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3rd International Conference on Flexible Electronics for Electrical Vehicle, FlexEV 2022

作者： Koulouris, Charalampos Dimitrios, Piromalis Al-Darraji, Izzat Tsaramirsis, Georgios Khadidos, Alaa Omar Khadidos, Adil Omar Papageorgas, Panagiotis Industrial Design and Production Engineering Department University of West Attica P. Ralit and Thivon 250 Egaleo12244 Greece Automated Manufacturing Department University of Baghdad Al-Khwarizmi College of Engineering Baghdad Iraq Abu Dhabi Womens’s College Higher Colleges of Technology Abu Dhabi United Arab Emirates Faculty of Computing and Information Technology Department of Information Systems King Abdulaziz University Jeddah Saudi Arabia Faculty of Computing and Information Technology Department of Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Electrical and Electronics Engineering University of West Attica P. Ralit and Thivon 250 Egaleo12244 Greece

ISBN: (纸本)9789819947942

The fourth industrial revolution, also known as Industry 4.0, has been impacted by the advancements of various technological areas, including microsatellites. The Internet of Space Things (IoST) and Space Sensor Networks (SSNs) are generally integrated into microsatellites as well as Internet of Things (IoT) devices that have recently been used in modern applications. Compared to the traditional microsatellites and IoT devices, the current IoST and SSN devices have the advantages of collecting IoT data in remote areas where there is no network or Internet, sending data with low power between mobile devices and satellite constellations, ensuring global coverage of data exchange, and so on. Due to the mentioned individual advantages, the IoST and SSNs devices have been widely applied in microsatellites and IoT devices. The IoST devices can be integrated with a satellite constellation and CubeSats or IoT receivers. Beside this, the IoST devices are implemented in other applications as a bigger satellite constellation which have both IoT receivers and space sensors such as satellite dishes and RF devices. This paper surveys the current research and technologies of IoST and SSNs within their applications in microsatellites and IoT devices. The impact of IoST on Industry 4.0 is also discussed in this paper. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024

关键词： Industry 4.0 Internet of Space Things Microsatellites Satellites Space sensor networks

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3D Printing in Fiber-Device Technology

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Advanced Fiber Materials 2021年第2期3卷 1-17页

作者： Louis van der Elst Camila Faccini de Lima Meve Gokce Kurtoglu Veda Narayana Koraganji Mengxin Zheng Alexander Gumennik Department of Intelligent Systems Engineering Luddy School of InformaticsComputing and EngineeringIndiana University Bloomington700 North Woodlawn AvenueBloomingtonIN 47408USA Fibers and Additive Manufacturing Enabled Systems Laboratory 2425 North Milo B Sampson LaneBloomingtonIN 47408USA

Recent advances in additive manufacturing enable redesigning material morphology on nano-,micro-,and meso-scale,for achieving an enhanced functionality on the *** non-planar and flexible electronic circuits,through biomechanically realistic surgical models,to shoe soles individualized for the user comfort,multiple scientific and technological areas undergo material-property redesign and enhancement enabled by 3D ***-device technology is currently entering such a *** this paper,we review the recent advances in adopting 3D printing for direct digital manufacturing of fiber preforms with complex cross-sectional architectures designed for the desired thermally drawn fiber-device ***,taking a recursive manufacturing approach,such fibers can serve as a raw material for 3D printing,resulting in macroscopic objects with enhanced functionalities,from optoelectronic to bio-functional,imparted by the fiber-devices properties.

关键词： Recursive manufacturing 3D printing Fibers Optical fibers Pervasive sensing

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Sailfish Optimization with Deep Learning Based Oral Cancer Classification Model

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Computer systems Science & engineering 2023年第4期45卷 753-767页

作者： Mesfer Al Duhayyim Areej A.Malibari Sami Dhahbi Mohamed K.Nour Isra Al-Turaiki Marwa Obayya Abdullah Mohamed Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversityMecca24382Saudi Arabia Department of Information Technology College of Computer and Information SciencesKing Saud UniversityRiyadh4545Saudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt

Recently,computer aided diagnosis(CAD)model becomes an effective tool for decision making in healthcare *** advances in computer vision and artificial intelligence(AI)techniques have resulted in the effective design of CAD models,which enables to detection of the existence of diseases using various imaging *** cancer(OC)has commonly occurred in head and neck *** identification of OC enables to improve survival rate and reduce mortality ***,the design of CAD model for OC detection and classification becomes ***,this study introduces a novel Computer Aided Diagnosis for OC using Sailfish Optimization with Fusion based Classification(CADOC-SFOFC)*** proposed CADOC-SFOFC model determines the existence of OC on the medical *** accomplish this,a fusion based feature extraction process is carried out by the use of VGGNet-16 and Residual Network(ResNet)***,feature vectors are fused and passed into the extreme learning machine(ELM)model for classification ***,SFO algorithm is utilized for effective parameter selection of the ELM model,consequently resulting in enhanced *** experimental analysis of the CADOC-SFOFC model was tested on Kaggle dataset and the results reported the betterment of the CADOC-SFOFC model over the compared methods with maximum accuracy of 98.11%.Therefore,the CADOC-SFOFC model has maximum potential as an inexpensive and non-invasive tool which supports screening process and enhances the detection efficiency.

关键词： Oral cancer computer aided diagnosis deep learning fusion model seagull optimization classification

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Saliency-based Explainable Artificial Intelligence (XAI) for Interpreting U-Net Semantic Segmentation of Synthetic Liquid Spray Images

Saliency-based Explainable Artificial Intelligence (XAI) for...

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2024 IEEE International Conference on computing, ICOCO 2024

作者： Chin, Teck Wai Lau, Sian Lun Wong, Richard T.K. Bravo, Luis Koneru, Rahul Babu Khare, Prashant Dept. of Computing and Information Systems School of Engineering and Technology Sunway University Bandar Sunway Malaysia Dept. of Engineering School of Engineering and Technology Sunway University Bandar Sunway Malaysia DEVCOM Army Research Laboratory Army Research Directorate Aberdeen Proving GroundMD21005 United States Department of Aerospace Engineering University of Cincinnati CincinnatiOH45221-0070 United States Department of Aerospace Engineering University of Maryland College ParkMD20742 United States

ISBN: (纸本)9798331530303

Deep learning models, particularly U-Net, have been widely used for image segmentation tasks in various domains such as medical, biological, autonomous driving, and industrial applications. However, the black-box nature of these models raises public concerns about the lack of transparency and interpretability of their decision-making processes. Unlike typical static objects, the segmentation of dynamic objects from synthetic fluid datasets are more complicated as the properties like position and shape change over time. This paper investigates these issues by exploring the uses of saliency-based Explainable Artificial Intelligence (XAI) techniques such as Class Activation Mapping (CAM), Gradient-weighted Class Activation Mapping (Grad-CAM), Grad-CAM++ and Segmentation Grad-CAM (Seg-Grad-CAM) to generate heatmap visualizations for providing insights into the prediction of U-net semantic segmentation. Using a dataset of synthetic liquid spray images, we systematically apply these techniques to visualize the model’s attention and highlights the properties and regions of the image that are influential in the model’s predictions for different classes such as lobe, ligaments and droplets. Our results demonstrate that while all techniques provide interpretable visualizations, Seg-Grad-CAM and Grad-CAM++ offer better localization of salient features, providing more precise explanations for U-Net’s segmentation decisions. The comparison of the results using quantitative analysis such as Pointing Game Accuracy and Mean Localization Error also indicated that Seg-Grad-CAM and Grad-CAM++ are more effective for interpreting complex segmentation models in liquid spray analysis. ©2024 IEEE.

关键词： Semantic Segmentation

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Bridging the gap between theory and practice in local energy sharing - energy communities in Croatia

Bridging the gap between theory and practice in local energy...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Mirna Gržanić Antić Tomislav Capuder Tomi Medved Department of Energy and Power Systems Faculty of Electrical Engineering and Computing University of Zagreb Croatia Laboratory of Energy Policy Faculty of Electrical Engineering University of Ljubljana Slovenia

ISBN: (数字)9798350390421

ISBN: (纸本)9798350390438

The European directives focus on unlocking the potential of final customers in the clean energy transition. With the recent trend of rising electricity and petrol prices, coupled with reduced investment costs in low-carbon technologies, the installation of photovoltaic solar panels for end customers has become more profitable compared to previous years, leading to a shorter payback period. Additionally, to enhance citizen engagement in the integration of renewable energy sources, various forms of local energy-sharing models are discussed in the literature. This paper elaborates on the regulatory framework and policies at the European level concerning renewable energy communities, citizen energy communities, and collective self-consumption, with a particular focus on Croatia’s perspective. It highlights the steps needed to accelerate the realization of energy communities in practice. Under current Croatian regulations, the profit from selling surplus community energy to the supplier cannot be directly remunerated to community members. The results indicate that community members could achieve 20% lower electricity costs annually if the surplus energy were directly remunerated. To avoid losing potential profits from excess energy, the paper models an investment in a community-owned battery storage system, aiming to increase community self-consumption and minimize the amount of energy sold to suppliers. It is shown that with a static sharing scheme, individual community members’ costs can decrease by 11% to 16%, and by up to 20% with a dynamic scheme compared to cases where profit from excess energy cannot be directly remunerated to community members.

关键词： Electric potential Costs Electricity Europe Production Regulation Batteries Smart grids Solar panels Investment

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Object Tracing with Colour range filtering in HSV Colour space and Object Following with Ros2

Object Tracing with Colour range filtering in HSV Colour spa...

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Information Technologies and Communications (GCITC), Global Conference on

作者： Aveen Vas Glenn Aston Dsouza Department of Intelligent Computing and Business Systems St. Joseph Engineering College Vamanjoor Mangalore India

This study proposes a unique method for object tracing with OpenCV (Open Source Computer Vision Library) using colour range filtering in the HSV (Hue Saturation Value) colour space and object following with ROS2. The suggested approach combines ROS2 (Robot Operating System 2) with OpenCV on a Raspberry Pi as the primary processing platform, allowing for effective communication and coordination between the two frameworks. Objects are identified based on colour range filtering, by utilizing the robust image processing capabilities of OpenCV using HSV colour space. The data of the detected object is then smoothly sent to ROS2 for additional processing and tracing. As the central node, it receives input from a camera, uses OpenCV to perform real-time object tracing and transmits the outcome to ROS2 nodes. Through this integration, OpenCV and ROS2 can work effectively together to give reliable solutions for object tracing and following. The feasibility and efficiency of the suggested strategy are demonstrated by the experimental evaluations carried out in a Raspberry Pi platform. The presented approach opens significant potential for many applications, including robotics, surveillance and interactive systems, where real-time object following are essential.

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EVALUATION MODEL OF ENHANCED GEOTHERMAL SYSTEM PROJECTS BASED ON MULTI-CRITERIA DECISION-MAKING 13

EVALUATION MODEL OF ENHANCED GEOTHERMAL SYSTEM PROJECTS BASE...

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13th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion, MEDPOWER 2022

作者： Raos, Sara Hranić, Josipa Rajšl, Ivan Faculty of Electrical Engineering and Computing Department of Energy and Power Systems Zagreb Croatia FieldCore Service Solutions Atlanta United States FieldCore Service Solutions Karlovac Croatia

ISBN: (纸本)9781839538445

This paper presents the main features and characteristics of the evaluation model of enhanced geothermal system projects based on multi-criteria decision-making. The model is a simulation model and evaluates the enhanced geothermal system projects in a holistic way. Considering geological setting characteristics, technology details, the economics of the project, social impact, and associated environmental impact enables a comprehensive assessment of such projects. The model is capable of calculating main performance metrics such as production quantities, losses, efficiencies, avoided CO2 emissions, and main economic metrics such as levelized cost of energy, the net present value of the project, yearly cash flows etc. Additionally, the model integrates a multi-criteria decision-making sub-module, enabling users to assess and compare different project options and scenarios. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Environmental impact

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A personalized learning system for mathematics utilizing artificial intelligence and machine learning for ordinary-level students in Sri Lanka

A personalized learning system for mathematics utilizing art...

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Advances in Technology and computing (ICATC), International Conference on

作者： D.N. Abeynayake N.M. Hakmanage A.M.L. Chamini Faculty of Computing University of Wolverhampton UK/ Cinec Campus Sri Lanka Department of Computer Systems Engineering University of Kelaniya Sri Lanka Faculty of Computing Cinec Campus Sri Lanka

ISBN: (数字)9798331518219

ISBN: (纸本)9798331518226

In the context of Sri Lanka, the use of technology in education has a radical change in the method of learning and has made it possible for even concepts in mathematics to be taught in a manner that is interesting and easy to understand. Mathematics is one of the main subjects that Sri Lankan ordinary-level (O/L) students find difficult to learn due to traditional teaching methods, limited resources, and language barriers. This study introduces an artificial intelligence (AI) and machine learning (ML)-powered personalized learning system to enhance existing education delivery to the intension level in mathematics for grade 10 and 11 students. Individual learning requirements are met by creating learning paths, implementing a chatbot to teach as the learner interacts with the material, and implementing a feedback mechanism to track progress. The agile methodology was adopted for this study. The MERN-Stack-based system and the integration of a linear regression model for individualized feedback are described. Student data, collected through surveys in both English and Sinhala languages, helped train the chatbot using the RASA framework. The system’s effectiveness in enhancing math comprehension has been demonstrated, with a higher rate of students improving mathematical performance. The iterative development approach, guided by Agile methodology, allowed for continuous improvements based on user feedback. Even though the system presents effective results, limitations such as scalability and broader testing are noted, with future efforts focused on refining the AI model and enhancing the feedback system. AI and ML-based personalized learning systems are recommended for schools to improve educational outcomes.

关键词： Learning systems Surveys Training Education Linear regression Learning (artificial intelligence) Machine learning Chatbots Mathematics Mathematical models

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Development of a Real-Time Trust/Distrust Metric Using Interactive Hybrid Cognitive Task Analysis

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Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2023年第1期67卷 2128 - 2136页

作者： Shiwen Zhou Xioyun Yin Matthew J. Scalia Ruihao Zhang Jamie C. Gorman Nathan J. McNeese Human Systems Engineering Arizona State University Mesa AZ USA Department of Human Centered Computing Clemson University Clemson SC USA

While there is increased interest in how trust spreads in Human Autonomy Teams (HATs), most trust measurements are subjective and do not examine real-time changes in trust. To develop a trust metric that consists of objective variables influenced by trust/distrust manipulations, we conducted an Interactive hybrid Cognitive Task Analysis (IhCTA) for a Remotely Piloted Aerial System (RPAS) HAT. The IhCTA adapted parts of the hybrid Cognitive Task Analysis (hCTA) framework. In this paper, we present the four steps of the IhCTA approach, including 1) generating a scenario task overview, 2) generating teammate-specific event flow diagrams, 3) identifying interactions and interdependencies impacted by trust/distrust manipulations, and 4) processing RPAS variables based on the IhCTA to create a metric. We demonstrate the application of the metric through a case study that examines how the influence of specific interactions on team state changes before and after the spread of distrust.

关键词： Cognitive task analysis Team performance/teamwork Trust

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Inaugural editorial for Discover Networks

Discover Networks

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Discover Networks 2025年第1期1卷 1-3页

作者： Poole, Chris De Rango, Floriano Meng, Weizhi Springer Nature London UK Department of Computer Modeling Electronic and Systems Engineering (DIMES) University of Calabria Arcavacata Italy School of Computing & Communications Lancaster University Lancaster UK

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