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AHFE Virtual Conference on Human Factors in Robots, Drones and Unmanned Systems, and the International Conference on Human Factors in Cybersecurity, 2020

作者： Grau, Antoni Guerrra, Edmundo Bolea, Yolanda Munguia, Rodrigo Automatic Control Department Technical University of Catalonia UPC Barcelona Spain Computer Science Department CUCEI University of Guadalajara Guadalajara Mexico

ISBN: (纸本)9783030517571

Robot navigation requires the use of a reliable map. Depending on the environment conditions, this map needs a constant update for a safe navigation. Autonomous robots use this map but at the same can contribute to the updating process, requiring a permanent connection to the cloud where the map is created and modified based on the robots’ information. In this paper, authors present a robot navigation scheme based in hybrid control behavior when the connection to the cloud is loss for some reason. Robot needs to recover a known position to restart its mission and the behavior definitions allow this fact. Results with real data are presented in front of different situations of network coverage. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Robots

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Graph-based semantic planning for adaptive human-robot-collaboration in assemble-to-order scenarios

Graph-based semantic planning for adaptive human-robot-colla...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Ruidong Ma Jingyu Chen John Oyekan Department of Automatic Control and System Engineering University of Sheffield Sheffield United Kingdom Department of Computer Science University of York York United Kingdom

Assemble-to-Order (ATO) has become a popular production strategy for the increasing demand for mass-customized manufacturing. In order to facilitate a flexible and automated Human-Robot-Collaboration system for ATO, we propose a Learning from Demonstration (LfD) framework based on 2D videos in this paper. We initially combine temporal hand motions with spatial hand-object interactions to detect assembly actions. Therefore, an assembly graph can be constructed using classified action sequences. Compared to previous studies on task planning for robots, our graph-based semantic planner can directly learn the demonstrated task structure and thus produce more detailed assistive robot actions for more effective collaboration. We validate our approach by applying it to a real-world ATO problem. The results demonstrated that our proposed system can produce actions adaptively in response to varying human action sequences, as well as guide human assembly when the robot is not involved. Our approach also shows generalizability to unseen human action sequences.

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Dynamic Optimization of Electric Vehicle Charging for Sustainable Power Grids using Sine Hunter-Prey Optimization with GRUs 3

Dynamic Optimization of Electric Vehicle Charging for Sustai...

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3rd IEEE International Conference on Electrical Power and Energy Systems, ICEPES 2024

作者： Syed, Mohd Khizar Chandra Behera, Prakash Josephson, P Joel Khan, Shamim Ahmad Kholmukhamedov, Timur Ponni Valavan, M. Kohinoor college Department of Physics Maharashtra India St. Claret College Department of Science Bangalore India Malla Reddy Engineering College Department of ECE Telangana Medchal Malkajgiri India Glocal School of Science & Technology Glocal University Uttar Pradesh India Turin Polytechnic University Automatic Control and Computer Engineering Department Tashkent Uzbekistan K.Ramakrishnan College of Engineering Department of AI & DS Tamil Nadu Trichy India

ISBN: (纸本)9798350390728

The integration of electric vehicles (EVs) into power grids presents a significant challenge for sustainability due to their dynamic charging demands and the need to accommodate renewable energy sources. Existing optimization methods often fail to adequately address the complexity of this problem. In this study, propose a novel approach that combines Sine Hunter-Prey Optimization (SHPO) with Gated Recurrent Units (GRUs) to dynamically optimize EV charging for sustainable power grid operation. Traditional optimization methods struggle to capture the nonlinear and time-varying nature of EV charging demands and renewable energy availability. To overcome this limitation, SHPO's ability to efficiently explore complex solution spaces and GRUs' capability to model temporal dependencies in sequential data. This proposed method optimizes EV charging schedules by considering grid stability, renewable energy utilization, and cost minimization. Validating the approach using historical data on EV charging patterns, renewable energy generation, and grid conditions. Performance metrics include grid stability indices, renewable energy integration rates, and cost savings compared to baseline strategies. Experimental results demonstrate that the method outperforms traditional optimization techniques, leading to more sustainable and resilient power grid operation with 92% stability. Overall, this study contributes to advancing the modern methods in dynamic optimization of EV charging, paving the way for more sustainable transportation and energy systems. © 2024 IEEE.

关键词： State of charge

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Linqstat-based Touch Sensor Keyboard for Physical Access Security

Linqstat-based Touch Sensor Keyboard for Physical Access Sec...

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International Conference on Semiconductor

作者： Cristian-Ovidiu Opriş Theodor Andrei Pruteanu Andrei Drăgulinescu Ana-Maria Drăgulinescu Petru-Lucian Milea Electronic Technology and Reliability Department POLITEHNICA Bucharest Bucharest Romania Faculty of Automatic Control and Computer Science POLITEHNICA Bucharest Bucharest Romania Telecommunications Department POLITEHNICA Bucharest Bucharest Romania

ISBN: (数字)9798350352078

ISBN: (纸本)9798350352085

With the purpose of increasing the security of physical access in restricted areas, the paper proposes the use of the Linqstat material for a touch sensor keyboard consisting of 19 keys with the possibility of expansion for later versions, model that is missing from the specialized literature. The advantage of this type of sensor is that it can also be applied to a curved surface, with the necessary calibration. The construction method of the keyboard including the touch sensor is presented. First, a neural network (NN) is used to locate the touch on the keyboard, then a second method detects each pressed key individually.

关键词： Keyboards Tactile sensors Artificial neural networks Calibration Security

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Cloud-Based DMPC Simulation for Autonomous Mobile Robot Platooning

Cloud-Based DMPC Simulation for Autonomous Mobile Robot Plat...

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International Conference on System Theory, control, and Computing (ICSTCC)

作者： Anca Maxim Ovidiu Pauca Razvan-Gabriel Lazar Cristian-Mihai Amarandei Constantin-Florin Caruntu Department of Automatic Control and Applied Informatics "Gheorghe Asachi" Technical University of Iasi Iasi Romania Department of Computer Science and Engineering "Gheorghe Asachi" Technical University of Iasi Iasi Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

Cloud computing can be an useful tool when dealing with spatially distributed resources, which need to be simultaneously managed. In this work, a simulation environment is developed using OpenStack to apply a Distributed Model Predictive control strategy for a platooning application consisting of multiple autonomous mobile robots. The idea is to develop the entire algorithm by taking advantage of the possibility of creating virtual machines. Thus, each autonomous mobile robot is treated as an individual agent and is modeled and controlled on a virtual machine, whereas the communication between the agents is performed in the cloud, using the TCP/IP protocol standard. The simulation results illustrate that the platooning application deployed in OpenStack outperforms the simulation performed on a single computer.

关键词： Cloud computing Protocols Computational modeling Stability criteria TCPIP Mathematical models Mobile robots MATLAB Autonomous robots Testing

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Research on Black-Box Adversarial Attack Based on Transformer 4

Research on Black-Box Adversarial Attack Based on Transforme...

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4th International Conference on Frontiers Technology of Information and computer, ICFTIC 2022

作者： Zheng, Wenjuan Luo, Hanxu Ji, YuXin Li, Haipeng Cong, Longjian National Key Laboratory of Aerospace Intelligent Control Technology Master of Computer Vision Beijing100854 China Beijing Aerospace Automatic Control Institute Beijing100854 China Wuhan University of Technology Department of Information Engineering Hubei Province Wuhan430000 China

ISBN: (纸本)9798350321951

The classic adversarial samples of black-box attacks are all aimed at the models of Convolutional neural networks (CNNs), but they do not perform well on the new recognition networks based on Transformer. In this paper, we propose an adversarial sample generation algorithm based on Vision transformers (VITs)' self-attention mechanism and patch partition. We noticed that different blocks in the VIT have uneven attention distribution, so we first generated a patch-based attention map and performed threshold segmentation, which is used as a mask to perform data enhancement operations based on patches with high weight and patches with low weight, and then exchanged information between patches to generate adversarial samples. The experiment of simulating black-box attack shows that the adversarial samples generated by the algorithm in this paper have a high success rate in all kinds of models based on Transformer of attacks, and also perform well on CNNs. © 2022 IEEE.

关键词： Convolutional neural networks

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Vision-based automatic fruit counting with UAV

arXiv

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arXiv 2025年

作者： Szolc, Hubert Wasala, Mateusz Mietla, Remigiusz Iwicki, Kacper Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Krakow al. Mickiewicza 30 Krakow30–059 Poland

The use of unmanned aerial vehicles (UAVs) for smart agriculture is becoming increasingly popular. This is evidenced by recent scientific works, as well as the various competitions organised on this topic. Therefore, in this work we present a system for automatic fruit counting using UAVs. To detect them, our solution uses a vision algorithm that processes streams from an RGB camera and a depth sensor using classical image operations. Our system also allows the planning and execution of flight trajectories, taking into account the minimisation of flight time and distance covered. We tested the proposed solution in simulation and obtained an average score of 87.27/100 points from a total of 500 missions. We also submitted it to the UAV Competition organised as part of the ICUAS 2024 conference, where we achieved an average score of 84.83/100 points, placing 6th in a field of 23 teams and advancing to the finals. © 2025, CC BY.

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Ranking of attributes-comparative study based on data from stylometric domain 26

Ranking of attributes-comparative study based on data from s...

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26th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2022

作者： Zielosko, Beata Stanczyk, Urszula Zabinski, Krzysztof Institute of Computer Science University of Silesia in Katowice B edzinska 39 Sosnowiec41-200 Poland Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

The area of feature selection methods constantly expands along with the development of artificial intelligence domain, and has great impact on almost every field, whenever data is processed and explored. The paper presents research where a ranking method was proposed, inspired by an approach which comes from an algorithm for induction of decision rules. The ranking procedure was based on calculation of standard deviation for attributes, taking into account assigned class labels. This method was compared with another ranking mechanism, a modified version of popular Relief algorithm, with incorporating characteristics of variables by supervised discretisation. Comparison of obtained results included the aspect of knowledge representation as well as the perspective of the accuracy for constructed rule-based classifiers. The experiments were performed on datasets from stylometry domain, where authorship attribution was considered as a classification task, and stylometric descriptors as characteristic features defining writing styles of authors. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 26th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2022)

关键词： Knowledge representation

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A CNN Approach To Predicting Non-Invasive Blood Pressure of Surgical Patients 5

A CNN Approach To Predicting Non-Invasive Blood Pressure of ...

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5th IEEE International Conference on Knowledge Innovation and Invention, ICKII 2022

作者： Ko, Pin-You Lai, Lien-Fu Ku, Tien-Hsiung Lin, Yue-Der National Changhua University of Education Department of Computer Science and Information Engineering Changhua Taiwan Changhua Christian Hospital Ai Development Center Department of Anesthesiology Changhua Taiwan Feng Chia University Department of Automatic Control Engineering Taichung Taiwan

ISBN: (数字)9781665479295

ISBN: (纸本)9781665479295

The occurrence of intraoperative hypertension/ hypotension may cause danger to a patient. Therefore, the monitoring of blood pressure change during surgical operation is momentous for anesthetized patient undergoing surgical procedures. The invasive method of measuring arterial blood pressure (ABP) provides accurate and much information, but it requires evaluation by an anesthesiologist and with its long-term use, it also brings a higher risk of infection, and even some patients have pain and discomfort at the catheter placement site after surgery, so it is not suitable for all types of patients. On the other hand, non-invasive method of measuring cuff blood pressure provides little information because of non-continuous measurement. With the emerging of deep learning, there has been more and more discussion on whether non-invasive Photoplethysmography (PPG) can replace invasive ABP as an alternative to continuous BP measurement and even BP prediction. This paper aims to implement a deep learning model to predict SBP/DBP signal by PPG, PPG extended data and Electrocardiography (ECG) data. We trained the CNN-based architecture on preprocessed data including ECG, PPG, 1st and 2nd derivative PPG signal extracted from MIMIC-III(Medical Information Mart for Intensive Care III) waveform database matched subset to predict continuous ABP signals using real world data. The value of SBP and DBP are directly predicted to effectively estimate the accuracy of the predictions. The prediction results fulfill the grade A in British Hypertension Society (BHS) standard and most part of Association for the Advancement of Medical Instrumentation (AAMI)'s standard. The proposed model has been applied for CHANGHUA CHRISTIAN HOSPITAL's IRB NO:191240. The results depict that our proposed model could effectively predict SBP/DBP signal and be deployed to the operating room realistically. © 2022 IEEE.

关键词： Blood pressure

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A frequency domain property driven search method for echo state network structures

A frequency domain property driven search method for echo st...

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Intelligent control, Measurement and Signal Processing and Intelligent Oil Field (ICMSP), International Conference on

作者： Zhaozhao Zhang Bofan Li Yingqin Zhu College Computer Science and Technology Xi'an University of Science and Technology Xi'an China Department of Control Automatic CINVESTAV-IPN(National Polytechnic Institute) Mexico City Mexico

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

To overcome the limitations of conventional echo state networks (ESN), such as redundant connections and insufficient frequency-domain feature extraction, this paper proposes a frequency-domain feature architecture search-based ESN (FS-ESN). Leveraging a frequency response fitness function, FS-ESN dynamically optimizes reservoir structures and incorporates frequency-domain features aligned with target tasks. Experimental results demonstrate that FS-ESN significantly improves feature extraction, adaptability, and predictive performance, outperforming traditional ESN models in task-specific applications.

关键词： Adaptation models Frequency-domain analysis Computational modeling Stochastic processes Echo state networks Predictive models Signal processing Feature extraction Reservoirs Frequency response

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