2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)最新文献_第4页

Short term load forecasting algorithm of substation bus based on multi source data characteristics 基于多源数据特征的变电站母线短期负荷预测算法

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456547

Quan Yuan, Qiang Zhang, A. Zhou

{"title":"Short term load forecasting algorithm of substation bus based on multi source data characteristics","authors":"Quan Yuan, Qiang Zhang, A. Zhou","doi":"10.1109/AIID51893.2021.9456547","DOIUrl":"https://doi.org/10.1109/AIID51893.2021.9456547","url":null,"abstract":"In order to avoid the adverse effects of load transfer, power outage and small power supply on bus load forecasting in bus power supply area, a short-term load forecasting algorithm for substation bus based on multi-source data characteristics is proposed. By converting the load of the bus to the ideal power load in the power supply area of the bus, the ideal power load is corrected as the historical load data, and the algorithm of multi-source data characteristic load forecasting is used to obtain the preliminary forecasting results. At the same time, the values of various influencing factors on the day to be forecasted are obtained. The forecasting results eliminate various influencing factors and indirectly predict the load value of the bus. Based on this, the experiment proves that the application of short-term load forecasting algorithm of substation bus based on multi-source data characteristics can significantly improve the accuracy of bus load forecasting with small power supply in the power supply area, compared with the direct forecasting method which takes the load value of bus network as historical data.","PeriodicalId":412698,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)","volume":"2 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":"131570483","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 Data Visualization Design for Police System 警务系统数据可视化设计研究

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456583

Zeng Xi, Wang Chunyu

{"title":"Research on Data Visualization Design for Police System","authors":"Zeng Xi, Wang Chunyu","doi":"10.1109/AIID51893.2021.9456583","DOIUrl":"https://doi.org/10.1109/AIID51893.2021.9456583","url":null,"abstract":"The increasingly complicated police work requires increasing information collection, analysis and transmission of the daily police system. From the design point of view, this paper intends to study how to improve the effectiveness and efficiency of data information transmission in police system. By analyzing the objectives and requirements of data information transmission in police system, from the perspective of user experience, the data visualization design model is optimized, the data visualization design principles are deduced, and the laws of human vision and cognition are combined to study the data visualization design strategy of police system to guide design practice. The verification results show that from the perspective of user experience, data visualization design practice can be effectively guided by the design scheme combining human vision and cognitive laws. The design practice process and results show that police data information can be efficiently conveyed, the user experience of police staff can be enhanced, and the efficiency of police work can be improved, based on the user's visual and cognitive laws of data visualization design.","PeriodicalId":412698,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)","volume":"4 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":"132453359","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

Using Bilinear-Siamese architecture for remote sensing scene classification 基于双线性-暹罗结构的遥感场景分类

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456523

Xu Cao, H. Zou, Xinyi Ying, Runlin Li, Shitian He, Fei Cheng

{"title":"Using Bilinear-Siamese architecture for remote sensing scene classification","authors":"Xu Cao, H. Zou, Xinyi Ying, Runlin Li, Shitian He, Fei Cheng","doi":"10.1109/AIID51893.2021.9456523","DOIUrl":"https://doi.org/10.1109/AIID51893.2021.9456523","url":null,"abstract":"The key challenge of remote sensing (RS) scene classification is that features generated by similar scenes are difficult to distinguish. To solve the problem, we present a Bilinear-Siamese architecture to learn to distinguish the subtle discriminative features between similar scenes. Specifically, a pair of images are sent to the feature extraction module. Then, the extracted paired features are sent to two branches: 1) A fully connected (FC) layer to generate the normal classification results. 2) A bilinear mix module and a FC layer to generate the bilinear mixed classification results. Finally, we introduce a discriminative fusion method to fuse the aforementioned classification results for final output. Noted that, the contrast loss of Bilinear-Siamese architecture improves the ability to distinguish similar scenes based on metric learning. In addition, we introduce the additional bilinear loss to improve the generalization and the robustness of our network. We conduct extensive experiments on benchmark RS datasets to demonstrate the effectiveness of our network and the experimental results show that the performance of the proposed method surpasses other existing methods.","PeriodicalId":412698,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)","volume":"98 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":"124977926","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

Detection of MMW Radar Target Based on Doppler Characteristics and Deep Learning 基于多普勒特性和深度学习的毫米波雷达目标检测

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456497

Chen Wang, Z. X. Chen, Xin Chen, Xiaojie Tang, Futai Liang

{"title":"Detection of MMW Radar Target Based on Doppler Characteristics and Deep Learning","authors":"Chen Wang, Z. X. Chen, Xin Chen, Xiaojie Tang, Futai Liang","doi":"10.1109/AIID51893.2021.9456497","DOIUrl":"https://doi.org/10.1109/AIID51893.2021.9456497","url":null,"abstract":"In recent years, unmanned technology has been continuously developed. millimeter - wave (MMW)radar has been widely used in driverless vehicles because of its performance characteristics. Target detection is also one of the hot issues studied by experts and scholars in the field of driverless driving. According to the target detection problem of millimeter - wave radar, a deep learning - based target detection method is proposed. It uses 77G HZ on - board millimeter - wave radar Spectro graph data to mark the target existence area and form a standard data set through data preprocessing. An improved model of Doppler image detection of RetinaNet radar was subsequently proposed. The model uses ResNet101 as a feature extraction network, uses group normalization (GN) as a normalization method, improves the network accuracy and convergence speed, introduces the attention mechanism in the feature extraction network, and enhances the feature expression capability of the model. The improved RetinaNet model improves the average accuracy of radar Doppler image detection by 7.2 % and 91.5%, which provides ideas for the development of radar target detection and unmanned driving technology, and has engineering application value.","PeriodicalId":412698,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)","volume":"12 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120917819","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

Robust Curb Detection Based on the Accessible Route Analysis and Key Frames Prediction 基于可达路由分析和关键帧预测的鲁棒抑制检测

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456591

Zehai Yu, Hui Zhu, Linglong Lin, Haozhe Yang

引用次数: 0

Research on automatic test system for elevator balance coefficient 电梯平衡系数自动测试系统的研究

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456503

Yanbin Zhang, Shuangchang Feng, Jie Chen, Kuangye Niu

{"title":"Research on automatic test system for elevator balance coefficient","authors":"Yanbin Zhang, Shuangchang Feng, Jie Chen, Kuangye Niu","doi":"10.1109/AIID51893.2021.9456503","DOIUrl":"https://doi.org/10.1109/AIID51893.2021.9456503","url":null,"abstract":"With the rapid advancement of urbanization in China, the newly installed elevator equipment has increased year by year. The number of elevators is increasing, and passengers pay more and more attention to the safety of elevators. Elevator balance coefficient is an important prerequisite for the safe operation of elevators. If the balance factor does not meet the standard requirements, serious personal injury accidents may occur. Although the current commonly used balance coefficient measurement method is widely used, their shortcomings are obvious, especially for the measurement of the balance coefficient of elevators without machine room. The current method cannot mark the middle position, the no-load power method cannot measure the speed, and the wire rope tension method is out of the way. In this paper, the author introduces an automatic test system for elevator balance coefficient, mainly from three aspects of the current balance coefficient measurement status, the working principle and advantages of the balance coefficient automatic test system and expounds the use method and application value of the system.","PeriodicalId":412698,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)","volume":"16 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":"128344647","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

Human behavior recognition based on convolutional long and short time memory network 基于卷积长短时记忆网络的人类行为识别

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456561

Chuanlin Zhang, K. Cao, Mengge Huang, Tao Deng

引用次数: 0

Design of RSM scheme against DPA suitable for LBlock algorithm 设计适合LBlock算法的抗DPA RSM方案

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456473

Bowei Chen, X. Xia, Shuai Guo, Weidong Zhong

引用次数: 0

A Medical Guidance Model Driven by Subjective and Objective Knowledge 主客观知识驱动的医学指导模式

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456581

He Yu, Liang Xiao

{"title":"A Medical Guidance Model Driven by Subjective and Objective Knowledge","authors":"He Yu, Liang Xiao","doi":"10.1109/AIID51893.2021.9456581","DOIUrl":"https://doi.org/10.1109/AIID51893.2021.9456581","url":null,"abstract":"Because of the impact of Corona Virus Disease 2019 (COVID-19), online medical services have developed rapidly and are widely accepted by people. People can find doctors for diagnosis and treatment by the name of the disease online. However, patients usually lack professional medical knowledge and have their own subjective preferences for health services, which makes it difficult for patients to accurately find a doctor that suits them. To this end, we proposed a medical guidance model driven by subjective and objective knowledge to provide decision support to patients. In the proposed model, the doctor's and disease's own information is regarded as objective knowledge, and the information of doctor feature extracted from patient reviews is regarded as subjective knowledge. They are fused into a knowledge graph. On this basis, a knowledge decision engine is designed to recommend the most suitable doctor based on the patient's objective conditions and subjective preferences. Finally, a prototype system is designed and developed to demonstrate the feasibility of the model as above. The system guides patients to improve their objective conditions and subjective preferences through inquiries, and returns recommended doctors to patients in an interpretable manner. The medical guidance model can effectively meet the personalized and professional needs of patients in online medical services, which has good practical value under the digital healthcare continues to become the trend of the future.","PeriodicalId":412698,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)","volume":"47 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":"127211426","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

Structure-Process-Result Evaluation Model with Computer-Aided Engineering in Grass-Roots Practice 计算机辅助工程在基层实践中的结构-过程-结果评价模型

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456543

Hui-jun Ni, Bo Zhou, Guang-Ming Zheng

引用次数: 0