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学者姓名：孙光民
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摘要 ：
In medical diagnostics, the invention of the computer-aided identification method has played a significant role in making essential decisions for human diseases. Lung cancer requires a greater focus among various diagnostic processes because both men and women are affected, contributing to high mortality rates. In addition, lung cancer is one of the leading causes of death worldwide. It can be treated if diagnosed at an early stage. Detecting and classifying lung lesions is challenging for radiologists. Radiologists typically use computer-aided diagnostic systems to screen for lung cancer. In recent years, computer specialists have proposed many techniques for diagnosing lung cancer. Conventional lung cancer prediction methods have failed to maintain the precision needed because the low-quality picture affects the segmentation process. Here, we propose a well-performing method to detect and classify lung cancer. We applied the Grey Wolf Optimization algorithm with a weighted filter to reduce noise in images, followed by segmentation using watershed transformation and dilation operations. In the end, we classified lung cancer among three classes using our method that showed high performance compared to previous studies: 98.33% accuracy, 100% sensitivity, and 93.33% specificity.
关键词 ：
Grey Wolf Optimization (GWO) Grey Wolf Optimization (GWO) Classification Classification Computer-Aided Diagnostic System (CAD) System Computer-Aided Diagnostic System (CAD) System Convolutional Neural Network (CNN) Convolutional Neural Network (CNN) Computed Tomography (CT) Computed Tomography (CT)
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| GB/T 7714 | Bilal, Anas , Sun, Guangmin , Li, Yu et al. Lung nodules detection using grey wolf optimization by weighted filters and classification using CNN [J]. | JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS , 2022 , 45 (2) : 175-186 . |
| MLA | Bilal, Anas et al. "Lung nodules detection using grey wolf optimization by weighted filters and classification using CNN" . | JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS 45 . 2 (2022) : 175-186 . |
| APA | Bilal, Anas , Sun, Guangmin , Li, Yu , Mazhar, Sarah , Latif, Jahanzaib . Lung nodules detection using grey wolf optimization by weighted filters and classification using CNN . | JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS , 2022 , 45 (2) , 175-186 . |
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摘要 ：
基于SM9算法的移动互联网身份认证方案研究
关键词 ：
单服务器环境 单服务器环境 移动互联网 移动互联网 身份认证 身份认证 SM9算法 SM9算法
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| GB/T 7714 | 张昱 , 孙光民 , 李煜 et al. 基于SM9算法的移动互联网身份认证方案研究 [J]. | 张昱 , 2021 , (4) : 1-9 . |
| MLA | 张昱 et al. "基于SM9算法的移动互联网身份认证方案研究" . | 张昱 4 (2021) : 1-9 . |
| APA | 张昱 , 孙光民 , 李煜 , 信息网络安全 . 基于SM9算法的移动互联网身份认证方案研究 . | 张昱 , 2021 , (4) , 1-9 . |
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摘要 ：
为了便于对建筑外墙瓷砖松动和开裂现象进行定期排查以保证周围居民的人身安全，本文提出了一种通过高分辨率相机拍摄的楼面图像进行微小缺陷自动检测的方法。首先，将原始检测任务划分为大尺度下的非墙体分割任务以及小尺度下的缺陷检测任务；其次，分别针对这些任务训练相应的深度模型并应用其进行处理；最后，将这些多尺度任务的处理结果进行融合，得到微小缺陷的最终检测结果。实验表明本文算法在精度和效率上都要明显优于单尺度方法。本文算法已在某小区实际部署运行并取得了良好的效果，具有很高的实用价值。
关键词 ：
墙面 墙面 多尺度 多尺度 卷积神经网络 卷积神经网络 缺陷 缺陷 负反馈技术 负反馈技术 高分辨率检测器 高分辨率检测器 目标检测 目标检测 滑窗 滑窗
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| GB/T 7714 | 孙光民 , 陈佳阳 , 李冰 et al. 双尺度网络高分辨率楼面影像微小缺陷检测 [J]. | 哈尔滨工程大学学报 , 2021 , (02) : 1-8 . |
| MLA | 孙光民 et al. "双尺度网络高分辨率楼面影像微小缺陷检测" . | 哈尔滨工程大学学报 02 (2021) : 1-8 . |
| APA | 孙光民 , 陈佳阳 , 李冰 , 李煜 , 闫冬 . 双尺度网络高分辨率楼面影像微小缺陷检测 . | 哈尔滨工程大学学报 , 2021 , (02) , 1-8 . |
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摘要 ：
移动互联网单服务器环境下传统身份认证方案存在用户需要针对不同的服务器记忆相应的不同口令,以及传统认证方式中的口令泄漏等安全问题。为解决以上问题,文章提出一种移动互联网单服务器环境下基于SM9算法的身份认证方案。用户针对不同的应用系统,仅需记忆统一的标识和口令,即可在不同的应用系统中通过身份认证,从而获得应用服务和访问资源的权限。文章方案将SM9标识密码算法与口令隐藏相结合,采用一次一密的方式实现密文传输、双向认证,达到了更高的安全性和健壮性,并能减轻用户的记忆负担,给用户带来更好的应用体验。通过安全性分析,文章方案能抵抗重放攻击、仿冒攻击、智能设备丢失攻击等常见攻击。通过性能对比,文章方案比同...
关键词 ：
SM9算法 SM9算法 单服务器环境 单服务器环境 身份认证 身份认证 移动互联网 移动互联网
引用：
复制并粘贴一种已设定好的引用格式，或利用其中一个链接导入到文献管理软件中。
| GB/T 7714 | 张昱 , 孙光民 , 李煜 . 基于SM9算法的移动互联网身份认证方案研究 [J]. | 信息网络安全 , 2021 , 21 (04) : 1-9 . |
| MLA | 张昱 et al. "基于SM9算法的移动互联网身份认证方案研究" . | 信息网络安全 21 . 04 (2021) : 1-9 . |
| APA | 张昱 , 孙光民 , 李煜 . 基于SM9算法的移动互联网身份认证方案研究 . | 信息网络安全 , 2021 , 21 (04) , 1-9 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
双尺度网络高分辨率楼面影像微小缺陷检测
关键词 ：
卷积神经网络 卷积神经网络 墙面 墙面 多尺度 多尺度 滑窗 滑窗 目标检测 目标检测 缺陷 缺陷 负反馈技术 负反馈技术 高分辨率检测器 高分辨率检测器
引用：
复制并粘贴一种已设定好的引用格式，或利用其中一个链接导入到文献管理软件中。
| GB/T 7714 | 孙光民 , 陈佳阳 , 李冰 et al. 双尺度网络高分辨率楼面影像微小缺陷检测 [J]. | 孙光民 , 2021 , 42 (2) : 286-293 . |
| MLA | 孙光民 et al. "双尺度网络高分辨率楼面影像微小缺陷检测" . | 孙光民 42 . 2 (2021) : 286-293 . |
| APA | 孙光民 , 陈佳阳 , 李冰 , 李煜 , 闫冬 , 哈尔滨工程大学学报 . 双尺度网络高分辨率楼面影像微小缺陷检测 . | 孙光民 , 2021 , 42 (2) , 286-293 . |
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摘要 ：
Deep learning-based segmentation algorithms for medical image require massive training datasets with accurate annotations, which is costly since it takes much human effort to manually labeling from scratch. Therefore, interactive image segmentation is important and may greatly improve the efficiency and accuracy of medical image labeling. Some interactive segmentation methods (e.g. Deep Extreme Cut and Deepgrow) may improve the labeling through minimal interactive input. However these methods only utilize the initial manually input information, while existing segmentation results (such as annotations produced by nonprofessionals or conventional segmentation algorithms) cannot be utilized. In this paper, an interactive segmentation method is proposed to make use of both existing segmentation results and human interactive information to optimize the segmentation results progressively. In this framework, the user only needs to click on the foreground or background of the target individual on the medical image, the algorithm could adaptively learn the correlation between them, and automatically completes the segmentation of the target. The main contributions of this paper are: (1) We adjusted and applied a convolutional neural network which takes medical image data and user's clicks information as input to achieve more accurate segmentation of medical images. (2) We designed an iterative training strategy to realize the applicability of the model to deal with different number of clicks data input. (3) We designed an algorithm based on false positive and false negative regions to simulate the user's clicks, so as to provide enough training data. By applying the proposed method, users can easily extract the region of interest or modify the segmentation results by multiple clicks. The experimental results of 6 medical image segmentation tasks show that the proposed method achieves more accurate segmentation results by at most five clicks. © 2021 SPIE.
关键词 ：
Convolution Convolution Convolutional neural networks Convolutional neural networks Image annotation Image annotation Image segmentation Image segmentation Deep learning Deep learning Iterative methods Iterative methods Image enhancement Image enhancement Medical image processing Medical image processing
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| GB/T 7714 | Bai, Yunkun , Sun, Guangmin , Li, Yu et al. Progressive medical image annotation with convolutional neural network-based interactive segmentation method [C] . 2021 . |
| MLA | Bai, Yunkun et al. "Progressive medical image annotation with convolutional neural network-based interactive segmentation method" . (2021) . |
| APA | Bai, Yunkun , Sun, Guangmin , Li, Yu , Shen, Le , Zhang, Li . Progressive medical image annotation with convolutional neural network-based interactive segmentation method . (2021) . |
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摘要 ：
Finger vein biometric technology has gained a lot of popularity over recent years. This is primarily due to the increased security and reliability level that comes with its non-intrusive nature. Non-intrusiveness became inevitable due to the pandemic of COVID-19. This paper introduces a unique and lightweight image enhancement method for person identification using Convolutional Neural Networks (CNN). As pre-processing steps, Contrast Limited Adaptive Histogram Equalization (CLAHE) followed by gamma correction is applied. Afterward, the image is sharpened and then passed through the median filter. These steps are followed by applying power law and contrast adjustment. As a final step, CLAHE is used yet again to bring out the enhanced vascular structure. The method was appraised using the four different openly accessible databases. These are regarded as the most challenging available finger vein database-s by many researchers. For recognition purposes, CNN was used with transfer learning. Transfer learning is implemented by modifying the 13 convolutional layers of VGG-16. The proposed model architecture also includes five max-pooling layers, one ReLU, and one Softmax layer. It is observed that with transfer learning, the accuracy could have reached up to 99% on finger-vein recognition on the experimented dataset, thus proved to be a highly accurate approach for finger vein recognition.
关键词 ：
convolutional neural networks convolutional neural networks finger vein finger vein image processing image processing pattern recognition pattern recognition Contrast limited adaptive histogram equalization Contrast limited adaptive histogram equalization transfer learning transfer learning
引用：
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| GB/T 7714 | Bilal, Anas , Sun, Guangmin , Mazhar, Sarah . Finger-vein recognition using a novel enhancement method with convolutional neural network [J]. | JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS , 2021 , 44 (5) : 407-417 . |
| MLA | Bilal, Anas et al. "Finger-vein recognition using a novel enhancement method with convolutional neural network" . | JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS 44 . 5 (2021) : 407-417 . |
| APA | Bilal, Anas , Sun, Guangmin , Mazhar, Sarah . Finger-vein recognition using a novel enhancement method with convolutional neural network . | JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS , 2021 , 44 (5) , 407-417 . |
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摘要 ：
Automatic segmentation of optic disc (OD) and optic cup (OC) is an essential task for analysing colour fundus images. In clinical practice, accurate OD and OC segmentation assist ophthalmologists in diagnosing glaucoma. In this paper, we propose a unified convolutional neural network, named ResFPN-Net, which learns the boundary feature and the inner relation between OD and OC for automatic segmentation. The proposed ResFPN-Net is mainly composed of multi-scale feature extractor, multi-scale segmentation transition and attention pyramid architecture. The multi-scale feature extractor achieved the feature encoding of fundus images and captured the boundary representations. The multi-scale segmentation transition is employed to retain the features of different scales. Moreover, an attention pyramid architecture is proposed to learn rich representations and the mutual connection in the OD and OC. To verify the effectiveness of the proposed method, we conducted extensive experiments on two public datasets. On the Drishti-GS database, we achieved a Dice coefficient of 97.59%, 89.87%, the accuracy of 99.21%, 98.77%, and the Averaged Hausdorff distance of 0.099, 0.882 on the OD and OC segmentation, respectively. We achieved a Dice coefficient of 96.41%, 83.91%, the accuracy of 99.30%, 99.24%, and the Averaged Hausdorff distance of 0.166, 1.210 on the RIM-ONE database for OD and OC segmentation, respectively. Comprehensive results show that the proposed method outperforms other competitive OD and OC segmentation methods and appears more adaptable in cross-dataset scenarios. The introduced multi-scale loss function achieved significantly lower training loss and higher accuracy compared with other loss functions. Furthermore, the proposed method is further validated in OC to OD ratio calculation task and achieved the best MAE of 0.0499 and 0.0630 on the Drishti-GS and RIM-ONE datasets, respectively. Finally, we evaluated the effectiveness of the glaucoma screening on Drishti-GS and RIM-ONE datasets, achieving the AUC of 0.8947 and 0.7964. These results proved that the proposed ResFPN-Net is effective in analysing fundus images for glaucoma screening and can be applied in other relative biomedical image segmentation applications.
关键词 ：
Deep learning Deep learning Glaucoma Screening Glaucoma Screening Joint OD and OC segmentation Joint OD and OC segmentation Convolutional neural network Convolutional neural network
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| GB/T 7714 | Sun, Guangmin , Zhang, Zhongxiang , Zhang, Junjie et al. Joint optic disc and cup segmentation based on multi-scale feature analysis and attention pyramid architecture for glaucoma screening [J]. | NEURAL COMPUTING & APPLICATIONS , 2021 , 35 (22) : 16129-16142 . |
| MLA | Sun, Guangmin et al. "Joint optic disc and cup segmentation based on multi-scale feature analysis and attention pyramid architecture for glaucoma screening" . | NEURAL COMPUTING & APPLICATIONS 35 . 22 (2021) : 16129-16142 . |
| APA | Sun, Guangmin , Zhang, Zhongxiang , Zhang, Junjie , Zhu, Meilong , Zhu, Xiao-rong , Yang, Jin-Kui et al. Joint optic disc and cup segmentation based on multi-scale feature analysis and attention pyramid architecture for glaucoma screening . | NEURAL COMPUTING & APPLICATIONS , 2021 , 35 (22) , 16129-16142 . |
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摘要 ：
Diabetic retinopathy (DR) is a primary cause of blindness in which damage occurs to the retina due to an accretion of sugar levels in the blood. Therefore, prior detection, classification, and diagnosis of DR can prevent vision loss in diabetic patients. We proposed a novel and hybrid approach for prior DR detection and classification. We combined distinctive models to make the DR detection process robust or less error-prone while determining the classification based on the majority voting method. The proposed work follows preprocessing feature extraction and classification steps. The preprocessing step enhances abnormality presence as well as segmentation; the extraction step acquires merely relevant features; and the classification step uses classifiers such as support vector machine (SVM), K-nearest neighbor (KNN), and binary trees (BT). To accomplish this work, multiple severities of disease grading databases were used and achieved an accuracy of 98.06%, sensitivity of 83.67%, and 100% specificity. © 2013 IEEE.
关键词 ：
Binary trees Binary trees Classification (of information) Classification (of information) Computer aided diagnosis Computer aided diagnosis Extraction Extraction Eye protection Eye protection Grading Grading Nearest neighbor search Nearest neighbor search Support vector machines Support vector machines
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| GB/T 7714 | Bilal, Anas , Sun, Guangmin , Li, Yu et al. Diabetic Retinopathy Detection and Classification Using Mixed Models for a Disease Grading Database [J]. | IEEE Access , 2021 , 9 : 23544-23553 . |
| MLA | Bilal, Anas et al. "Diabetic Retinopathy Detection and Classification Using Mixed Models for a Disease Grading Database" . | IEEE Access 9 (2021) : 23544-23553 . |
| APA | Bilal, Anas , Sun, Guangmin , Li, Yu , Mazhar, Sarah , Khan, Abdul Qadir . Diabetic Retinopathy Detection and Classification Using Mixed Models for a Disease Grading Database . | IEEE Access , 2021 , 9 , 23544-23553 . |
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摘要 ：
To reduce the high pre-weaning mortality rate of new-born piglets crushed by sows, a kind of recognition and evaluation method for sows' behavior based on the scheme of time-sharing and multiplexing by adopting triaxial acceleration and video sensors at day and night is proposed in this paper. For darker scene at night, random forest classifier with optimal 43-dimensional feature vector subset proposed in this paper is adopted to recognize four kinds of macro behaviors of sows roughly by adopting triaxial acceleration sensor MPU6050. The recognition rate can reach 89.4%. For brighter light scene during the day, an improved bilinear convolutional neural network method based on CBAM module is proposed in this paper to recognize seven kinds of micro behaviors of sows by video sensor. The recognition rate can reach 84.4%. The methods proposed in this paper can meet the requirement of real-time to recognize the behavior of sows during 24 hours on the premise of ensuring accuracy. Finally, an evaluation model of sows' maternal behavior level is set up in this paper. The achivement of the study can not only help the farm to select sows with higher maternal ability for breeding piglets, but also avoid the large-scale economic losses caused by the high mortality rate of piglets before weaning of the farm. © 2013 IEEE.
关键词 ：
Behavioral research Behavioral research Convolutional neural networks Convolutional neural networks Decision trees Decision trees Losses Losses Population statistics Population statistics
引用：
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| GB/T 7714 | Sun, Guangmin , Shi, Chong , Liu, Jie et al. Behavior Recognition and Maternal Ability Evaluation for Sows Based on Triaxial Acceleration and Video Sensors [J]. | IEEE Access , 2021 , 9 : 65346-65360 . |
| MLA | Sun, Guangmin et al. "Behavior Recognition and Maternal Ability Evaluation for Sows Based on Triaxial Acceleration and Video Sensors" . | IEEE Access 9 (2021) : 65346-65360 . |
| APA | Sun, Guangmin , Shi, Chong , Liu, Jie , Ma, Pan , Ma, Jingyan . Behavior Recognition and Maternal Ability Evaluation for Sows Based on Triaxial Acceleration and Video Sensors . | IEEE Access , 2021 , 9 , 65346-65360 . |
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