen Xiangyang , Lai Yuanzhe , Deng Chenzhou 
Published: January 2021

Abstract
Because the traditional feature extraction is based on the statistical information such as document frequency and word frequency, the selection of feature words is ignored, and the semantic correlation between words in the text is ignored. The feature selection method based on complex network takes into account the semantic association between words, but does not take into account the statistical information such as word frequency. The above methods are not satisfactory for the selection of feature words, which affects the effect of text classification. Therefore, this paper combines the two, proposes a new method for feature selection, and in order to solve the problem of low accuracy rate of single classification algorithm, USES integrated learning [1] to strengthen the classification algorithm. The results show that this method is feasible and achieves good classification effect.
Lightweight and real-time object detection model on edge devices with model quantization.
Authors: Wang Jiayi 
Published: January 2021

Abstract
Environment perception is of vital importance in autonomous driving as it serves as autonomous car’s “eye” to perceive the surrounding environment. We propose a solution to achieve real-time environment perception on resource constrained edge devices like mobile phones, even though they have limited computation resources. We used float-to-int quantization based on TensorFlow Lite to quantilize the SSD model. TensorFlow Lite achieves accuracy lossless quantization with quantization-aware training by inserting fake quant operator during training, which imitates the loss introduced by quantization in feedforward training. Our experiment shows that superiority of our proposed method, which is highly speed, light weight and relatively high accuracy.
Research on Intelligent Party Building Information System Based On Big Data Platform
Authors: Gao Fei 
Published: January 2021

Abstract
With the advent of the era of network information, the development of information and network has penetrated into every corner of society. In the social situation, the work of grass-roots party organizations is also inseparable from network information technology. In order to adapt to and use the technical advantages in the building of grass-roots party organization under the new situation, based on evolutionary game theory, this paper, from the perspective of information platform adverse information and Party building information, establishes an evolutionary game model, and analyzes the dynamic evolution process of information platform adverse information and Party building information strategic behavior, and the dynamic development and change characteristics of information security risk of information platform. Through the analysis of evolutionary game model between the adverse information of information platform and the information of party building, it is found that there is only one evolutionary stable strategy in the whole information platform risk evolution system, that is, all groups will evolve to dishonest groups, and the behavior evolution of party building system will tend to adopt the strategy of keeping faith to reduce the credit risk of information platform.
Research on Consistency of IoT Data Communication Based on Eigenvector Vector
Authors: Tang Xiaoqiang , Gao Fei , Ji Yuxiang , Wang Shengfei 
Published: January 2021

Abstract
Aiming at the large deviation of the traditional Internet of things (IoT) communication consistency algorithm, this paper designs the IoT information communication consistency algorithm. According to the obtained eigenvectors, the communication consensus protocol is generated, and the truncation factor is introduced to solve the problem that the cooperative variable deviation between the new node or restart node and the eigenvector is too large in the process of consistency, so as to realize the information communication consistency of the Internet of things. The experimental results show that the deviation value of this method is lower than that of the traditional algorithm, and is close to the standard value. This design ensures the consistency of the information communication of the Internet of things, and has certain application significance.
Research on Voltage Quality Model of Distribution Network Based on Convolution Neural Optimization
Authors: Song Chao , Xu Zhichao , Xu Jia 
Published: January 2021

Abstract
In order to realize the optimization design of distribution network voltage quality monitoring model based on convolution neural optimization, this paper adopts the method of big data fusion scheduling and statistical information mining to monitor the voltage quality of distribution network. Firstly, the prior data of distribution network voltage quality is analyzed, and then the evaluation and decision of distribution network voltage quality are carried out according to the analysis results of distribution network voltage quality prior data analysis and fuzzy parameter fusion method is used to model the voltage quality monitoring of distribution network to realize the optimization of quality monitoring. The experimental results show that the confidence level of this method is high and the error is low.
Research of Multi-band Image Recognition Applied to Ships
Authors: Lu Jiahao , Hu Guosheng , Xia Jun , Song Zhenjun 
Published: January 2021

Abstract
Aiming at the shortcomings of the traditional local feature recognition method of multi-band ship image target, the recognition speed is slow, and a local feature recognition method of multi-band ship image target is proposed. First, by detecting key points and feature matching, the local features of the multi-band ship image target are extracted; then, the multi-classifier combination is used to train and identify the local features through the linear classifier and the non-linear classifier, so that the design of local feature recognition method of multi-band ship image target is completed. Through comparison experiments, compared with the traditional local feature recognition method of multi-band ship image target, the experimental results show that the proposed local feature recognition method of multi-band ship image target has faster recognition speed.
Applications of the Image Processing Technology in Casting Surface Roughness Detection Technology
Authors: Lu Jiahao , Hu Guosheng , Xia Jun , Song Zhenjun 
Published: January 2021

Abstract
The casting performance and life is directly affected by the degree of casting surface roughness. In this thesis, the image processing technology was used to establish an automatic detection system of casting surface roughness and to realize the median filtering, image edge enhancement, and binaryzation processing during the image pretreatment process. Moreover, the casting surface roughness parameters were analyzed. By combining with examples, effectiveness and practicability of the computer image processing technology in the casting surface roughness detection technology were verified. The findings in this thesis laid a foundation on developing the digital images’ 3D evaluation technology for the casting surface roughness.
Analysis of grabbing space debris by software manipulator
Authors: Shan Lijun , Li Yunfei 
Published: January 2021

Abstract
Aiming at the problem that space debris is difficult to deal with, a software manipulator grabbing device is designed. The process of space debris grabbing by a software manipulator is analyzed, and the process of space debris grabbing is simulated based on the finite element method. The simulation results verify the rationality of space debris grabbing by the manipulator. To solve the small grasping range of the original manipulator, the structure of the manipulator was optimized, and its working process was simulated and analyzed.