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

Research on University Product System under Computer Science Aided Operation 计算机辅助操作下的高校产品系统研究

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456481

Ying Yu, Yue Sun

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

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

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

Research on Image Encryption Technology Based on Hyperchaotic System and DNA Encoding 基于超混沌系统和DNA编码的图像加密技术研究

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456457

Mengmeng Zhang, Wangchun Wu

引用次数: 5

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

Research on face recognition algorithm based on multi task deep learning 基于多任务深度学习的人脸识别算法研究

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456559

Hainie Meng, Yunli Cheng

{"title":"Research on face recognition algorithm based on multi task deep learning","authors":"Hainie Meng, Yunli Cheng","doi":"10.1109/AIID51893.2021.9456559","DOIUrl":"https://doi.org/10.1109/AIID51893.2021.9456559","url":null,"abstract":"With the vigorous development of the new generation of information technology, deep learning technology based on big data has gradually become one of the mainstream technologies in the field of artificial intelligence. Face recognition is an important topic in the field of artificial intelligence and biometrics.It is widely used in business, security, identity authentication and many other aspects, and has become a dynamic research field.With the continuous improvement of application requirements, face recognition technology is no longer only for face identification, face attribute recognition is becoming more and more important.Firstly, a simplified multi task face recognition model is proposed and designed to speed up the operation;Secondly, the correlation among multiple learning tasks is used to improve the recognition accuracy of the model;After model training and selection, an end-to-end multi task face recognition model is obtained.The multi task face recognition algorithm can be fast and accurate in a short time, which can be widely used in intelligent driving behavior analysis, intelligent navigation and other fields.","PeriodicalId":412698,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)","volume":"9 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":"126969852","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

An Improved MSCNN Method for Underwater Image Defogging 一种改进的MSCNN水下图像去雾方法

2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456545

Haoyue Wang, Xiangning Chen, Bijie Xu, S. Du, Yinan Li

{"title":"An Improved MSCNN Method for Underwater Image Defogging","authors":"Haoyue Wang, Xiangning Chen, Bijie Xu, S. Du, Yinan Li","doi":"10.1109/AIID51893.2021.9456545","DOIUrl":"https://doi.org/10.1109/AIID51893.2021.9456545","url":null,"abstract":"Underwater imagery is an important carrier and presentation of underwater information, which plays a vital role in the exploration, exploitation and utilization of marine resources. However, due to the limitations of objective imaging environment and equipment, the quality of underwater images is always poor, with degradation phenomena such as low contrast, blurred details and colour deviation, which seriously restrict the development of related fields. Therefore, how to enhance and recover degraded underwater images through post-production algorithms has received increasing attention from scholars. In recent years, with the rapid development of deep learning technology, great progress has been made in underwater image enhancement and restoration based on deep learning. In this paper, we propose an improved MSCNN underwater image defogging method, which combines Retinex and CLAHE for brightness equalization and contrast enhancement of underwater images, making the method more advantageous for complex situations such as low illumination, uneven illumination and obvious Rayleigh scattering phenomena in underwater environments, and conduct objective analysis and comparison of the recovered images to prove the effectiveness of this algorithm in underwater defogging and colour correction. The effectiveness of the algorithm for underwater defogging and colour correction is demonstrated by objective analysis and comparison of the recovered images.","PeriodicalId":412698,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)","volume":"72 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":"131278216","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

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