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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Ioannis K. Chatzipaschalis Ioannis Tompris Georgios Kleitsiotis Theodoros Panagiotis Chatzinikolaou Iosif-Angelos Fyrigos Michail-Antisthenis Tsompanas Andrew Adamatzky Phil Ayres Antonio Rubio Georgios Ch. Sirakoulis Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi Greece Department of Electronic Engineering Universitat Politècnica de Catalunya Barcelona Spain Department of Computer Science and Creative Technologies University of the West of England Bristol UK Institute of Architecture and Technology Royal Danish Academy Copenhagen Denmark

ISBN: (数字)9798350356830

ISBN: (纸本)9798350356847

Mycelium, the vegetative structure of fungi, exhibits complex electrical signaling patterns when stimulated that resemble neural-like activity, which can be leveraged for bio-inspired computing and sensing applications. To replicate this activity, a 100x100 grid-based circuit has been designed capable of spiking behavior and adaptable configuration thanks to memristive technology aligned with fabricated devices, mimicking the dynamics seen in mycelial networks. Simulations have been carried out to demonstrate that the memristive grid successfully replicates key aspects of mycelium’s electrical activity recorded from experimental setups, including response to environmental stimuli, spiking signal propagation, and eradication.

关键词： Fungi Adaptation models Computational modeling Biological system modeling Emulation Memristors Sensors Bio-inspired computing Integrated circuit modeling Oscillators

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From Detection to Traversal: A Probabilistic Framework for UAS-Assisted Landmine Mapping and Circumvention

From Detection to Traversal: A Probabilistic Framework for U...

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2025 International Conference on Unmanned Aircraft Systems, ICUAS 2025

作者： Steckenrider, J. Josiah Kim, Dongbin Manjunath, Pratheek Simpson, Jack Rosbrook, Charles Weiss, Micah United States Military Academy Department of Civil and Mechanical Engineering West Point NY United States College of Engineering Technology and Architecture University of Hartford West Hartford CT United States United States Military Academy Department of Electrical Engineering and Computer Science West Point NY United States

ISBN: (纸本)9798331513283

This research presents a robust probabilistic framework for minefield localization, mapping, and avoidance, addressing a technological gap in the field of aerial countermine intelligence, while bypassing the well-established techniques of landmine detection. Our approach propagates the pose uncertainty matrix delivered by a drone's flight controller's Kalman filter to probabilistically estimate the location of detected mines. This probability map then seeds an artificial potential field path generator which creates a safe path for ground traversal by producing waypoints through the minefield. The system's performance is evaluated in simulations and validated through flight trials, demonstrating its potential to improve the efficiency and safety of UAV-assisted minefield navigation and threat avoidance. © 2025 IEEE.

关键词： Landmine detection

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A Review and Benchmark on State-of-the-Art Steel Defects Detection

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SN computer science 2024年第1期5卷 114页

作者： Chazhoor, Anthony Ashwin Peter Ho, Edmond S. L. Gao, Bin Woo, Wai Lok Computer and Information Science Department Northumbria University ELB 108 Ellison Pl Newcastle upon Tyne NE1 8ST United Kingdom School of Computing Science University of Glasgow 18 Lilybank Gardens Glasgow G12 8RZ United Kingdom School of Automation Engineering University of Electronic Science and Technology of China 4 1st Ring Rd East 2 Section Chenghua District Sichuan Chengdu 610056 China

Steel, a critical material in construction, automobile, and railroad manufacturing industries, often presents defects that can lead to equipment failure, significant safety risks, and costly downtime. This research aims to evaluate the performance of state-of-the-art object detection models in detecting defects on steel surfaces, a critical task in industries such as railroad and automobile manufacturing. The study addresses the challenges of limited defect data and lengthy model training times. Five existing state-of-the-art object detection models (faster R-CNN, deformable DETR, double head R-CNN, Retinanet, and deformable convolutional network) were benchmarked on the Northeastern University (NEU) steel dataset. The selection of models covers a broad spectrum of methodologies, including two-stage detectors, single-stage detectors, transformers, and a model incorporating deformable convolutions. The deformable convolutional network achieved the highest accuracy of 77.28% on the NEU dataset following a fivefold cross-validation method. Other models also demonstrated notable performance, with accuracies within the 70–75% range. Certain models exhibited particular strengths in detecting specific defects, indicating potential areas for future research and model improvement. The findings provide a comprehensive foundation for future research in steel defect detection and have significant implications for practical applications. The research could improve quality control processes in the steel industry by automating the defect detection task, leading to safer and more reliable steel products and protecting workers by removing the human factor from hazardous environments. © 2023, The Author(s).

关键词： Automated defect detection Deep learning models Steel defect detection Steel surface

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Energetic and Smart Design of Battery Management Scheme to Improve the Lifetime of Electric Vehicles

Energetic and Smart Design of Battery Management Scheme to I...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Varatharaju, V.M. Chidambaranathan, C.M. Karthik, Mamidala Vijay Sathi, G. Pandi, V. Samuthira Shobana, D. University of Technology and Applied Science Electrical Section Engineering Department Ibra Oman Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India Cmr Engineering College Department of Electrical & Electronics Engineering Telangana Hyderabad India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Tamil Nadu Chennai India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India Rajalakshmi Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350313987

In order to maximize efficiency, electric vehicles use Battery Management Systems (BMS) to manage their rechargeable batteries. The battery's safety and dependability are ensured by the battery management system, and the battery's age is extended without being damaged. Various monitoring methods are used to keep track of the battery pack's charging capacity, voltage-levels, current-ratings and ecological pressure ratio. A variety of analogue and digital sensors are employed with microcontrollers for this purpose. Several powerful batteries have been developed as a result of the tremendous advances in the field of batteries over the past ten years. High-power batteries with suitable battery management systems are essential for the safe and reliable operation of Electric Vehicles (EVs). This paper will address the power sources utilized by electric automobiles and the primary problems of battery control systems, as well as compare both the lithium ion battery as well as the metal based nickel-hydride batteries in regards to growing older along with the impact caused by climate through the use of the State-of-Charge (SOC) and closed device electricity. This study's goal is to compare and contrast different intelligence techniques along with the control methods used in electric vehicle battery management platforms. The study assesses the characteristics, structure, configuration, accuracy, benefits, and downsides of intelligent algorithms for estimating the condition of a battery. Focusing on parts, characteristics, aims, outcomes, advantages, and restrictions, this research analyses the various controllers used for warming, cooling, balancing, and protecting batteries. © 2023 IEEE.

关键词： Battery Management Systems BMS Electric Vehicles EVs SOC State-of-Charge

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Investigating human-robot cooperation in a hospital environment: Scrutinising visions and actual realisation of mobile robots in service work 21

Investigating human-robot cooperation in a hospital environm...

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2021 ACM Designing Interactive Systems Conference: Nowhere and Everywhere, DIS 2021

作者： Tornbjerg, Kristina Kanstrup, Anne Marie Skov, Mikael B. Rehm, Matthias Department of Planning Aalborg University Denmark Department of Computer Science Aalborg University Denmark Department of Architecture Design and Media Technology Aalborg University Denmark

ISBN: (纸本)9781450384766

This study analysed work activity in a hospital basement where humans and robots interacted and cooperated on logistics tasks. The robots were deployed to automate parts of courier processes and improve the work environment for the hospital's kitchen staff. Human-robot cooperation was studied through ethnographic fieldwork relating to mobile service robots and hospital kitchen staff. The results highlighted problems arising through the assumption that the gplug and play' service robots could effectively automate work tasks. The analysis revealed the complexity of human-robot interaction in dynamic work settings such as hospitals and identified contradictions between the envisioning and realisation of robots at work, as well as the visible and invisible procedures underpinning human-robot cooperation. Consequently, we emphasise the importance of considering robots as agents of change and draw attention to the new work practices that arise when robots assume the roles of workers in dynamic work settings. © 2021 Owner/Author.

关键词： Hospitals

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Optimal Design of Heliostat Field Using Random Search Algorithm

Optimal Design of Heliostat Field Using Random Search Algori...

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IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS)

作者： Chenglin Mou Kaiqi Wu Pengcheng Du Kaibo Yang Department of Engineering Mechanics School of Civil Engineering and Architecture Henan University of Science and Technology Luoyang China Department of Computer Science and Technology School of Information Engineering Henan University of Science and Technology Luoyang China Department of Automation School of Information Engineering Henan University of Science and Technology Luoyang China Department of Civil Engineering School of International Education Henan University of Science and Technology Luoyang China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

Tower solar thermal power generation is a new type of low-carbon and environmentally friendly clean energy technology. In this paper, a single-objective optimization model is established with the maximum annual average thermal output per unit mirror area of the heliostat field as the objective function. By optimizing the heliostat width, heliostat height and heliostat coordinate, the annual average output thermal power can be maximized while meeting the rated power. The heliostat field is distributed in a circular shape. As the seasons change, the light intensity received by the heliostat field is uneven, so the optical efficiency of the heliostats is also different. We simplify the heliostat field into S area (area with higher optical efficiency) and W area (area with lower optical efficiency) based on the optical efficiency distribution of heliostats. At different locations, According to the heliostat width, heliostat height and heliostat coordinate generated by random functions, substituting these parameters into the efficiency model in this paper, and the random search algorithm is used to traverse the maximum annual output thermal power and the corresponding heliostat parameters. In the shadow occlusion loss model, we have grid to set up the heliostat and use the coordinate conversion to solve the shadow occlusion loss efficiency.

关键词： Integrated optics Green energy Shape Optimization models Computational modeling Poles and towers Linear programming Mirrors Power generation

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Balancing accuracy and interpretability: AI-driven predictive modeling of construction schedule performance in India

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Asian Journal of Civil Engineering 2025年第7期26卷 3083-3098页

作者： Maurya, Sudhanshu Lakkimsetty, Nageswara Rao Manjunath, T.C. Shukla, Anoop Kumar Sethy, Barada Prasad Behera, Rashmi Rekha Symbiosis Institute of Technology Symbiosis International (Deemed University) Nagpur Campus Nagpur India Department of Chemical and Petroleum Engineering School of Engineering American University of Ras Al Khaimah (AURAK) Ras al Khaimah United Arab Emirates Ras al Khaimah United Arab Emirates Department of Computer Science & Engineering Cyber Security & Blockchain Technology Rajarajeswari College of Engineering Bengaluru India Manipal School of Architecture and Planning Manipal Academy of Higher Education Manipal India NIST University Berhampur India Centurion University of Technology and Management Gajapati India

Construction schedule delays are a persistent challenge in India’s infrastructure sector, leading to significant cost overruns, stakeholder dissatisfaction, and inefficient resource utilization. In this context, the integration of Artificial Intelligence (AI) into project forecasting offers a promising solution. This study presents an AI-driven predictive modeling framework aimed at estimating construction schedule adherence, with a dual focus on predictive accuracy and model interpretability. Three machine learning models—multiple linear regression (MLR), artificial neural networks (ANN), and gradient boosting machine (GBM)—were developed and evaluated using real-world data from 150 construction projects across India. The models were assessed based on standard performance metrics (MAE, RMSE, R²), computational efficiency, and transparency. While MLR offered high interpretability, it lacked accuracy in handling nonlinear interactions. ANN showed high accuracy but limited explainability. GBM outperformed both, achieving the best predictive performance (R² = 0.94) and a favorable trade-off between accuracy and interpretability. Feature importance and sensitivity analyses identified equipment utilization, material availability, and labor productivity as key influencers. Case study validation further reinforced the practical value of GBM in diverse project settings. The findings advocate for the strategic adoption of interpretable AI tools like GBM in construction management to enhance scheduling precision and decision-making efficiency. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Artificial intelligence Construction schedule prediction Gradient boosting machine (GBM) Interpretability in machine learning Project performance analytics

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THE EFFECT OF LIMITED MOBILITY ON THE EXPERIENCED SEGREGATION OF FOREIGN-BORN MINORITIES

arXiv

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arXiv 2024年

作者： Liao, Yuan Gil, Jorge Yeh, Sonia Pereira, Rafael H.M. Alessandretti, Laura Department of Space Earth and Environment Chalmers University of Technology Gothenburg Sweden Department of Architecture and Civil Engineering Chalmers University of Technology Gothenburg Sweden - Brazil Data Science Division Brazil Department of Applied Mathematics and Computer Science Technical University of Denmark Lyngby Denmark

Segregation is a key challenge in promoting more diverse and inclusive cities. Research based on large-scale mobility data indicates that segregation between majority and minority groups persists in daily activities beyond residential areas, like visiting shops and restaurants. Aspects including lifestyle differences, homophily, and mobility constraints have been proposed as drivers of this phenomenon, but their contributions remain poorly quantified. Here, we elucidate how different mechanisms influence segregation outside home, looking at the distinctive segregation experienced by native and foreign-born individuals. Our study is based on the movement of ∼320,000 individual smartphone devices collected in Sweden, where immigration creates profound divides. We find that while day-to-day activities lead to mixing for native-born individuals, foreign-born individuals remain segregated in their out-of-home activities. Using counterfactual simulations, we show that this heterogeneous effect of mobility on experienced segregation results mainly from two mechanisms: homophily and limited travel, i.e. foreign-born individuals (i) tend to visit destinations visited by similar individuals, and (ii) have limited mobility ranges. We show that homophily, as represented by destination preference, plays a minor role, while limited mobility, associated with reduced transport access, limits opportunities for foreign-born minorities to diversify their encounters. Our findings suggest that enhancing transport accessibility in foreign-born concentrated areas could reduce social segregation. © 2024, CC BY-SA.

关键词： Smartphones

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Health care data analysis and visualization using interactive data exploration for sportsperson

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science China(Information sciences) 2022年第6期65卷 85-97页

作者： Hao LIU Yang ZHANG Ke LIAN Yifei ZHANG Oscar Sanjuán MARTíNEZ Rubén González CRESPO Department of Sport and Exercise Sciences Kunsan National University Department of Naval Architecture and Ocean Engineering Ludong University Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja

Sports have scored significant attention among the public in this multifaceted world. Diverse training strategies are followed by many athletics and even flexible to adapt comfortable and optimal techniques. This fact has led physicians and educators to encourage remote health surveillance as one of the core strategies in athletic training. The need for innovative data exploration methodologies capable of facing Big Data's influence to make remote monitoring services viable has been raised by the growing ties of networks that deliver high quantities of real-time data. This paper presents an interactive healthcare data exploration and visualization(IHDEV) model to enhance multi-scaling data analysis and visualization in the athletic health vision platform. This paper aims to simplify optimization methods to measure sportsperson muscle tension. This model illustrates a three-layer architecture with a raw data acquisition layer, data analysis layer, and visualization layer. The first layer considers the acquisition of health-related data from the athletes for remote monitoring using IoT and stores it into the cloud. The data analysis layer adapts artificial intelligence(AI) in data mining. The final layer introduces an intelligent interactive data visualization model assisted by a reactive workflow mechanism, enabling analysis and visualization solutions to be composed in a personalized data flow appropriate to the athletic training. This experimental study extended with two healthcare datasets to show the feasibility of IHDEV in promoting healthcare based athletic monitoring and improves the accuracy ratio of 96.7%, prediction ratio of 96.2%, an efficiency ratio of 96.8%, Pearson correlation coefficient of 98.2%, and reduces the error rate of 18.7% compared to other conventional models.

关键词： artificial intelligence health monitoring data visualization data analysis athletics

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SOCIO-SPATIAL SEGREGATION AND HUMAN MOBILITY: A REVIEW OF EMPIRICAL EVIDENCE

arXiv

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arXiv 2024年

Socio-spatial segregation is the physical separation of different social, economic, or demographic groups within a geographic space, often resulting in unequal access to resources, services, and opportunities. The literature has traditionally focused on residential segregation, examining how individuals' residential locations are distributed differently across neighborhoods based on various social attributes, e.g., race, ethnicity, and income. However, this approach overlooks the complexity of spatial segregation in people's daily activities, which often extend far beyond residential areas. Since the 2010s, emerging mobility data sources have enabled a new understanding of socio-spatial segregation by considering daily activities such as work, school, shopping, and leisure visits. From traditional surveys to GPS trajectories, diverse data sources reveal that daily mobility can result in spatial segregation levels that differ from those observed in residential segregation. This literature review focuses on three critical questions: (a) What are the strengths and limitations of segregation research incorporating extensive mobility data? (b) How do human mobility patterns relate to individuals' residential vs. experienced segregation levels? and (c) What key factors explain the relationship between one's mobility patterns and experienced segregation? Our literature review enhances the understanding of socio-spatial segregation at the individual level and clarifies core concepts and methodological challenges in the field. Our review explores studies of key themes: segregation, activity space, co-presence, and the built environment. By synthesizing their findings, we aim to offer actionable insights for reducing segregation. © 2024, CC BY-SA.

关键词： Leisure

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