2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)最新文献_第10页

Image Segmentation of Chinese Gong-che Notation Sheet Music Based on Clustering Analysis 基于聚类分析的中国宫车谱图像分割

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456519

Gen-Fang Chen

引用次数: 0

Fast location method of underground power distribution facilities based on low power Bluetooth beacon 基于低功耗蓝牙信标的地下配电设施快速定位方法

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456554

Wenping Xiang, Tao Ma, Yu Liang, Zhan-gang Yang, Jing Peng

{"title":"Fast location method of underground power distribution facilities based on low power Bluetooth beacon","authors":"Wenping Xiang, Tao Ma, Yu Liang, Zhan-gang Yang, Jing Peng","doi":"10.1109/AIID51893.2021.9456554","DOIUrl":"https://doi.org/10.1109/AIID51893.2021.9456554","url":null,"abstract":"For the problem that the output range of traditional location method is large and the optimal solution can not be obtained, a fast location method of underground space distribution facilities based on low-power Bluetooth beacon is designed. According to the overall architecture of the positioning method, a low-power Bluetooth beacon is established to collect the transmitted Bluetooth signals. The relationship between signal strength and distance is solved, and the interference data with large fluctuation is eliminated, and the Bluetooth signal propagation ranging model is established. A fast location algorithm for distribution facilities in underground space is designed, and the least square iterative method is used to solve the problem until the result meets the threshold value. The experimental results show that, compared with the traditional location method of distribution facilities in underground space, the location error of this method is reduced by 0.56m, 0.7m and 1.11m, which can achieve a more accurate and stable estimation of the location and meet the application needs of practical engineering.","PeriodicalId":412698,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116855185","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

Crowd counting method based on feature fusion and attention mechanism 基于特征融合和注意机制的人群计数方法

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456541

Jiaming Niu, Guobin Li, Yu Yang

{"title":"Crowd counting method based on feature fusion and attention mechanism","authors":"Jiaming Niu, Guobin Li, Yu Yang","doi":"10.1109/AIID51893.2021.9456541","DOIUrl":"https://doi.org/10.1109/AIID51893.2021.9456541","url":null,"abstract":"Aiming at the problem of background noise interference and occlusion in complex crowded crowd scenes, a crowd counting network FANet based on feature fusion and attention mechanism is proposed. By introducing a feature fusion layer and a crowd region recognition module, FANet can effectively eliminate the influence of background interference and occlusion, thereby improving counting performance. As a supplement to the feature extraction network, the feature fusion layer aims to fuse low-level texture features and high-level features to avoid a large amount of loss of features, thereby enabling the model to have higher multi-scale information perception capabilities and improving training efficiency. The crowd region recognition module generates a corresponding attention weight map for the image through convolution and up-sampling operations, and based on this, achieves the purpose of suppressing background interference. Finally, the evaluation was conducted on two data sets. The experiment showed that the MAE of the proposed method on ShanghaiTech and UCF-QNRF achieved 1.1%,3% and 1.1% improvement respectively.","PeriodicalId":412698,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131302867","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

A Framework For Network Intrusion Detection Based on Unsupervised Learning 一种基于无监督学习的网络入侵检测框架

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456542

Wang Hui, Wang Dongming, Li Dejian, Zeng Lin, Wang Zhe

引用次数: 0

Design and Implementation of Online Monitoring System for Soil Salinity and Alkalinity in Yangtze River Delta Tideland 长江三角洲滩涂土壤盐碱度在线监测系统的设计与实现

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456521

Yong Liu, Yunsheng Wang, Shiyao Xu, Wenwen Hu, Yingjing Wu

{"title":"Design and Implementation of Online Monitoring System for Soil Salinity and Alkalinity in Yangtze River Delta Tideland","authors":"Yong Liu, Yunsheng Wang, Shiyao Xu, Wenwen Hu, Yingjing Wu","doi":"10.1109/AIID51893.2021.9456521","DOIUrl":"https://doi.org/10.1109/AIID51893.2021.9456521","url":null,"abstract":"Soil salinity and alkalinity is an important index concerned by planting industry. In order to meet the demand of long-term observation of soil salinity and alkalinity in precision agriculture and eco-environmental protection, and to solve the current pain points of long sampling period and high cost of soil salinity measurement, this paper designs and implements an online monitoring system for soil salinity alkalinity in tideland in the Yangtze River Delta for crop planting and soil remediation. This system uses solar power supply system and maintenance-free digital sensor, which can be arranged in monitoring area for a long time to collect soil temperature, humidity and salinity data. The collected data can be stored in SD card locally and transmitted to cloud server in real time through 4G network. Up to now, the system has been running stably for nearly two years under the condition of unattended and maintenance free. More than 30000 soil salinity data have been collected from 5 monitoring points, which can be used for long-term observation of the interaction between salinity and plant growth, so as to improve the soil and improve the quality of agriculture products.","PeriodicalId":412698,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127852598","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

Algorithm of Detection of Articles Left behind in Vehicles 车辆遗留物品检测算法

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456575

Yang Bo, Luo Renjun

引用次数: 0

Design and Implementation of System for Generating MOFs for Hydrogen Storage in Hydrogen-Fueled vehicles 氢燃料汽车储氢用mof生成系统的设计与实现

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456565

Chunming Tang, Shan Fu, Fengyang Liu

{"title":"Design and Implementation of System for Generating MOFs for Hydrogen Storage in Hydrogen-Fueled vehicles","authors":"Chunming Tang, Shan Fu, Fengyang Liu","doi":"10.1109/AIID51893.2021.9456565","DOIUrl":"https://doi.org/10.1109/AIID51893.2021.9456565","url":null,"abstract":"New energy vehicles replace nonrenewable energy such as gasoline with renewable resources. On the one hand, it effectively reduces the use of nonrenewable energy and protects the natural environment. On the other hand, it improves the atmospheric environment. Because hydrogen energy has the characteristics of high efficiency and energy saving, the use of hydrogen energy has become one of the directions for the development of clean energy. Metal-organic framework (MOF) has been widely studied in the field of gas adsorption due to its porous structure and large specific surface area. In this paper, we propose a system for generating MOF based on Monte Carlo Tree Search (MCTS). By improving the activation function in the gated recurrent unit (GRU) structure, the convergence speed and accuracy of the neural network can be improved. The improved GRU is used as a policy network to guide MCTS to generate MOFs with superior hydrogen adsorption performance. The improved GRU has an accuracy of 90.31% on the SMILES string dataset, which is 1.19% higher than the accuracy of the traditional GRU; For specific metal nodes and topologies, the system can generate MOFs with larger hydrogen adsorption capacity than the experimentally synthesized MOF materials.","PeriodicalId":412698,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130576400","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

A High-Precision Fast Smoky Vehicle Detection Method Based on Improved Yolov5 Network 基于改进Yolov5网络的高精度快速烟熏车辆检测方法

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456462

Chengpeng Wang, Huanqing Wang, Fajun Yu, Wangjin Xia

{"title":"A High-Precision Fast Smoky Vehicle Detection Method Based on Improved Yolov5 Network","authors":"Chengpeng Wang, Huanqing Wang, Fajun Yu, Wangjin Xia","doi":"10.1109/AIID51893.2021.9456462","DOIUrl":"https://doi.org/10.1109/AIID51893.2021.9456462","url":null,"abstract":"A high-precision and fast smoky vehicle detection method was proposed. Since the existing target detection models deployed on embedded devices cannot meet the needs of rapid detection, an improved lightweight network based on Yolov5 was adopted in this paper. The backbone of Yolov5s was improved by Mobilenetv3-small to reduce the number of model parameters and calculations. In order to detect motor vehicle exhaust with high precision, a vehicle exhaust dataset is collected and established. Due to the interference of vehicle shadows and the occlusion between vehicles, Cutout and saturation transformation were applied to expand the self-built dataset, which was finally expanded to 6102 images. Experiments results show that after using data augmentation, the detection accuracy is increased by 8.5%. The improved network is deployed on embedded devices, and the detection speed of the network can reach 12.5FPS, which is 2 times higher than Yolov5's. The amount of improved network parameters is only 0.48M. This research proposes an efficient target detection model, and provides a possible method for the development of low-cost and rapid vehicle exhaust detection equipment.","PeriodicalId":412698,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131719390","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: 15

Research on PLC Electrical Control Simulation Experimental Device Based on Internet of Things Technology 基于物联网技术的PLC电气控制仿真实验装置研究

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456489

Yunqiang Wu

引用次数: 0

Multi-target Optimized Cooperative Attack Task Allocation Technology 多目标优化协同攻击任务分配技术

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456587

Zhang Yijie, Zhang Weijie, Mao Qinghua, Gao Dengwei, Huang Xulei, Liao Haiqing

引用次数: 0