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

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Path-following cooperative control strategies for driverless vehicles 无人驾驶车辆路径跟随协同控制策略
2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456456
Wang Peiyan
{"title":"Path-following cooperative control strategies for driverless vehicles","authors":"Wang Peiyan","doi":"10.1109/AIID51893.2021.9456456","DOIUrl":"https://doi.org/10.1109/AIID51893.2021.9456456","url":null,"abstract":"In order to improve the tracking accuracy of unmanned vehicle paths, while taking into account steering frequency and vehicle stability, and to be able to identify nonlinear road realities, a comprehensive collaborative governance strategy for unmanned vehicle driving is proposed, taking into account real-time influencing factors on the road as far as possible. On the basis of lateral trajectory following control, a driver operation discrimination module is introduced, a vehicle dynamics model and a tyre model are established, and an upper trajectory following lateral controller is designed using strong robust structural control theory based on the assumption of steady-state circular motion, optimal pre-sighting theory. The fuzzy controller with gain adjustment and the underlying actuator controller are used to reduce the sliding mode controller jitter in the automatic steering mode and to reduce the error of the theoretical analysis. For the ESP system, a more robust non-linearised fuzzy PID control strategy is used to improve it.","PeriodicalId":412698,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)","volume":"200 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":"131542228","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
RDUnet-A: A Deep Neural Network Method with Attention for Fabric Defect Segmentation Based on Autoencoder RDUnet-A:一种基于自编码器的织物缺陷分割的深度神经网络方法
2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456576
Huaijin Chen, D. Chen, Haoran Dai
{"title":"RDUnet-A: A Deep Neural Network Method with Attention for Fabric Defect Segmentation Based on Autoencoder","authors":"Huaijin Chen, D. Chen, Haoran Dai","doi":"10.1109/AIID51893.2021.9456576","DOIUrl":"https://doi.org/10.1109/AIID51893.2021.9456576","url":null,"abstract":"In industrial production, fabric products will always inevitably appear flaws due to uncontrollable factors such as production and transportation. However, there are many problems with the manual inspection methods used by manufacturers, such as low efficiency of fabric defects, high false detection rate, and high missed detection rate. While the diversity and complexity of fabric flaws also lead to the unsatisfactory results of existing flaw detection. Therefore, improving the detection and classification of fabric defects has become the key to problem solving. In this article, we propose a new deep convolutional network with attention mechanism (RDUnet-A) to solve the problems in fabric defect detection. The network is more efficient through training, and it is more helpful to realize the defect recognition of the image. We evaluated our model and the classic CNN model on the AITEX public data set, and the experimental results demonstrate that the newly proposed RDUnet-A model can achieve densely distributed defect detection, with Pixel Accuracy up to 0.600 and mlou up to 0.466, which is better than other classic models. This model effectively improves the accuracy and precision of fabric defect detection, and can obtain the defect location, which can meet industrial production needs basically.","PeriodicalId":412698,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)","volume":"82 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":"116475188","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
Aircraft sensor Fault Diagnosis Method Based on Residual Antagonism Transfer Learning 基于残差对抗迁移学习的飞机传感器故障诊断方法
2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456530
Xiaoya Li, Xiangwei Kong
{"title":"Aircraft sensor Fault Diagnosis Method Based on Residual Antagonism Transfer Learning","authors":"Xiaoya Li, Xiangwei Kong","doi":"10.1109/AIID51893.2021.9456530","DOIUrl":"https://doi.org/10.1109/AIID51893.2021.9456530","url":null,"abstract":"With the rapid development of electronic information technology, aircraft has entered a completely electrified era, and the number of sensors has increased exponentially. Although key sensors have redundant designs, many aviation accidents in recent years are caused by sensor failures. Therefore, early detection of Aircraft sensor faults is of great significance for ensuring flight safety. Faced with a large number of unlabeled and uneven sample sensor data, a method for fault diagnosis of Aircraft sensors based on residual countermeasure migration learning is proposed. This method can help deep learning. The product neural network requires the limitation of a large number of labeled data, and uses the rich label data from different but related auxiliary fields to reuse and transfer the data of the target domain to achieve the purpose of transfer learning and realize the fault diagnosis of Aircraft sensors.","PeriodicalId":412698,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)","volume":"12 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":"114447488","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: 5
An Under-Sampling Algorithm Based on SVM 基于支持向量机的欠采样算法
2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456573
Zheng Hengyu
{"title":"An Under-Sampling Algorithm Based on SVM","authors":"Zheng Hengyu","doi":"10.1109/AIID51893.2021.9456573","DOIUrl":"https://doi.org/10.1109/AIID51893.2021.9456573","url":null,"abstract":"Tradition classification algorithms often get poor performance in imbalanced datasets because they are proposed under the assumption that the datasets are nearly balanced. Random under-sampling(RUS) algorithm is a popular algorithm to solve imbalance problem through removing some majority class samples randomly. However, RUS algorithm may neglect some key information of datasets. A new under-sampling algorithm based on SVM is proposed in this paper. The proposed algorithm aims to reserve samples distribution information in undersampling process. The simulation results show that the proposed algorithm could achieve satisfying performance.","PeriodicalId":412698,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)","volume":"210 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":"121849788","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 Green Wireless Mobile Communication Technology 绿色无线移动通信技术研究
2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456568
Guo Fang
{"title":"Research on Green Wireless Mobile Communication Technology","authors":"Guo Fang","doi":"10.1109/AIID51893.2021.9456568","DOIUrl":"https://doi.org/10.1109/AIID51893.2021.9456568","url":null,"abstract":"Green wireless communication technology can not only effectively reduce the energy consumption in the transmission and transmission, but also help to improve the actual work effect. In actual work, it is necessary to strengthen the research on the rationality of green wireless mobile communication technology. According to the requirements and standards set by the system, new working concepts and technical models are incorporated, so that the green wireless mobile communication technology can develop faster and save unnecessary energy consumption.","PeriodicalId":412698,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)","volume":"190 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":"121074422","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 database controlled memory authorization application model based on ARM architecture 基于ARM架构的数据库控制内存授权应用模型研究
2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456582
Wuqiang Shen, Iinbo Zhang, Guiquan Shen
{"title":"Research on database controlled memory authorization application model based on ARM architecture","authors":"Wuqiang Shen, Iinbo Zhang, Guiquan Shen","doi":"10.1109/AIID51893.2021.9456582","DOIUrl":"https://doi.org/10.1109/AIID51893.2021.9456582","url":null,"abstract":"The traditional database authorization application model uses forward and generalized authorization rules to authorize the database, which requires all authorization items to be examined in turn when authorizing, and the authorization granularity is too coarse, leading to a large amount of data redundancy in the authorization library and the complexity of the authorization library is increased. To address the above problems, the application model of controlled memory authorization for database based on ARM architecture will be studied. After database semantic view reconstruction in ARM architecture virtualization platform, the distribution of data controllable memory authorization rules is determined. The database memory authorization information is encrypted and managed using the NTRU algorithm, and the database controlled memory authorization application model is constructed and implemented on the basis of the RBAC access control model. The simulation experimental results show that the authorization model studied has an authorization card redundancy ratio lower than 8%, which is much smaller than the traditional authorization model, and the authorization granularity of this authorization model is small, which can effectively ensure the database security.","PeriodicalId":412698,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)","volume":"132 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":"115899613","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
A Music Similarity Model Based on Data Analysis and Algorithm Application 基于数据分析的音乐相似度模型及算法应用
2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456465
Yunsong Jia, Yiming Liu
{"title":"A Music Similarity Model Based on Data Analysis and Algorithm Application","authors":"Yunsong Jia, Yiming Liu","doi":"10.1109/AIID51893.2021.9456465","DOIUrl":"https://doi.org/10.1109/AIID51893.2021.9456465","url":null,"abstract":"Rapid analysis of the influences and musical similarities among a large number of composers and genres is of great significance to the study of music development and social change. In this paper, the similarity model based on Minkowski distance is proposed to realize the rapid evaluation, and the validity of the model is verified. It can quickly identify the similarity between music or music groups based on music features. Based on the analysis and mining of ICM data, the characteristics of the individual, genre development mode, influence, and change in music history are obtained.","PeriodicalId":412698,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)","volume":"46 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":"115384721","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
Image Enhancement of Optical Coherence Tomography using Deep Learning 基于深度学习的光学相干断层成像图像增强
2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456470
Guohong Qin, Congrui Yang, Yixin Du
{"title":"Image Enhancement of Optical Coherence Tomography using Deep Learning","authors":"Guohong Qin, Congrui Yang, Yixin Du","doi":"10.1109/AIID51893.2021.9456470","DOIUrl":"https://doi.org/10.1109/AIID51893.2021.9456470","url":null,"abstract":"Optical coherence tomography (OCT) images are widely used in the clinical diagnosis of diseases because they can obtain high-resolution images in real-time. However, due to the noise interference generated during the signal acquisition, there will be pixel jitters in the OCT images. Aiming at the pixel imaging jitter problem caused by signal transmission interference, an improved UNET network framework in deep learning is proposed to construct an OCT image correction model. This model forms a mapping from the input image X to the output image Y by taking advantage of the deep network structure of UNET. Through 200 iteration training, the loss value is reduced to the lowest level in this model to realize OCT image correction. Finally, the validity of the proposed method was proved by calculating the similarity of corrected images.","PeriodicalId":412698,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)","volume":"50 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":"121717548","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
Construction of bisection model of SPECT bone scan image based on VGGNet 基于VGGNet的SPECT骨扫描图像分割模型构建
2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456458
Ziwen Zheng, Liangxia Liu, Xiaoyan Chen, Qiang Lin
{"title":"Construction of bisection model of SPECT bone scan image based on VGGNet","authors":"Ziwen Zheng, Liangxia Liu, Xiaoyan Chen, Qiang Lin","doi":"10.1109/AIID51893.2021.9456458","DOIUrl":"https://doi.org/10.1109/AIID51893.2021.9456458","url":null,"abstract":"Nuclear medical SPECT imaging is an advanced medical imaging equipment, which plays an important role in the discovery, diagnosis and treatment of bone metastases in the process of medical diagnosis. Computerized SPECT imaging can accurately diagnose whether patients have bone metastasis, which can help doctors quickly identify whether there is disease. In this paper, the whole body bone scanning imaging data is effectively expanded by mirroring, translating and rotating the existing data, and then an image classifier is constructed based on VGGNet model. The experimental evaluation and analysis of a group of real tomographic data by image classifier shows that VGGNet7 model can effectively distinguish disease from normal, and the accuracy Acc, PRecision pre, recall rec and F-1 scores of experimental evaluation are 0.99, 0.99, 0.99 and 0.99, respectively.","PeriodicalId":412698,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)","volume":"18 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":"115049443","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
An expression detection technique based on multi-input convolutional neural network for incomplete face images 基于多输入卷积神经网络的不完整人脸图像表情检测技术
2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) Pub Date : 2021-05-28 DOI: 10.1109/AIID51893.2021.9456454
Anbang Wang, Dan Liu, Wentao Zhao
{"title":"An expression detection technique based on multi-input convolutional neural network for incomplete face images","authors":"Anbang Wang, Dan Liu, Wentao Zhao","doi":"10.1109/AIID51893.2021.9456454","DOIUrl":"https://doi.org/10.1109/AIID51893.2021.9456454","url":null,"abstract":"An expression detection technique based on feature fusion multi-input convolutional neural network is proposed. In view of the negative effect of occlusion objects in occlusion face image on expression recognition task, the multi-input convolutional neural network is proposed to use the multi-input property, so that the multi-classifier coupling network can learn more complex prediction model. The local feature level fusion method was used to extract the features from the image, and the local micro-features of the region of interest were taken as the multi-branch input of the multi-input neural network, so as to reduce the influence of the contribution rate of the missing part of the incomplete image and improve the robustness and accuracy of the expression detection.","PeriodicalId":412698,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)","volume":"1 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":"128651598","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
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