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16th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2024

作者： Mironela, Pirnau Iustin, Priescu Mihaela, Priescu Catalina Claudiu, Pirnau Daniel, Ghiculescu Liviu 'Titu Maiorescu' University Faculty of Informatics Department of Informatics Bucharest Romania National University of Science and Technology Politehnica Bucharest Faculty of Automatic Control and Computer Science Bucharest Romania National University of Science and Technology Politehnica Bucharest Faculty of Industrial Engineering and Robotics Manufacturing Engineering Department Bucharest Romania

ISBN: (纸本)9798350371154

This study proposes methods that can be used to examine and interpret comments that users have made after watching videos on YouTube on a particular topic. YouTube tutorials are very popular among young people. They have become an important pillar in informal education, thus contributing to the rapid acquisition of skills and knowledge. Under these circumstances, we were interested in analysing the YouTube videos comments, knowing that the platform has a continuous increase in popularity, which is also due to the opportunities of sharing them. Since ChatGPT-themed YouTube videos have seen a significant surge in popularity since 2022, we were interested to analyse some videos that approach the topics of 'ChatGPT, AI clone, AI robot and Deep Learning.' © 2024 IEEE.

关键词： Deep learning

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Output Tracking Control of a Nonlinear System Based on Takagi-Sugeno Fuzzy Model: Generalized Partial Eigenstructure Assignment Approach

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IEEE Access 2024年 12卷 18520-18535页

作者： Soltanian, Farzad Shasadeghi, Mokhtar Mobayen, Saleh Skruch, Pawel University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2M9 Canada Shiraz University of Technology Electrical Engineering Faculty Shiraz71557-13876 Iran National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan AGH University of Science and Technology Department of Automatic Control and Robotics Kraków30-059 Poland

This paper introduces an innovative optimal control approach to achieve output tracking while incorporating H2-performance specifications in a specific class of nonlinear dynamics modeled by the Takagi-Sugeno fuzzy model (TSFM). The primary innovation lies in extending partial eigenstructure assignment to TSFM-based nonlinear systems within the framework of sliding mode control (SMC). We propose a two-step methodology for designing optimal sliding surface gains. Initially, optimal state feedback gains are computed for each rule consequence containing a linear subsystem, adhering to predetermined eigenvalues and satisfying H2-performance criteria. Subsequently, using a convex combination, the overall state feedback gain is calculated and utilized to design sliding matrix gains. The sliding matrix gains are then determined by strategically combining previously calculated state-feedback gains in a convex optimization problem. We reframe the output tracking strategy as a stabilization problem using a virtual control input and reformulate the optimization task concerning tracking state errors. This process yields state feedback gains, sliding gains, and the formulation of the virtual control input. The effectiveness of our approach is verified through comprehensive simulations, emphasizing its capability in addressing output tracking challenges within nonlinear systems. © 2013 IEEE.

关键词： Transient analysis

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Energy-efficient and Secure Wireless Communication for Telemedicine in IoT

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computer Systems science & Engineering 2022年第12期43卷 1111-1130页

作者： Shital Joshi S.Manimurugan Ahamed Aljuhani Umar Albalawi Amer Aljaedi Department of Computer Science Oklahoma State UniversityOklahomaUSA College of Computing and Information Technology University of TabukTabuk71491Saudi Arabia Industrial Innovation and Robotics Center University of TabukTabukSaudi Arabia

The Internet of Things (IoT) represents a radical shifting paradigm fortechnological innovations as it can play critical roles in cyberspace applications invarious sectors, such as security, monitoring, medical, and environmental sectors,and also in control and industrial applications. The IoT in E-medicine unleashedthe design space for new technologies to give instant treatment to patients whilealso monitoring and tracking health conditions. This research presents a systemlevel architecture approach for IoT energy efficiency and security. The proposedarchitecture includes functional components that provide privacy managementand system security. Components in the security function group provide securecommunications through Multi-Authority Ciphertext-Policy Attributes-BasedEncryption (MA-CPABE). Because MA-CPABE is assigned to unlimited devices,presuming that the devices are reliable, the user encodes data with AdvancedEncryption Standard (AES) and protects the ABE approach using the solutionsof symmetric key. The Johnson’s algorithm with a new computation measure isused to increase network lifetime since an individual sensor node with limitedenergy represents the inevitable constraints for the broad usage of wireless sensornetworks. The optimal route from a source to destination turns out as the cornerstone for longevity of network and its sustainability. To reduce the energy consumption of networks, the evaluation measures consider the node’s residualenergy, the number of neighbors, their distance, and the link dependability. Theexperiment results demonstrate that the proposed model increases network lifeby about 12.25% (27.73%) compared to Floyd–Warshall’s, Bellman–Ford’s,and Dijkstra’s algorithms, lowering consumption of energy by eliminating thenecessity for re-routing the message as a result of connection failure.

关键词： Energy-efficiency energy consumption Internet of Things telemedicine wireless sensor networks

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Convergence Analysis for Delay Dependent LFC Scheme for Power Systems Networks Over Open Communication Networks with Disturbances

Convergence Analysis for Delay Dependent LFC Scheme for Powe...

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2023 IEEE International Conference on Industrial Technology, ICIT 2023

作者： Islam, Shafiqul Ncf 263 Xavier University of Louisiana Dual-Degree Computer Science and Engineering Intelligent Robotics Mechatronics and Autonomous Systems Lab New OrleansLA70125 United States

ISBN: (纸本)9798350336504

This paper presents a delay-dependent convergence criterion for load frequency control (LFC) for smart power grid networks with the presence of disturbances and time-varying delays. The LFC scheme comprises delayed remote measurement and control signals with local state feedback signals. The convergence criteria are developed by using Lyapunov-Krasovskii functional for both symmetrical and asymmetrical time-varying delays associated with remote measurement and control channel. First, the analysis shows that all the power system states are bounded and asymptotically stable without the presence of disturbances. Then, the convergence analysis proves that all the power systems states are bounded and their bounds guarantee the convergence property of the H∞ control with the presence of bounded disturbances. These convergence properties are achieved by using well know Linear Matrix Inequalities (LMIs). Compared with the existing design, the proposed analysis uses both symmetrical and asymmetrical time-varying delays in the measurement channel from RTU to the control center and in the control channel from the control center to the power generation unit. In contrast with reported designs, the proposed design and analysis uses the disturbances associated with the closed loop systems appearing from modeling errors and other external load disturbances and faults. © 2023 IEEE.

关键词： Closed loop systems

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Early Detection of Diabetes Using ML Based Classification Algorithms 13th

Early Detection of Diabetes Using ML Based Classification Al...

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13th International Advanced Computing Conference, IACC 2023

作者： Ashisha, G.R. Mary, X. Anitha Chowdhury, Subrata Karthik, C. Choudhury, Tanupriya Kotecha, Ketan Electronics and Instrumentation Engineering Karunya Institute of Technology and Science Tamil Nadu Coimabtore India Robotics Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India Computer Science and Engineering Sreenivasa Institute of Technology and Management Studies Andhra Pradesh Chittoor India Robotics Engineering Jyothi Enngineering College Kerala Cheruthuruthy India Maharashtra Pune412115 India Maharashtra Pune412115 India

ISBN: (纸本)9783031567025

This article introduces a method for classifying diabetes based on machine learning (ML) methods. In recent years, significant focus have been put onto increasing disease classification performance through the use of ML approaches. This paper outlines the use of five interpretable ML algorithms: Bagging classifier, Random Forest, AdaBoost, Multilayer Perceptron, and Restricted Boltzmann Machine. All the ML classifiers were trained and tested in a benchmark Biostat Diabetes Dataset using Python programming. Each technique’s performance is evaluated to discover which has the finest accuracy, precision, recall, F1-score, specificity, and sensitivity. Experimental findings and assessment reveal that the Random Forest technique outperforms all other ML techniques by achieving 98% precision, 98% recall, 98% F1-score, 75% sensitivity, 96% specificity, and accuracy of 97.5%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Multilayer neural networks

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RiskMap: A Unified Driving Context Representation for Autonomous Motion Planning in Urban Driving Environment

RiskMap: A Unified Driving Context Representation for Autono...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Xin, Ren Wang, Sheng Chen, Yingbing Cheng, Jie Liu, Ming Ma, Jun The Hong Kong University of Science and Technology Division of Emerging Interdisciplinary Areas Hong Kong Hong Kong Robotics and Autonomous Systems Thrust Guangzhou511453 China The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong

ISBN: (纸本)9781665481090

Motion planning is a complicated task that requires the combination of perception, map information integration and prediction, particularly when driving in heavy traffic. Developing an extensible and efficient representation that visualizes sensor noise and provides basis to real-time planning tasks is desirable. We aim to develop an interpretable map representation, which offers prior of driving cost in planning tasks. In this way, we can simplify the planning process for dealing with complex driving scenarios and visualize sensor noise. Specifically, we propose a unified context representation empowered by deep neural networks. The unified representation is a differentiable risk field, which is an analytical representation of statistical cognition regarding traffic participants for downstream planning tasks. This representation method is nominated as RiskMap. A sampling-based planner is adopted to train and compare RiskMap generation methods. In this paper, the RiskMap generation tools and model structures are explored, the results illustrate that our method can improve driving safety and smoothness, and the limitation of our method is also discussed. © 2024 IEEE.

关键词： Motion planning

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MuST: Multi-Head Skill Transformer for Long-Horizon Dexterous Manipulation with Skill Progress

arXiv

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arXiv 2025年

作者： Gao, Kai Wang, Fan Aduh, Erica Randle, Dylan Shi, Jane Amazon Robotics MA United States Department of Computer Science Rutgers University NJ United States

Robot picking and packing tasks require dexterous manipulation skills, such as rearranging objects to establish a good grasping pose, or placing and pushing items to achieve tight packing. These tasks are challenging for robots due to the complexity and variability of the required actions. To tackle the difficulty of learning and executing long-horizon tasks, we propose a novel framework called the Multi-Head Skill Transformer (MuST). This model is designed to learn and sequentially chain together multiple motion primitives (skills), enabling robots to perform complex sequences of actions effectively. MuST introduces a "progress value" for each skill, guiding the robot on which skill to execute next and ensuring smooth transitions between skills. Additionally, our model is capable of expanding its skill set and managing various sequences of sub-tasks efficiently. Extensive experiments in both simulated and real-world environments demonstrate that MuST significantly enhances the robot’s ability to perform long-horizon dexterous manipulation tasks. Copyright © 2025, The Authors. All rights reserved.

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A Pilot Usability Study of a Humanoid Avatar to Assist Therapists of ASD Children 15th

A Pilot Usability Study of a Humanoid Avatar to Assist Th...

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15th International Conference on Social robotics, ICSR 2023

作者： Fournier, Carole Michelon, Cécile Tanguy, Arnaud Audoyer, Paul Granit, Véronique Baghdadli, Amaria Kheddar, Abderrahmane CNRS -University of Montpellier Laboratory of Computer Science Robotics and Microelectronics of Montpellier Montpellier France Centre Ressources Autisme Languedoc-Roussillon and Center of Excellence for Autism and Neurodevelopmental Disorders CHU Montpellier Montpellier France Faculty of Medicine University of Montpellier Montpellier France CNRS-AIST Joint Robotics Laboratory IRL3218 Tsukuba Japan

ISBN: (纸本)9789819987146

In this article, we report on a pilot study consisting of an evaluation of the usability satisfaction and effectiveness of a preliminary telerobotic system to assist therapists of children with ASD. Unlike existing pre-programmed robotic systems, our solution beamed therapists in a humanoid robot (Pepper) to reproduce in real-time the therapist’s gestures, speech and visual feedback aiming to embody the therapist in a humanoid robot avatar and be able to perform activities during an ESDM intervention. Evaluations of our system, used by eleven therapists in internal tests during mock session without children, are reported and suggest that future use in real therapy sessions with ASD children can begin. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Anthropomorphic robots

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Design and Evaluation of Wearable Haptics Device for Weight Perception in Vocational Trade 23

Design and Evaluation of Wearable Haptics Device for Weight ...

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6th International Conference on Advances in robotics, AIR 2023

作者： Sasi, Deepu Krishnachandran, S. Rohith, P. James, Jose Ramesh, Shanker Vishnu Rajendran, S. Bhavani, Rao R. Ammachi Labs Amrita Vishwa Vidyapeetham India Brown Robotics Dept of Computer Science Brown University United States University of Victoria Canada University of Lincoln United Kingdom

ISBN: (纸本)9781450399807

Wearable devices have a good role in haptics and Virtual Reality (VR) technologies. Weight perception during the VR interactions would give a more realistic immersive experience to the user and that would give psycho-motor skill training. User performance is another aspect that would add to the advantages of wearable haptic devices. Scaffolding work is a very complex assembling task in the construction industry and proper training is insufficient in this area. The technology-based training would be helpful for reducing accidents and improving the outcome. So VR-based solutions along with wearable haptics would be the best option for this. This paper described the design and prototype development of a wearable haptic device based on the systematic approach that can be used for scaffolding work. This paper also reported the user experiments conducted with the participants and presented their experiences. These studies revealed the details of the weight perception and comfort level of the participants when experiencing the device. © 2023 ACM.

关键词： Virtual reality

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Customized SAM 2 for Referring Remote Sensing Image Segmentation

arXiv

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arXiv 2025年

作者： Rong, Fu Lan, Meng Zhang, Qian Zhang, Lefei School of Computer Science Wuhan University China Hong Kong University of Science and Technology Hong Kong Horizon Robotics China

Referring Remote Sensing Image Segmentation (RRSIS) aims to segment target objects in remote sensing (RS) images based on textual descriptions. Although Segment Anything Model 2 (SAM 2) has shown remarkable performance in various segmentation tasks, its application to RRSIS presents several challenges, including understanding the text-described RS scenes and generating effective prompts from text descriptions. To address these issues, we propose RS2-SAM 2, a novel framework that adapts SAM 2 to RRSIS by aligning the adapted RS features and textual features, providing pseudo-mask-based dense prompts, and enforcing boundary constraints. Specifically, we first employ a union encoder to jointly encode the visual and textual inputs, generating aligned visual and text embeddings as well as multimodal class tokens. Then, we design a bidirectional hierarchical fusion module to adapt SAM 2 to RS scenes and align adapted visual features with the visually enhanced text embeddings, improving the model’s interpretation of text-described RS scenes. Additionally, a mask prompt generator is introduced to take the visual embeddings and class tokens as input and produce a pseudo-mask as the dense prompt of SAM 2. To further refine segmentation, we introduce a text-guided boundary loss to optimize segmentation boundaries by computing text-weighted gradient differences. Experimental results on several RRSIS benchmarks demonstrate that RS2-SAM 2 achieves state-of-the-art performance. Copyright © 2025, The Authors. All rights reserved.

关键词： Image segmentation

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