2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)最新文献_第5页

Fault Prediction of Intelligent Electricity Meter Based on Multi-classification Machine Learning Model 基于多分类机器学习模型的智能电表故障预测

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9497987

Yang Jincheng, Guo Zelin, Yuan Tiejiang, Qi Shangmin, Li Ning

{"title":"Fault Prediction of Intelligent Electricity Meter Based on Multi-classification Machine Learning Model","authors":"Yang Jincheng, Guo Zelin, Yuan Tiejiang, Qi Shangmin, Li Ning","doi":"10.1109/ICAICA52286.2021.9497987","DOIUrl":"https://doi.org/10.1109/ICAICA52286.2021.9497987","url":null,"abstract":"Aiming at the characteristics of intelligent electricity meter fault data, such as large scale, high dimension, complex structure, error and abnormal data, a fault prediction method of intelligent electricity meter based on multi-classification machine learning model was proposed. Firstly, the normal distribution completion and box diagram method were used to fill the missing values and replace the outliers in the original data set. By calculating the correlation coefficient between feature attributes and fault types, redundant and irrelevant features are eliminated to form feature subset. To solve the problem of unbalanced fault data, a mixed sampling strategy was constructed, which oversampled a few samples and undersampled a majority of samples. Secondly, the prediction accuracy of intelligent electricity meter fault data processed by support vector machine (SVM), BP neural network and random forest algorithm was calculated, and the confusion matrix representing the performance of each classifier was constructed. Considering the recognition ability of each classifier for different fault types, weight is assigned to each classifier, and then the multi-classifier fusion decision function is constructed. Finally, public data sets and actual electricity consumption data are used as samples to verify the effectiveness of the proposed method.","PeriodicalId":121979,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121775746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

An IB-PSO algorithm for unconstrained nonlinear programming problems 无约束非线性规划问题的IB-PSO算法

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9498010

Qing Shao, Jianbo Wang, Qing Yu, Tao Xu, Yoshino Tatsuo

{"title":"An IB-PSO algorithm for unconstrained nonlinear programming problems","authors":"Qing Shao, Jianbo Wang, Qing Yu, Tao Xu, Yoshino Tatsuo","doi":"10.1109/ICAICA52286.2021.9498010","DOIUrl":"https://doi.org/10.1109/ICAICA52286.2021.9498010","url":null,"abstract":"Particle swarm optimizer (PSO) is an efficient algorithm to find the best solution in engineering. However, the classical particle swarm optimization algorithm and the existing variational algorithm still have the problems of prematurity and slow convergence speed in the late iteration in different application problems. To make up the above demerits, we proposed a hybrid algorithm PSO to combine an improved PSO (IPSO) with an improved Broyden-Fletcher-Goldfarb-Shanno (BFGS) method. The hybrid algorithm introduced the convergence direction of the RL-BFGS to correct the evolution direction and enhance global search capability. And the initial point of the RL-BFGS is decided by the IPSO. For the sake of the effectiveness evaluation of IB-PSO, The performance of eight improved PSO algorithms was compared by optimizing six benchmark functions with different characteristics, such as single mode, multi-mode and rotation. The results show that compared with the other eight algorithms, the proposed IB-PSO has good performance in solving multimodal problems. Furthermore, the proposed approach can be used in engineering problems to obtain high-quality solutions.","PeriodicalId":121979,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115141145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on Weld Recognition Based on MESR Adaptive Threshold Algorithm 基于MESR自适应阈值算法的焊缝识别研究

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9497900

Yalong Wang, Youwang Hu, Xiao-yan Sun, Feng He

{"title":"Research on Weld Recognition Based on MESR Adaptive Threshold Algorithm","authors":"Yalong Wang, Youwang Hu, Xiao-yan Sun, Feng He","doi":"10.1109/ICAICA52286.2021.9497900","DOIUrl":"https://doi.org/10.1109/ICAICA52286.2021.9497900","url":null,"abstract":"To realize the automatic welding of welding robots using machine vision technology, it is necessary to accurately identify and track the weld trajectory. The key is to solve the position of the weld centerline through image processing. The quality of the weld image will be affected by noise, which greatly reduces the accuracy and efficiency of weld recognition. In order to eliminate the interference of the noise in the image on the extraction of the weld, this paper proposes the idea based on the Maximum Stable Extremum Region (MSER) algorithm, combined with the median filtering and morphological methods, and adaptively determines the optimal threshold of each image. The target area of the weld is segmented to extract the foreground from the background, and the center of the skeleton of the target area of the weld is fitted with a straight line as the position information of the weld. Through the processing experiments on a large number of weld seam image samples, the accuracy and effectiveness of the image segmentation algorithm and the straight line fitting algorithm are tested and evaluated. The results show that the algorithm in this paper has high weld seam recognition accuracy and anti-noise ability, and can provide image coordinate information of weld centerline for weld tracking and positioning.","PeriodicalId":121979,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132806865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Method for Segmentation of Surface Defects in Non-flat Area Based on Deep Learning 基于深度学习的非平坦区域表面缺陷分割方法

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9497895

Jinwei Mao, Wang Luo, Weidong Yang, Lei Zhang

{"title":"A Method for Segmentation of Surface Defects in Non-flat Area Based on Deep Learning","authors":"Jinwei Mao, Wang Luo, Weidong Yang, Lei Zhang","doi":"10.1109/ICAICA52286.2021.9497895","DOIUrl":"https://doi.org/10.1109/ICAICA52286.2021.9497895","url":null,"abstract":"Surface defect detection based on computer vision has become a valuable and promising research field, which has a very high direct impact on the application field of visual inspection, especially industrial production. With the development of computer vision technology, deep-learning has become the most suitable method to solve the problem. By using the sample image as a reference, deep learning enables the inspection system to learn to detect surface defects. However, in the actual industrial environment, fewer defective samples cause difficulties in data collection. This paper proposes a segmentation method, which is specially designed for the segmentation of surface defects in non-flat areas, and demonstrates the detection effect of this method. The method only uses a small number of defective samples for training, which is a very significant advantage for industrial applications. The paper compares the proposed method with related deep-learning detection methods, and the result shows that this method is superior to other methods in detecting surface defects in specific areas on the surface of non-flat areas. We created a new dataset for experimentation based on real cases. The results of experiments show off that the proposed approach apply small number of defected surfaces to fit network parameters, using only approximately 40-50 training samples for each type of defects, and the detection effect is not inferior to the related methods. Because of this few-shot characteristics, the proposed approach has high industrial application value.","PeriodicalId":121979,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132909692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-UAV Reconnaissance Task Assignment Based on User Satisfaction 基于用户满意度的多无人机侦察任务分配

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9497913

Hua Yang, Jungang Yang, Bingpeng Zhang, Chengyuan Wang

{"title":"Multi-UAV Reconnaissance Task Assignment Based on User Satisfaction","authors":"Hua Yang, Jungang Yang, Bingpeng Zhang, Chengyuan Wang","doi":"10.1109/ICAICA52286.2021.9497913","DOIUrl":"https://doi.org/10.1109/ICAICA52286.2021.9497913","url":null,"abstract":"In traditional field reconnaissance scenarios, where multiple unmanned aerial vehicles (UAVs) need to conduct the reconnaissance of particular targets in sequence and send the information back to the BS for information distribution, the satisfaction of users towards the information acquisition is raraly concerned. In the paper, the user’s satisfaction towards information acquisition is considered in the process of task assignment, and a user priority-driven satisfaction model is proposed. Then, a user satisfaction maximization problem is formulated, which aims to maximize the priority-weighted satisfaction of users by realizing effective task assignment. And a multi-population co-operation based genetic algorithm (MPCGA) by introducing the idea of \"exploration-exploitation\" into traditional GAs, which is able to solve the task assignment problem in polynomial time, is proposed. Simulation results show that, compared with the algorithm without considering users’ satisfaction, the satisfaction of users can be improved by about 20% based on our algorithm, with the task completion time increased slightly.","PeriodicalId":121979,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134165309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Channel simulation analysis of seawater laser communication 海水激光通信信道仿真分析

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9498183

Wang Fei, Guan Yunjing

引用次数: 1

Identification of patterns in the involvement of novice software developers in software testing processes 识别新软件开发人员参与软件测试过程的模式

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9498137

Gustavo Caiza, Fernando Ibarra Torres, Marcelo V. García

{"title":"Identification of patterns in the involvement of novice software developers in software testing processes","authors":"Gustavo Caiza, Fernando Ibarra Torres, Marcelo V. García","doi":"10.1109/ICAICA52286.2021.9498137","DOIUrl":"https://doi.org/10.1109/ICAICA52286.2021.9498137","url":null,"abstract":"Background: Testing is often used to make decisions about whether or not to implement software projects. How the software industry effectively engages novice software developers in both general development and testing is less clear; however, it is especially important to determine the views of more experienced developers on this issue. Objectives: We seek to explore how software companies involve novice software developers in software development and testing; to understand how experienced developers involve novice programmers in testing; and to discover systematic patterns in experienced developers' opinions about the involvement of novice software developers. Methods: We conducted a survey of three private software companies and one public institution, taking into account experienced developers. We asked respondents to indicate how they involve novice software developers in their work activities, as well as their perspectives on software testing. Results: We identified 4 patterns that describe the involvement of novice programmers. We identified that, due to the lack of clear processes for involving novice developers in software testing, a key pattern that was identified is how the novice developer is limited when testing a product.","PeriodicalId":121979,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130309014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A New ID-Based Threshold Proxy Signature Scheme 一种新的基于id的阈值代理签名方案

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9498100

P. Wei, Bai Xia

引用次数: 0

Sentiment classification method of hierarchical retrieval based on comment data 基于评论数据层次检索的情感分类方法

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9497974

Cheng-yue Liu, Jian Zhang

引用次数: 0

Design of Intelligent Socket Based on Cloud Platform of IoT 基于物联网云平台的智能插座设计

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9497943

Juhua Guan, Kewang Zhang, Xuebin Zhong

引用次数: 0