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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Qing Li Huifang Feng Kanle Shi Yue Gao Yi Fang Yu-Shen Liu Zhizhong Han BNRist School of Software Tsinghua University Beijing China School of Informatics Xiamen University Xiamen China Kuaishou Technology Beijing China Center for Artificial Intelligence and Robotics New York University Abu Dhabi Abu Dhabi UAE Department of Computer Science Wayne State University Detroit USA

We propose a novel method called SHS-Net for oriented normal estimation of point clouds by learning signed hyper surfaces, which can accurately predict normals with global consistent orientation from various point clouds. Almost all existing methods estimate oriented normals through a two-stage pipeline, i.e., unoriented normal estimation and normal orientation, and each step is implemented by a separate algorithm. However, previous methods are sensitive to parameter settings, resulting in poor results from point clouds with noise, density variations and complex geometries. In this work, we introduce signed hyper surfaces (SHS), which are parameterized by multi-layer perceptron (MLP) layers, to learn to estimate oriented normals from point clouds in an end-to-end manner. The signed hyper surfaces are implicitly learned in a high-dimensional feature space where the local and global information is aggregated. Specifically, we introduce a patch encoding module and a shape encoding module to encode a 3D point cloud into a local latent code and a global latent code, respectively. Then, an attention-weighted normal prediction module is proposed as a decoder, which takes the local and global latent codes as input to predict oriented normals. Experimental results show that our SHS-Net outperforms the state-of-the-art methods in both unoriented and oriented normal estimation on the widely used benchmarks. The code, data and pretrained models are available at https://***/LeoQLi/SHS-Net.

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Dual-loop joint torque control based on lower limb exoskeleton robots

Dual-loop joint torque control based on lower limb exoskelet...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Meitong Zhang Chi Zhang Feng Yi Xindong Shu Ting Yuan Zheng Li School of Electrical Engineering Hebei University of Science and Technology Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Ningbo China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo Institute of Materials Technology and Engineering Chinese Academy of Science Ningbo China School of Electrical Engineering Hebei University of Science and Technology Shijiazhuang China

In the framework of impedance control, the torque trajectory of the joint is unpredictable due to nonlinear factors during the movement of the exoskeleton. To precisely predict command torque trajectory and compensate for the disturbances, we propose a dual-loop joint torque control system including predictive feedforward and a disturbance observer. A predictive feedforward module is adopted as the control outer loop based on the feedback control, and a general compensation module is used to compensate for the prediction error. The inner loop adopts a disturbance observer to estimate and compensate for low-frequency external disturbances. The simulation, which utilizes the actual torque of hip and knee joints as a reference, is conducted to prove the effectiveness of the proposed method. The comparison results demonstrate that the proposed control method can realize high bandwidth for exoskeleton joint control.

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InOR-Net: Incremental 3D Object Recognition Network for Point Cloud Representation

arXiv

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arXiv 2023年

作者： Dong, Jiahua Cong, Yang Sun, Gan Wang, Lixu Lyu, Lingjuan Li, Jun Konukoglu, Ender The State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China The University of Chinese Academy of Sciences Beijing100049 China The Computer Science Department Northwestern University Evanston United States The Sony AI Tokyo108-0075 Japan The School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China The Computer Vision Lab ETH Zurich Zürich8092 Switzerland

3D object recognition has successfully become an appealing research topic in the real-world. However, most existing recognition models unreasonably assume that the categories of 3D objects cannot change over time in the real-world. This unrealistic assumption may result in significant performance degradation for them to learn new classes of 3D objects consecutively, due to the catastrophic forgetting on old learned classes. Moreover, they cannot explore which 3D geometric characteristics are essential to alleviate the catastrophic forgetting on old classes of 3D objects. To tackle the above challenges, we develop a novel Incremental 3D Object Recognition Network (i.e., InOR-Net), which could recognize new classes of 3D objects continuously via overcoming the catastrophic forgetting on old classes. Specifically, a category-guided geometric reasoning is proposed to reason local geometric structures with distinctive 3D characteristics of each class by leveraging intrinsic category information. We then propose a novel critic-induced geometric attention mechanism to distinguish which 3D geometric characteristics within each class are beneficial to overcome the catastrophic forgetting on old classes of 3D objects, while preventing the negative influence of useless 3D characteristics. In addition, a dual adaptive fairness compensations strategy is designed to overcome the forgetting brought by class imbalance, by compensating biased weights and predictions of the classifier. Comparison experiments verify the state-of-the-art performance of the proposed InOR-Net model on several public point cloud datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Object recognition

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Exploring Innovations in Autonomous robotics and Mechatronics: Application in Manufacturing and Healthcare

Exploring Innovations in Autonomous Robotics and Mechatronic...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： T.Grace Shalini L. Ganeshabu Priyanka Chemudugunta Nagendar Yamsani Ch.Ashok Kumar A. Athiraja Department of Computational Intelligence School of Computing SRM Institute of Science and Technology (SRMIST)Kattankulathur Chengalpattu Tamil Nadu India Department of Robotics and Automation Rajalakshmi Engineering College Rajalakshmi Nagar Thandalam Mevalurkuppam Chennai Tamil Nadu India Department of Robotics and Automation Karpaga Vinayaga College of Engineering and Technology Chengalpattu Tamil Nadu India Department of School of Computer Science and Artificial Engineering SR University Warangal India Department of Mechanical Engineering Malla Reddy Engineering College Main Campus Maisammaguda Hyderabad Telangana India Department of CSE (AIML) Malla Reddy College of Engineering Secunderabad Telangana India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Autonomous robotics and mechatronics have drastically changed the manufacturing and healthcare sectors by increasing productivity, precision, and flexibility. This work addresses the pressing need for new methods to advance these sectors by merging intelligent algorithms with autonomous systems. A hybrid strategy was employed, combining Deep Reinforcement Learning (DRL) and Adaptive Control Systems (ACS), to develop and optimize robotic operations. Extensive studies were conducted to assess the success of this strategy, which demonstrated a 25% reduction in operating expenses and a 30% increase in production efficiency in manufacturing settings. The method produced a precision rate of about 99% in the healthcare industry, which is significantly better than the 93% average of current systems. It also reduced patient recovery times by an estimated 20%. These results highlight the improved performance and promise of ACS with DRL combination in autonomous robotics, which may have revolutionary implications for the manufacturing and healthcare industries. The success, highlighted by economic and medical benefits, demonstrates the efficacy of our methodology and provides a strong platform for future developments in autonomous robotics and mechatronics.

关键词： Industries Technological innovation Mechatronics Service robots Scalability Medical services Manufacturing Adaptive control Tuning Testing

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PTC-FOZNN: A Strictly Predefined-Time Convergent Fractional-Order Recurrent Neural Network for Solving Time-Variant Quadratic Programming

PTC-FOZNN: A Strictly Predefined-Time Convergent Fractional-...

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International Conference on Control and Automation (ICCA)

作者： Yi Yang Weibing Li Jianshu Zhou Junda Huang Jinfei Hu Richard M. Voyles Xin Ma Department of Mechanical and Automation Engineering Multi-Scale Medical Robotics Center The Chinese University of Hong Kong Hong Kong China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong China School of Engineering Technology Purdue University West Lafayette IN USA

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

This study presents a Predefined-Time Convergent Fractional-Order Zeroing Neural Network (PTC-FOZNN) as an innovative solution for Time-Invariant Quadratic Programming (TIQP) and Time-Variant Quadratic Programming (TVQP) problems. This model represents a noteworthy integration of fractional calculus into recurrent neural networks for cyclic motion planning of robots. The PTC-FOZNN diverges from the conventional Zeroing Neural Network (ZNN) by implementing a conformable fractional derivative that complies with the Leibniz rule-a standard often unmet by existing fractional derivatives. Additionally, it employs a novel predefined-time stabilizer as its activation function. When compared to the traditional ZNN, the PTC-FOZNN, configured with $0 < \alpha\leq 1$ , exhibits enhanced convergence speed and greater positional accuracy for both TIQP and TVQP tasks. Through meticulous theoretical and empirical validation, including simulation trials with a Franka Emika Panda robotic arm, the PTC-FOZNN model has proven its effectiveness in precise trajectory tracking and computational efficacy.

关键词： Recurrent neural networks Trajectory tracking Kinematics Manipulators Planning Quadratic programming Task analysis

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Cyberbullying Classification Using a Natural Language Processing and Machine Learning Techniques

Cyberbullying Classification Using a Natural Language Proces...

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Advances in Computing Research on science Engineering and technology (ACROSET), International Conference on

作者： Nikhilesh Pramod Mankar Tushar Mahure Anushka A. Joshi Sagar Dhanraj Pande Aditya Khamparia Ankita Shrikrushna Nathe Fadi Al Turjman School of Engineering and Technology Pimpri Chinchwad University (PCU) Pune India Independent Researcher Amravati India Department of Computer Science Babasaheb Bhimrao Ambedkar University Amethi India Department of Information Technology Kavikulguru Institute of Technology and Science Nagpur India Artificial Intelligence Software Information Systems Engineering Departments AI and Robotics Institute Near East University Nicosia Turkey Research Center for AI and IoT Faculty of Engineering University of Kyrenia Kyrenia Turkey

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

The usage of online entertainment has increased dramatically after some time with the enhancement of the Internet and has turned into the most compelling systems administration stage in this century. Notwithstanding, the upgrade of a social network regularly makes adverse consequences on society that add to two or three terrible peculiarities, for example, online maltreatment, badgering cyberbullying, cybercrime, and internet savaging. Cyberbullying habitually prompts genuine mental and actual misery, especially for ladies and kids, and even occasionally make them to use self-destruction methods. Multiple number of episodes have as of late happened worldwide because of online badgering, like sharing private talks, bits of hearsay, and sexual comments. In this way, the distinguishing proof of harassing messages or messages via online entertainment has gained a significant measure for development for consideration with in scientists. The ground or motive for this exploration is to create a full-fledged way or a method to differentiate online harmful and tormenting drafts by combining various methods and techniques in the domain of natural language processing and AI

关键词： Cyberbullying Entertainment industry Machine learning Gain measurement Natural language processing Internet Bayes methods computer crime

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UDE-based Robust Control for a Class of Nonaffine Nonlinear Systems with Uncertainties and Output Constraint

UDE-based Robust Control for a Class of Nonaffine Nonlinear ...

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第40届中国控制会议

作者： Tian Chen Sainan Zhang Shuzhi Sam Ge School of Computer Science and Engineering Center for RoboticsUniversity of Electronic Science and Technology of China Department of Mechanical Engineering Biomechatronics and Intelligent Roboticsthe City College of New York Department of Electrical and Computer Engineering National University of Singaporeand also with the Institute for Center for RoboticsUniversity of Electronic Science and Technology of China

In this paper,we study the robust control problem of a class of nonaffine nonlinear systems based on uncertainty and disturbance estimator（UDE） under unknown system dynamics and output constraint.A proposed UDE control method is used to estimate uncertainties that contain the uncertainty of *** prominent feature of UDE is to avoid the construction of inverse *** control design requires only the bandwidth information of ***,the barrier Lyapunov function（BLF） is used directly to address the problem of output *** remarkable feature of the BLF compared to the Lyapunov function based on pure tracking error is to relax the feasible initial output signal to the whole constraint *** on the control of UDE,by using Lyapunov’s stability theory,the consistent boundary of the closed-loop system is *** the simulation studies of model uncertainty and external disturbance,the validity of the proposed UDE-based control method is proved and compared with the traditional UDE control method.

关键词： Nonaffine Nonlinear Systems Uncertainty and Disturbance Estimator Barrier Lyapunov Function

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Design and Implementation of Neuro-Fuzzy Control Approach for Robot's Trajectory Tracking 7

Design and Implementation of Neuro-Fuzzy Control Approach fo...

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7th International Conference on Electronics, Communication and Aerospace technology, ICECA 2023

作者： Kiruba Sandou, D. Sunad Kumara, A.N. Choudhary, Binod Kumar Gurpur, Shashikala Sarishma Natrayan, L. Sivaramkumar, M. Manakula Vinayagar Institute of Technology Department of Robotics and Automation Pondicherry605107 India BGS Institute of Technology Department of Computer Science and Engineering Adichunchanagiri University Karnataka Mandya571448 India ARKA Jain University Department of Electrical and Electronics Engineering Jharkhand Jamshedpur832108 India Maharashtra Pune411014 India Graphic Era Deemed to Be University Department of Management Studies Uttarakhand Dehradun India Saveetha School of Engineering SIMATS Department of Mechanical Engineering Tamilnadu Chennai602105 India Karpagam Academy of Higher Education Department of EEE Tamiinadu Coimbatore India

ISBN: (纸本)9798350340600

Achieving precise trajectory tracking while minimizing errors, reducing settling time, and curbing overshoot presents a significant challenge in the domain of robot-manipulator control. In response, this paper proposes a novel controller design for robot manipulators, incorporating two distinct approaches: a conventional fuzzy controller and an advanced Neuro-Fuzzy controller. These controllers are tailored to elevate trajectory tracking performance by simultaneously optimizing time characteristics in robot manipulator responses. To assess their efficacy, a range of test scenarios is devised, encompassing different trajectory types, including linear and nonlinear paths. Upon comparative analysis, the results highlight an intriguing contrast: while the fuzzy controller exhibits higher maximum overshoot and longer settling times in the robot's response, the Neuro-Fuzzy controller consistently demonstrates superior performance. The comprehensive error analysis such as Integral Square Error (ISE) and Integral Absolute Error (IAE) further underscores the capability of the Neuro-Fuzzy-based robot manipulator in delivering trajectory tracking precision. This study contributes to the advancement of trajectory control techniques for robot manipulators, paving the way for enhanced automation and performance across various applications. © 2023 IEEE.

关键词： Controllers

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Cognitive Improvement Estimation using EEG Imaging after tDCS Therapy

Cognitive Improvement Estimation using EEG Imaging after tDC...

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Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE), International Conference on

作者： Mudassar Ayub Kaleem Ullah Muhammad Jawad Khan Hassan Farooq Afzaal Khan U.S. - Pakistan Centers for Advanced Studies in Energy (USPCAS-E) National University of Science & Technology (NUST) Islamabad Pakistan School of Electrical Engineering and Computer Science National University of Science & Technology (NUST) Islamabad Pakistan Department of robotics and artificial intelligence SMME National University of Science & Technology (NUST) Islamabad Pakistan

ISBN: (纸本)9781665492430

Brain is the most important organ in the human body. The effects of brain impairment are wide ranging and include cognition, fatigue, sleep issues, headaches, dizziness, impaired self-awareness, clinical depression, attention and concentration problems, epilepsy, struggle in making decisions and many more. The objective of this paper is to the efficacy of noninvasive neuro modulatory technique Transcranial direct current stimulation (tDCS) in the estimation of brain cognitive state improvement using changes in small electrical brain voltages recorded by Electroencephalogram (EEG). Event related De synchronization (ERDs) is applied to the Motor Imagery Period (MIP) and Rest Period (RP) of pre stimulation and post stimulation EEG data of ten subjects. Common Spatial Pattern (CSP) is used for feature extraction and Linear Discrimination Analysis (LDA) a machine learning model is applied on these features for classification. The results suggest a decrease of contralateral ERDs oscillatory activity related to an event as per the hypothesis in anodal post stimulation than pre stimulation across all channels for six subjects. Further LDA applied on CSP proved that the classification accuracy between Motor imagery period (MIP) and the Rest period (RP) after the stimulation therapy is higher than the Pre stimulation Motor Imagery period (MIP) and Rest period (RP).

关键词： Analytical models Medical treatment Estimation Machine learning DC motors Feature extraction Brain modeling

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Energy Efficiency Optimization for Full-Duplex D2D Communications Underlaying Distributed Antenna Systems

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年

作者： Liu, Zhan Liao, Zhiyuan Li, Chunquan Zhang, Zhijun Yu, Junzhi Liu, P.X. Hunan University of Humanities Science and Technology School of Information Loudi417000 China Nanchang University School of Information Engineering Nanchang330031 China Jiangxi Provincial Key Laboratory of Intelligent Systems and Human-Machine Interaction Nanchang330031 China South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics College of Engineering Beijing100871 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

In this paper, we investigate the total system energy efficiency (EE) of full-duplex (FD) device-to-device (D2D) communications underlaying distributed antenna systems (DAS), where remote access units (RAUs), D2D users (DUs), and cellular users (CUs) are all capable of FD operation. Specifically, we jointly optimize subcarrier assignment and power allocation under the quality of service (QoS) requirements of CUs and DUs and the maximum power constraints of RAUs, CUs, and DUs. In addition, we propose a novel spectrum sharing strategy that allows each subcarrier to be assigned to multiple CUs and/or multiple D2D pairs (DPs) for flexibility. To solve the formulated non-convex optimization problem, we first employ fractional programming to transform the objective function in the optimization problem from the fractional form into the equivalent subtractive form. Then, an efficient iterative resource allocation algorithm is proposed, which needs to solve an inner problem in each iteration. After relaxing the variables and introducing penalty factors, the non-convex inner problem is transformed into a convex problem through the successive convex approximation (SCA) method and solved by iterative algorithm. Simulation results demonstrate that the proposed algorithm can considerably improve the system EE compared to other benchmark schemes. Furthermore, the proposed spectrum sharing strategy is superior to the existing sharing strategies. © 2017 IEEE.

关键词： Convex optimization

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