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A Multimodal Fusion Model for Estimating Human Hand Force Comparing surface electromyography and ultrasound signals

IEEE ROBOTICS & AUTOMATION MAGAZINE 2022年第4期29卷 10-24页

作者： Zou, Yongxiang Cheng, Long Li, Zhengwei Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Inst Automat Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Biomimetic robots have received significant attention in recent years. Among them, the wearable exoskeleton, which imitates the functions of the musculoskeletal system to assist humans, is a typical biomimetic robot. Given that safe human-robot interaction plays a critical role in the successful application of wearable exoskeletons, this work studies the clinical readiness of a multimodal fusion model that estimates hand force based on the surface electromyography (sEMG) and A-mode ultrasound signals of the forearm muscles. The proposed multimodal fusion model affords the biomimetic hand exoskeleton assisting the elderly in completing daily tasks or quantitatively assessing the recovery level of poststroke patients. The suggested fusion model is called Optimization of Latent Representation for the Self-Attention Convolutional Neural Network (OLR-SACNN), which utilizes a common component extraction module (CCEM) and a complementary component retention module (CCRM) to optimize latent representation of the multiple modalities. Then the optimized latent representations are fused with the self-attention mechanism. The experiments conducted on a self-collected multimodal data set verify performance of the proposed OLR-SACNN model. Specifically, compared to solely employing sEMG or A-mode ultrasound signals, the force estimation's normalized mean-square error (NMSE) based on the multiple modalities decreases by 97.7 and 38.92%, respectively. Furthermore, the OLR-SACNN model has been used to estimate the hand force of some poststroke patients and attained the desired performance.

关键词： Force Ultrasonic imaging Muscles Robots Feature extraction Estimation Exoskeletons

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BNAS-v2: Memory-Efficient and Performance-Collapse-Prevented Broad Neural Architecture Search

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IEEE TRANSACTIONS ON systemS MAN CYBERNETICS-systemS 2022年第10期52卷 6259-6272页

作者： Ding, Zixiang Chen, Yaran Li, Nannan Zhao, Dongbin Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In this article, we propose BNAS-v2 to further improve the efficiency of broad neural architecture search (BNAS), which employs a broad convolutional neural network (BCNN) as the search space. In BNAS, the single-path sampling-updating strategy of an overparameterized BCNN leads to terrible unfair training issue, which restricts the efficiency improvement. To mitigate the unfair training issue, we employ a continuous relaxation strategy to optimize all paths of the overparameterized BCNN simultaneously. However, continuous relaxation leads to a performance collapse issue that leads to the unsatisfactory performance of the learned BCNN. For that, we propose the confident learning rate (CLR) and introduce the combination of partial channel connections and edge normalization. Experimental results show that 1) BNAS-v2 delivers state-of-the-art search efficiency on both CIFAR-10 (0.05 GPU days, which is 4x faster than BNAS) and ImageNet (0.19 GPU days) with better or competitive performance;2) the above two solutions are effectively alleviating the performance collapse issue;and 3) BNAS-v2 achieves powerful generalization ability on multiple transfer tasks, e.g., MNIST, FashionMNIST, NORB, and SVHN. The code is available at https://***/zixiangding/BNASv2.

关键词： Computer architecture Training Graphics processing units Optimization Convolution Topology Task analysis Broad neural architecture search (BNAS) confident learning rate (CLR) continuous relaxation image classification partial channel connections (PC)

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Learning Accurate and Stable Point-to-Point Motions: A Dynamic system Approach

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IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第2期7卷 1510-1517页

作者： Zhang, Yu Cheng, Long Li, Houcheng Cao, Ran Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

This letter proposes a dynamic system approach to learn point-to-point motions while keeping the stability of the dynamic system. The proposed approach is grounded on a Learning from Demonstration (LfD) method based on a neural network, which gets a better reproduction performance while guaranteeing the generalization ability. The proposed approach has been experimentally validated on the LASA dataset and by the "pick-and-place" task of Franke Emika robot, and experimental results demonstrate that: (1) compared with the state-of-the-art results, the trajectory generated by the proposed approach achieves higher accuracy (approximately 24.79%) in terms of the similarity with respect to the demonstration;(2) the proposed approach can handle high dimensional data and learn from one or more demonstrations;(3) the proposed approach can guarantee the performance regardless of the variation of starting points even in the case of high dimensional complex motions.

关键词： Point-to-point tasks neural network dynamic system generalization performance high dimensional data

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Relentless False Data Injection Attacks Against Kalman-Filter-Based Detection in Smart Grid

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IEEE TRANSACTIONS ON control OF NETWORK systemS 2022年第3期9卷 1238-1250页

作者： Liu, Yifa Cheng, Long Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

As one of the most dangerous cyber attacks in smart grids, the false data injection attacks pose a serious threat to power system security. To detect the false data, the traditional residual method and other improved methods, such as the Kalman-filter-based detector, have been proposed. However, these methods often have defects, especially in a very complex networked system with noises. By investigating the tolerance to the uncertainty in the residual detection method and properties of noises, the attack magnitude planning has been presented to hide the attack behind noises, which can bypass the residual detection method. As to the Kalman-filter-based detector, this article designs a specific attack strategy that can successfully deceive the Kalman-filter-based detector. Under this strategy, the false data injected at each step are used to balance the anomalies caused by previous false data, making the system look quite normal in monitoring, while deviating the system from normal operation eventually.

关键词： Attack sequence false data injection Kalman filter smart grid security state estimation

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Data-efficient image captioning of fine art paintings via virtual-real semantic alignment training

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NEUROCOMPUTING 2022年 490卷 163-180页

作者： Lu, Yue Guo, Chao Dai, Xingyuan Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

The image captioning of fine art paintings aims at generating a sentence to describe the painting content. Compared with photographic images, there are few annotated data of clear and precise content descriptions for paintings. Besides, painting images usually have abstract expressions, making it hard to extract their representative features. In this paper, we propose a virtual-real semantic alignment training process to address these challenges in painting captioning. To provide sufficient training data, we generate a virtual painting captioning dataset by applying style transfer to a large-scale photographic image captioning dataset and maintaining their annotations. To tackle the difficulty of abstract expressions, we employ a semantic alignment loss between photographic image features and virtual painting features to guide the training of the painting feature extractor. We evaluate our method in two data-hungry scenarios where only a few or no annotated painting data for training. According to the evaluation results on a public painting captioning dataset and our annotated painting captioning dataset, our model achieves significant improvements and higher data efficiency than the baselines in the two data-hungry scenarios on all datasets. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Image captioning Fine art paintings Semantic alignment

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Rethinking Appearance-Based Deep Gait Recognition: Reviews, Analysis, and Insights From Gait Recognition Evolution

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systemS 2025年第6期36卷 9777-9797页

作者： Li, Jingqi Zhang, Yuzhen Zeng, Yi Ye, Changxin Xu, Wenzheng Ben, Xianye Wang, Fei-Yue Zhang, Junping Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China Shandong Univ Shenzhen Res Inst Shenzhen 518063 Peoples R China Shandong Univ Sch Informat Sci & Engn Jinan 250100 Peoples R China Obuda Univ DeSci Ctr Parallel Intelligence H-1034 Budapest Hungary Macau Univ Sci & Technol Intelligent Syst Robot & Automat Lab Macau Peoples R China CASIA State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Gait recognition is a prominent biometric recognition technique extensively employed in public security. Appearance-based and model-based gait recognition are two categories of methods commonly used. Specifically, appearance-based methods, which use silhouettes to represent body information, typically outperform model-based methods that rely on skeleton data, making them more popular. Recently, the shift from single-frame templates to multiframe silhouettes has advanced appearance-based gait recognition with better spatiotemporal representation. However, there is a notable lack of comprehensive studies that deepen the understanding of multiframe appearance-based gait recognition methods. This article reviews various methods to trace the evolution of gait recognition. Furthermore, we unify various performant models in one framework, study the overlooked effects on data arrangement, and explore the scaling ability of existing methods. Besides the advancement in gait recognition, we also summarize the current challenges and future prospects to foster future research.

关键词： Gait recognition Feature extraction Data models Reviews Training Skeleton Shape Analytical models Deep learning Clothing Appearance-based biometric deep learning gait recognition GaitBase

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Parallel Medical Devices and Instruments: Integrating Edge and Cloud Intelligence for Smart Treatment and Health systems

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IEEE-CAA JOURNAL OF AUTOMATICA SINICA 2025年第4期12卷 651-654页

作者： Lin, Fei Gao, Tommy Sun, Dali Ni, Qinghua Ding, Xianting Wang, Jing Gao, David Wenzhong Wang, Fei-Yue Macau Univ Sci & Technol Fac Innovat Engn Dept Engn Sci Macau 999078 Peoples R China Univ Denver Knoebel Inst Hlth Aging Fac Dept Elect & Comp Engn Denver CO 80210 USA Shanghai Jiao Tong Univ Inst Personalized Med Sch Biomed Engn Shanghai 200030 Peoples R China Shanghai Jiao Tong Univ Sch Biomed Engn Shanghai 200030 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Denver Dept Elect & Comp Sci Denver CO 80210 USA Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

With the rapid development of technologies such as Artificial Intelligence (AI), edge computing, and cloud intelligence, the medical field is undergoing a fundamental transformation [1]. These technologies significantly enhance the medical system's capability to process complex data and also improve the real-time response rate to patient needs. In this wave of technological innovation, parallel intelligence, along with Artificial systems, Computational experiments, and Parallel execution (ACP) approach [2] will play a crucial role. Through parallel interactions between virtual and real systems, this approach optimizes the functionality of medical devices and instruments, enhancing the accuracy of diagnoses and treatments while enabling the autonomous evolution and adaptive adjustment of medical systems.

关键词： Electronic health record

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ShadowPainter: Active Learning Enabled Robotic Painting through Visual Measurement and Reproduction of the Artistic Creation Process

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JOURNAL OF INTELLIGENT & ROBOTIC systemS 2022年第3期105卷 1-17页

作者： Guo, Chao Bai, Tianxiang Wang, Xiao Zhang, Xiangyu Lu, Yue Dai, Xingyuan Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

In this paper, we present an active learning enabled robotic painting system, called ShadowPainter, which acquires artist-specific painting information from the artwork creating process and achieves robotic reproduction of the artwork. The artist's painting process information, including interactive trajectories of paintbrushes with the environment and states of the canvas, is collected by a novel Visual Measurement system (VMS). A Robotic Painting system (RPS), accompanied by the VSM, is developed to reproduce human paintings by actively imitating the measured painting process. The critical factors that influence the final painting performance of the robot are revealed. At the end of this paper, the reproduced artworks and the painting ability of the RPS are evaluated by local and global criteria and metrics. The experimental results show that our ShadowPainter can reproduce human-level brush strokes, painting techniques, and overall paintings. Compared with the existing work, our system produces natural strokes and painting details that are closer to human artworks.

关键词： Painting reproduction Robotic painting Robotic system Vision-based measurement

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A Robotic End-Effector for Screwing and Unscrewing Bolts From the Side

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IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第4期7卷 9793-9800页

作者： Tao, Rui Fan, Junfeng Jing, Fengshui Jun, Hou Xing, Shiyu Ma, Yunkai Tan, Min Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

This letter presents a novel robotic end-effector for screwing and unscrewing bolts. In many industrial scenarios, it is required to manipulate bolts from the side using robots with end-effectors. Besides, the reaction torque during tightening needs to be balanced to the stability of the robot. To address these challenges, we design a robotic end-effector that can realize side screwing and torque counteraction. Specifically, through an open gear set and a width-adjustable screwing mouth, the end-effector can approach the bolt from the side in any direction and then screw and unscrew it. A gripper with force-magnification and self-locking drive is equipped to counteract the reaction torque during screwing and unscrewing. Considering the extensions for different application scenarios, a method to extend the end-effector by switching screwing mouths is given, and the extended end-effector is able to screw a variety of objects. After further analysis and optimization of the force and size, experiments are carried out. The results show that the end-effector can screw and unscrew the target bolt robustly and counteract the reaction torque. It is also applicable in different scenarios.

关键词： Fasteners End effectors Robots Torque Mouth Gears Grippers Screwing and unscrewing grippers and other end-effectors mechanism design assembly

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Robust Detection of Malicious URLs With Self-Paced Wide & Deep Learning

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IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 2022年第2期19卷 717-730页

作者： Liang, Yunji Wang, Qiushi Xiong, Kang Zheng, Xiaolong Yu, Zhiwen Zeng, Daniel Northwestern Polytech Univ Sch Comp Sci Xian 710060 Shaanxi Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100080 Peoples R China

As cybercrimes grow in scale with devastating economic costs, it is important to protect potential victims against diverse attacks. In spite of the diversity of cybercrimes, it is the uniform resource locators (URLs) that connect vulnerable users with potential attacks. Although numerous solutions (e.g., rule-based solutions and machine learning-based methods) are proposed for malicious URL detection, they cannot provide robust performance due to the diversity of cybercrimes and cannot cope with the explosive growth of malicious URLs with the evolution of obfuscation strategies. In this paper, we propose a deep learning-based system, dubbed as CyberLen, to detect malicious URLs robustly and effectively. Specifically, we use factorization machine (FM) to learn the latent interaction among lexical features. For the deep structural features, position embedding is introduced for token vectorization to reduce the ambiguity of URL tokens. Meanwhile, temporal convolution network (TCN) is utilized to learn the long-distance dependency among URL tokens. To fuse heterogeneous features, self-paced wide & deep learning strategy is proposed to train a robust model effectively. The proposed solution is evaluated on a large-scale URL dataset. Our experimental results show that position embedding is constructive to reducing the ambiguity of URL tokens, and the self-paced wide & deep learning strategy shows superior performance in terms of F1 score and convergence speed.

关键词： paced learning temporal convolutional network factorization machine wide & deep malicious URL detection

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