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学者姓名:姜海燕
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Abstract :
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 等. "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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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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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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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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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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本发明提出一种多传感器融合反馈调整的止鼾枕,其特征在于,包括:止鼾枕本体、布设在止鼾枕本体上的分布式压力传感器、布设在止鼾枕本体内的多个带有气泵的气囊,以及控制器、心电脉搏波传感器、声音传感器和设置在每个气囊内的气压传感器;所述分布式压力传感器、心电脉搏波传感器、声音传感器、气压传感器和气泵的驱动结构分别连接控制器。其对使用者的睡眠干扰小,使用体验佳。所提取的特征值对鼾声的辨别准确,通过分布式压力传感器网络能识别人体体位信息,对于气压的检测保证了使用的安全性。能在使用者无感知中调整睡眠体位,保证睡眠质量。
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GB/T 7714 | 姜海燕 , 许先静 , 陈艳 et al. 多传感器融合反馈调整的止鼾枕 : CN202110458686.9[P]. | 2021-04-27 . |
MLA | 姜海燕 et al. "多传感器融合反馈调整的止鼾枕" : CN202110458686.9. | 2021-04-27 . |
APA | 姜海燕 , 许先静 , 陈艳 , 黄书萍 , 杜民 . 多传感器融合反馈调整的止鼾枕 : CN202110458686.9. | 2021-04-27 . |
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Surface electromyographic (sEMG) signal contains abundant information such as joint torque and joint motion, which is widely used in human-computer interactive intelligent rehabilitation equipment. In this work, the ankle torque of lower limb is taken as the research object, and the feature parameters of sEMG which represent the fatigue state are analysed. Advance prediction of fatigue features for specific time periods was performed using a normalized minimum average square (NLMS) filter. While the modified cerebellar model neural network (WFCMNN) is used to classify fatigue, which can be divided into three states, namely no fatigue, transition to fatigue, and fatigue. The results show that the accuracy of classification is 96.429%, which is better than other advanced models. At the same time, sEMG signal is used to predict fatigue in advance, which can solve the problem of differences between different individuals. Such strategy is helpful for doctors and physiotherapists to carry out rehabilitation treatment for patients, as a pre judgment and diagnosis index. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 Licence.
Keyword :
Biomedical signal processing Biomedical signal processing Diagnosis Diagnosis Human rehabilitation equipment Human rehabilitation equipment Joints (anatomy) Joints (anatomy) Muscle Muscle Patient rehabilitation Patient rehabilitation Patient treatment Patient treatment
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GB/T 7714 | Huang, Shuping , Jang, Haiyan , Chen, Yan et al. Analysis of lower limb muscle fatigue based on surface electromyographic signal [C] . 2021 . |
MLA | Huang, Shuping et al. "Analysis of lower limb muscle fatigue based on surface electromyographic signal" . (2021) . |
APA | Huang, Shuping , Jang, Haiyan , Chen, Yan , Shi, Shaoyun . Analysis of lower limb muscle fatigue based on surface electromyographic signal . (2021) . |
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BACKGROUND: Implantable medical sensors for monitoring and transmitting physiological signals like blood glucose, blood oxygen, electrocardiogram, and endoscopic video present a new way for health care and disease prevention. Nevertheless, the signals transmitted by implantable sensors undergo significant attenuation as they propagate through various biological tissue layers. OBJECTIVE: This paper mainly aims to investigate the power loss of an out-to-in body wireless radio frequency link at 2.45 GHz. METHODS: Two simulation models including the single-layer human tissue model and three-layer human tissue model were established, applying the finite element method (FEM). Two experiments using physiological saline and excised porcine tissue were conducted to measure the power loss of a wireless radio frequency link at 2.45 GHz. Various communication distances and implantation depths were investigated in our study. RESULTS: The results from our measurements show that each 2 cm increase in implantation depth will result in an additional power loss of about 10 dB. The largest difference in values obtained from the measurements and the simulations is within 4 dB, which indicates that the experiments are in good agreement with the simulations. CONCLUSIONS: These results are significant for the estimate of how electromagnetic energy changes after propagating through human tissues, which can be used as a reference for the link budget of transceivers or other implantable medical devices.
Keyword :
finite element method finite element method Implantable medical sensors Implantable medical sensors power loss power loss radio frequency link radio frequency link
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GB/T 7714 | Chen, Xingguang , Chen, Zhiying , Gao, Yueming et al. An investigation on power loss of an out-to-in body wireless radio frequency link [J]. | TECHNOLOGY AND HEALTH CARE , 2021 , 29 (6) : 1089-1098 . |
MLA | Chen, Xingguang et al. "An investigation on power loss of an out-to-in body wireless radio frequency link" . | TECHNOLOGY AND HEALTH CARE 29 . 6 (2021) : 1089-1098 . |
APA | Chen, Xingguang , Chen, Zhiying , Gao, Yueming , Liu, Wenzhu , Jiang, Ruixin , Du, Min et al. An investigation on power loss of an out-to-in body wireless radio frequency link . | TECHNOLOGY AND HEALTH CARE , 2021 , 29 (6) , 1089-1098 . |
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Joint torque prediction plays an important role in quantitative limb rehabilitation training and the exoskeleton robot. The Surface electromyography signal (sEMG) with the advantages of non-invasive and easy collection can be applied to the prediction of human muscle force. By utilizing the sEMG, the recurrent cerebellar model neural network (RCMNN), which has better generalization and computational power than the traditional neural network has been used to predict the joint torque. In this work, a smooth function with adaptive coefficient is employed to polish the results of RCMNN, the proposed method shows great performance on torque prediction with the correlation coefficient between the torque and the estimation result up to 98.43%, such advanced model paves the way to the application on the quantitative rehabilitation training.
Keyword :
ankle joint ankle joint RCMNN RCMNN sEMG sEMG Torque prediction Torque prediction
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GB/T 7714 | Jiang, Hai-Yan , Yu, Shou-Yan , Lin, Chih-Min et al. Torque Prediction of Ankle Joint from Surface Electromyographic Using Recurrent Cerebellar Model Neural Network [J]. | ACTA POLYTECHNICA HUNGARICA , 2021 , 18 (8) : 183-199 . |
MLA | Jiang, Hai-Yan et al. "Torque Prediction of Ankle Joint from Surface Electromyographic Using Recurrent Cerebellar Model Neural Network" . | ACTA POLYTECHNICA HUNGARICA 18 . 8 (2021) : 183-199 . |
APA | Jiang, Hai-Yan , Yu, Shou-Yan , Lin, Chih-Min , Chen, Yan , Huang, Shu-Ping . Torque Prediction of Ankle Joint from Surface Electromyographic Using Recurrent Cerebellar Model Neural Network . | ACTA POLYTECHNICA HUNGARICA , 2021 , 18 (8) , 183-199 . |
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