2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)最新文献_第3页

An Improved Composite Differential Evolutionary Algorithm with Self-adaptive Mutation Strategy for Identifying Photovoltaic Model Parameters 基于自适应突变策略的光伏模型参数识别改进复合差分进化算法

2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731329

Jing J. Liang, Hao Guo, Kunjie Yu, B. Qu, C. Yue, Kangjia Qiao

{"title":"An Improved Composite Differential Evolutionary Algorithm with Self-adaptive Mutation Strategy for Identifying Photovoltaic Model Parameters","authors":"Jing J. Liang, Hao Guo, Kunjie Yu, B. Qu, C. Yue, Kangjia Qiao","doi":"10.1109/acait53529.2021.9731329","DOIUrl":"https://doi.org/10.1109/acait53529.2021.9731329","url":null,"abstract":"With the rapid growth of solar energy demand, the optimization of the photovoltaic model becomes significant. The conversion efficiency of the photovoltaic model is mainly determined by its structural parameters, and the multi-modal property of parameter search space brings challenges to the existing evolutionary algorithms. Therefore, this paper proposes an improved composite differential evolutionary algorithm with a self-adaptive mutation strategy (CoDESA). In CoDESA, three complementary strategies are selected into the strategy pool, and each parent will produce three offspring according to their selection probabilities. Moreover, the selection probability of each strategy is dynamically adjusted using a self-adaptive mechanism, so that the algorithm can utilize the more suitable strategies at specific evolutionary stages. The proposed CoDESA is examined on the parameter identification of three photovoltaic models. It is compared with seven commonly used evolutionary algorithms, and more accurate parameters are identified.","PeriodicalId":173633,"journal":{"name":"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125405374","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: 1

Robots for pipeline inspection tasks—A survey of design philosophy and implementation technologies 用于管道检测任务的机器人——设计理念和实现技术综述

2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731152

Mingyuan Wang, Jianjun Yuan, Sheng Bao, Liang Du, Shugen Ma

引用次数: 1

Research on Music Emotion Classification Based on CNN-LSTM Network 基于CNN-LSTM网络的音乐情感分类研究

2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731277

Yin Yu

引用次数: 0

Research on Face Recognition Technology Fusion Deep Learning Under Different Light Intensity Changes 不同光强变化下人脸识别技术融合深度学习研究

2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731292

Yanqing Yang, Xing Song

引用次数: 1

Intelligent Detection of Underwater Fish Speed Characteristics Based on Deep Learning 基于深度学习的水下鱼类速度特征智能检测

2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731159

Xianghui Li, Xin Xia, Zhuhua Hu, Bingtao Han, Yaochi Zhao

{"title":"Intelligent Detection of Underwater Fish Speed Characteristics Based on Deep Learning","authors":"Xianghui Li, Xin Xia, Zhuhua Hu, Bingtao Han, Yaochi Zhao","doi":"10.1109/acait53529.2021.9731159","DOIUrl":"https://doi.org/10.1109/acait53529.2021.9731159","url":null,"abstract":"At present, the breeding area of Hainan province is 58,000 hectares, and the breeding industry is an important economic source of Hainan province. As an important breeding object in Hainan province, the daily activities and abnormal behaviors of the fish have a direct impact on the breeding yield and the breeding income. For mariculture fish, changes in behaviour are often reflected in the important behavioral feature of swimming speed. Fishes swim at different speeds when they are in different situation. Of course, the change of fish speed is not only related to their own behavior and health state, but also related to the water quality. When the water quality changes or the fish are subjected to some abnormal stimulation, fish swimming speed will change. Therefore, accurate and rapid acquisition of fish swimming speed can not only reflect the change of fish behavior intuitively, but also reflect the water quality to a certain extent, which is of great significance to the large breeding province. Based on this, in this paper, tracking algorithm combined YOLOv5 deep learning network and Kalman filter is used to conduct intelligent detection of the speed characteristics of underwater fish, and track and calculate the speed of a single fish, a number of fish and fish swarm respectively. The experimental results show that the tracking algorithm proposed in this paper can track the underwater fish and calculate the corresponding speed well.","PeriodicalId":173633,"journal":{"name":"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"340 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122754549","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: 2

An multi-task head pose estimation algorithm 一种多任务头部姿态估计算法

2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731346

Heng Song, Tianbao Geng, Maoli Xie

引用次数: 0

Construction and application of biological visual nerve computing model in robot 生物视觉神经计算模型在机器人中的构建与应用

2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731313

Naigong Yu, Hejie Yu, Tong Qiu, Jia Lin

{"title":"Construction and application of biological visual nerve computing model in robot","authors":"Naigong Yu, Hejie Yu, Tong Qiu, Jia Lin","doi":"10.1109/acait53529.2021.9731313","DOIUrl":"https://doi.org/10.1109/acait53529.2021.9731313","url":null,"abstract":"Biological vision is very effective and accurate in scene classification and recognition. Based on this, this paper proposes a biological visual neural computing model based on the anatomical structure of rat brain, which is characterized by: constructing a biological visual scene memory model (visual word bag), imitating the biological brain’s storage of environmental scene information, and calculating the similarity between the current scene information and the visual template; designing and constructing the object details located in the lateral entorhinal cortex and the peripheral olfactory cortex Cell discharge model. Experimental results show that the proposed model can effectively extract image features and generate visual word bag model based on image features. Compared with ratslam scan line strength model, the retrieval time of this model is greatly shortened; The object cell discharge model with image similarity information as input can show similar expression of discharge rate as physiological research, which verifies the effectiveness and efficiency of the proposed model. The research results lay a foundation for the research of robot environment cognition method based on brain cognitive mechanism.","PeriodicalId":173633,"journal":{"name":"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132115428","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 Leakage Monitoring of Metal Oxide Surge Arrester Based on Hybrid Particle Swarm Algorithm 基于混合粒子群算法的金属氧化物避雷器泄漏监测研究

2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731291

Kai Zhang, Yongyan Xu

{"title":"Research on Leakage Monitoring of Metal Oxide Surge Arrester Based on Hybrid Particle Swarm Algorithm","authors":"Kai Zhang, Yongyan Xu","doi":"10.1109/acait53529.2021.9731291","DOIUrl":"https://doi.org/10.1109/acait53529.2021.9731291","url":null,"abstract":"The leakage monitoring of the metal oxide arrester(MOA) is completed in the process of extracting the resistive component of the leakage current of the arrester, that is, the leakage monitoring of the arrester is realized by analyzing the operation of the resistive component of current. It is shown in relevant studies and literature that in the process of using metal oxide arrester, resistive component of current changes due to the aging of metal oxides. Therefore, the problem appears to be the leakage of the arrester. In view of this, aiming at the problem that previous intelligent algorithms are difficult to monitor the leakage of MOA accurately, this paper proposes an online monitoring algorithm based on hybrid particle swarm optimization by combining the classical particle swarm optimization algorithm and the nonlinear classification method. That is, after constructing the objective function of MOA, the characteristic parameters C and α which can effectively reflect the operation of MOA are solved. And then the current resistive component of the leakage current function equation is extracted to monitor the leakage of the metal oxide arrester. The research results show that in this study, the characteristic parameters C and α solved by the hybrid particle swarm algorithm are 502.19 and 24.9786 respectively, and the corresponding mean errors are 0.438% and 0.086% respectively. At the same time, the resistive current curve obtained by the hybrid particle swarm optimization algorithm is closer to the actual situation than that obtained by the particle swarm optimization algorithm. Thus, it can improve the accuracy of MOA Leakage Monitoring effectively.","PeriodicalId":173633,"journal":{"name":"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133210013","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

POC: Periodical Orthogonal Center Loss For Open Set Classification 开集分类的周期正交中心损失

2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731132

Yusheng Pu, Ruonan Liu, Qian Chen, Dongyue Chen, Wenlong Yu, Di Cao

引用次数: 0

Frame-Level Multiple Sound Sources Localization Based on Visual Understanding 基于视觉理解的帧级多声源定位

2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731148

Li Fang, Long Ye, Xinglong Ma, Ruiqi Wang, Wei Zhong, Qin Zhang

引用次数: 0