2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID)最新文献_第6页

Automatic Cell Counting and Labeling for Fluorescence Microscope Images 荧光显微镜图像的自动细胞计数和标记

2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID) Pub Date : 2019-10-01 DOI: 10.1109/ICASID.2019.8925211

Yuefei Lin, Y. Diao, Jianguang Zhang, Ling Li

引用次数: 0

A Blockchain-based Communication Non-repudiation System for Conversational Service 基于区块链的会话服务通信不可抵赖系统

2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID) Pub Date : 2019-10-01 DOI: 10.1109/ICASID.2019.8924991

Zhaozheng Li, W. Lei, Hanyun Hu, Wei Zhang

引用次数: 0

A Precise Step Counting Algorithm Based on Acceleration Correlation Analysis 一种基于加速度相关分析的精确步长计数算法

2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID) Pub Date : 2019-10-01 DOI: 10.1109/ICASID.2019.8925156

Yanhu Huang, Lvqing Bi, Wentao Fu, Ruizhao Yang, Xueguang Bi

引用次数: 1

Application of Locally Connected Spiking Neural Network in Image Processing 局部连接脉冲神经网络在图像处理中的应用

2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID) Pub Date : 2019-10-01 DOI: 10.1109/ICASID.2019.8925219

Zenan Huang, Hongyin Luo, Donghui Guo

引用次数: 1

Insulator Faults Detection Based on Deep Learning 基于深度学习的绝缘子故障检测

2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID) Pub Date : 2019-10-01 DOI: 10.1109/ICASID.2019.8925094

Mohamed Witti Adou, Huarong Xu, Guanhua Chen

{"title":"Insulator Faults Detection Based on Deep Learning","authors":"Mohamed Witti Adou, Huarong Xu, Guanhua Chen","doi":"10.1109/ICASID.2019.8925094","DOIUrl":"https://doi.org/10.1109/ICASID.2019.8925094","url":null,"abstract":"Electrical insulators are mainly used in transmission system for electrical insulation and mechanical support purpose. Since the insulators are exposed to environment, they can be victim of stresses (electrical, mechanical, environmental) which can lead to bunch-drop of insulators. Bunch-drop of insulators can occur due to aging, overloading, corrosion and so on. So, in this paper we propose a new method which can detect bunch-drop of insulators. We propose an object detection algorithm (YOLO, you only look once) in order to perform insulator localization and its bunch-drop detection. YOLO is the stat-of-the-art object detection method which is based on deep learning. YOLO adopts supervised learning mode. It takes as input labeled dataset. So, we trained YOLOv3 on insulator dataset of 2000 images with corresponding labels for two classes, insulator and the defect. Features are extracted by those images using several convolutional layers (53 layers). Then logistic regression is used for performing classes probabilities and labels predictions. The advantage of YOLO over other object detection method is based on its speed; thus, it is fast and can process 45 frames per second. The experiment results show that the proposed method can effectively localize insulator and detect its bunch-drop.","PeriodicalId":422125,"journal":{"name":"2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131019502","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: 17

Classification of Thyroid Ultrasound Standard Plane Images using ResNet-18 Networks 基于ResNet-18网络的甲状腺超声标准平面图像分类

2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID) Pub Date : 2019-10-01 DOI: 10.1109/ICASID.2019.8925267

Minghui Guo, Yongzhao Du

{"title":"Classification of Thyroid Ultrasound Standard Plane Images using ResNet-18 Networks","authors":"Minghui Guo, Yongzhao Du","doi":"10.1109/ICASID.2019.8925267","DOIUrl":"https://doi.org/10.1109/ICASID.2019.8925267","url":null,"abstract":"The thyroid ultrasound standard plane (TUSP) classification is quite essential for the ultrasound diagnosis of thyroid disease. The traditional method relies entirely on the ultrasonography doctor to do it manually, which is not only time-consuming and labor-intensive but also subjectively influenced by the doctor's experience and knowledge reserve. Therefore, a TUSP automatic classification method is desirable in the clinical diagnosis of thyroid ultrasound. In this paper, we proposed that using deep learning convolutional neural network (CNN) method to achieve the automatic classification of TUSP images, and the classification effect of CNN models with different structures is also compared. In our experiment, 4,509 TUSP images collected from the hospital's real data are randomly divided into 3,386 sheets as the training set and 1,123 sheets as the test set. The test set experimental results show that the 18-layer CNN model ResNet has a good performance for automatic classification of TUSP images, and the accuracy of TUSP images classification reaches 83.88%. This indicates that the deep learning method can classify TUSP images effectively, which lays a foundation for the automatic diagnosis of thyroid diseases.","PeriodicalId":422125,"journal":{"name":"2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID)","volume":"518 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123111313","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: 22

Function Verification of SRAM Controller Based on UVM 基于UVM的SRAM控制器功能验证

2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID) Pub Date : 2019-10-01 DOI: 10.1109/ICASID.2019.8925105

Xiaohong Peng, Shengxia Wang, Hong Wang, Haonan Tang, Yu Wang, Sen Wang, Meijuan Chen

引用次数: 4

Femtosecond Laser Fault Injection into External SRAM Implementations 飞秒激光故障注入外部SRAM实现

2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID) Pub Date : 2019-10-01 DOI: 10.1109/ICASID.2019.8925078

Qihua Deng, C. Shao, Huiyun Li, Jiayan Fang

引用次数: 0

Cervical Lesion Detection Net 宫颈病变检测网

2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID) Pub Date : 2019-10-01 DOI: 10.1109/ICASID.2019.8925284

Bing Bai, Yongzhao Du, Ping Li, Yuchun Lv

{"title":"Cervical Lesion Detection Net","authors":"Bing Bai, Yongzhao Du, Ping Li, Yuchun Lv","doi":"10.1109/ICASID.2019.8925284","DOIUrl":"https://doi.org/10.1109/ICASID.2019.8925284","url":null,"abstract":"Colposcopy is one of the important steps in the clinical screening of cervical intraepithelial neoplasia (CIN) and early cervical cancer. It directly affects the patient's diagnosis and treatment program. Therefore, it is widely used for cervical cancer screening. The present work proposes a cervical lesion detection net (CLDNet) model based on the deep convolutional neural network. The Squeeze-Excitation convolutional neural network (SE-CNN) is employed to extract depth features of the whole image. Moreover, the region proposal network (RPN) is used to generate a proposal box of the region of interest (ROI). Finally, the region of interest is classified and proposal box regression is performed to locate the cervical lesion region. The Squeeze-Excitation (SE block) is used to strengthen important features and suppress non-primary features, improve feature extraction ability, which is beneficial to feature classification and proposal box regression in the regions of interest. It is found that the average accuracy of the model extraction lesion region is 91.87% and the average recall rate is 84.53%, which can play a good role in the auxiliary diagnosis.","PeriodicalId":422125,"journal":{"name":"2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123959029","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: 4

Research on Fruit Category Classification Based on Convolution Neural Network and Data Augmentation 基于卷积神经网络和数据增强的水果类别分类研究

2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID) Pub Date : 2019-10-01 DOI: 10.1109/ICASID.2019.8925265

Dongmei Zhu, Min Wang, Qin Zou, Dingcai Shen, Jiamei Luo

{"title":"Research on Fruit Category Classification Based on Convolution Neural Network and Data Augmentation","authors":"Dongmei Zhu, Min Wang, Qin Zou, Dingcai Shen, Jiamei Luo","doi":"10.1109/ICASID.2019.8925265","DOIUrl":"https://doi.org/10.1109/ICASID.2019.8925265","url":null,"abstract":"Since fruit category classification plays an important role in fruit production, processing, transportation and sales, research on intelligent methods of fruit classification have a very important practical significance. Based-image machine learning methods of fruit classification are depend heavily on feature design, and its performance is not high. To resolve the defects of traditional intelligent methods, this paper proposes the fruit classification method based on convolution neural network (CNN). Through optimizing the network structure of AlexNet, we obtain a improved network model called IANet. IANet is a 10-layer network which consists of five convolutional layers, four fully connection layers and one output layer. Furthermore, based on Fruits-360 dataset, we use data augmentation methods to generate more train images. Data augmentation improves better classification performance. Finally, we compare the classification performance of IANet with other convolutional neural network models, AlexNet, 13-LayerCNN and HoreaCNN. The experimental results show that the overall accuracy of the IANet without data augmentation achieves 98.06%. While the overall accuracy of the IANet with data enhancement is 98.60%. The performance of fruit classification is better than that of state-of-the-art CNNs model.","PeriodicalId":422125,"journal":{"name":"2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131950907","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: 4