检索结果-内蒙古大学图书馆

SSRN 2023年

作者： Zhang, Ke Song, Chengqun Cheng, Jun Liu, Jiancheng Wang, Lei Hao, Fusheng Wu, Fuxiang Lou, Jie Dong, Qi CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China The Chinese University of Hong Kong 999077 Hong Kong China Nuclear Power Operations Co. Ltd China

Patrolling is crucial for maintaining the safety and security of real estate such as buildings, factories, and public areas. In general, a mobile patrol system (MPS) is most suited for circumstances when the environment is hazardous, difficult to reach, or where repetitive activities must be completed. MPS must overcome practical obstacles in order to provide thorough regional coverage and concentrated attention to high-risk regions. To address the drawbacks, a novel multi-target path updating and optimization method for MPS in nuclear power plants is proposed in this paper. The method leverages a Gaussian Mixture Model (GMM) to construct a risk heat map using environmental and security risk information. The high-risk points on the map are identified as targets for a single patrol path, which is dynamically updated to ensure that unpatrolled areas are considered during the next patrol. The static risk heat map is updated if any danger is detected. The multi-target Rapidly-Exploring Random Tree star (MT-RRT*) algorithm is used to obtain the patrol order and a feasible path, and then an efficient optimization approach is employed to generate a multi-target patrol path. The method has been shown to effectively achieve both comprehensive regional coverage and targeted monitoring of high-risk areas through multiple patrols. Although the proposed method is introduced from the scene of nuclear power plants, it can also be applied to the patrol of other scenes, e.g. military areas, hazardous waste sites, and industrial sites. © 2023, The Authors. All rights reserved.

关键词： Motion planning

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FDN: Finite difference network with hierarchical convolutional features for text-independent speaker verification

arXiv

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arXiv 2021年

作者： Li, Jin Yan, Nan Wang, Lan CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

In recent years, using raw waveforms as input for deep networks has been widely explored for the speaker verification system. For example, RawNet and RawNet2 extracted speaker’s feature embeddings from waveforms automatically for recognizing their voice, which can vastly reduce the front-end computation and obtain state-of-the-art performance. However, these models do not consider the speaker’s high-level behavioral features, such as intonation, indicating each speaker’s universal style, rhythm, etc. This paper presents a novel network that can handle the intonation information by computing the finite difference of different speakers’ utterance variations. Furthermore, a hierarchical way is also designed to enhance the intonation property from coarse to fine to improve the system accuracy. The high-level intonation features are then fused with the low-level embedding features. Experimental results on official VoxCeleb1 test data, VoxCeleb1-E, and VoxCeleb-H protocols show our method outperforms and robustness existing state-of-the-art systems. To facilitate further research, code is available at https://***/happyjin/FDN Copyright © 2021, The Authors. All rights reserved.

关键词： Speech recognition

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A control method with terrain classification and recognition for lower limb soft exosuit 22

A control method with terrain classification and recognition...

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22nd IEEE International Conference on E-Health Networking, Application and Services, HEALTHCOM 2020

作者： Ni, Jiangpeng Chen, Chunjie Wang, Zhuo Liu, Youfu Wu, Xinyu Liu, Yida Shenzhen Institutes of Adanced Technology Chinese Academy of Sciences Shenzhen China ShenZhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China

ISBN: (纸本)9781728162676

Soft Exosuit is a kind of Lower-limb wearable robots to augment and assist the wearer's performance. The wearer need different assistance modes to reduce the metabolic rate when walking on different terrains. Therefore, assistance modes need to be selected according to different terrains for the wearer of soft Exosuit. Recently, our team has designed a stable terrain classification and recognition system (TCRS) for the soft Exosuit to discriminate terrains and estimating environmental features. Through this system, soft Exosuit can perceive the environment to auxiliary control of the locomotion modes. A depth sensor with an inertial measurement unit(IMU) can acquire to stabilize the point cloud of environments. Subsequently, the 2D point cloud is extracted from the origin 3D point cloud. Then, they are classified to estimate terrain environmental features, including the incline angle of the slope, the width, and the stairs' height. Finally, the TCRS was evaluated by classifying and recognizing five basic terrains in three different experimental scenarios while the subject was wearing the soft Exosuit with the TCRS module. The results show that the terrain classification accuracy rate reaches 97.74 %, and the environmental features estimation error is less than 15 %. The promising results indicate the robustness and the potential application of the presented TCRS to provide proper auxiliary force to reduce the metabolic rate of wearers on different terrains. © 2021 IEEE.

关键词： Metabolism

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Time-frequency decomposition-based weighted ensemble learning for motor imagery EEG classification

Time-frequency decomposition-based weighted ensemble learnin...

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2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Zheng, Liangsheng Ma, Yue Li, Mengyao Xiao, Yang Feng, Wei Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen518055 China Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (纸本)9781665436786

Motor imagery brain-computer interface system based on Electroencephalogram (EEG) is an effective way to help the disabled recover part of their motor abilities. However, decoding the movement intention contained in the EEG signal accurately presents many challenges. In this paper, we propose a time-frequency decomposition-based weighted ensemble learning (TFDWEL) method, which aims to improve the classification performance of motor imagery EEG signals. The TFDWEL method divides the EEG signal into multiple subsets, and uses four time-frequency processing methods to extract the time-frequency sub-bands of each subset. Then the feature extraction model and classifier model of each subset trained by the common spatial pattern (CSP) algorithm and the support vector machine (SVM) algorithm are used to build a set of base learners. The least square error estimation method is used to learn the weight of each base learner, and finally the weighted summation method is used to obtain the final decision. The classification performance of the TFDWEL method is evaluated on the BCI Competition IV Data Set 2b, and the results show that the classification accuracy of 81.58% can be obtained. Superior classification performance indicates that the TFDWEL method can be used in further research to help the rehabilitation of the disabled. © 2021 IEEE.

关键词： Electroencephalography

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Feature Fusion of Gramian Angular Field Deep Learning EEG-Based Epileptic Seizure Classification 17

Feature Fusion of Gramian Angular Field Deep Learning EEG-Ba...

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17th International Convention on Rehabilitation Engineering and Assistive Technology, i-CREATe 2024

作者： Aboyeji, Sunday Timothy Wang, Xin Chen, Yan Ahmad, Ijaz Li, Lin Liu, Zhenzhen Yao, Chen Zhang, Yu Zhao, Guoru Li, Guanglin Chen, Shixiong Shenzhen Institute of Advanced Technology Cas Key Laboratory of Human-Machine Intelligence Synergy Systems Chinese Academy of Sciences Guangdong Shenzhen518055 China University of Chinese Academy of Sciences Shenzhen College of Advanced Technology Guangdong Shenzhen518055 China Surgery Division Epilepsy Center Shenzhen Children's Hospital Shenzhen518036 China First Affiliated Hospital of Shenzhen University Shenzhen Second People's Hospital The National Key Clinic Specialty Shenzhen Key Laboratory of Neurosurgery Department of Neurosurgery Shenzhen518035 China Naval Medical University Translational Medicine Center Shanghai200433 China The Chinese Univ of Hong Kong School of Medicine Guangdong Shenzhen518172 China

ISBN: (纸本)9798350355154

Deep learning, particularly convolutional neural networks (CNNs), is increasingly used for epileptic seizure detection, leveraging their ability to directly extract features from EEG signals. Since CNNs excel with image data, various methods have been developed to convert signals into images, including the Gramian Angular Field (GAF) transformation algorithm. The algorithm is subtyped into the Gramian Angular Summation Field (GASF) and the Gramian Angular Difference Field (GADF). Each of these transformation techniques has been employed individually in earlier studies. However, each method focuses on unique properties of the EEG signal during image construction. Therefore, we proposed a feature fusion of the two transformation methods using the CNN model to learn richer representations. The experiment was conducted on publicly available Bonn datasets. Initially, the datasets are segmented into epochs of length 0.5s and step size 0.2s. Images are generated for each of the segments, resulting in 8600 images for each set of Bonn datasets. The generated images from each of the transformations are fed into parallel CNNs, and the extracted features are fused for improved performance. We conducted binary, ternary, and multiclass (5-class) classifications. The results show that feature fusion of GASF and GADF improves the classification performance with an average accuracy increase of 0.255%, 0.295%, and 0.660% for binary, ternary, and multiclass, respectively. This feature fusion of the GAF transformation approach shows promising potential for distinguishing various EEG patterns, underscoring its potential in seizure classification tasks. © 2024 IEEE.

关键词： Convolutional neural networks

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Design and simulation of a hip exoskeleton for lateral walking

Design and simulation of a hip exoskeleton for lateral walki...

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2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Zhang, Zhewen Cao, Wujing Chen, Chunjie Yu, Hongliu Wu, Xinyu Meng, Qiaoling Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System University of Shanghai for Science and Technology Rehabilitation Engineering and Technology Institute CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology

ISBN: (纸本)9781665436786

In this article, a rehabilitation exoskeleton assisted by lateral walking is designed to help patients with lower limb movement disorders to solve the problem of lateral walking assistance, so as to achieve the effect of rehabilitation training. Based on the characteristics of lateral movement of human body, the exoskeleton designed in this paper uses linear push rod motor and thigh binding to assist hip abduction and adduction respectively. Based on the research of human transverse movement gait, a reasonable linkage mechanism was designed and kinematics simulation was carried out. The dynamics model of human lower limbs was established and calculated with MATLAB simulation software. the moment of hip joint lateral movement is obtained. Finally, the structural parameters of the driving mechanism were analyzed by dynamic simulation, and the simulation results were compared with the target motion function to verify the rationality of the design. © 2021 IEEE.

关键词： MATLAB

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A convolutional generative adversarial framework for data augmentation based on a robust optimal transport metric 23

A convolutional generative adversarial framework for data au...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Su, Liyilei Fu, Xianjun Hu, Qingmao Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Zhejiang College of Security Technology Wenzhou325016 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781665494571

Enhancement of the vanilla generative adversarial network (GAN) to preserve data variability in the presence of real world noise is of paramount significance in deep learning. In this study, we proposed a new distance metric of cosine distance in the framework of optimal transport (OT), and presented and validated a convolutional neural network (CNN) based GAN framework. In comparison with state-of-the-art methods based on Graphics Processing Units (GPU), the proposed framework could maintain the data diversity and quality best in terms of inception score (IS), Fréchet inception distance (FID) and enhancing the classification network of bone age, and is robust to noise degradation. The proposed framework is independent of hardware and thus could also be extended to more advanced hardware such as specialized Tensor Processing Units (TPU), and could be a potential built-in component of a general deep learning networks for such applications as image classification, segmentation, registration, and object detection. © 2021 IEEE.

关键词： Measurement Deep learning Tensors Smart cities Neural networks Graphics processing units Object detection

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Real-time Terrain Recognition based on Transformer and Gait Prediction based on CNN-LSTM-Attention for Exosuit

Real-time Terrain Recognition based on Transformer and Gait ...

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2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023

作者： Yang, Fangliang Chen, Chunjie Wang, Zhuo Xu, Yinsheng Liu, Yao Wu, Xinyu University of Chinese Academy of Sciences Shenzhen College of Advanced Technology Shenzhen518055 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China Guilin University of Technology Guilin541004 China

ISBN: (纸本)9798350327182

For wearable soft exosuits, an imprecise control strategy can easily injure the wearer, while real-time terrain recognition and accurate gait phase estimation can effectively improve the control strategy of the exosuit and make the wearer comfortable and safe. In this paper, a real-time terrain recognition algorithm based on Transformer and a gait estimation algorithm based on CNN-LSTM-Attention are implemented. The Transformer-based recognition of different terrains improves the recognition accuracy to some extent, and the CNN-LSTM-Attention based feature extraction for temporal signals such as gait phase is also extremely noticeable. Experiments show that the Transformer-based algorithm achieves 99.64 % recognition accuracy in six different terrain environments. In the gait phase estimation experiment, CNN-LSTM-CBAM achieved the best performance with an evaluation index r2 of 0.9221. The aforementioned terrain recognition algorithms and gait phase estimation algorithms may have a positive impact on soft exosuit and dynamic prosthetics research. © 2023 IEEE.

关键词： Landforms

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Asynchronous steady-state visual evoked potential brain-computer interface application: True and false positive rate comparison between with and without eye-tracking switch paradigms

Asynchronous steady-state visual evoked potential brain-comp...

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2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Xie, Jun Zhang, Huanqing Liu, Yi Fang, Peng Yu, Hongwei He, Liushi Li, Guanglin Yuan, Song Xu, Guanghua Li, Min School of Mechanical Engineering Xi'An Jiaotong University Xi'an710049 China Beijing Institute of Astronautical Systems Engineering Beijing100076 China Shenzhen Institute of Advanced Technology CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Engineering Laboratory of Neural Rehabilitation Shenzhen518055 China

ISBN: (纸本)9781665436786

Due to the bypass of muscle activity or peripheral nerve control, brain-computer interface (BCI) technique has advantages in different fields such as medical field for the rehabilitation of paralyzed patients. Steady-state visual evoked potential (SSVEP) has been widely adopted in BCI applications. SSVEP based BCIs have the advantages of high information transfer rate, less or no need of training, and strong anti-interference, which could be used in the more natural asynchronous BCI application with control of the users rather than the operant system of the synchronous mode. In order to solve the problem of high false positive rate (FPR) in common asynchronous SSVEP BCI applications, this paper proposed an eye-tracking switch based BCI paradigm to reduce the FPR and to improve the performance of the asynchronous BCI system. In the proposed paradigm, the fixation point position instead of EEG signal is used to determine whether the system is in idle state. Experimental results showed that when eye-tracking switch was applied in the asynchronous SSVEP BCI, the FPR was reduced to less than 10% and the recognition accuracy (i.e., the true positive rate, TPR) can also be improved to a certain extent, which proved the applicability of the eye-tracking switch in asynchronous BCI applications. © 2021 IEEE.

关键词： Brain computer interface

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Performance Evaluation of Deep Learning Techniques on Classifying Commercial Sensor-based Auditory Brainstem Response 16

Performance Evaluation of Deep Learning Techniques on Classi...

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16th International Conference on Sensing Technology, ICST 2023

作者： Huang, Kaiyang Wang, Xin He, Yuchao Ji, Junyu Xu, Yangjie Jia, Desheng Zhao, Chunrui Huang, Jinzhu Tao, Yuan Pan, Hongguang Li, Guanglin Chen, Shixiong Guangzhou University of Chinese Medicine School of Medical Information Engineering Guangzhou China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China Reliability and Trust University of Luxembourg Interdisciplinary Centre for Security Luxembourg Luxembourg Shenzhen Children's Hospital Department of Otolaryngology Shenzhen China The First Dongguan Affiliated Hospital of Guangdong Medical University Department of Endocrine Dongguan China Peking University Shenzhen Hospital Department of Otorhinolaryngology Shenzhen China

ISBN: (纸本)9798350395341

To objectively evaluate the health condition of the auditory pathway, Auditory Brainstem Response (ABR) is used in the clinic. The clinical diagnosis is concluded by the audiologist according to the morphology and characteristic waves of ABR through manual screening. It is a time-consuming and subjective task that not only reduces the efficiency of the physicians but also introduces subjective error which might cause an incorrect diagnosis. Therefore, it is necessary to propose an objective method that can solve the abovementioned problems. Deep Learning (DL) technique develops fast and is widely used in many fields, including biomedical engineering. Hence, DL might help with the objective classification of ABR and release the physicians from the heavy workload. In this paper, we evaluated the classification performance of three commonly used DL methods, namely EEGNet, DeepConvNet and ShallowConvNet, on several metrics. The results showed that on the task of classifying normal and abnormal ABR signals, EEGNet had the best performance, with the accuracy of 0.9995 ± 0.0011, the precision of 0.9995 ± 0.0011, the recall of 0.9995 ± 0.0011, the F1-score of 0.9995 ± 0.0011, and the AUC of 1.0000 ± 0.0000. It is believed that DL can help with objective ABR classification and release the physicians' workload. © 2023 IEEE.

关键词： Biomedical engineering

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