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学者姓名：姜海燕
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Abstract ：
针对传统机器学习在表面肌电信号手势识别领域的适应性和准确性不足，以及新用户因个体生理和行为差异在已有模型上表现不佳的问题，提出一种利用卷积神经网络模型并有效克服肌电数据分布差异的算法，用于提升手势识别的性能。首先对肌电信号进行变分模态分解，构建易于识别的表面肌电图像，并提出了一种卷积神经网络模型进行手势识别，提升用户相关的肌电信号手势识别准确率；同时利用迁移学习中的领域自适应和模型微调技术，提升用户无关的肌电信号手势识别准确率，并将所提算法在NinaPro DB1肌电数据集中进行了3分类、4分类、5分类和12分类共4组评估验证。结果表明：在4组评估验证中，用户相关的肌电信号手势识别平均准确率分别达到了99.28%、99.30%、98.39%和93.40%,用户无关的肌电信号手势识别平均准确率分别达到了94.05%、92.60%、88.38%和70.03%,表明本文提出的算法在表面肌电信号手势识别中具有良好的效果，为实现人机交互中的普适性的肌电设备开发提供了一种可行的方案。
Keyword ：
卷积神经网络 卷积神经网络 变分模态分解 变分模态分解 手势识别 手势识别 表面肌电信号 表面肌电信号 领域自适应 领域自适应
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| GB/T 7714 | 姜海燕 , 许先静 , 钟凌珺 et al. 采用变分模态分解与领域自适应的表面肌电信号手势识别 [J]. | 西安交通大学学报 , 2024 , 58 (05) : 75-87 . |
| MLA | 姜海燕 et al. "采用变分模态分解与领域自适应的表面肌电信号手势识别" . | 西安交通大学学报 58 . 05 (2024) : 75-87 . |
| APA | 姜海燕 , 许先静 , 钟凌珺 , 李竹韵 . 采用变分模态分解与领域自适应的表面肌电信号手势识别 . | 西安交通大学学报 , 2024 , 58 (05) , 75-87 . |
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Perineural invasion (PNI) is the process of neoplastic invasion of the nerves and is a prognostic factor in gastric cancer. However, such examination is a labor-intensive task in high-resolution digital pathological images. To alleviate this problem, we propose a multi-task deep learning-based framework to highlight diagnostically significant PNI regions in whole slide images (WSIs) of human gastric cancer tissue sections. The proposed framework includes a gastric cancer segmentation model, neural detection model, and PNI decision-making module, which realizes the segmentation of the gastric cancer region while completing the task of identifying PNI. Adequate comparative experiments were performed on our own gastric cancer PNI dataset called GC-PNI. Experiments have shown that our proposed model can effectively diagnose PNI with a high sensitivity of 0.972 and a specificity of 0.933, illustrating the potential of this practical application.
Keyword ：
Deep learning Deep learning Gastric cancer segmentation Gastric cancer segmentation Perineural infiltration detection Perineural infiltration detection Transformer Transformer Whole slide images Whole slide images
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| GB/T 7714 | Hu, Ziwei , Deng, Yanglin , Lan, Junlin et al. A multi-task deep learning framework for perineural invasion recognition in gastric cancer whole slide images [J]. | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2023 , 79 . |
| MLA | Hu, Ziwei et al. "A multi-task deep learning framework for perineural invasion recognition in gastric cancer whole slide images" . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL 79 (2023) . |
| APA | Hu, Ziwei , Deng, Yanglin , Lan, Junlin , Wang, Tao , Han, Zixin , Huang, Yuxiu et al. A multi-task deep learning framework for perineural invasion recognition in gastric cancer whole slide images . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2023 , 79 . |
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多无人机的协同目标分配是一个多模型、多约束的组合优化问题,其解空间随无人机的数量成指数上升;当以无人机协同目标分配的角度解决无人机编队切换的问题时,标准的匈牙利算法存在单个无人机飞行路径过长的问题,导致个别无人机的电量下降迅速,因此相较于其他无人机会提前降落,而且会导致其他无人机在编队切换过程中等待时间过长,进而影响整体编队飞行时长;考虑到无人机的工作环境处于三维空间及其编队切换时间的协同性,文章提出了改进的匈牙利算法,以保证在无人机飞行总移动距离尽可能小的前提下,减小单个无人机的最大移动距离,从而延长整个编队的飞行时长;经仿真对比多种算法,验证了该算法相较于匈牙利算法等其他算法具有更好的效果,能够很好地解决多无人机的编队切换问题.
Keyword ：
任务分配 任务分配 匈牙利算法 匈牙利算法 无人机 无人机 算法改进 算法改进 编队切换 编队切换
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| GB/T 7714 | 许先静 , 姜海燕 . 多无人机协同目标分配与航迹规划 [J]. | 计算机测量与控制 , 2023 , 31 (1) : 127-132 . |
| MLA | 许先静 et al. "多无人机协同目标分配与航迹规划" . | 计算机测量与控制 31 . 1 (2023) : 127-132 . |
| APA | 许先静 , 姜海燕 . 多无人机协同目标分配与航迹规划 . | 计算机测量与控制 , 2023 , 31 (1) , 127-132 . |
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The surface electromyography (sEMG) is one of the basic processing techniques to the gesture recognition because of its inherent advantages of easy collection and non-invasion. However, limited by feature extraction and classifier selection, the adaptability and accuracy of the conventional machine learning still need to promote with the increase of the input dimension and the number of output classifications. Moreover, due to the different characteristics of sEMG data and image data, the conventional convolutional neural network (CNN) have yet to fit sEMG signals. In this paper, a novel hybrid model combining CNN with the graph convolutional network (GCN) was constructed to improve the performance of the gesture recognition. Based on the characteristics of sEMG signal, GCN was introduced into the model through a joint voting network to extract the muscle synergy feature of the sEMG signal. Such strategy optimizes the structure and convolution kernel parameters of the residual network (ResNet) with the classification accuracy on the NinaPro DBl up to 90.07%. The experimental results and comparisons confirm the superiority of the proposed hybrid model for gesture recognition from the sEMG signals. © 2023 Beijing Institute of Technology. All rights reserved.
Keyword ：
deep learning deep learning gesture recognition gesture recognition graph convolutional network (GCN) graph convolutional network (GCN) residual network (ResNet) residual network (ResNet) surface electromyographic (sEMG) signals surface electromyographic (sEMG) signals
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| GB/T 7714 | Xu, X. , Jiang, H. . A Hybrid Model Based on ResNet and GCN for sEMG-Based Gesture Recognition [J]. | Journal of Beijing Institute of Technology (English Edition) , 2023 , 32 (2) : 219-229 . |
| MLA | Xu, X. et al. "A Hybrid Model Based on ResNet and GCN for sEMG-Based Gesture Recognition" . | Journal of Beijing Institute of Technology (English Edition) 32 . 2 (2023) : 219-229 . |
| APA | Xu, X. , Jiang, H. . A Hybrid Model Based on ResNet and GCN for sEMG-Based Gesture Recognition . | Journal of Beijing Institute of Technology (English Edition) , 2023 , 32 (2) , 219-229 . |
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The article proposes a qualitative identification scheme of fluorescent immunoassay strips based on residual networks to address problems such as poor strip positioning accuracy and inadequate strip size specifications in current photoelectric fluorescent immunoassay quantitative detection systems, which result in low accuracy and detection efficiency. The proposed method employs the Hough line detection algorithm, which is based on Canny edge detection, to extract the tilt angle of the test strip. It then combines this with contour extraction of the strip image to calculate its contour center. By utilizing the test strip tilt angle and contour center, the article accurately locates the test strip position. The residual network is utilized for extracting strip features, while the extreme learning machine is employed for discriminating the validity and positive/negative results of the fluorescent strips. Validation experiments on a novel coronavirus fluorescent immunoassay strip demonstrate that the residual network model based on extreme learning machine proposed in this article achieves 100% accuracy, precision, recall, and F1-score values for strip classification, effectively improving the recognition accuracy and detection efficiency of the fluorescent immunoassay quantitative detection system. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
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| GB/T 7714 | Zhong, L. , Jiang, H. , Xu, X. . Qualitative identification of fluorescence immunochromatography strips based on residual networks [未知]. |
| MLA | Zhong, L. et al. "Qualitative identification of fluorescence immunochromatography strips based on residual networks" [未知]. |
| APA | Zhong, L. , Jiang, H. , Xu, X. . Qualitative identification of fluorescence immunochromatography strips based on residual networks [未知]. |
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Gastric cancer is the third most common cause of cancer-related death in the world. Human epidermal growth factor receptor 2 (HER2) positive is an important subtype of gastric cancer, which can provide significant diagnostic information for gastric cancer pathologists. However, pathologists usually use a semi-quantitative assessment method to assign HER2 scores for gastric cancer by repeatedly comparing hematoxylin and eosin (H&E) whole slide images (WSIs) with their HER2 immunohistochemical WSIs one by one under the microscope. It is a repetitive, tedious, and highly subjective process. Additionally, WSIs have billions of pixels in an image, which poses computational challenges to Computer-Aided Diagnosis (CAD) systems. This study proposed a deep learning algorithm for HER2 quantification evaluation of gastric cancer. Different from other studies that use convolutional neural networks for extracting feature maps or pre-processing on WSIs, we proposed a novel automatic HER2 scoring framework in this study. In order to accelerate the computational process, we proposed to use the re-parameterization scheme to separate the training model from the deployment model, which significantly speedup the inference process. To the best of our knowledge, this is the first study to provide a deep learning quantification algorithm for HER2 scoring of gastric cancer to assist the pathologist's diagnosis. Experiment results have demonstrated the effectiveness of our proposed method with an accuracy of 0.94 for the HER2 scoring prediction.
Keyword ：
CNN CNN deep learning deep learning gastric cancer gastric cancer HER2 score prediction HER2 score prediction re-parameterization re-parameterization
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| GB/T 7714 | Han, Zixin , Lan, Junlin , Wang, Tao et al. A Deep Learning Quantification Algorithm for HER2 Scoring of Gastric Cancer [J]. | FRONTIERS IN NEUROSCIENCE , 2022 , 16 . |
| MLA | Han, Zixin et al. "A Deep Learning Quantification Algorithm for HER2 Scoring of Gastric Cancer" . | FRONTIERS IN NEUROSCIENCE 16 (2022) . |
| APA | Han, Zixin , Lan, Junlin , Wang, Tao , Hu, Ziwei , Huang, Yuxiu , Deng, Yanglin et al. A Deep Learning Quantification Algorithm for HER2 Scoring of Gastric Cancer . | FRONTIERS IN NEUROSCIENCE , 2022 , 16 . |
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Background and Objective: A thyroid nodule is an abnormal lump that grows in the thyroid gland, which is the early symptom of thyroid cancer. In order to diagnose and treat thyroid cancer at the earliest stage, it is desired to characterize the nodule accurately. Ultrasound thyroid nodules segmentation is a challenging task due to the speckle noise, intensity heterogeneity, low contrast and low resolution. In this paper, we propose a novel framework to improve the accuracy of thyroid nodules segmentation. Methods: Different from previous work, a super-resolution reconstruction network is firstly constructed to upscale the resolution of the input ultrasound image. After that, our proposed N-shape network is utilized to perform the segmentation task. The guidance of super-resolution reconstruction network can make the high-frequency information of the input thyroid ultrasound image richer and more comprehensive than the original image. Our N-shape network consists of several atrous spatial pyramid pooling blocks, a multi-scale input layer, a U-shape convolutional network with attention blocks and a proposed parallel atrous convolution(PAC) module. These modules are conducive to capture context information at multiple scales so that semantic features can be fully utilized for lesion segmentation. Especially, our proposed PAC module is beneficial to further improve the segmentation by extracting high-level semantic features from different receptive fields. We use the UTNI-2021 dataset for model training, validating and testing. Results: The experimental results show that our proposed method achieve a Dice value of 91.9%, a mIoU value of 87.0%, a Precision value of 88.0%, a Recall value 83.7% and a F1-score value of 84.3%, which outperforms most state-of-the-art methods. Conclusions: Our method achieves the best performance on the UTNI-2021 dataset and provides a new way of ultrasound image segmentation. We believe that our method can provide doctors with reliable auxiliary diagnosis information in clinical practice. (C) 2022 Elsevier B.V. All rights reserved.
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| GB/T 7714 | Lin, Xingtao , Zhou, Xiaogen , Tong, Tong et al. A Super-resolution Guided Network for Improving Automated Thyroid Nodule Segmentation [J]. | COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE , 2022 , 227 . |
| MLA | Lin, Xingtao et al. "A Super-resolution Guided Network for Improving Automated Thyroid Nodule Segmentation" . | COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 227 (2022) . |
| APA | Lin, Xingtao , Zhou, Xiaogen , Tong, Tong , Nie, Xingqing , Wang, Luoyan , Zheng, Haonan et al. A Super-resolution Guided Network for Improving Automated Thyroid Nodule Segmentation . | COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE , 2022 , 227 . |
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The application of deep learning in the medical field has continuously made huge breakthroughs in recent years. Based on convolutional neural network (CNN), the U-Net framework has become the benchmark of the medical image segmentation task. However, this framework cannot fully learn global information and remote semantic information. The transformer structure has been demonstrated to capture global information relatively better than the U-Net, but the ability to learn local information is not as good as CNN. Therefore, we propose a novel network referred to as the O-Net, which combines the advantages of CNN and transformer to fully use both the global and the local information for improving medical image segmentation and classification. In the encoder part of our proposed O-Net framework, we combine the CNN and the Swin Transformer to acquire both global and local contextual features. In the decoder part, the results of the Swin Transformer and the CNN blocks are fused to get the final results. We have evaluated the proposed network on the synapse multi-organ CT dataset and the ISIC 2017 challenge dataset for the segmentation task. The classification network is simultaneously trained by using the encoder weights of the segmentation network. The experimental results show that our proposed O-Net achieves superior segmentation performance than state-of-the-art approaches, and the segmentation results are beneficial for improving the accuracy of the classification task. The codes and models of this study are available at https://github.com/ortonwang/O-Net..
Keyword ：
classification classification CNN CNN deep learning deep learning medical image segmentation medical image segmentation transformer transformer
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| GB/T 7714 | Wang, Tao , Lan, Junlin , Han, Zixin et al. O-Net: A Novel Framework With Deep Fusion of CNN and Transformer for Simultaneous Segmentation and Classification [J]. | FRONTIERS IN NEUROSCIENCE , 2022 , 16 . |
| MLA | Wang, Tao et al. "O-Net: A Novel Framework With Deep Fusion of CNN and Transformer for Simultaneous Segmentation and Classification" . | FRONTIERS IN NEUROSCIENCE 16 (2022) . |
| APA | Wang, Tao , Lan, Junlin , Han, Zixin , Hu, Ziwei , Huang, Yuxiu , Deng, Yanglin et al. O-Net: A Novel Framework With Deep Fusion of CNN and Transformer for Simultaneous Segmentation and Classification . | FRONTIERS IN NEUROSCIENCE , 2022 , 16 . |
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The joint torque prediction plays an important role in rehabilitation medicine, clinical medicine, sports training and other fields. The continuous and real-time torque prediction can make the human-computer interaction equipment better feedback and reproduce the intention of human motion. To provide a safe, active and comfortable rehabilitation training environment for patients and enhance the compliance of the human-computer interaction equipment, a novel method of joint torque prediction is proposed, which is based on an improved recursive cerebellar model neural network. In this method, muscle synergy analysis is used to reduce the dimensionality of surface electromyographic (sEMG) signals. Then, the reduced-dimension sEMG feature vector, joint angular velocity and joint angle are used as the input data of the prediction model. In addition, recursive unit and fuzzy logic rules are introduced into the cerebellar model neural network, while the wavelet function is used as membership function. Hence, the generalization ability of the network is optimized. The RWFCMNN model realizes the time series prediction of the dynamic torque of ankle dorsiflexion and plantarflexion in three states, non-fatigue, transitional fatigue and fatigue. The average Pearson correlation coefficient and the average normalized root mean square error between the predicted torque and the actual torque are 0.933 5 and 0.159 8, respectively. These numerical values verify the accuracy and effectiveness of this method for continuous prediction of lower limb joint torque. © 2022, Science Press. All right reserved.
Keyword ：
Brain Brain Correlation methods Correlation methods Forecasting Forecasting Fuzzy logic Fuzzy logic Human computer interaction Human computer interaction Joints (anatomy) Joints (anatomy) Mean square error Mean square error Medical computing Medical computing Membership functions Membership functions Muscle Muscle Neural network models Neural network models Numerical methods Numerical methods Sports Sports Torque Torque
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| GB/T 7714 | Jiang, Haiyan , Li, Zhuyun , Chen, Yan . Joint torque prediction of lower limb of sEMG signals based on improved cerebellar model [J]. | Chinese Journal of Scientific Instrument , 2022 , 43 (11) : 172-180 . |
| MLA | Jiang, Haiyan et al. "Joint torque prediction of lower limb of sEMG signals based on improved cerebellar model" . | Chinese Journal of Scientific Instrument 43 . 11 (2022) : 172-180 . |
| APA | Jiang, Haiyan , Li, Zhuyun , Chen, Yan . Joint torque prediction of lower limb of sEMG signals based on improved cerebellar model . | Chinese Journal of Scientific Instrument , 2022 , 43 (11) , 172-180 . |
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关节力矩预测在康复医学、临床医学和运动训练等领域有着重要作用,对力矩连续、实时地预测可以使人机交互设备更好地反馈、复刻人体运动意图.为了给患者提供一个安全、主动、舒适的康复训练环境,提升人机交互设备的柔顺性,提出了一种改进型递归小脑模型神经网络模型关节力矩预测方法.该方法采用肌肉协同分析对采集的相关肌肉的表面肌电信号(sEMG)进行降维,将降维后的sEMG特征向量与关节角速度、关节角度作为输入信号,并在小脑模型神经网络中加入递归单元和模糊逻辑规则,以小波函数作为隶属度函数,对非疲劳、过渡疲劳及疲劳这3种状态下的踝关节背屈跖屈运动的动态力矩进行连续预测.力矩预测值与实际值之间的平均皮尔逊相关系数和平均标准均方根误差分别为0.933 5和0.159 8,实验结果验证了该方法对下肢关节力矩连续预测的准确性和有效性.
Keyword ：
关节力矩预测 关节力矩预测 小脑模型神经网络 小脑模型神经网络 肌肉协同分析 肌肉协同分析 表面肌电信号 表面肌电信号
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| GB/T 7714 | 姜海燕 , 李竹韵 , 陈艳 . 基于改进小脑模型的sEMG下肢关节力矩预测 [J]. | 仪器仪表学报 , 2022 , 43 (11) : 172-180 . |
| MLA | 姜海燕 et al. "基于改进小脑模型的sEMG下肢关节力矩预测" . | 仪器仪表学报 43 . 11 (2022) : 172-180 . |
| APA | 姜海燕 , 李竹韵 , 陈艳 . 基于改进小脑模型的sEMG下肢关节力矩预测 . | 仪器仪表学报 , 2022 , 43 (11) , 172-180 . |
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