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Annual Conference of Industrial Electronics Society

作者： Yihang Wu Ziyao Qu Xiaocong Chang Zhijian Hu Rongni Yang Renjie Ma State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Control Science and Engineering Shandong University Jinan China

Microrobots can work in small, enclosed spaces and complete complex tasks. It has potential applications in the field of biomedical and life sciences. This paper focuses on investigating robust dynamic tracking for electromagnetic-actuated microrobot via sliding mode control. First, we generalize the planar motion to the case of spatial trajectory tracking and then establish the three-dimensional tracking error model of microrobot. Then, we deploy a novel sliding control strategy and ensure the stability of sliding mode dynamics, once the robotic state reaches the sliding surface and sustains there in the sequel. Finally, the numerical simulation is conducted, compared to the traditional proportion-integration-differentiation control strategy, to verify the effectiveness and advantages of our proposed control scheme.

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Industrial Wireless Network Access Selection Based on Multilevel Fuzzy Neural Network

Industrial Wireless Network Access Selection Based on Multil...

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Chinese Control and Decision Conference, CCDC

作者： Yan Xu Zhijun Gao Sichao Zhang School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

In order to solve the issues in the heterogeneous industrial wireless network access selection such as delay and reliability, this paper present an industrial wireless network access selection based on multilevel fuzzy neural network. The algorithm divides the input attributes into three categories: network attributes, terminal attributes, and environment attributes. It synchronously inputs them into the fuzzy neural network to obtain the candidate network scores, which effectively reduces the number of fuzzy rules and reduces the algorithm complexity. Furthermore, this paper also consider the influence of the industrial environment on network selection and quantitatively reduce the number of switching and blocking rate. The simulation experimental results show that this algorithm is able to achieve fast network access selection in noisy and occluded industrial heterogeneous wireless networks, and the blocking rate can remain low in the presence of dense terminals.

关键词： Wireless networks Neural networks Switches Mean square error methods Interference Fuzzy neural networks Delays

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Edge Detectors Can Make Deep Convolutional Neural Networks More Robust

arXiv

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arXiv 2024年

作者： Ding, Jin Zhao, Jie-Chao Sun, Yong-Zhi Tan, Ping Wang, Jia-Wei Ma, Ji-En Fang, You-Tong School of Automation and Electrical Engineering Key Institute of Robotics of Zhejiang Province Zhejiang University of Science and Technology Hangzhou310023 China State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou310027 China School of Electrical Engineering Zhejiang University Hangzhou310027 China

Deep convolutional neural networks (DCNN for short) are vulnerable to examples with small perturbations. Improving DCNN’s robustness is of great significance to the safety-critical applications, such as autonomous driving and industry automation. Inspired by the principal way that human eyes recognize objects, i.e., largely relying on the shape features, this paper first employs the edge detectors as layer kernels and designs a binary edge feature branch (BEFB for short) to learn the binary edge features, which can be easily integrated into any popular backbone. The four edge detectors can learn the horizontal, vertical, positive diagonal, and negative diagonal edge features, respectively, and the branch is stacked by multiple Sobel layers (using edge detectors as kernels) and one threshold layer. The binary edge features learned by the branch, concatenated with the texture features learned by the backbone, are fed into the fully connected layers for classification. We integrate the proposed branch into VGG16 and ResNet34, respectively, and conduct experiments on multiple datasets. Experimental results demonstrate the BEFB is lightweight and has no side effects on training. And the accuracy of the BEFB integrated models is better than the original ones on all datasets when facing FGSM, PGD, and C&W attacks. Besides, BEFB integrated models equipped with the robustness enhancing techniques can achieve better classification accuracy compared to the original models. The work in this paper for the first time shows it is feasible to enhance the robustness of DCNNs through combining both shape-like features and texture features. © 2024, CC BY-NC-SA.

关键词： Convolutional neural networks

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A High-Precision Edge Detection Algorithm Based on Improved Sobel Operator-Assisted HED

A High-Precision Edge Detection Algorithm Based on Improved ...

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IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent systems

作者： Yeborui Yuan Wenyuan Chen Jie Tang Jia Yang School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China State Key Laboratory of Robotics Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

In response to the problem of poor detection performance of the traditional Sobel operator in edge detection, a high-precision edge detection algorithm based on Sobel operator-assisted Holistically-nested Edge Detection (HED) network was proposed, which is named ISAHED here. The algorithm is designed as follows. Firstly, an improved Sobel operator based on the traditional Sobel operator was proposed through adding gradient direction. Then, the image output by the improved Sobel operator is taken as the ground-truth image and as the end-to-end comparison object in a HED-like network. Finally, the ISAHED algorithm outputs edge images with better quality through iterative learning of the HED-like neural network. The experimental results prove that the proposed ISAHED algorithm has better edge detection performance by visually observing the final edge images and further evaluating the values of FOM. This work provides an important reference for the further development of the edge detection technology.

关键词： Automation Image edge detection Image processing Neural networks Control systems Iterative algorithms Intelligent systems

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FISS GAN:A Generative Adversarial Network for Foggy Image Semantic Segmentation

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IEEE/CAA Journal of Automatica Sinica 2021年第8期8卷 1428-1439页

作者： Kunhua Liu Zihao Ye Hongyan Guo Dongpu Cao Long Chen Fei-Yue Wang School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510006China State Key Laboratory of Automotive Simulation and Control and the Department of Control Science and Engineering Jilin University(Campus Nanling)Changchun 130025China Mechanical and Mechatronics Engineering Department at the University of Waterloo 200 University Avenue West WaterlooON N2L 3G1Canada State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

Because pixel values of foggy images are irregularly higher than those of images captured in normal weather(clear images),it is difficult to extract and express their *** method has previously been developed to directly explore the relationship between foggy images and semantic segmentation *** investigated this relationship and propose a generative adversarial network(GAN)for foggy image semantic segmentation(FISS GAN),which contains two parts:an edge GAN and a semantic segmentation *** edge GAN is designed to generate edge information from foggy images to provide auxiliary information to the semantic segmentation *** semantic segmentation GAN is designed to extract and express the texture of foggy images and generate semantic segmentation *** on foggy cityscapes datasets and foggy driving datasets indicated that FISS GAN achieved state-of-the-art performance.

关键词： Edge GAN foggy images foggy image semantic segmentation GAN semantic segmentation

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Learning to Turn: Deformation Control of a Novel Flexible Fishtail Actuated by Artificial Muscles

Learning to Turn: Deformation Control of a Novel Flexible Fi...

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IEEE International Conference on Cyborg and Bionic systems (CBS)

作者： Junwen Gu Yukai Feng Zhengxing Wu Jian Wang Junzhi Yu Key Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Department of Advanced Manufacturing and Robotics State Key Laboratory for Turbulence and Complex Systems College of Engineering Peking University Beijing China

ISBN: (数字)9798350388039

ISBN: (纸本)9798350388046

Achieving agile, fish-like turning remains a critical challenge in the development of robotic fish. The flexible deformation of a fish’s body significantly contributes to its turning ability. Consequently, researchers have devoted substantial efforts to enhancing the compliance and agility of robotic fish. This paper presents a novel flexible fishtail, inspired by the musculoskeletal structure of real fish, which can control the deformation of its flexible components during turns, improving the turning performance of robotic fish. The fishtail primarily consists of a carbon fiber plate with attached artificial muscles. These muscles can contract and stretch, causing the carbon fiber tail to bend and deform. To enable precise deformation control, a dynamic model of the fishtail is established based on Hamilton’s principle. Subsequently, deep reinforcement learning methods such as deep deterministic policy gradient and soft actor-critic are employed to develop a deformation controller under joint deflection. Cross-validation ensures the controller’s accuracy and robustness. The experimental results demonstrate that this controller facilitates the deformation control of the flexible tail across various turning amplitudes and speeds, allowing the fishtail to exhibit adjustable compliance or resistance to fluids during turning motion. Consequently, this enhances the agility of the robotic fish.

关键词： Artificial muscles Deformation Tail Learning (artificial intelligence) Muscles Turning Fish Robustness Carbon Robots

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MTPA-based Haptic Feedback Controller for Master Manipulator in Minimally Invasive Surgery

MTPA-based Haptic Feedback Controller for Master Manipulator...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Zhao, Wenda Chen, Jian Hong, Yilun Liu, Hongbin School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Hong Kong Institute of Science and Innovation Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics Hong Kong Hong Kong School of Biomedical Engineering and Imaging Sciences King's College London LondonSE1 7EU United Kingdom

ISBN: (纸本)9781665481090

Haptic feedback is critical for teleoperation in surgical robots, particularly in Natural Orifice Transluminal Endoscopic Surgery (NOTES). This paper introduces a haptic controller designed to enhance force generation efficiency and resolution, surpassing traditional motor control frameworks. We propose a Maximum Torque Per Ampere (MTPA)-based Field-Oriented Control (FoC) strategy for haptic kinesthetic feedback controller, optimizing the performance of a master manipulator's joint with a brushless DC (BLDC) motor under stalled and ultra-slow conditions. This approach enables a master manipulator with three degrees of freedom (DoF) for force feedback. Its effectiveness is validated through simulator and surgical trials, demonstrating improved haptic feedback in surgical applications. © 2024 IEEE.

关键词： Brushless DC motors

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Collaborative turning and jumping control of a cyborg locust via sensory stimulation 3

Collaborative turning and jumping control of a cyborg locust...

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3rd IEEE International Conference on Electrical, Control and Instrumentation engineering, ICECIE 2021

作者： Liu, Peng Ma, Songsong Liu, Shen Li, Yao Li, Bing Harbin Institute of Technology School of Mechanical Engineering and Automation Shenzhen518055 China Harbin Institute of Technology State Key Laboratory of Robotics and Systems Shenzhen518055 China

ISBN: (纸本)9781665449663

In this study, we describe the first-ever demonstration of the turning and jumping control of locusts based on electrical stimulation, which can realize a cyborg jumper. In terms of perception, walking distance, speed, payload, maneuverability, and adaptability to complex environments, cyborgs can reveal unmatchable advantages to insect-scale artificial robots. Herein, live locusts (Locusta migratoria) were selected as the study platform. Antennae and cercus stimulations using electrical pulses were applied to elicit the insect to turn and jump. We examined both the turning angle and the angular velocity by tuning the stimulus amplitude or frequency. It is found the turning rate induced by antenna stimulation can be graded. Besides, the stimulation of the cercus at the distal abdomen triggered the jump reliably. Moreover, the directional jumping control of the locust was achieved by the collaboration of turning and jumping control. Such results provide the potential to establish a more precise and more efficient model to develop a direction-controllable cyborg jumper. © 2021 IEEE.

关键词： Antennas

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BLIP-Adapter: Bridging Vision-Language Models with Adapters for Generalizable Face Anti-spoofing 18

BLIP-Adapter: Bridging Vision-Language Models with Adapters ...

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18th IEEE International Joint Conference on Biometrics, IJCB 2024

作者： Zhang, Bin Zhu, Xiangyu Gao, Li Zhang, Xiaoyu Lei, Zhen Chinese Academy of Sciences Institute of Information Engineering China University of Chinese Academy of Sciences School of Cyber Security China China Mobile Financial Technology Co. Ltd. China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation China University of Chinese Academy of Sciences School of Artificial Intelligence China Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Hong Kong

ISBN: (纸本)9798350364132

Face anti-spoofing is essential for ensuring the security of facial recognition systems against spoofing attacks. Recent methods have transferred Vision-Language models to face anti-spoofing (e.g., FLIP and CLIPC8), demonstrating that learning perception from supervision in natural language can enhance the model's detection performance. However, such methods exhibit limited depth in the interaction between images and texts, resulting in poor performance on fine-grained understanding tasks such as face anti-spoofing. Besides, the lack of diversity in image-text pairs for face anti-spoofing further hinders such methods from playing their best. To address these issues, we propose a novel fine-tuning strategy for Vision-Language models in face anti-spoofing. This strategy introduces the Bootstrapping Language-Image Pre-training model (BLIP), known for its novel interaction mechanisms and superior image-text comprehension, to construct a more generalized feature representation for face anti-spoofing. Furthermore, we propose an Adapter module for the text branch to reduce the negative impact of insufficient data diversity and catastrophic forgetting. Extensive experiments conducted on various cross-domain testing benchmarks demonstrate the significant superiority of our method over the state-of-the-art, highlighting its effectiveness and robustness. © 2024 IEEE.

关键词： Visual languages

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All-weather,natural silent speech recognition via machine-learning-assisted tattoo-like electronics

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npj Flexible Electronics 2021年第1期5卷 183-191页

作者： Youhua Wang Tianyi Tang Yin Xu Yunzhao Bai Lang Yin Guang Li Hongmiao Zhang Huicong Liu YongAn Huang State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhanChina Flexible Electronics Research Center Huazhong University of Science and TechnologyWuhanChina School of Mechanical and Electric Engineering Jiangsu Provincial Key Laboratory of Advanced RoboticsSoochow UniversitySuzhouChina State Key Laboratory of Industrial Control Technologyg Institute of Cyber Systems and ControlZhejiang UniversityHangzhouChina

The internal availability of silent speech serves as a translator for people with aphasia and keeps human–machine/human interactions working under various *** paper develops a silent speech strategy to achieve all-weather,natural *** strategy requires few usage specialized skills like sign language but accurately transfers high-capacity information in complicated and changeable daily *** the strategy,the tattoo-like electronics imperceptibly attached on facial skin record high-quality bio-data of various silent speech,and the machine-learning algorithm deployed on the cloud recognizes accurately the silent speech and reduces the weight of the wireless acquisition module.A series of experiments show that the silent speech recognition system(SSRS)can enduringly comply with large deformation(~45%)of faces by virtue of the electricitypreferred tattoo-like electrodes and recognize up to 110 words covering daily vocabularies with a high average accuracy of 92.64%simply by use of small-sample machine *** successfully apply the SSRS to 1-day routine life,including daily greeting,running,dining,manipulating industrial robots in deafening noise,and expressing in darkness,which shows great promotion in real-world applications.

关键词： translator weather silent

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