bluuebunny/crossref_metadata_2025 huggingface dataset:
Developing Learning Model Base On PMR Approach at Senior High School to Improve Student’s Motivation
Authors: Azizah N , Fauzan A , Lufri  , Arnawa I M
Published: January 2021

There are several factors that cause the low achievement of students' mathematics learning outcomes in several senior high schools in the Dharmasraya district, the main factor is many students have difficulties in solve mathematic problems in their daily life, that caused the low of students achievement of students, including daily assessment and national assessment. The purpose of this research is for developing learning model base on realistic mathematic education (RME) approach. Developing learning model on topic Implementation of sequences and series based on RME is an effort for helping students in solving mathematics problems that they find in their daily life. The subject of this research is students grade XI of SMAN 1 Koto Salak. The method of this research used is design research using the Plomp development model. The techniques of data collection implemented using observation, questionnaires and assessment of mathematical test. The techniques of data analysis used are descriptive quanlitative and quantitative. The results of this research showed that the learning design with topic Implementation of sequences and series based on PMR approach has; The average value of HLT validity is 4.11 with a very valid category. The practical test of learning books applying PMR-based sequences and series by teachers and students is 85.02 and is classified as very practical. The average value of students' motivation in learning after being tested 83.3 and it means PMR approach also can improve student’s motivation in mathematics learning.
The effect of mind mapping on the concept comprehension abilities for grade x automotive student of vocational public schools in Sijunjung regency
Authors: Sefriyanti D , Syarifuddin H , Arnawa I M , Asmar Ali
Published: January 2021

Concept comprehension abilities is a very important aspect in learning mathematics. The low of concept comprehension abilities will affect the quality of student learning, which in turn has an impact on the low achievement of students in school. This also happened at the Vocational public School in Sijunjung regency. One effort that can be done in reacting to it is through the selection of learning models that can create a learning environment that makes students active. Mind Mapping technique is an alternative to increase student activity in learning. This type of research was quasi-experimental. The instrument used in this study was a test consisting of an initial ability test to see students 'initial abilities consisting of high and low initial abilities, a final test to see students' mathematical concept comprehension abilities. Data was analyzed by using the Mann Whitney U test and two-way ANOVA. Based on the results of data analysis, several conclusions were obtained, namely: (1) The Concept comprehension abilities of students who learn with Mind Mapping Techniques was higher than students who learn with conventional learning; (2) Concept comprehension abilities of high, moderate, and low prior knowledge who learn with Mind Mapping techniques was higher than students who study with conventional learning; (3) there is no interaction between model of learning and prior knowledge.
The Effects of Problem Based Learning (PBL) Model on the Improvement of Student’s Mathematical Problem-Solving Skill in MAN in Pekanbaru City
Authors: Jannah N , Yerizon  , Arnawa I M
Published: January 2021

Based on the preliminary study indicate that the problem-solving abilities of students of MAN Pekanbaru City are still low. The purpose of this research is to investigate the effect of PBL model on the improvement of student’s problem-solving skills in the 11thclass MAN Pekanbaru city. The data in this research was collected by using problem-solving test, and analyzed by using paired t-test. The results showed that the problem-solving skills of students after implementing PBL model is better than before. This result means PBL model takes effect on the improvement of student’s problem-solving skills.
Discussion on the Application of Layering Technology in the Development of Computer Software
Authors: Wang Aihua
Published: January 2021

Computer software is an important way for computers to achieve various functions. With the popularization and application of computer technology, people also put forward new requirements for computer software development technology. Traditional single-level software development technology can no longer meet the actual needs of computer technology applications at this stage, so we must actively apply layered development technology. Computer software developers should strengthen their understanding and research on layering technology, and make full use of layering technology to improve the function, quality and speed of computer software. This will help newly developed computer software to better adapt to the requirements of the network information society in the new era, thereby promoting the modernization of China’s social economy.
RETRACTED: Access Point Authentication Scheme of SCADA System Based on Cloud Computing Technology
Authors: Shen Jiayuan , Xu Jun , Cai Kun , Ji Yan
Published: January 2021

Aiming at the security problem of the SCADA system introduced into cloud computing that lacks a credible identity authentication mechanism, this paper proposes a cloud computing-based SCADA system access point authentication scheme. In this scheme, a cloud service security access protocol for SCADA system is proposed. The protocol uses password based identity authentication technology to screen the identity of the terminal node, and effectively identifies the illegal access point by authenticating the access point. By this method, the user’s private information is prevented from being intercepted and tampered maliciously.
A Detection System for Rail Defects Based on Machine Vision
Authors: Zhou Qianxi
Published: January 2021

In this work, a machine vision based detection system is designed and applied to identify the region of rail track need to be polished. The CCD industrial camera is employed to obtain the RGB color space images on the railway line in real time. To reduce the effect of environmental brightness, the obtained RGB space images are transformed into HSI color space. Then, the noise was removed from the grayscale image by the Gaussian blur to remove the interference elimination of the acquisition signal. According to the calculated saturation and hue values, the threshold of saturation and hue values is set for the following image segmentation, respectively. At last, the accurate position information of the rail track region needs to be polished is obtained for the subsequent grinding processing. By employing the machine vision system, high-efficient automatic rail defects grinding could be realized.
Research on LRU Hierarchy based on Optimal Granularity Level
Authors: Fei Yang , Dong Shuo , Shi Li-Zhong
Published: January 2021

In order to improve the convenience of replacing faulty equipment on the route and realize LRU(Line Replaceable Unit) level planning reasonably and effectively, a LRU hierarchical division method based on optimal granularity level is proposed. Based on the analysis of the relationship between the components in the product structure, the design structure matrix theory is introduced to construct the comprehensive relationship matrix, which quantifies the relationship between the components from two levels of function and structure. By defining modularization index, combining with the hierarchical traversal of product structure tree, the optimal granularity level is calculated. This method can provide a division scheme based on hierarchical factors for LRU planning and provide support for getting more optimized LRU planning and design.
Research on the Selection of Maintenance Equipment Purchasing Mode in Equipment Distribution Process
Authors: Yue Shuai , Shi Xianming , Wang Yabin , Rong Liqing , Tian Wenjie
Published: January 2021

Maintenance equipment is an important material basis of equipment distribution, and the procurement of maintenance equipment is a key link in the implementation of equipment distribution, which plays a very important role in ensuring the integrity rate of equipment. Compared with the traditional purchase of weapons and equipment, the purchase of equipment maintenance equipment has its own unique situation. In the process of choosing purchasing methods, there is a lack of specific theoretical guidance. Aiming at the practical problems of maintenance equipment procurement in current equipment distribution, this paper focuses on the characteristics of equipment maintenance equipment procurement, combs the applicability, advantages and disadvantages of various procurement methods, and puts forward some suggestions for reference.
Optimal Parameter Study of the External Inverting Die
Authors: Wen Hailong , Zheng Yuqing , Fei Yiting
Published: January 2021

In the external inverting process of thin-walled circular tube, buckling instability often occurs in the straight line section of thin-walled tube due to the size mismatching of forming die. Based on LS-DYNA971, the finite element modeling was built up and a series of simulation calculations were conducted about the external inverting process of thin-walled circular tubes on two circular arc dies under axial compression. The inverting plastic deformation of a Q235 steel thin-walled tube with an outer diameter of 60mm and a wall thickness of 2mm was reproduced on two die types with different fillet radii. The simulation results show that the optimal induced fillet radii of the dies is 10.7±0.1m, and meanwhile the thin-walled tube head has good formability in the early stage, which is convenient for the subsequent shape operation of the invertube. This paper provides a method reference for the die structure design used for inverting the thin-walled tube.
Dynamic simulation of robot milling process based on ADAMS
Authors: Linxin Wang , Hao Luo , Shujun Li
Published: January 2021

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

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

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

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

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

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

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

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

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

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

The 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

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

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

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

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

In 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

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

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

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

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

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

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

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

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.
Human speech emotion recognition via feature selection and analyzing
Authors: Lun Xiangmin , Wang Fang , Yu Zhenglin
Published: January 2021

Speech emotion recognition is one of the important research topics in the field of multimedia processing and human-machine interface. To obtain the most influential features of the speech data for emotion recognition, in this paper, 64 statistical features of the speech signal including short-term energy, pitch, frame, format, and spectrum energy were extracted with speech emotion database. Mean Impact Value (MIV) and the improved Correlation-based Feature Selection (CFS) were employed to select the most influential feature set. BP neural network (BPNN) was used to identify the accuracy. The proposed MIV-CFS method selected the features related to speech emotion, with less recognition error, the recognition accuracy all higher than 88%, and the highest recognition accuracy is 91.61%.
Automatic Insulator Detection for Power Line Using Aerial Images Powered by Convolutional Neural Networks
Authors: Tan Jicheng
Published: January 2021

The inspection and fault diagnosis of power transmission line via aerial images is becoming widely used in smart grids and electrical power systems. However, the accuracy of detecting objects with complicated backgrounds still remains an important challenge in the defect detection system. In this article, a deep-learning based method is presented to realize the automation of insulator fault detection. In the proposed approach, the insulator localization network based on Single Shot Multibox Detector (SSD), a one-stage detecting backbone, is utilized to extract multi-level features and make predictions in order to determine the insulator situation in the aerial images. Additionally, Densely Connected Convolutional Networks (DenseNet) is newly introduced to strengthen the insulator detecting system’s classification ability. To address the data scarcity problem, a two-stage data augmenting strategy is also applied. The first stage is mainly based on the image combination, while the second stage includes random affine transformation, Gaussian blur, brightness and contrast transformation, and salt-and-pepper noise. The insulator detection network and the classification network precisions of the proposed method have reached 0.95 and 0.98 using the text dataset, respectively. The results have shown that the proposed method can ensure robustness and accuracy during insulator detection.
Application of MIV-NARX to Identify Road Roughness
Authors: He Yijie , Zhang Guofang , Song Jingfen
Published: January 2021

Aiming at the deficiencies in the existing research on road roughness recognition based on neural networks, the road roughness and 16 vehicle response data are simulated based on the filtered white noise model and the smoothness seven-degree-of-freedom model, NARX neural network is built to identify road roughness. The coefficient of determination and the root mean square error are introduced as the evaluation indicators of the model, the MIV method is used to evaluate and screen each input response. Research shows that MIV method improves the performance of NARX neural networks, MIV-NARX can effectively identify road roughness.
Digest-based Security Protocol for IoT devices
Authors: Wang Huinan , Zhang Jianmin
Published: January 2021

Short output universal hash functions are widely used in manual authentication protocols. A digest function is one new universal hashing algorithm. The aim of this algorithm is to reduce the CPU processing time, and have the best proven bounds. However, typical manual authentication protocols using digest are based on asymmetric key algorithms, which is not suitable in many Internet of things scenarios. This paper thus proposes a series of digest protocols only use symmetric key algorithms.
The Scattering Characteristics of Vortex Radio-Frequency Waves for Wireless Communication
Authors: Hui Yan , Wang Wennai
Published: January 2021

As a solution to the shortage of transmission capacity, the vortex radio frequency waves have been applied to wireless transmission technology. The potential of vortex radio frequency waves applying to the fields of wireless communication, radar detection and radar imaging will be invaluable. The scattering characteristics of the vortex waves are the basis for its application in radar detection imaging and identification. This paper focuses on the theoretical derivation, including the main effects on the scattering characteristics, and the future applications and prospects of scattering characteristics. The state of art in research area is reviewed, followed by a brief discussion on future directions.
Home-style smart medicine box for the elderly
Authors: Miao Fengyi , Zhang Yu , Wang Xincheng , Xiong Xinhong
Published: January 2021

With the intensification of the aging of our country’s population, the health care of the elderly has become a social problem that must be solved in my country. Elderly people often suffer from chronic diseases and need long-term medication. However, it is difficult for the elderly to take medicines on time and in doses due to deterioration of their physical functions. Based on the above background, the project team researched and designed a smart medicine box that can intelligently remind and automatically dispense medicine. The device is mainly divided into four functional modules: medicine dispensing module, medicine taking module, medicine dispensing module, and control module. It realizes the five basic functions of medicine addition, temporary storage and sealing, medicine absorption, automatic medicine dispensing, and timing reminder and Additional features such as data sharing.
Analysis on the Maintainability of Smart Meter Software for Full Lifetime
Authors: Qiao Shuya , Qian Xiaoyao , Zhou Ming , Wang Yang
Published: January 2021

With the deepening of Internet concept and electricity marketization reform, more requirements are put forward for metering equipment, and the scale and complexity of software also increase. The new generation of intelligent electricity meters meet the IR46 standard and adopt the multi-core modular design scheme, which also puts forward higher requirements for the maintainability of software. This paper starts from the key control points of the whole life cycle of intelligent electricity meters and combines the characteristics of software testing, so as to analyze and evaluate the maintainability of the software. Firstly, the factors that influence the maintainability of watt-hour meter products are analyzed, and the software key data is considered from the perspective of software development and design. Then, the index system of the maintainability of the intelligent software is established, and the key points of the maintainability test and the weight of each index are analyzed in combination with the testing technology of the intelligent meter software and the analytic hierarchy process.
Feature extraction of target echo based on wavelet energy spectrum
Authors: Zhou Bo
Published: January 2021

The characteristics of target echo are related to many complicated factors, such as transmission waveform, the structure, attitude and motion state of the target and multi-channels transmission of ocean channel. The characteristics of target echo in different time-frequency spaces are discussed. The echo is recognized by the combination of different characteristic spaces such as energy, frequency band, entropy, kurtosis and variance of the wavelet energy spectrum. Firstly, the wavelet energy spectrum in time domain by using the continuous wavelet transformation, then the energy spectrum entropy describing quantitatively signals varying with time was calculated along the time axis, and their time-wavelet energy spectrum were different. The data processing shows that segmented spectrum center, spectral bandwidth can classify different types of underwater echoes to some extent.
Wi-Fi Devices Individual Identification based on Envelope Front Characteristic
Authors: Lv Dongze , Chen Junlin , Che Yanting
Published: January 2021

There are some characteristics are used for traditional Wi-Fi devices individual identification methods, such as: amplitude and phase characteristics during the period that Wi-Fi devices are turned-on or turned-off, MAC address, and IP address. In fact, on one hand, MAC address and IP address are easily to be changed. Traditional methods can’t identify individual Wi-Fi devices if MAC address or IP address changed. On the other hand, the amplitude and phase characteristics of Wi-Fi devices can only be identified during the short period of turned-on or turned-off. The traditional methods have application limitations. Therefore, the Wi-Fi devices individual identification based on envelope front characteristic is proposed in this paper. Firstly, envelopes of Wi-Fi signals are extracted. Secondly, front characteristics of Wi-Fi envelope are extracted. Then envelope front characteristics are used to compare and match with models saved in the library to realize Wi-Fi devices individual identification. By using the method proposed in this paper, Wi-Fi devices individual identification can be realized whether MAC address or IP address changed. Scope of identification application can be effectively expanded into the whole time when Wi-Fi devices transmit signals by using this method. Since envelope front characteristics have the advantages of non-changing and non-copying, accuracy of Wi-Fi individual identification is also improved by using this method.
Internet of Things Access Control System Based on Hyperledger
Authors: Yang Zheming , Shao Di , Qu Liangyong , Zhang Mei
Published: January 2021

The current access control of IoT devices adopts the traditional method of centralized control, which leads to the shortcomings of single point of failure and low efficiency of the entire system. Due to the increasing amount of data generated by the Internet of Things network and the more complex data structure, the traditional access control system has exposed the problem of insufficient security when communicating. Based on the characteristics of blockchain decentralization, non-tampering, and programmable Hyperledger, an attribute-based access control mechanism combining blockchain with the Internet of Things is proposed to realize dynamic changes to the access control strategy and ensure the access control of the Internet of Things Safety and reliability.
Failure Section Identification in Smart Distribution Network Based on IFIAPN Considering the Auto-reclosing and DG
Authors: Cheng Xuezhen , Xu Guangyuan , Li Jiming
Published: January 2021

This paper proposes a fault location method for smart distribution networks based on the intuitionistic fuzzy inhibitor arc Petri net (IFIAPN) technique that employs discrete evidence such as the status of protective devices and fault indicators (FIs) to estimate the faulted section. To eliminate the possible false fault section estimations and pinpoint the actual faulted location, an IFIAPN algorithm is improved. It amends the certainty and uncertainty of the information according to the action logic of the protection system and the direction of fault propagation. The authors further utilize the directional fault indicator to cope with the impact on the bidirectional power flows caused by distributed generation, and introduce the inhibitor arc to represent the action characteristics of auto-reclosing to optimize the diagnostic model. The proposed method can precisely determine the fault location in distribution systems with many laterals and sub-laterals in the presence of distributed generators (DGs). Finally, feasibility, fault tolerance, and effectiveness of the proposed method are verified by the IEEE 33 node feeder distribution system.
Design of Intelligent Counting Equipment System for Uniform Materials
Authors: Xiao Suhua , Wang Zhiyong , Qiao Mingjuan , Mo Ling , Wang Wei
Published: January 2021

In fields such as manufacturing and agriculture, manual methods are generally used to count materials such as hardware parts and seeds, which had problems such as slow speed, low efficiency, and low accuracy, and a lot of pressure was put on people. Based on machine vision and motion control, a uniform material counting equipment system was designed. The equipment uses a two-stage conveyor belt for material transportation, and vibration frequency control and speed control of materials of different specifications were achieved. Because of application of precision motion control technology, the camera collects the image of the material and identifies the material to be inspected, and automatically counts the quantity of the material by calculating the area of the material. The test was implemented with screws, hole gaskets, soy, jujubes and other materials, and the results show that: materials with a size of 2mm-20mm can be detected; the detection accuracy rate is 100%.the system has realized high-speed, high-efficiency, high-accuracy automatic counting, and has good application value.
Detection of Hidden Disease of Concrete Bridge Based on Infrared Thermal Imaging
Authors: Jin Hui , Zou Lan-Lin
Published: January 2021

Any object with temperature above absolute zero can radiate infrared energy outward [1]. According to this characteristic, the infrared thermal imaging method is applied to diseases of concrete bridges such as internal voids. The relationship between the hidden disease of concrete bridge and infrared thermal image is analyzed theoretically. The concrete model with internal cavity defects is established and the finite element simulation analysis is carried out to verify the feasibility of infrared thermal imaging method to detect the internal defects of bridge concrete. The final finite element simulation results are in agreement with the theoretical analysis, indicating that the method of detecting the internal defects of bridge concrete by infrared thermal imaging is feasible.