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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Luo, Haiyi Sun, Zhenyu Jing, Xiaobei Shu, Bairui Chen, Shixiong Yong, Xu Yokoi, Hiroshi Department of Mechanical Engineering and Intelligent Systems The University of Electro-Communications 1-5-1 Chofugaoka Tokyo Chofu182-8585 Japan CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518055 China Center for Neuroscience and Biomedical Engineering The University of Electro-communications 1-5-1 Chofugaoka Tokyo Chofu182-8585 Japan

ISBN: (纸本)9781665481090

Exoskeleton robots are now prevalent in hand rehabilitation medical training, and they can effectively drive a variety of rehabilitative movements in a hand that has lost its motor ability. To adapt to the hand's physiological structure and motion characteristics, a hybrid-driven exoskeleton hand based on tendon rope and linkage and its validation experiments are proposed in this paper. The exoskeleton hand can assist one to five fingers independently or even assist a joint alone. Wearing the robot retains the physiological touch of the hand to the maximum extent, which is beneficial to rehabilitation. In addition, patients can also carry out rehabilitation training independently, and the control mode is simple and practical. To verify whether the exoskeleton can reach the grip standard of healthy hands, the Leap Motion Controller is also used to conduct experimental verification of finger movement wearing the exoskeleton. The results show that the maximum average differences between the angles of the finger flexion motion joints (MCP and PIP) with and without the exoskeleton are 10.33 degrees and 11.06 degrees. It was verified that the exoskeleton could meet the requirements of finger flexion and extension for assisted motion within a specific error range. © 2022 IEEE.

关键词： Physiology

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CAANet: CAM-guided Adaptive Attention Network for Weakly Supervised Semantic Segmentation of Thyroid Nodules

CAANet: CAM-guided Adaptive Attention Network for Weakly Sup...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ruiguo Yu Xianzhi Zhang Mankun Zhao Yang Yan Ming Li Mei Yu College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China Tianjin Key Laboratory of Advanced Networking Tianjin China Tianjin Medical University General Hospital Tianjin China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Deep learning-based thyroid ultrasound image segmentation is of great importance in clinical diagnosis. The Weakly Supervised Semantic Segmentation (WSSS) models requiring only Image-level Labels (IL) reduce the dependence on pixel-level labels. However, due to the lack of position of the objects in image-level labels, WSSS methods with IL are generally based on Class Activation Map (CAM) that can locate the objects. Nowadays, channel attention, which is one of the effective tools to extract object features from images, has been widely used in semantic segmentation tasks. Unfortunately, due to the large differences in the size and discriminative features of thyroid nodules, the mainstream channel attention is unable to perform flexible feature extraction for nodules, leading to the problem of under-segmentation or over-segmentation in the predicted results of the model. To overcome this issue, we propose a dynamic channel attention network that can extract features of thyroid nodules adaptively, called the CAM-guided Adaptive Attention Network (CAANet). In detail, CAANet can pay different attention to the overall and discriminative features of nodules based on the information provided by CAM. Finally, to verify the effectiveness of our method, we perform experimental comparisons with recent WSSS methods and the mainstream channel attention methods on the thyroid ultrasound image dataset. The evaluation results show that our method has better performance improvement, with a mean Intersection over Union (IoU) of 52.713%.

关键词： Ultrasonic imaging Adaptive systems Semantic segmentation Semantics Predictive models Feature extraction Lesions

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Gray Learning from Non-IID Data with Out-of-distribution Samples

arXiv

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arXiv 2022年

作者： Zhao, Zhilin Cao, Longbing Wang, Chang-Dong The Data Science Lab School of Computing and DataX Research Centre Macquarie University SydneyNSW2109 Australia The School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory of Computational Science Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

The integrity of training data, even when annotated by experts, is far from guaranteed, especially for non-IID datasets comprising both in- and out-of-distribution samples. In an ideal scenario, the majority of samples would be in-distribution, while samples that deviate semantically would be identified as out-of-distribution and excluded during the annotation process. However, experts may erroneously classify these out-of-distribution samples as in-distribution, assigning them labels that are inherently unreliable. This mixture of unreliable labels and varied data types makes the task of learning robust neural networks notably challenging. We observe that both in- and out-of-distribution samples can almost invariably be ruled out from belonging to certain classes, aside from those corresponding to unreliable ground-truth labels. This opens the possibility of utilizing reliable complementary labels that indicate the classes to which a sample does not belong. Guided by this insight, we introduce a novel approach, termed Gray Learning (GL), which leverages both ground-truth and complementary labels. Crucially, GL adaptively adjusts the loss weights for these two label types based on prediction confidence levels. By grounding our approach in statistical learning theory, we derive bounds for the generalization error, demonstrating that GL achieves tight constraints even in non-IID settings. Extensive experimental evaluations reveal that our method significantly outperforms alternative approaches grounded in robust statistics. Copyright © 2022, The Authors. All rights reserved.

关键词： machine learning

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CoMT: A Novel Benchmark for Chain of Multi-modal Thought on Large Vision-Language Models

arXiv

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arXiv 2024年

作者： Cheng, Zihui Chen, Qiguang Zhang, Jin Fei, Hao Feng, Xiaocheng Che, Wanxiang Li, Min Qin, Libo School of Computer Science and Engineering Central South University China Key Laboratory of Data Intelligence and Advanced Computing in Provincial Universities Soochow University China Research Center for SCIR Harbin Institute of Technology Harbin China National University of Singapore Singapore

Large Vision-Language Models (LVLMs) have recently demonstrated amazing success in multi-modal tasks, including advancements in Multi-modal Chain-of-Thought (MCoT) reasoning. Despite these successes, current benchmarks still follow a traditional paradigm with multi-modal input and text-modal output, which leads to significant drawbacks such as missing visual operations and vague expressions. Motivated by this, we introduce a novel Chain of Multi-modal Thought (CoMT) benchmark to address these limitations. Different from the traditional MCoT benchmark, CoMT requires both multi-modal input and multi-modal reasoning output, aiming to mimic human-like reasoning that inherently integrates visual operations. Specifically, CoMT consists of four categories: (1) Visual Creation, (2) Visual Deletion, (3) Visual Update, and (4) Visual Selection to comprehensively explore complex visual operations and concise expression in real scenarios. We evaluate various LVLMs and strategies on CoMT, revealing some key insights into the capabilities and limitations of the current approaches. We hope that CoMT can inspire more breakthroughs on introducing multi-modal generation into the reasoning process. The project page is available at https://***/czhhzc/CoMT. © 2024, CC BY-NC-SA.

关键词： Chains

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Solving Multitrip Pickup and Delivery Problem With Time Windows and Manpower Planning Using Multiobjective Algorithms

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IEEE/CAA Journal of Automatica Sinica 2020年第4期7卷 1134-1153页

作者： Jiahai Wang Yuyan Sun Zizhen Zhang Shangce Gao the Department of Computer Science and also with the Key Laboratory of Machine Intelligence and Advanced ComputingMinistry of Educationand also with the Guangdong Key Laboratory of Big Data Analysis and ProcessingSun Yat-sen UniversityGuangzhou 510275China the Department of Computer Science Sun Yat-sen UniversityGuangzhou 510275China the Faculty of Engineering University of ToyamaToyama-shi 930-8555Japan

The multitrip pickup and delivery problem with time windows and manpower planning(MTPDPTW-MP)determines a set of ambulance routes and finds staff assignment for a hospital. It involves different stakeholders with diverse interests and objectives. This study firstly introduces a multiobjective MTPDPTW-MP(MO-MTPDPTWMP) with three objectives to better describe the real-world scenario. A multiobjective iterated local search algorithm with adaptive neighborhood selection(MOILS-ANS) is proposed to solve the problem. MOILS-ANS can generate a diverse set of alternative solutions for decision makers to meet their requirements. To better explore the search space, problem-specific neighborhood structures and an adaptive neighborhood selection strategy are carefully designed in MOILS-ANS. Experimental results show that the proposed MOILS-ANS significantly outperforms the other two multiobjective algorithms. Besides, the nature of objective functions and the properties of the problem are analyzed. Finally, the proposed MOILS-ANS is compared with the previous single-objective algorithm and the benefits of multiobjective optimization are discussed.

关键词： Adaptive neighborhood selection manpower planning multiobjective optimization multitrip pickup and delivery problem with time windows

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An Efficient Color and Geometric Feature Fusion Module for 6D Object Pose Estiamtion 12

An Efficient Color and Geometric Feature Fusion Module for 6...

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12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2022

作者： Li, Jiangeng Liu, Hong Huang, Gao Zuo, Guoyu Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China Faculty of Information Beijing University of Technology Beijing100124 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing100081 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

6D pose estimation is widely used in robot tasks such as sorting and grasping. RGB-D-based methods have recently attained brilliant success, but they are still susceptible to heavy occlusion. Our critical insight is that color and geometry information in RGBD images are two complementary data, and the crux of the pose estimation problem under occlusion is fully leveraging them. Towards this end, we propose a new color and geometry feature fusion module that can efficiently leverage two complementary data sources from RGB-D images. Unlike prior fusion methods, we conduct a two-stage fusion strategy to do color-depth fusion and local-global fusion successively. Specifically, we fuse the color features extracted from RGB images into the point cloud in the first stage. In the second stage, we extract local and global features from the fused point cloud using an ASSANet-like network and splice them together to obtain the final fusion features. We conducted experiments on the widely used LineMod and YCB-Video datasets, which shows that our method improves the prediction accuracy while reducing the training time. © 2022 IEEE.

关键词： Color

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Distilling the Unknown to Unveil Certainty

arXiv

引用

arXiv 2023年

作者： Zhao, Zhilin Cao, Longbing Zhang, Yixuan Lin, Kun-Yu Zheng, Wei-Shi The Data Science Lab School of Computing Macquarie University SydneyNSW2109 Australia The DataX Research Centre Macquarie University SydneyNSW2109 Australia The China-Austria Belt and Road Joint Laboratory on Artificial Intelligence and Advanced Manufacturing Hangzhou Dianzi University Zhejiang Hangzhou310005 China The School of Computer Science and Engineering Sun Yat-sen University Guangzhou510275 China The School of Computer Science and Engineering Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Sun Yat-sen University Guangzhou510275 China

Out-of-distribution (OOD) detection is essential in identifying test samples that deviate from the in-distribution (ID) data upon which a standard network is trained, ensuring network robustness and reliability. This paper introduces OOD knowledge distillation, a pioneering learning framework applicable whether or not training ID data is available, given a standard network. This framework harnesses unknown OOD-sensitive knowledge from the standard network to craft a certain binary classifier adept at distinguishing between ID and OOD samples. To accomplish this, we introduce Confidence Amendment (CA), an innovative methodology that transforms an OOD sample into an ID one while progressively amending prediction confidence derived from the standard network. This approach enables the simultaneous synthesis of both ID and OOD samples, each accompanied by an adjusted prediction confidence, thereby facilitating the training of a binary classifier sensitive to OOD. Theoretical analysis provides bounds on the generalization error of the binary classifier, demonstrating the pivotal role of confidence amendment in enhancing OOD sensitivity. Extensive experiments spanning various datasets and network architectures confirm the efficacy of the proposed method in detecting OOD samples. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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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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Efficient Hierarchical Exploration with An Active Subgoal Generation Strategy

Efficient Hierarchical Exploration with An Active Subgoal Ge...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Xu, Xin Zuo, Guoyu Li, Jiangeng Huang, Gao The Faculty of Information Technology Beijing University of Technology Beijing100124 China The Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China Faculty of Information Beijing University of Technology Beijing100124 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing100081 China

ISBN: (纸本)9781665481090

Goal-conditioned hierarchical reinforcement learning (HRL) is a promising approach in long-horizon and complex tasks. It has been successfully used by decomposing tasks via subgoals. However, the existing goal-conditioned HRL methods often suffer from training inefficiency in the long-horizon tasks with sparse external rewards. For this problem, this paper proposes a novel framework that can train high-level policy efficiently with an effective sub goal generation method. The key component of the framework is an active subgoal generation strategy which consists of (a) an efficient high-level policy learning mechanism and (b) two measures of novelty and coverage for filtering effective subgoals. We also introduce the HER mechanism to the framework to solve the problem of sparse reward in the environment. Building upon the proposed framework, this paper develops an active hierarchical exploration strategy with an efficient subgoal generation method. We test our method on a variety of continuous control tasks. Experimental results demonstrate that the strategy outperforms the state-of-the-art HRL approaches. © 2022 IEEE.

关键词： Reinforcement learning

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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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