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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Kumar, Vijay Gupta, Sachin School of Electronics and Control Engieening Lovely Professional University Department of Robotics and Control Engineering Phagwara India

ISBN: (纸本)9798331540821

As per latest research in the main medical sector, paralysis sight as is sight as observed as one of the main foremost causes of disability in human subjects trailing only behind chronic illnesses such as cancer and heart disease. In this sight as study, we examined health-related signals to uncover potential indicators of paralysis related disability. In collaboration with medical experts, we identified eight crucial parameters that could assist in determining whether an individual shows symptom of paralysis. Our research involved assembling an observational database comprising over 40,000 human subjects, spanning the main period from January 2018 to December 2023. We employed a three-layer Recurrent Neural Network to conduct categorical analyses involving training, validation, and testing phases. Our results suggest that ECG, blood pressure, heart rate and PPG signals are essential in evaluating an individual's risk of paralysis sight as disorders. The main model achieved an impressive accuracy rate of 94.3%, outperforming other models observed during the main study. These observed findings underscore the main potential of advanced machine learning techniques for facilitating the main early detection of paralysis sight as symptoms. By providing timely notifications to healthcare providers and human subjects at risk, our methodology can significantly enhance patient care and outcomes. This sight as research not only enriches the main scientific understanding of paralysis sight as but also offers practical solutions for improving human health through timely intervention strategies. Ultimately aiding in the main prevention of severe consequences associated with this sight as debilitating condition. © 2024 IEEE.

关键词： Blood pressure

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Efficient Multi-task Learning for Road Scene Classification: Scene, Time, and Weather Predictions 20

Efficient Multi-task Learning for Road Scene Classification:...

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20th IEEE International Conference on Intelligent Computer Communication and Processing Conference, ICCP 2024

作者： Prykhodchenko, Roman Skruch, Pawel Agh University of Science and Technology Department of Automatic Control and Robotics Cracow Poland

ISBN: (纸本)9798331539979

This paper addresses the task of road scene classification by leveraging multi-task learning (MTL) to improve model performance and computational efficiency. We employ the BDD100K dataset, which provides diverse driving scenarios, and apply data augmentation and balancing techniques to mitigate class imbalances. Our methodology integrates a ResNet18 backbone pre-trained on ImageNet, extended with task-specific heads for scene type, time of day, and weather classification. Uniform Manifold Approximation and Projection (UMAP) is used for dimensionality reduction and visualization of feature embeddings. Experiments demonstrate that our MTL approach significantly enhances classification accuracy across tasks, with notable performance in time-of-day classification. The proposed model achieves robust results despite class imbalances, validated through confusion matrices and UMAP visualizations. Our findings highlight the benefits of MTL in road scene classification, offering insights for future research in autonomous driving and computer vision. © 2024 IEEE.

关键词： Dimensionality reduction

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A hierarchical framework for distributed resilient control of islanded AC microgrids under false data injection attacks

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ISA Transactions 2023年第1期139卷 191-204页

作者： Zarei, Mahdi Sadegh Atrianfar, Hajar Tehran Iran Center of Excellence on Control and Robotics Department of Electrical Engineering Amirkabir University of Technology Tehran Iran

In this paper, we propose a control scheme to ensure the microgrid control layers are resilient to cyberattacks. The studied microgrid consists of several distributed generation (DG) units and we consider the hierarchical control structure that is common for microgrids. The use of communication channels among DGs has made microgrids more vulnerable and this is where cybersecurity issues arise. In this work we added three algorithms, reputation-based, Weighted Mean Subsequence Reduced (W-MSR) and Resilient Consensus Algorithm with Trusted Nodes (RCA-T), to the secondary control layer of the microgrid and made them resilient to false data injection (FDI) attacks. In reputation-based control, some procedures are used for detecting the attacked DGs and isolating them from the others. W-MSR and RCA-T are Mean Subsequence Reduced (MSR)-based algorithms that fade the effect of attacks without finding them. These algorithms use a simple strategy that ignores some extreme values of neighboring agents, so an attacker can simply get ignored. Our analysis of the reputation-based algorithm is based on scrambling matrices, so the communication graph can switch in a prescribed set. In each of the above cases, to evaluate the performance of the designed controllers, in addition to theoretical analysis, we evaluated and compared the controllers using simulation. © 2023 ISA

关键词： controllers

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Path Planning of Guide Robot with Nonholonomic Constraints for Visually Impaired Assistance 38

Path Planning of Guide Robot with Nonholonomic Constraints f...

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38th Annual European Simulation and Modelling Conference, ESM 2024

作者： Grzejszczak, Tomasz Lasak, Michal Department of Automatic Control and Robotics Silesian University of Technology Gliwice44-100 Poland

ISBN: (纸本)9789492859334

This paper presents a novel trajectory planning algorithm for a vehicle whose task is to guide a blind person. The kinematic model is used to accurately represent the mechanical structure and motion capabilities of the platform with nonholonomic constraints. Then the heuristic algorithm is employed to generate efficient and optimal paths for various tasks. The algorithm is implemented in a simulation environment and tested for various variants of leash length, distance between intermediate points and robot possible actions. The results show that the algorithm has a comparable total number of nodes to effective nodes ratio to the literature results, yet it is simpler to implement than proposed in literature RRT modification. This research contributes to the advancement of assistive robotics, paving the way for more autonomous and user-friendly navigation solutions for the visually impaired. © 2024 Modelling and Simulation 2024 - 38th Annual European Simulation and Modelling Conference 2024, ESM 2024. All rights reserved.

关键词： Heuristic algorithms

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Utilization of Haptic Feedback to facilitate Navigation for Visually Impaired Individuals 38

Utilization of Haptic Feedback to facilitate Navigation for ...

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38th Annual European Simulation and Modelling Conference, ESM 2024

作者： Lasak, Michal Grzejszczak, Tomasz Department of Automatic Control and Robotics Silesian University of Technology Gliwice44-100 Poland

ISBN: (纸本)9789492859334

This article presents the results of a study conducted to identify the most effective method for guiding visually impaired individuals in navigating their surroundings. The study aimed to address the persistent challenges faced by visually impaired individuals in achieving independent mobility. These challenges are exacerbated by the limited effectiveness of existing assistive technologies and the high costs associated with advanced solutions. Therefore, the focus was on developing and evaluating practical, cost-effective methods leveraging haptic feedback technology. The primary objective was to determine which haptic feedback configurations provided the most intuitive and effective guidance. By evaluating factors such as response time, accuracy in following designated routes, and user satisfaction, the study aimed to establish evidence-based recommendations for improving navigation aids. These findings are crucial as they contribute to the development of accessible technologies that can be readily integrated into daily life without imposing significant financial burdens or usability challenges. © 2024 Modelling and Simulation 2024 - 38th Annual European Simulation and Modelling Conference 2024, ESM 2024. All rights reserved.

关键词： Assistive technology

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DFU-Ens: End-to-End Diabetic Foot Ulcer Segmentation Framework with Vision Transformer Based Detection 3rd

DFU-Ens: End-to-End Diabetic Foot Ulcer Segmentation Framewo...

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3rd Diabetic Foot Ulcers Grand Challenge, DFUC 2022, held in Conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022

作者： Kucharski, Dariusz Kostuch, Aleksander Noworolnik, Filip Brodzicki, Andrzej Jaworek-Korjakowska, Joanna Department of Automatic Control and Robotics AGH UST Krakow Poland

ISBN: (纸本)9783031263538

Diabetic foot ulceration (DFU) is an open wound that occurs in approximately 15% of patients with diabetes, and is mostly located on the sole. Currently, it is one of the major challenges for healthcare systems around the world and a serious complication of diabetes. DFU causes infection and ischemia which can significantly prolong treatment and result in limb amputation and terminal illness. Thus, regular monitoring of the DFU area is necessary to assess the healing process and improve patient care. In this regard, we propose an end-to-end ensemble fully convolutional network (DFU-Ens), which mainly includes three modules: the U-Net module (DFU-Seg), the hybrid approach (DFU-Det) containing the YOLOv4 based detection module and the Vision Transformer DETR detection approaches. The ensemble solution is based on a high ranking strategy which combines DFU-Seg with a hybrid solution of bounding-box detection (performed using the latest DETR vision transformer architecture and YOLOv4) and patch segmentation. On the DFUC2022 validation set, we achieved 0.643 Dice score for the ensemble approach, 0.648 for DFU-Seg, and 0.556 and 0.581 for hybrid approaches based on YOLOv4 and DETR, respectively, with DETR having the best sensitivity. On the later published test set, DFU-Seg achieve a Dice score of 0.67 while the ensemble method achived 0.66. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Diseases

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Segmentation of Retina Vessels in 2D OCT-Reconstructed Fundus Images with 3D UNet 26

Segmentation of Retina Vessels in 2D OCT-Reconstructed Fundu...

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26th IEEE Signal Processing: Algorithms, Architectures, Arrangements, and Applications, SPA 2023

作者： Marciniak, Tomasz Stankiewicz, Agnieszka Zaradzki, Przemyslaw Poznan University of Technology Division of Electronic Systems and Signal Processing Institute of Automatic Control and Robotics Department of Control Robotics and Electrical Engineering Poznan Poland

ISBN: (纸本)9798350304985

This paper presents an analysis of the effectiveness of blood vessel segmentation in human retinal images. The segmentation process was carried out for three-dimensional optical coherence tomography (OCT) scans using the author's publicly available CAVRI-C database. A 3D version of the U-Net artificial neural network was used. Various dimensions of the input blocks were investigated to determine their influence on the segmentation process. The effectiveness of the proposed solutions was evaluated using various metrics and the results obtained were compared with the 2D solution, also based on neural networks. The use of the proposed solutions allowed us to improve the segmentation even by 15% (in the case of the F1-score). © 2023 Division of Signal Processing and Electronic Systems, Poznan University of Technology (DSPES PUT).

关键词： Optical tomography

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Identification of New EMC Factors of UAV FHSS Datalink in the Pre-Compliance Test Phase

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Journal of Engineering Science and Technology Review 2023年第3期16卷 109-116页

作者： Imrane, Bennageh Hassane, Mahmoudi Department of power electronics intelligent control techniques automation and robotics Mohammadia School of Engineers Mohammed V University of Rabat Morocco

This document presents a cost-effective methodology to diagnose the various parameters that influence the electromagnetic susceptibility (EMS) of a datalink using the frequency hopping transmission technique in drone applications. The innovative method is applied during the electromagnetic compatibility (EMC) pre-compliance test of a drone datalink. The paper outlines essential guidelines for establishing a reliable and affordable EMC test bench setup. It focuses on modifying specific parameters of the frequency hopping datalink that are typically not considered in conventional EMC testing. The results obtained reveal important new parameters of the datalink that utilizes hopping spread spectrum transmission techniques, including the communication protocol, datalink frequency bandwidth, and the number of transmission channels. By manipulating these key parameters, the electromagnetic immunity of the datalink used in experiments significantly improves. © 2023 School of Science, IHU. All rights reserved.

关键词： Drones

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Aggressive and robust low-level control and trajectory tracking for quadrotors with deep reinforcement learning

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Neural Computing and Applications 2025年第3期37卷 1223-1240页

作者： Chen, Shiyu Li, Yanjie Lou, Yunjiang Lin, Ke Department of Control Science and Engineering Harbin Institute of Technology Shenzhen Shenzhen518055 China Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Shenzhen518055 China

Executing accurate trajectory tracking tasks using a high-performance low-level controller is crucial for quadrotors to be applied in various scenarios, especially those involving uncertain disturbances. However, due to the uncertainties in disturbed environments, developing effective low-level controllers with traditional model-based control schemes is challenging. This paper presents an aggressive and robust reinforcement learning (RL)-based low-level control policy for quadrotors. The policy maps the observed quadrotor state directly to motor thrust commands, without requiring the quadrotor dynamics. Additionally, a trajectory generation pipeline is developed to improve the accuracy of trajectory tracking tasks based on differential flatness. With the learned low-level control policy, extensive simulations and real-world experiments are implemented to validate the performance of the policy. The results indicate that our RL-based low-level control policy outperforms traditional proportional–integral–derivative (PID) control methods and related learning-based policies in terms of accuracy and robustness, particularly in environments with uncertain disturbances. Furthermore, the proposed RL-based control policy exhibits an aggressive response in trajectory tracking, even when the speed of the desired trajectory is increased to 6 m/s. Moreover, the learned policy demonstrates strong vibration suppression capabilities and enables the quadrotor to recover to a hovering state from random initial conditions with shorter response time. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Deep reinforcement learning

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Coverage Path Planning of Agricultural Spraying Drones with Graph Convolutional Network 12

Coverage Path Planning of Agricultural Spraying Drones with ...

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12th RSI International Conference on robotics and Mechatronics, ICRoM 2024

作者： Khaloujini, Amirmasood Talaeizadeh, Amin Taheri, Alireza Alastya, Aria Advanced Research Lab for Control and Agricultural Robotics Sharif University of Technology Social and Cognitive Robotics Lab. Department of Mechanical Engineering Tehran Iran

ISBN: (纸本)9798331529734

Optimal route planning for autonomous robots is a crucial element of contemporary agriculture, especially for activities like spraying and pollination. This paper presents a Graph Convolutional Network (GCN) model especially developed to address the Coverage Path Planning (CPP). The aim of this paper is to convert the CPP problem into a Traveling Salesman Problem (TSP), so that by solving the TSP using the proposed neural network, an optimal route can be extracted for each agricultural field in the shortest possible time and with sufficient accuracy, ensuring full coverage of the field's area. The proposed model is a GCN, augmented with a transformer layer and a Multi-Layer Perceptron (MLP), was trained using datasets produced by a Genetic Algorithm (GA) Method. The model obtained a network error of 0.12 while solving TSP cases with 20 points. It showed robust generalization skills, as evidenced by validation and test errors of 0.1261 and 0.1264, respectively. Significantly, the GCN model reduces the solution time to approximately one-tenth compared to conventional approaches, a benefit that increases as the number of points increases. The effectiveness of the model in producing ideal coverage routes highlights its potential for wider use in optimization and coverage path planning, especially in dynamic and extensive agricultural environments. © 2024 IEEE.

关键词： Traveling salesman problem

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