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4th International Conference on Computing, Mathematics and engineering Technologies, iCoMET 2023

作者： Qureshi, Falaq Uddin, Riaz Ned University of Engineering and Technology Haptics Human-Robotics and Condition Monitoring Lab National Center of Robotics and Automation - Ncra HEC/PC Pakistan Department of Electrical Engineering Karachi75270 Pakistan

ISBN: (纸本)9798350335316

In this paper, we discuss the design of different models for teleoperation control by presenting the significant benchmark results for various important force reflecting control architectures used in 1-DOF bilateral teleoperation. They are used to analyze the results of force and distance tracking graphs in free motion and contact mode obtained from various control models by varying dynamics of master, slave, environment, and communication channel. In this regard, different force reflecting teleoperation control architectures are simulated via proposed model design, such as two-channel (2C) architectures, three-channel (3C) and finally four-channel (4C) architecture. These benchmark results are presented for each architecture via MATLAB/Simulink-based simulator. © 2023 IEEE.

关键词： Remote control

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Grounded Answers for Multi-agent Decision-making Problem through Generative World Model 38

Grounded Answers for Multi-agent Decision-making Problem thr...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Liu, Zeyang Yang, Xinrui Sun, Shiguang Qian, Long Wan, Lipeng Chen, Xingyu Lan, Xuguang National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Application Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China

Recent progress in generative models has stimulated significant innovations in many fields, such as image generation and chatbots. Despite their success, these models often produce sketchy and misleading solutions for complex multi-agent decision-making problems because they miss the trial-and-error experience and reasoning as humans. To address this limitation, we explore a paradigm that integrates a language-guided simulator into the multi-agent reinforcement learning pipeline to enhance the generated answer. The simulator is a world model that separately learns dynamics and reward, where the dynamics model comprises an image tokenizer as well as a causal transformer to generate interaction transitions autoregressively, and the reward model is a bidirectional transformer learned by maximizing the likelihood of trajectories in the expert demonstrations under language guidance. Given an image of the current state and the task description, we use the world model to train the joint policy and produce the image sequence as the answer by running the converged policy on the dynamics model. The empirical results demonstrate that this framework can improve the answers for multi-agent decision-making problems by showing superior performance on the training and unseen tasks of the StarCraft Multi-Agent Challenge benchmark. In particular, it can generate consistent interaction sequences and explainable reward functions at interaction states, opening the path for training generative models of the future. © 2024 Neural information processing systems foundation. All rights reserved.

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MEG Channel Selection Using Copula Entropy-Based Transfer Entropy for Motor Imagery BCI 46

MEG Channel Selection Using Copula Entropy-Based Transfer En...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Tang, Chao Jiang, Dongyao Chen, Badong Xi'an Jiaotong University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an China

ISBN: (纸本)9798350371499

Multi-channel magnetoencephalography (MEG) data provides high spatiotemporal resolution for motor imagery (MI)-based brain-machine interfaces (BCIs). However, not all channels contribute to the performance of BCIs. Taking into account the importance of specific channels in measuring their causal relationships with other channels during MI tasks, a novel channel selection method using copula entropy-based transfer entropy (CTE) is proposed to select task-relevant channels. Experiments on a publicly available dataset validate the effectiveness of the proposed methods. Compared to using all channels, channel selection based on CTE can significantly (p © 2024 IEEE.

关键词： Magnetoencephalography

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Smart Railway Level Crossing System for Avoiding Accidents

Smart Railway Level Crossing System for Avoiding Accidents

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2023 International Conference on robotics and Automation in Industry, ICRAI 2023

作者： Mirza, Ghulam Fiza Junejo, Yahya Sameen Shaikh, Faisal Karim Chowdhry, Bhawani Shankar Shah, Ali Akbar NED University of Engineering and Technology Department of Telecommunications Karachi Pakistan Haptics Human Robotics and Condition Monitoring Lab National Center of Robotics and Automation Mehran University of Engineering and Technology Jamshoro Pakistan

ISBN: (纸本)9781665464727

In railways, one of the foremost issue is the safety of railway level crossing. Crashes related to railway level crossing lead to a great loss with respect to goods and lives. However, the existing methods used for the accidents' avoidance are manual, sensors and cameras based which possess certain drawbacks. Manual control of crossing gates is not only time-consuming but requires human labor and generate errors. Besides, camera based control systems are costly, involve experts to process the images, require human intervention, and do not provide clear images in fog/rain. On the other hand, sensors based systems have short range issues and are dependent on environmental conditions. This paper reviews the existing control systems of railway infrastructure and their classification in different countries. It then presents an automatic, novel, high range and cost-effective approach to prevent critical accidents intelligently. The novel concept avoids level crossing accidents using Internet of Things (IoT) technology. The train GPS based on-board and track controller nodes communicate to control level crossing gates and alert road users. The communication between modules (i.e., on-board train and track devices) and remote database establishes using WiFi. The review of effective communication network for critical and non-critical communication is presented with respect to mobility, capacity, coverage, availability and reliability. This research provides concept which can greatly mitigate the accidents' frequency associated with the trains, pedestrians and road vehicles on crossing using smart technologies. © 2023 IEEE.

关键词： Control systems

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Power in Unity: Combining in-Domain and out-of-Domain Pre-Training Strategies for EEG-Based Person Identification

Power in Unity: Combining in-Domain and out-of-Domain Pre-Tr...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Garoufis, Christos Glytsos, Marios Chourdaki, Ioanna Filntisis, Panagiotis P. Maragos, Petros School of ECE National Technical University of Athens Athens Greece Robotics Institute Athena Research Center Athens Greece

ISBN: (纸本)9798350368741

We present the NTUA-IRAL team's solution for the Person Identification track of the Signal Processing EEG-Music Emotion Recognition Grand Challenge, hosted at ICASSP. Our approach employs an ensemble of three CNNs, each pretrained using a distinct strategy: contrastive pre-training, traditional ImageNet pre-training, and task-specific pre-training on a publicly available EEG dataset. This diverse pre-training regimen enabled our models to achieve a test set accuracy of 100%, earning third place in the challenge subtrack. © 2025 IEEE.

关键词： contrastive convolutional neural networks EEG person identification pre-training

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Semi-Supervised Curriculum Learning for Ultra-Wide-Angle fundus Optic Disc Segmentation

Semi-Supervised Curriculum Learning for Ultra-Wide-Angle fun...

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2024 China Automation Congress, CAC 2024

作者： Wang, Yingkai Chen, Yiyi Wu, Jiayi Xin, Jingmin National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China

ISBN: (纸本)9798350368604

Segmentation of the optic disc in ultra-wide-angle fundus images could aid in detection and diagnosis of diabetic kidney disease and diabetic retinopathy. Due to the wide field of view, large image size, and small targets of ultra-wide-angle fundus images, it’s hard to annotate them. In this case, semi-supervised methods could solve this problem. However, standalone semi-supervised training does not take into account the differences between samples. We developed a new semi-supervised training method incorporating curriculum learning, where the model initially trains on easier samples and gradually progresses to more hard ones. This strategy could avoid the possible negative impact of the unlabeled samples in early stages in training and strengthen model’s robustness and serves as a plug- and-play approach and is adaptable to existing semi-supervised semantic segmentation methods. More concretely, we design a difficulty measurer to estimate the training difficulty of samples from the perspective of the entire dataset and a pace controller to control training time and duration of different samples according to their training difficulty. The experiment result shows the method we proposed could improves the performance. © 2024 IEEE.

关键词： Semantic Segmentation

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Unified Control of Voltage, Frequency and Angle in Electrical Power Systems: A Passivity and Negative-Imaginary based Approach 63

Unified Control of Voltage, Frequency and Angle in Electrica...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Chen, Yijun Shi, Kanghong Petersen, Ian R. Ratnam, Elizabeth L. The Australian National University School of Engineering Australia The University of Melbourne Department of Electrical and Electronic Engineering Australia The University of Sydney The Australian Center for Robotics Australia

ISBN: (纸本)9798350316339

This paper proposes a unified methodology for voltage regulation, frequency synchronization, and rotor angle control in power transmission systems considering a one-axis generator model with time-varying voltages. First, we formulate an output consensus problem with a passivity and negativeimaginary (NI) based control framework. We establish output consensus results for both networked passive systems and networked NI systems. Next, we apply the output consensus problem by controlling large-scale batteries co-located with synchronous generators that have real-time voltage phasor measurements. By controlling the battery storage systems so as to dispatch real and reactive power, we enable simultaneous control of voltage, frequency, and power angle differences across a transmission network. Validation through numerical simulations on a four-area transmission network confirms the robustness of our unified control framework. © 2024 IEEE.

关键词： Synchronous generators

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Keep it Simple: Understanding Natural Language Commands for General-Purpose Service Robots

Keep it Simple: Understanding Natural Language Commands for ...

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2024 IEEE/SICE International Symposium on System Integration, SII 2024

作者： Ortuno-Chanelo, Stephany Contreras, Luis Savage, Jesús Okada, Hiroyuki School of Engineering National Autonomous University of Mexico Biorobotics Laboratory Mexico Tamagawa University Advance Intelligence and Robotics Research Center Japan

ISBN: (纸本)9798350312072

Service robots are designed to perform useful tasks for humans, which involve managing and combining a variety of skills in the form of global and local plans to solve any given task. In this work, we propose a framework to process natural language commands for general-purpose service robots where the robot should perform an arbitrary spoken command requested by a non-expert operator. Our system uses a Natural Language Processing (NLP) parser and a Conceptual Dependency (CD) builder to create CD structures that an expert system can employ to generate a global plan that the robot will execute. An extra simplification step using Large Language Models (LLM) was tested and evaluated in order to improve the accuracy of the system. Finally, our system has been tested in challenging environments in robot competitions and we have achieved promising results. © 2024 IEEE.

关键词： Expert systems

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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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2024 International Convention on Rehabilitation engineering and Assistive Technology and World Rehabilitation Robot Convention, i-CREATE and WRRC 2024

作者： Cao, Wujing Guo, Xinqiang Zou, Yupeng Zhang, Shuo Luo, Mingxiang Kobsiriphat, Worawarit Wu, Xinyu Yin, Meng Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen518005 China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society SIAT-CUHK Joint Laboratory of Robotics and Intelligent Systems China School of Mechanical Engineering China University of Petroleum China National Metal and Materials Technology Center Thailand

ISBN: (纸本)9798350355123

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. © 2024 IEEE.

关键词： Convolutional neural networks

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On Designing Control Systems for Electron-Beam Processing Plants

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Russian Electrical engineering 2024年第7期95卷 579-584页

作者： Kozhechenko, A.S. Sliva, A.P. Maslov, A.N. Shcherbakov, A.V. Goncharov, A.L. Rodyakina, R.V. Dragunov, V.K. Department of Power Supply of Industrial Enterprises and Electric Technologies National Research University "Moscow Power Engineering Institute" Moscow111250 Russia Department of Metal Technologies National Research University "Moscow Power Engineering Institute" Moscow111250 Russia Department of Robotics Mechatronics Dynamics and Machine Strength National Research University "Moscow Power Engineering Institute" Moscow111250 Russia

This study shows the need for a common structured approach to designing control systems for electron-beam welding, surfacing, and additive shaping plants. The main controlled parameters of the process are highlighted. The three-level hierarchical structure of the control system is described. The control processes are differentiated taking into account the process requirements for electron-beam treatment processes and the features of the vacuum system, the electron gun, and the positioning system, as well as the system of taking images of the treated area according to the signal of backscattered electrons. The types and quantitative characteristics of the parameters of the signals necessary for running the system are given. It is shown that it is effective to divide the control processes in three groups, depending on the requirements on the rapid response of the actuators, as well as on the need for synchronizing some processes with others over time. The presented differentiation is used to propose a method for selecting technical facilities for each of the mentioned groups. An example is provided of constructing a control system for an electron-beam process plant using hardware components available for purchase. © Allerton Press, Inc. 2024.

关键词： Numerical control systems

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