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2023 China Automation Congress, CAC 2023

作者： Chen, Zikai Wu, Jiayi Li, Jincheng Zuo, Weiliang Li, Chenzdong Xin, Jingmin Zheng, Nanning Institute of Artificial Intelligence and Robotics Xi'An Jiaotong University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center of Visual Information and Applications Xi'an China College of Clinical Medicine Medical College of Henan University of Science and Technology The First Affiliated Hospital Luoyang China

ISBN: (纸本)9798350303759

In recent years, the convolutional neural network represented by U-net has achieved great success in the field of medical image segmentation. However, due to different data acquisition protocols and differences in instrument parameters, the data in actual clinical applications will have a distribution shift problem with the training data of the model, also known as the domain shift problem. This can lead to a decrease in model performance, hindering the application of the model in real-world clinical settings. Traditional convolutional neural networks have local perception properties, and their inability to model long-range dependencies hinders the improvement of domain generalization performance. In addition, the traditional DG method of feature space domain randomization is not effective in medical image segmentation tasks due to the limited search space of feature styles and the inability to keep semantic information unchanged. To address the above issues, this paper proposes a medical image segmentation network named GFDR-U net for domain generalization. The network utilizes globally learnable filters to model long-range dependencies, and introduces a low-frequency domain randomization method to solve the problem of semantic invariance. Extensive experiments show that our method outperforms the state-of-the-art methods. © 2023 IEEE.

关键词： Convolution

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Safety-Aware Trajectory Tracking using High-Order Control Barrier Functions

Safety-Aware Trajectory Tracking using High-Order Control Ba...

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IEEE Conference on Decision and Control

作者： Panagiotis Rousseas Dimitra Panagou Kostas Kyriakopoulos Control Systems Laboratory School of Mechanical Engineering National Technical University of Athens Greece Department of Robotics and the Department of Aerospace Engineering University of Michigan MI USA Center of AI & Robotics (CAIR) New York University Abu Dhabi

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

A novel method on safety-aware trajectory tracking is presented in this work. The proposed scheme employs Control Barrier Functions (CBFs) along with a latent-space representation over the barrier and its derivatives. While such representations have also been employed in the literature, the innovation of the proposed method lies in formulating a novel tracking problem on the latent space, enabling for the shape of linear class $-\mathcal{K}$ functions that are included in the safety condition induced by the control barrier function to be chosen constructively. Additionally, the aforementioned safety condition is modified based on the latent-space tracking through the addition of an extra term. This enables maintaining safety guarantees and is demonstrated to provide decreased tracking errors compared to the nominal control barrier function scheme. Numerical simulations validate the efficacy of the proposed scheme, as well as its ability for online implementation.

关键词： Technological innovation Trajectory tracking Shape Aerospace electronics Numerical simulation Safety

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Development of SimuLink-Based Physical Experiment Platform for EtherCAT Bipedal Robots

Development of SimuLink-Based Physical Experiment Platform f...

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robotics, Control and Automation (ICRCA), International Conference on

作者： Feng Yang Hao Huang Jinxin Xu Hongrun Lu Yizhuang Sun Huibin Li Xiaofeng Yang Robotics Engineering Center The 21ST Research Institute of China Electronic Technology Group Corporation Shanghai China

ISBN: (数字)9798350344721

ISBN: (纸本)9798350344738

In order to run the robot simulation algorithms in Matlab on a physical robot, and to solve the communication and control of the bipedal robot algorithms and joints during real-time motion between them, recorded the translations in XYZ axes by means of an attitude sensor. And the force on the robot plantar foot during motion is recorded by a six-dimensional force sensor, which is used to determine whether there is a moment component in the local coordinate system of the plantar foot in any direction other than the z-axis. The EtherCAT Ethernet control automation technology is used as the basis of the system. EtherCAT Ethernet control automation technology as the basis, combined with Matlab's SimuLink Real-Time control technology, the 12-joint robot biped is unified for control. The 12-joint robot biped is controlled and managed in a unified way. The control management includes device communication, state control, motion control optimization, signal reading and feedback, processing and analysis, and so on. The robot kinematics and dynamics control algorithms are implemented to compare the angular velocity trajectories given by the algorithms with the actual positional velocity trajectories of the robot. By comparing the angular velocity trajectory given by the algorithm and the actual position and velocity trajectory of the robot, the usability and practicality of the experimental platform are verified.

关键词： Automation Software packages Attitude control Robot kinematics Heuristic algorithms Process control Robot sensing systems

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High-Speed Trajectory Tracking Control for Quadrotors via Deep Reinforcement Learning

High-Speed Trajectory Tracking Control for Quadrotors via De...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Hean Hua Hang Zhong Hui Zhang Yongchun Fang Yaonan Wang College of Electrical and Information Engineering Hunan University Changsha China National Engineering Research Center for Robot Visual Perception and Control Technology Changsha China School of Robotics Hunan University Changsha China Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

This paper presents a learning-based high-speed trajectory tracking control strategy for quadrotors, which achieves efficient learning and strong reliability by the collaboration of deep reinforcement learning (RL) and self-tuning mechanism. Different from existing methods, the proposed strategy is designed to explore optimal control performance by taking advantage of model-based self-tuning mechanism and deep reinforcement learning. Specifically, the self-tuning guided deep RL scheme is put forward for quadrotors, with superior learning efficiency and strong adaptability. Firstly, a novel self-tuning mechanism is constructed and some auxiliary variables are introduced to enhance the tracking performance. Then, based on the model-driven self-tuning design, the deep RL is proposed to achieve model-guided learning, where the tuning actions are adopted in the evaluation process during training, aiming at removing the bad explorations by the carefully designed parallel evaluation. Finally, the convergence is analyzed based on the proposed learning framework, which indicates the efficient cooperation of exploration and self-tuning mechanism. To verify the effectiveness of the proposed controller, the guided training and hardware experiments are implemented to show efficient cooperation and satisfactory high-speed trajectory tracking control of the proposed method.

关键词： Training Adaptation models Trajectory tracking Heuristic algorithms Optimal control Deep reinforcement learning Hardware

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Revolutionizing waste-to-energy: harnessing the power of triboelectric nanogenerators

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Advanced Composites and Hybrid Materials 2024年第3期7卷 1-42页

作者： Kumar, Khanapuram Uday Hajra, Sugato Mohana Rani, Gokana Panda, Swati Umapathi, Reddicherla Venkateswarlu, Sada Kim, Hoe Joon Mishra, Yogendra Kumar Kumar, Rajaboina Rakesh Lab National Institute of Technology Warangal506004 India Department of Robotics and Mechatronics Engineering Daegu Gyeongbuk Institute of Science and Technology Daegu42988 Korea Republic of NanoBio High-Tech Materials Research Center Department of Biological Sciences and Bioengineering Inha University Incheon22212 Korea Republic of Department of Materials Science and Engineering National Taiwan University of Science and Technology Keelung Road Taipei10607 Taiwan Department of Chemistry Gachon University Seongnam13120 Korea Republic of Smart Materials NanoSYD Mads Clausen Institute University of Southern Denmark Alsion 2 Sønderborg6400 Denmark

Recently, there has been a lot of focus on developing new waste-to-energy technologies because they help us to provide sustainable energy solutions for future generations. This review paper investigates an innovative waste-to-energy technology known as triboelectric nanogenerators (TENGs), which uses the electrostatic induction and contact electrification principles of physics. The underlying physics of TENG technology allows for a wide range of material choices for its fabrication;as a result, waste materials are utilized for energy production using TENGs. It comprehensively discusses how various types of waste, including plastic, electronic, medical, household, and biowaste, can be integrated into TENG technology for efficient energy production. Furthermore, various applications of waste-based TENGs are discussed in detail. Finally, we projected challenges and future directions for creating a sustainable, green energy landscape. Graphical abstract: The review article presents a detailed exploration of triboelectric nanogenerators (TENGs) as novel waste-to-energy technologies that utilize waste materials. (Figure presented.) © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Nanogenerators

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Toward Robust Incomplete Multimodal Sentiment Analysis via Hierarchical Representation Learning 38

Toward Robust Incomplete Multimodal Sentiment Analysis via H...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Li, Mingcheng Yang, Dingkang Liu, Yang Wang, Shunli Chen, Jiawei Wang, Shuaibing Wei, Jinjie Jiang, Yue Xu, Qingyao Hou, Xiaolu Sun, Mingyang Qian, Ziyun Kou, Dongliang Zhang, Lihua Academy for Engineering and Technology Fudan University Shanghai China Institute of Metaverse & Intelligent Medicine Fudan University Shanghai China Cognition and Intelligent Technology Laboratory Shanghai China Jilin Provincial Key Laboratory of Intelligence Science and Engineering Changchun China Engineering Research Center of AI and Robotics Ministry of Education Shanghai China

Multimodal Sentiment Analysis (MSA) is an important research area that aims to understand and recognize human sentiment through multiple modalities. The complementary information provided by multimodal fusion promotes better sentiment analysis compared to utilizing only a single modality. Nevertheless, in real-world applications, many unavoidable factors may lead to situations of uncertain modality missing, thus hindering the effectiveness of multimodal modeling and degrading the model's performance. To this end, we propose a Hierarchical Representation Learning Framework (HRLF) for the MSA task under uncertain missing modalities. Specifically, we propose a fine-grained representation factorization module that sufficiently extracts valuable sentiment information by factorizing modality into sentiment-relevant and modality-specific representations through crossmodal translation and sentiment semantic reconstruction. Moreover, a hierarchical mutual information maximization mechanism is introduced to incrementally maximize the mutual information between multi-scale representations to align and reconstruct the high-level semantics in the representations. Ultimately, we propose a hierarchical adversarial learning mechanism that further aligns and adapts the latent distribution of sentiment-relevant representations to produce robust joint multimodal representations. Comprehensive experiments on three datasets demonstrate that HRLF significantly improves MSA performance under uncertain modality missing cases. © 2024 Neural information processing systems foundation. All rights reserved.

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EMG Based Rehabilitation Gesture Recognition Using DAE-CNN-LSTM Hybrid Model

EMG Based Rehabilitation Gesture Recognition Using DAE-CNN-L...

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World Rehabilitation Robot Convention (WRRC)

作者： Wujing Cao Xinqiang Guo Yupeng Zou Shuo Zhang Mingxiang Luo Worawarit Kobsiriphat Xinyu Wu Meng Yin Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China SIAT-CUHK Joint Laboratory of Robotics and Intelligent Systems SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society School of Mechanical Engineering China University of Petroleum East China National Metal and Materials Technology Center Thailand

ISBN: (数字)9798350355123

ISBN: (纸本)9798350355130

Stroke, as one of the leading causes of long-term disability globally, often results in motor impairments, particularly in the hands, significantly affecting patients' daily activities and causing profound psychological trauma. Rehabilitation gesture recognition, as one of the key means of active rehabilitation medicine, holds significant potential in stroke rehabilitation, providing real-time feedback on patient progress and enabling personalized interventions to meet individual needs. Traditional gesture recognition methods face numerous challenges when dealing with stroke patient data, such as noise, motion blur, and individual differences. To address these challenges, this paper proposes a novel approach for stroke patient gesture recognition using deep learning models. We adopt a strategy combining denoising autoencoder (DAE), convolutional neural network (CNN), and long short-term memory (LSTM) to enhance the accurate recognition of hand movements in stroke patients. Specifically, DAE is utilized for denoising EMG signals, extracting features, and reducing noise to improve the signal's noise resistance. CNN is employed for spatial feature extraction, while LSTM captures temporal dependencies in gesture sequences. By integrating these three deep learning models, our aim is to enhance the accuracy and robustness of rehabilitation gesture recognition. We validate the proposed method's effectiveness on an EMG dataset from seven subjects through experiments and compare it with traditional machine learning and individual CNN and LSTM algorithms. Experimental results demonstrate significant performance improvements of our hybrid model in rehabilitation gesture recognition tasks, indicating promising application prospects and practical significance.

关键词： Support vector machines Deep learning Accuracy Noise Gesture recognition Stroke (medical condition) Feature extraction Electromyography Convolutional neural networks Long short term memory

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The ways in which Artificial Intelligence improves several facets of Cyber Security-A survey 1

The ways in which Artificial Intelligence improves several f...

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Salama, Ramiz Al-Turjman, Fadi Altrjman, Chadi Bordoloi, Dibyahash Ai and Robotics Institute Research Center for Ai and IoT Near East University Nicosia Department of Computer Engineering Mersin10 Turkey Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept. Mersin Nicosia10 Turkey University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Mersin10 Turkey Waterloo University Canada Graphic Era Hill University Dehradun Rs Cse Graphuc Era University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350338027

With the increase in computer use for the last 10 years, it increases the size of cyber-attacks as well. Since most computers are connected to the network, they may suffer cyber-attacks. Because of these cyber threats, Cyber Security is becoming a really important area in the information technology area. Cyber Security is an area which is mainly responsible for dealing with cyber-attacks by hackers. The improvements and advancements in the information technology area allow cyber criminals to do really complicated and dangerous cyber-attacks. Cyber-attack methods that are being used today are much more effective than the ones in the past. Because of that, there is always a war between cyber security specialists and cyber attackers or hackers. Cyber security analysts, on the other hand, try to come up with new solutions to the methods developed by cyber criminals or hackers. The competition between them continuously advances and improves cyber security technologies every day. The improvements can be either in cyber security or cybercrime side. These days, traditional cyber security solutions are enough to fight against cyber-attacks, but can these solutions be further developed? This is the question everyone in the cyber security area is trying to answer. Of course, yes. Thanks to AI, which is one of the most popular technologies in the information technology area, it is used in many areas ranging from e-commerce, advertising, human resource & recruiting, video games, transportation, surveillance systems etc. AI has been used in cyber security over the last years. Although it also has negative effects, we are going to mostly talk about its positive sides in this article. We are going to go deep into how and why AI is used in cyber security, how AI improves and enhances the current traditional cyber security methods. Then, we will also talk about which AI techniques play an important role in the cyber security area. © 2023 IEEE.

关键词： Artificial intelligence

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Real-Time Plant Recognition and Crop Row Navigation for Autonomous Precision Agricultural Sprayer Robot

Real-Time Plant Recognition and Crop Row Navigation for Auto...

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International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Fazal Nasir Muhammad Haris Bilawal Khan Muhammad Tufail Muhammad Tahir Khan Zhang Dong Advanced Robotics and Automation Lab National Center of Robotics and Automation Peshawar Pakistan Department of Mechatronics Engineering University of Engineering and Technology Peshawar Pakistan Institute of Automation Qilu University of Technology (Shandong Academy of Sciences) Jinan China

This paper presents a vision-based selective spraying technique for an autonomous agricultural sprayer robot. In traditional methods, excessive chemical spraying cause deleterious effects on human health, environment and becomes uneconomical. In order to reduce the agrochemical wastage encounters in a broadcast spraying, a selective plant spraying method is used for crop chemical treatment. The sensor-based approach assisted with YOLOv7 model is deployed on a custom designed robot for recognizing and localizing the lettuce plants in field. The PID-based pressure controller is designed that minimizes the undesirable fluctuations cause by the opening/closing of solenoid-valve-nozzles (SVNs) during spraying. Thus the nozzle's spraying quality is maintained by keeping the pressure constant to a desired value. A visual servoing scheme for row tracking is presented that uses the detected plant's spatial features. The robustness of the visual-based navigation is validated in the real field experiments.

关键词： Service robots Plants (biology) Spraying Crops Wheels Robot sensing systems Visual servoing

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The use of machine learning (ML) in sustainable systems-An Overview 1

The use of machine learning (ML) in sustainable systems-An O...

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Salama, Ramiz Al-Turjman, Fadi Altrjman, Chadi Bordoloi, Dibyahash Ai and Robotics Institute Research Center for Ai and IoT Near East University Nicosia Department of Computer Engineering Mersin10 Turkey Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept. Mersin Nicosia10 Turkey University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Mersin10 Turkey Waterloo University Canada Graphic Era Hill University Dehradun Rs Cse Graphic Era University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350338027

Machine learning has the ability to dramatically improve sustainable systems by anticipating needs, maximizing resource use, raising output, and lowering waste. An overview of earlier studies on the incorporation of machine learning into sustainable systems is presented together with a case study of how machine learning was used to lower energy use in a residential structure. The results show that machine learning may be used to increase energy efficiency and save a lot of money. Wearable technology has added a whole new dimension to the already vast field of personal electronics. The mobile phone gave devices their true individuality. Because so many services are designed around mobile phones, the market has opened up for a brand-new personalized experience utilizing wearable technologies. Fabric sensors may now be combined with wearable microcontrollers like the flora and lily pad to monitor stretch, pressure, bend, and even the direction that the body is being braced. The connections between them are based on conductive threads that follow the curve of the fabric. We'll examine how different teams used their in-depth understanding of wearable technologies to accomplish their goals in this study. © 2023 IEEE.

关键词： Machine learning

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