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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Okwu, M.O. Eruero, O.P. Abubakar, N. Edward, B.A. Oreko, B.U. Otanocha, O.B. Orikpete, O.F. Maware, C. Ezekiel, K.C. Ori, C. Tartibu, L. Department of Mechanical Engineering Federal University of Petroleum Resources Effurun Nigeria Computer Science Department Brunel University London UxbridgeUB8 3PH United Kingdom University of Port Harcourt Choba Nigeria Fujio Cho Engineering Technology University of Kentucky 210E Center for Robotics and Manufacturing Building United States Maintenance Engineering and Asset Management University of Manchester United Kingdom 814-131 Wentworth Drive HalifaxNS Canada Department of Mechanical and Industrial Engineering Technology University of Johannesburg South Africa

Abundant solar resources and increasing electricity demand make solar energy a promising renewable energy source in Sub-Saharan Africa. However, conventional stationary Photovoltaic (PV) systems face challenges in efficiently capturing solar irradiation. To address this limitation, the implementation of solar tracking systems becomes essential, as they optimize the energy output of PV systems by dynamically adjusting the orientation of the panels to align with the sun's rays. This study presents a comprehensive design and performance evaluation of single-axis solar tracking systems in Delta State, Nigeria. The investigation focused on the energy provision efficiency of these systems, revealing that the single-axis tracker reached peak performance at year-end, providing 9.333 kWh of available solar energy and 9.296 kWh of user-available energy. Over the year, the system delivered a total of 100.625 kWh of available solar energy and 96.483 kWh of user-available energy. These findings highlight the significant role of solar tracking systems in maximizing energy harvest and underscore their potential for improving energy sustainability in Sub-Saharan Africa. © 2024 The Authors. Published by Elsevier B.V.

关键词： Solar irradiance

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Convergent effects of peptides on the initiation of feeding motor programs in the mollusk Aplysia

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Journal of Neurophysiology 2025年第5期133卷 1368-1379页

作者： Evans, Colin G. Barry, Michael A. Reaver, Carrie N. Patel, Paras R. Chestek, Cynthia A. Perkins, Matthew H. Jing, Jian Cropper, Elizabeth C. Department of Neuroscience Friedman Brain Institute Icahn School of Medicine at Mount Sinai New York NY United States Department of Biomedical Engineering Biointerfaces Institute University of Michigan Ann Arbor MI United States Department of Electrical Engineering and Computer Science Neurosciences Program Robotics Program University of Michigan Ann Arbor MI United States State Key Laboratory of Pharmaceutical Biotechnology Institute for Brain Sciences Chemistry and Biomedicine Innovation Center Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology Advanced Institute for Life Sciences School of Life Sciences Nanjing University Nanjing China

Neuropeptides configure the feeding network of Aplysia. For example, egestive activity is promoted by small cardioactive peptide (SCP), and ingestive activity is promoted by a combination of feeding circuit activating peptide (FCAP) and cerebral peptide 2 (CP-2). In addition, SCP and FCAP/CP-2 have a common network effect that does not contribute to motor program specification. They increase the excitability of an interneuron, B63. In this report, we further characterized the effects of peptides on B63. We performed voltage-clamp experiments and used a step protocol to look at steady-state currents. We found that SCP and FCAP/CP-2 both induced an inward current that was virtually absent in low-sodium saline. Previous work has established that B63 is unusual in the feeding circuit in that subthreshold depolarizations are autonomously generated that can trigger motor programs. Here, we show that this autonomous activity is more frequent in the presence of peptides. Previous studies have also shown that activity of the feeding central pattern generator (CPG) can be initiated by neurons that excite B63, e.g., by cerebral buccal interneuron 2 (CBI-2), a projection neuron that triggers biting-like motor programs. Here, we show that the latency of CBI-2-induced activity is decreased by stimulation of the esophageal nerve (EN) (which releases endogenous SCP). These results, taken together with previous results, indicate that peptides that act divergently to configure network activity additionally act convergently to promote motor program induction. We present data that suggest that this arrangement facilitates brief switches between ingestive and egestive motor activity. © 2025 The Authors.

关键词： central pattern generator invertebrate modulation motor program neuropeptide

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Analyzing Risk Level from RoI and VoI for Robust Data Privacy using Fuzzy Inference System

Analyzing Risk Level from RoI and VoI for Robust Data Privac...

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International Conference on robotics,Electrical and Signal Processing Techniques (ICREST)

作者： Rabab Khan Rongon M. Akhtaruzzaman Krishna Das Shekh Tanjil Sharif Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh Quantum Robotics and Automation Research Group (QRARG) Dhaka Bangladesh International University of Business Agriculture and Technology (IUBAT) Dhaka Bangladesh

ISBN: (数字)9798331530952

ISBN: (纸本)9798331530969

In the modern digital environment, securing data privacy has become increasingly critical as organizations confront rising threats from cyber-attacks and data breaches. To safeguard sensitive information, businesses must invest in robust data protection measures, yet balancing these investments with their potential benefits remains challenging. This research addresses this issue by proposing a novel framework that integrates Return on Investment (RoI) and Value of Information (VoI) within a Fuzzy Inference System (FIS) to assess data privacy risks effectively. RoI measures the financial return relative to the cost of data protection investments, indicating the economic value derived from such measures. VoI, on the other hand, evaluates the worth of the information protected by these investments, reflecting its impact on decision-making and operational efficiency. By employing a FIS, which accommodates uncertainties and imprecise data, this framework provides a nuanced approach to risk assessment, enabling organizations to optimize their data privacy strategies. The integration of RoI and VoI within the FIS allows for a comprehensive evaluation of risk levels, facilitating more informed decision-making and improved risk management. This research aims to enhance data privacy practices by offering a methodical and adaptable tool for evaluating and balancing investments in data protection.

关键词： Fuzzy logic Analytical models Uncertainty Decision making Data protection Signal processing algorithms Organizations Mathematical models Risk management Investment

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Motion planning for highly-dynamic unconditioned reflexes based on chained Signed Distance Functions

arXiv

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arXiv 2025年

作者： Lin, Ken Ye, Qi Lam, Tin Lun Li, Zhibin Chen, Jiming Li, Gaofeng College of Control Science and Engineering Zhejiang University Hangzhou China Robotics and Artificial Intelligence Laboratory The Chinese University of Hongkong Shenzhen China Department of Computer Science University College London London United Kingdom

The unconditioned reflex (e.g., protective reflex), which is the innate reaction of the organism and usually performed through the spinal cord rather than the brain, can enable organisms to escape harms from environments. Accordingly, it is essential to endow robots the highly-dynamic unconditioned reflexes to humans and/or environments, such that it can work collaboratively with humans or serve people in daily life. Although various sampling-based and optimization-based motion planning methods have been developed for robots, their limited computational speed is not suitable for rapid motion planning in highly-dynamic environments. In this paper, we propose an online, highly-dynamic motion planning algorithm to enable highly-dynamic unconditioned reflexes for manipulators. Our method is based on a chained version of Signed Distance Functions (SDFs), which can be pre-computed and stored. Our proposed algorithm is divided into two stages. In the offline stage, we create 3 groups of local SDFs to store the geometric information of the manipulator and its working environment. The SDFs in the first group are to describe the geometric information of the static environment. The SDFs in the second group are to describe the geometric information of each link. While the SDFs in the last group are to describe the reachability of each link’s end of the manipulator. In the online stage, the precomputed local SDFs are chained together according the configuration of the manipulator, to provide global geometric information about the environment. While the point clouds of the dynamic objects serve as query points to look up these local SDFs for quickly generating escape velocity. Then we propose a modified geometric Jacobian matrix and use the Jacobian-pseudo-inverse method to generate real-time reflex behaviors to avoid the static and dynamic obstacles in the environment. The benefits of our method are validated in both static and dynamic scenarios. In the static scenario, our meth

关键词： Jacobian matrices

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Predictive Models for the Early Diagnosis and Prognosis of Knee Osteoarthritis Using Deep Learning Techniques 1st

Predictive Models for the Early Diagnosis and Prognosis of K...

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1st International Conference on Intelligent Systems in Computing and Communications, ISCComm 2023

作者： Malathi, S.Y. Bharamagoudar, Geeta Shiragudikar, Sharada K. G. Totad, Shashikumar CSE Department KLEIT Karnataka Hubballi India School of Computer Science and Engineering KLE Technological University Karnataka Hubballi India Department of Automation and Robotics KLE Technological University Karnataka Hubballi India

ISBN: (纸本)9783031756047

Uncovering hidden patterns and relationships in knee OA data using machine learning algorithms can be incredibly useful for predicting illness progression and developing more effective treatments. By analyzing large amounts of data, machine learning algorithms can identify patterns and relationships that might not be immediately apparent to human researchers. This can help us better understand the mechanisms behind knee OA and identify potential targets for treatment. By developing more potent treatments, we may be able to slow the progression of the disease and improve patient outcomes. The knee osteoarthritis dataset information was initially acquired from Kaggle and the information on knee osteoarthritis is processed before model training. The study aims to develop and apply advanced machine learning methods, such as deep learning techniques like CNN-SVM, Mobilenet v2, Resnet152v2, Resnet 50, and EfficientnetB5, to analyze knee OA data and develop accurate prediction models for early diagnosis and prognosis. The study evaluates the performance of these models on a dataset of individuals with knee OA to determine their efficacy and accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： CNN EfficientNetB5 Knee osteoarthritis mobilenetV2 ResNet50

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Spectrum from Defocus: Fast Spectral Imaging with Chromatic Focal Stack

arXiv

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arXiv 2025年

作者： Kerem Aydin, M. Hung, Yi-Chun Pytlarz, Jaclyn Guo, Qi Alexander, Emma Department of Computer Science McCormick School of Engineering Northwestern University United States Dolby Laboratories Inc. Elmore Family School of Electrical and Computer Engineering Purdue University United States Center for Robotics and Biosystems McCormick School of Engineering Northwestern University United States

Hyperspectral cameras face harsh trade-offs between spatial, spectral, and temporal resolution in an inherently low-photon regime. Computational imaging systems break through these trade-offs with compressive sensing, but require complex optics and/or extensive compute. We present Spectrum from Defocus (SfD), a chromatic focal sweep method that recovers state-of-the-art hyperspectral images with a small system of off-the-shelf optics and © 2025, CC BY-NC-ND.

关键词： Hyperspectral imaging

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Extinguishing Real Fires by Fully Autonomous Multirotor UAVs in the MBZIRC 2020 Competition

Field Robotics

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Field robotics 2025年 2卷 406-436页

作者： Viktor Walter Vojtěch Spurný Matěj Petrlík Tomáš Báča David Žaitlík Lyubomyr Demkiv Martin Saska Department of Cybernetics Faculty of Electrical Engineering Czech Technical University in Prague http://mrs.felk.cvut.cz Robotics Group of SoftServe Inc. and with the Institute of Computer Science and Information Technologies Lviv Polytechnic National University Ukraine

In this paper, we describe a system for combating real fires with sprayed liquid extinguishing agent using a team of multirotor UAVs. The system design relies on onboard sensors and operates without the need for human intervention throughout its complex mission, from its takeoff to landing. The core UAV platform can estimate its state, self-localize, navigate and locate and extinguishing fires. Thermal and RGB cameras are used, each with a specialized computer vision subsystem and are combined with planar LIDAR for 3D localization of fires on multistory building facades. The system conducts aerial firefighting with a software stack that addresses flight dynamics and sensor limitations and a liquid-spraying subsystem incorporating a rigidly-attached water nozzle. The approach presented in this paper was motivated by the Mohamed Bin Zayed International robotics Challenge (MBZIRC 2020) firefighting scenario, which focused on coordinated multi-UAV teams that can autonomously combat high-rise building fires. The MBZIRC series places particular emphasis on fast and reliable deployment of robots in realistic conditions. These contests promote development of real-world applications that are greatly needed by society, but which still exceed State-of-the-Art in the robotics community. To our knowledge, our system was the only MBZIRC 2020 contender to extinguish a facade fire successfully in autonomous mode without using an RTK-GNSS system. Our approach contributed to victory in the overall competition and we have now adapted it into an industrial prototype for a firefighting UAV. A video attachment to this paper is available at http://***/fr2020firechallenge-facadefires.

关键词： Robots Buildings Autonomous aerial vehicles Location awareness Robot kinematics Inspection Navigation Cameras Monitoring Sensor systems

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LearningFlow: Automated Policy Learning Workflow for Urban Driving with Large Language Models

arXiv

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arXiv 2025年

作者： Peng, Zengqi Wang, Yubin Han, Xu Zheng, Lei Ma, Jun Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology Guangzhou China Data Science and Analytics Thrust The Hong Kong University of Science and Technology Guangzhou China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology China

Recent advancements in reinforcement learning (RL) demonstrate the significant potential in autonomous driving. Despite this promise, challenges such as the manual design of reward functions and low sample efficiency in complex environments continue to impede the development of safe and effective driving policies. To tackle these issues, we introduce LearningFlow, an innovative automated policy learning workflow tailored to urban driving. This framework leverages the collaboration of multiple large language model (LLM) agents throughout the RL training process. LearningFlow includes a curriculum sequence generation process and a reward generation process, which work in tandem to guide the RL policy by generating tailored training curricula and reward functions. Particularly, each process is supported by an analysis agent that evaluates training progress and provides critical insights to the generation agent. Through the collaborative efforts of these LLM agents, LearningFlow automates policy learning across a series of complex driving tasks, and it significantly reduces the reliance on manual reward function design while enhancing sample efficiency. Comprehensive experiments are conducted in the high-fidelity CARLA simulator, along with comparisons with other existing methods, to demonstrate the efficacy of our proposed approach. The results demonstrate that LearningFlow excels in generating rewards and curricula. It also achieves superior performance and robust generalization across various driving tasks, as well as commendable adaptation to different RL algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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Design and testing of Hybrid Slime Mold Algorithm for Real-Time Adaptive Mobile Robot Path Planning in Dynamic Environments

Design and testing of Hybrid Slime Mold Algorithm for Real-T...

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Sachin Gupta Shivali Gupta Shaik Mohammed Raith Anand Kumar Salil Bharany Vishnu Kant Ch.Sandeep Department of Robotics and Control Engineering School of Electronics and Control Engineering Lovely Professional University Phagwara India Department of Chemistry School of Chemical Engineering and Physical Sciences Lovely Professional University Phagwara India Department of Computer Science and Engineering Lovely Professional University Phagwara Punjab India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura India School of Computer Science and Artificial Intelligence SR University Warangal Telangana

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Dynamic path planning and collision prevention is one of the key tasks for autonomous vehicles in relation to dynamic movement of obstacles. In this paper, a novel hybrid approach based on integration of Slime Mold Algorithm (SMA), Triangle Inequality Principle (TIP), and Partition Method Strategy (PMS) for solution of this problem statement. The SMA algorithm is very flexible and adaptive by nature due to its biological working mechanism which is associated with TIP and PMS strategies in relation to their optimization and efficient path planning capabilities and robust solutions. This integrated module is tested in virtual environment and the results are compared with other updated algorithms such as A*, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) for the same static and dynamic environment. It has been observed from the results that this hybrid strategy significantly improves the path planning task several times than other algorithms in terms of multiple aspects. Under testing conditions, the path length was reduced to 25% than other algorithms in a dynamic environment. During this calculation, the computational efficiency of the system has been improved to 60% than conventional methods. The chances of collision have been improved to 34.23% especially in environment where the obstacles are moving. The capability of SMA has been improved due to the synergy between TIP and PMS. The dynamic and adaptive nature of this algorithm is suitable for various applications such as search-and-rescue operations, manufacturing units’ automation and other hazardous environment exploration.

关键词： Navigation Heuristic algorithms Virtual environments Path planning Partitioning algorithms Safety Mobile robots Vehicle dynamics Testing Genetic algorithms

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A Comprehensive Analysis of IoT and IoT Security Attacks: Understanding the Threat Landscape

A Comprehensive Analysis of IoT and IoT Security Attacks: Un...

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Himanshi Dhayal Vikas Siwach Harkesh Sehrawat Renu Dalai Department of Computer Science and Engineering UIET Maharshi Dayanand University Rohtak Haryana India University School of Automation and Robotics (USAR) GGSIPU Guru Gobind Singh Indraprastha University Delhi India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

IoT has remarkably changed the internet landscape since its introduction. Today, even an isolated physical device is provided with the internet, which allows it to exchange information with other devices. As much flexibility as these devices offer, they also have numerous security issues, causing threats to its users. Adversaries increasingly exploit the vulnerabilities in Internet of Things devices to execute attacks that can inflict significant harm on individuals, communities, and organizations. Consequently, prioritizing the detection, prevention and mitigation of the attacks is imperative to ensure safety and security. The paper provides an in-depth examination of internet of things (IoT) taxonomy, highlighting the associated challenges and security attacks within the IoT ecosystem, which subsequently gives insights into the vulnerabilities of the IoT ecosystem. Additionally, it includes a comparative analysis of various security attacks along with their impacts.

关键词： Prevention and mitigation Ecosystems Taxonomy Organizations Communications technology Safety Security Internet of Things Computational intelligence

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