2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA)最新文献_第4页

Non-dense Feature Aggregation for Video Object Detection 用于视频目标检测的非密集特征聚合

2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) Pub Date : 2021-05-01 DOI: 10.1109/AIEA53260.2021.00073

Zhubin Liang, Weibin Liu, Weiwei Xing

引用次数: 0

Research on the Training Model of Marketing Professionals under the Background of Internet + Big Data 互联网+大数据背景下营销人才培养模式研究

2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) Pub Date : 2021-05-01 DOI: 10.1109/AIEA53260.2021.00066

Yanyang Ma

引用次数: 0

Research on Textile Defect Detection Based on Improved Cascade R-CNN 基于改进级联R-CNN的纺织品缺陷检测研究

2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) Pub Date : 2021-05-01 DOI: 10.1109/aiea53260.2021.00017

Yu Li, Ziwei Wang

{"title":"Research on Textile Defect Detection Based on Improved Cascade R-CNN","authors":"Yu Li, Ziwei Wang","doi":"10.1109/aiea53260.2021.00017","DOIUrl":"https://doi.org/10.1109/aiea53260.2021.00017","url":null,"abstract":"Textile fabric defects are an important factor affecting the quality of textile fabrics. Traditional manual-based textile defect detection methods are slow and inefficient, and cannot meet the production needs of the modern textile industry. Aiming at the process and technical difficulties of textile defect detection, this paper analyzes different defect detection algorithms based on deep learning through theoretical research and experimental comparison, and finally designs and implements a defect detection algorithm based on improved Cascade R-CNN. Through the introduction of FPN on the basic structure of Cascade R-CNN, the defect detection experiment based on improved Cascade R-CNN is designed and realized. This paper discusses the defect detection model design and network structure based on the improved Cascade R-CNN, and uses the multi-scale detection characteristics of FPN to detect textile defects. The experimental results show that, compared with the defect detection based on Cascade R-CNN, the accuracy of the improved Cascade R-CNN model defect detection is increased by 4.09% to 95.43%, and the improved model has better detection of multi-scale defects effect.","PeriodicalId":223292,"journal":{"name":"2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116402673","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

Study on Flood Optimal Operation of Reservoir based on SA-FOA 基于SA-FOA的水库洪水优化调度研究

2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) Pub Date : 2021-05-01 DOI: 10.1109/AIEA53260.2021.00061

Youchun Liu, Xiaopeng Bai, Yafei Dong, Han Wang, Y. Diao

引用次数: 0

Parameter identification of hybrid-driven underwater glider based on differential evolution algorithm 基于差分进化算法的混合动力水下滑翔机参数辨识

2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) Pub Date : 2021-05-01 DOI: 10.1109/AIEA53260.2021.00078

Chao Zhang, Hua Zhang, Yong Zhang, Jian Wang, Donglin Xiao, Lingling Xu, Wei Chen, Yuan Cao

{"title":"Parameter identification of hybrid-driven underwater glider based on differential evolution algorithm","authors":"Chao Zhang, Hua Zhang, Yong Zhang, Jian Wang, Donglin Xiao, Lingling Xu, Wei Chen, Yuan Cao","doi":"10.1109/AIEA53260.2021.00078","DOIUrl":"https://doi.org/10.1109/AIEA53260.2021.00078","url":null,"abstract":"With a view to the difficulty of the hybrid-driven underwater glider (HUG) hydrodynamic parameter identification, a method based on Differential Evolution (DE) algorithm which has designed adaptive factors is proposed. On the basis of dynamic modeling and analysis, this method is used to identify parameters from simulation observed data of the horizontal motions of HUG. After comparing with the actual value, the identification error is within the allowable range. The identification results are also compared with the results of Particle Swarm Optimization (PSO) algorithm and Least Square (LS) algorithm. The simulation results show that the parameter identification results using DE algorithm are significantly better than other comparison methods. The effectiveness of the algorithm is illustrated, and the method is not affected by the selection of initial parameter values, and has good robustness and global optimization characteristics. Finally, the model is verified by motion simulation, and the results show that the algorithm can effectively identify hydrodynamic parameters, which can provide a reference for engineering practice.","PeriodicalId":223292,"journal":{"name":"2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130572462","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

Revolution: A Spatial-specific Convolution for Image Super-Resolution 革命:用于图像超分辨率的空间特定卷积

2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) Pub Date : 2021-05-01 DOI: 10.1109/AIEA53260.2021.00068

Qi Zhang, Jifeng Sun, Yinggang Li, Junwei Hu, Shuai Zhao, Yibin Lin

{"title":"Revolution: A Spatial-specific Convolution for Image Super-Resolution","authors":"Qi Zhang, Jifeng Sun, Yinggang Li, Junwei Hu, Shuai Zhao, Yibin Lin","doi":"10.1109/AIEA53260.2021.00068","DOIUrl":"https://doi.org/10.1109/AIEA53260.2021.00068","url":null,"abstract":"Recently, deep convolution neural networks achieve remarked performance in single image super-resolution(SISR) due to their strong feature representation ability. However, most existing SR methods mainly use standard convolution in each layer, neglecting to explore the various feature of spatial domain. Standard convolution focuses on spatial invariance by using the same convolution kernel in space. It deprives kernels of the ability to adapt the different spatial positions, hence hindering the diverse of feature. Later, a new convolution operator, Involution, was proposed to implement spatial-specific convolution. But it calculated the convolution kernel parameters of surrounding pixels only by utilizing the characteristics of the central pixel, which was unreasonable and limited its performance. To address these issues, in this paper, we propose a novel convolution named Revolution, which not only incorporates spatial-specific to capture different spatial information, but also considers the relevance of the pixels to determine convolution kernel parameters to further learn more realistic feature representations. On the other hand, the Revolution we proposed makes the number of parameters smaller. Experiments demonstrate that our method obtains obvious improvement in terms of indicators and visual effects.","PeriodicalId":223292,"journal":{"name":"2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129118182","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 Design and Implementation of Smart Energy System in Digital Economy Era 数字经济时代智能能源系统的设计与实现研究

2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) Pub Date : 2021-05-01 DOI: 10.1109/AIEA53260.2021.00090

Wang Lele, Xu Wentao, Wang Guobin, Liu Hui, An Jing, Wang Xiaochuang, Ma Jun

引用次数: 1

Self-Adaptive Semantics Masking for Text Classification 用于文本分类的自适应语义屏蔽

2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) Pub Date : 2021-05-01 DOI: 10.1109/AIEA53260.2021.00088

Jinfan Zhang, Jiawei Wu, Hao Zhang

引用次数: 0

Experimental Teaching Platform of Simple Harmonic Motion Based on Human-machine Engineering 基于人机工程的简谐运动实验教学平台

2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) Pub Date : 2021-05-01 DOI: 10.1109/AIEA53260.2021.00027

Jiyuan Qiu, Haolin Wei, Yunting Fan, Xueru Liu

引用次数: 0

Effect of Ge content and annealing temperature on resistance properties of Cu-8Mn alloy Ge含量和退火温度对Cu-8Mn合金电阻性能的影响

2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) Pub Date : 2021-05-01 DOI: 10.1109/AIEA53260.2021.00037

Wangbing, Zouhonghui, Mazhixin, Wenjunguo, Lidefu, Guoshengli

{"title":"Effect of Ge content and annealing temperature on resistance properties of Cu-8Mn alloy","authors":"Wangbing, Zouhonghui, Mazhixin, Wenjunguo, Lidefu, Guoshengli","doi":"10.1109/AIEA53260.2021.00037","DOIUrl":"https://doi.org/10.1109/AIEA53260.2021.00037","url":null,"abstract":"The as-cast Cu-8Mn-xGe (x=0, 1.35wt%, 1.7wt%, 2.2wt%) alloys were prepared by horizontal continuous casting process. The annealed alloy samples were obtained by multi-pass drawing and annealing process. The effects of Ge content and annealing temperature on the resistivity and resistance temperature coefficient of the alloys were investigated by means of optical microscope, scanning electron microscope, DC resistance tester and resistance temperature coefficient tester. The results show that the resistivity of Cu-8Mn-xGe alloy increases from 0.26 µΩ•m to 0.36 µΩ•m with the increase of Ge content. The resistivity of Cu-8Mn-1.7Ge alloy decreases with the increase of annealing temperature. In terms of the temperature coefficient of resistance (TCR), the TCR of Cu-8Mn-xGe alloy decreases with the increase of the content of Ge atom, from 21.74 × 10-6 °C-1•to -13.00 × 10-6 °C-1. The TCR of Cu-8Mn-xGe alloy is significantly reduced by Ge element. The TCR of Cu-8Mn-1.7Ge alloy increases with the increase of annealing temperature from 26.90 × 10-6 °C-1 to 118.00 × 10-6 °C-1. In terms of microstructure, the average grain size of Cu-8Mn-1.7Ge alloy increases from 5.68µm to 22.06µm with the increase of heat treatment temperature.","PeriodicalId":223292,"journal":{"name":"2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121497643","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