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1st International Conference on intelligent systems, Blockchain, and Communication Technologies, ISBCom 2024

作者： Elhenawy, Mohammed Abdelhay, Ahmed Alhadidi, Taqwa I. Ashqar, Huthaifa I. Jaradat, Shadi Jaber, Ahmed Glaser, Sebastien Rakotonirainy, Andry Accident Research and Road Safety Queensland Queensland University of Technology 130 Victoria Park Rd Kelvin Grove BrisbaneQLD4059 Australia Centre for Data Science Queensland University of Technology 130 Victoria Park Rd Kelvin Grove BrisbaneQLD4059 Australia Computer and Systems Engineering Department Faculty of Engineering Minia University Minia Egypt Civil Engineering Department Al-Ahliyya Amman University Al-Saro Al-Salt Amman Jordan Arab American University Palestine and Columbia University JeninNY United States Department of Transport Technology and Economics Faculty of Transportation Engineering and Vehicle Engineering Budapest University of Technology and Economics Műegyetem Rkp. 3. Budapest1111 Hungary

ISBN: (纸本)9783031823763

Multimodal Large Language Models (MLLMs) have demonstrated proficiency in processing diverse modalities, including text, images, and audio. These models leverage extensive pre-existing knowledge, enabling them to address complex problems with minimal to no specific training examples, as evidenced in few-shot and zero-shot in-context learning scenarios. This paper investigates the use of MLLMs’ visual capabilities to ‘eyeball’ solutions for the Traveling Salesman Problem (TSP) by analyzing images of point distributions on a two-dimensional plane. Our experiments aimed to validate the hypothesis that MLLMs can effectively ‘eyeball’ viable TSP routes. The results from zero-shot, few-shot, self-ensemble, and self-refine zero-shot evaluations show promising outcomes. We anticipate that these findings will inspire further exploration into MLLMs’ visual reasoning abilities to tackle other combinatorial problems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Zero-shot learning

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A PID-incorporated Latent Factorization of Tensors Approach to Dynamically Weighted Directed Network Analysis

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 533-546页

作者： Hao Wu Xin Luo MengChu Zhou Muhyaddin J.Rawa Khaled Sedraoui Aiiad Albeshri Chongqing Institute of Green and Intelligent Technology Chinese Academy of SciencesChongqing 400714and also with the University of Chinese Academy of SciencesBeijing 100049China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA IEEE Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia College of Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Computer Science King Abdulaziz UniversityJeddah 21481Saudi Arabia

A large-scale dynamically weighted directed network(DWDN)involving numerous entities and massive dynamic interaction is an essential data source in many big-data-related applications,like in a terminal interaction pattern analysis system(TIPAS).It can be represented by a high-dimensional and incomplete(HDI)tensor whose entries are mostly *** such an HDI tensor contains a wealth knowledge regarding various desired patterns like potential links in a DWDN.A latent factorization-of-tensors(LFT)model proves to be highly efficient in extracting such knowledge from an HDI tensor,which is commonly achieved via a stochastic gradient descent(SGD)***,an SGD-based LFT model suffers from slow convergence that impairs its efficiency on large-scale *** address this issue,this work proposes a proportional-integralderivative(PID)-incorporated LFT *** constructs an adjusted instance error based on the PID control principle,and then substitutes it into an SGD solver to improve the convergence *** studies on two DWDNs generated by a real TIPAS show that compared with state-of-the-art models,the proposed model achieves significant efficiency gain as well as highly competitive prediction accuracy when handling the task of missing link prediction for a given DWDN.

关键词： Big data high dimensional and incomplete(HDI)tensor latent factorization-of-tensors(LFT) machine learning missing data optimization proportional-integral-derivative(PID)controller

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Virtual Reality-Based Teleoperation System for Robot Forklifts

Virtual Reality-Based Teleoperation System for Robot Forklif...

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： Manuel B. Couto Marcelo R. Petry Abel Mendes Manuel F. Silva CRIIS - Centre for Robotics in Industry and Intelligent Systems INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal FEUP - Faculty of Engineering University of Porto Porto Portugal Flowbotic Coimbra Portugal ISEP - Instituto Superior de Engenharia do Porto Instituto Politécnico do Porto Porto Portugal

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

The growing reliance on e-commerce and the demand for efficient intralogistics operations have increased the need for automation, while labour shortages continue to pose significant challenges. When combined with the inherent risks of forklift operation, this circumstance prompted businesses to look for robotic solutions for intralogistics tasks. However, robots are still limited when they come across situations that are outside of their programming scope and often need assistance from humans. To achieve the long-term goal of enhancing intralogistics operation, we propose the development of a virtual reality-based teleoperation system that allows remote operation of robot forklifts with minimal latency. Considering the specificities of the teleoperation process and network dynamics, we conduct detailed modelling to analyse latency factors, optimise system performance, and ensure a seamless user experience. Experimental results on a mobile robot have shown that the proposed teleoperation system achieves an average glass-to-glass latency of 368 ms, with capturing latency contributing to approximately 60% of the total delay. The results also indicate that network os-cillations significantly impact image quality and user experience, emphasising the importance of a stable network infrastructure.

关键词： Radio frequency Visualization 5G mobile communication System performance Robot vision systems Virtual reality Cameras User experience Wireless fidelity Testing

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Clustering Approach for Modeling Course Difficulty Level in Adaptive Learning

Clustering Approach for Modeling Course Difficulty Level in ...

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IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement systems and Applications

作者： Imamah Umi Laili Yuhana Arif Djunaidy Eric Pardede Mauridhi Hery Purnomo Departement of Electrical Engineering Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Departement of Informatics Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Departement of Information Systems Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Departement of Computer Science and Information Technology La Trobe University Bundoora VIC Australia

ISBN: (数字)9798350322996

ISBN: (纸本)9798350323009

The variations in students’ abilities present challenges in achieving desired learning outcomes. Students exhibit different levels of comprehension regarding the complexity of learning materials. In this study, we aim to model the difficulty levels of learning materials using a clustering approach to offer personalized learning material recommendations within an adaptive learning framework. data were collected from student scores of data Structure course obtained between 2020 and 2022 at the University of Trunojoyo Madura. The dataset modeling process adheres to the CRISP-DM Methodology, employing the K-Means clustering method and Euclidean Distance. We employed the Within-Cluster Sum of Squares (WCSS) metric and silhouette score to identify the optimal K-value, which resulted in a value of 5, accompanied by a silhouette score of 0.629. Our analysis pinpointed Stack and Deque as the easiest, while Linked List emerged as the most challenging course material. Accordingly, we will assign a label of 1 to the easiest materials, 2 to those of medium difficulty, and 3 to the most challenging ones. These labels will serve as criteria for recommending suitable learning materials to students based on their knowledge level. We will assess the ratio between the overall knowledge level score and the difficulty level of the material. The ratio value will serve as a reference for determining material recommendations for students. Materials categorized with the highest difficulty levels are exclusively suggested to students demonstrating faster learning abilities with a high value of ratio, and vice versa. This approach is feasible for implementation in adaptive learning.

关键词： Measurement Adaptive learning Adaptation models Computational modeling Clustering methods Virtual environments Euclidean distance

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Advanced Analytics in Smart Factories: Towards an actionable Taxonomy for Prescriptive Analytics Use Cases

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Procedia CIRP 2025年 134卷 229-234页

作者： Julian Weller Sumit Nalavade Oliver Gmelch Nico Migenda Tim Heuwinkel Roman Dumitrescu Fraunhofer Institute for Mechatronic Systems Design Zukunftsmeile 1 33102 Paderborn Germany Technische Hochschule Ingoldstadt“ Esplanade 10 85049 Ingolstadt Center for Applied Data Science Gütersloh Faculty of Engineering and Mathematics Bielefeld University of Applied Sciences and Arts Schulstraße 10 33330 Gütersloh Germany Chair for Advanced Systems Engineering Heinz Nixdorf Institute Fürstenallee 11 33102 Paderborn Germany

Prescriptive analytics use cases support in the decision-making process and focus on providing actionable guidance (e.g. in the smart factory) based on a set of problems and possible solutions. Smart factories represent the core of Industry 4.0 and can greatly benefit from the implementation of advanced analytics use cases. Prescriptive analytics enables operational excellence in smart factories by providing actionable insights and decisions to (autonomously) steer and govern areas of a factory. Still, a widespread adoption of principles of prescriptive analytics is not reached, due to the complexity and interconnectivity of different use cases. Additionally, a lack of methodological support for developing prescriptive analytics use cases is observed. We support the adoption and ideation of prescriptive analytics use cases by providing a taxonomy for prescriptive analytics use cases in smart factories for researchers. To make the findings from the taxonomy actionable, we develop a concept on how to transform existing analytics use cases or use case ideas into prescriptive analytics use cases to support practitioners. We focus on the transformation of existing analytics use cases into prescriptive analytics use cases to lower the barrier to entry for the development of prescriptive use cases. The findings are based on the taxonomy development method by Nickerson. Our evaluation is supported by expert interviews as well as focus groups.

关键词： "Smart Factory Advanced Analytics Prescriptive Analytics Taxonomy"

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Reliability Assessment of X-ray Machines: A Machine Learning-Based Prioritization Model

Reliability Assessment of X-ray Machines: A Machine Learning...

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Biomedical engineering Conference (NBEC), IEEE National

作者： En Xin Neo Aizat Hilmi Zamzam Mohd Effendi Amran Khin Wee Lai Mohd Istajib Mokhtar Khairunnisa Hasikin Dept. of Biomedical Engineering Faculty of Engineering Universiti Malaya Kuala Lumpur Malaysia Engineering Services Division Ministry of Health Putrajaya Malaysia Dept. of Science and Technology Studies Faculty of Science Universiti Malaya Kuala Lumpur Malaysia Dept. of Biomedical Engineering / Center of Intelligent Systems for Emerging Technology (CISET) Faculty of Engineering Universiti Malaya Kuala Lumpur Malaysia

Poorly maintained medical equipment has a tendency to perform poorly, increasing user hazards and necessitating large maintenance costs to keep it functioning at an acceptable level. In this paper, we present the case study of an X-ray machine as one of the critical devices commonly used in public or private healthcare facilities for corrective maintenance prioritization prediction. The proposed technique of Naïve Bayes attains the best performance among other techniques with an accuracy of 95.90%, Area under the ROC curve (AUC) of 0.97, and only requires 15.37 seconds of training time. With sensitivity levels of 0.5 in Class 1 and 1.0 in Class 2 and 3, the Naive Bayes can divide X-ray machines into three classes (i.e., Class 1, 2, and 3). Class 1, Class 2, and Class 3 each had specificities of 1.00, 0.94, and 1.00, respectively. For Class 1, the accuracy value and F-score were 1.00 and 0.67, while for Class 2, they were 0.90 and 0.95. The accuracy values and F-Score for Class 3 were both 1.00. The promising results attained from this case study have proved the feasibility of using machine learning algorithm in determining the prioritization level of medical devices’ corrective maintenance. The pilot findings from this study can be adapted to other medical devices in ensuring reliability and performance of healthcare services.

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Tobacco Plant Disease dataset 14

Tobacco Plant Disease Dataset

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14th International Conference on Digital Image Processing, ICDIP 2022

作者： Lin, Hong Tse, Rita Tang, Su-Kit Qiang, Zhenping Ou, Jinliang Pau, Giovanni Faculty of Applied Sciences Macao Polytechnic University China Engineering Research Centre of Applied Technology on Machine Translation and Artificial Intelligence of Ministry of Education Macao Polytechnic University China College of Big Data and Intelligent Engineering Southwest Forestry University KunMing China Department of Computer Science and Engineering-DISI University of Bologna Italy Samueli Computer Science Department University of California Los Angeles United States

ISBN: (纸本)9781510657564

Tobacco is a valuable plant in agricultural and commercial industry. Any disease infection to the plant may lower the harvest and interfere the operation of supply chain in the market. Image-based deep learning methods are cutting-edge technologies that can facilitate the diagnosis of diseases efficiently and effectively when large-scale dataset is available for training. However, there is not a public dataset about tobacco currently. A comprehensive dataset is appealed to take advantage of deep learning methods in tobacco cultivation urgently. In this paper, we propose to create a specific dataset for tobacco diseases, called Tobacco Plant Disease dataset (TPDD). 2721 tobacco leaf images are taken in field. The dataset serves for two purposes: disease classification and leaf detection. For classification, we identify 12 classes and provide two types of disease annotations: 1) Whole Leaf Section;2) Disease Fragment Section. For leaf detection, we provide two kinds of bounding box: rectangle bounding box and polygon bounding box. In addition, we conduct baseline experiments to illustrate the usefulness of TPDD: 1) using deep learning model to detect single disease and multiple diseases;2) using YOLO-v3 and Mask-RCNN to detect leaves. We hope that the dataset could support the tobacco industry, also be a benchmark in fine-grained vision classification. © 2022 SPIE.

关键词： Computer vision

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Optical Evanescent Field for Direct Observation of Sub-100nm Particle Motion on Glass Surface

Optical Evanescent Field for Direct Observation of Sub-100nm...

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Robotics, engineering, science, and Technology (RESTCON), International Conference on

作者： Panart Khajornrungruang Thitipat Permpatdechakul Keisuke Suzuki Aran Blattler Yutaka Terayama Daiki Goto Pipat Phaisalpanumas Natthaphon Bun-Athuek Amornphun Phunopas Dept. of Intelligent and Control System Faculty of Computer Science and Systems Engineering Mechanical Science and Technology Division Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Interdisciplinary Informatics Graduate School of Computer Science and Systems Engineering Mechanical Information Science and Technology Division Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Production and Robotics Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand Dept. of Intelligent and Control System Graduate School of Computer Science and Systems Engineering Mechanical Science and Technology Course Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Industiral Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand Dept. of Mechanical Engineering Technology College of Industrial Technology King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand

To directly investigate the dynamic nanoscale phenomenon on the surface being processed in wet conditions such as precision polishing, and cleaning in semiconductor industrial, an optical method for visualization and observation of each sub-100 nm sized particle that is moving on an interface such as a silica glass surface by applying an evanescent field have been proposing. Subsequently, we developed an experimental apparatus equipped with an optical microscopy system for verifying the moving particle observation method in a laboratory scale. This article introduces some experimentally direct observation results of duplicated wet processes.

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Proxel-Based Simulation of Fault Trees in R 22

Proxel-Based Simulation of Fault Trees in R

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Proceedings of the Winter Simulation Conference

作者： Parisa Niloofar Hossein Haghbin Sanja Lazarova-Molnar Mærsk Mc-Kinney Møller Institute University of Southern Denmark Odense DENMARK Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr IRAN Institute of Applied Informatics and Formal Description Methods Karlsruhe Institute of Technology Karlsruhe GERMANY

Simulation is an advantageous solution to calculate reliability of complex systems when analytical methods are not able to deliver results in time or when we do not have the resources to apply analytical methods. Proxel-based simulation has shown to be very efficient in reliability analysis of complex systems. In this paper, we present a package called ftaproxim for proxel-based simulation of complex systems using fault trees, developed in the R programming language. ftaproxim can calculate and plot instantaneous unavailabilities of repairable events and the system as a whole.

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Optimal Participation of Virtual Power Plants in the Electricity Market Considering Multi-Energy systems

Optimal Participation of Virtual Power Plants in the Electri...

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International Conference on Smart Energy systems and Technologies (SEST)

作者： Mohammad Sadegh Javadi Gerardo J. Osório André S. Parente João P.S. Catalão Institute for Systems and Computer Engineering Technology and Science (INESC TEC) Porto Portugal Portucalense University Infante D. Henrique (UPT) / REMIT Porto Portugal Faculty of Engineering University of Porto Porto Portugal Research Center for Systems and Technologies (SYSTEC) Advanced Production and Intelligent Systems Associate Laboratory (ARISE) Faculty of Engineering University of Porto Porto Portugal

The growth and modernization of the power system are the keys to enabling economic progress. The deregulation, added to the new emerging production technologies, conversion, and storage, triggered a change in the way of managing the power system worldwide. This work analyses the optimal dispatch of a virtual power plant (VPP) with active participation in the electricity market, considering multi-energy systems. The objective is to minimize the total operating cost of the power plant. The power plant is fed by two external networks: electrical and natural gas. The VPP is composed of energy production, conversion, and storage technologies, also considering the integration of a wind turbine and a set of electric vehicles (EVs). In addition to the Grid-to-Vehicle (G2V) charging, the advantage of Vehicle-to-Grid (V2G) technology is also verified, which allows the injection of power into the grid through the vehicles and Vehicle-to-Load (V2L) technology, enabling EVs to contribute to the satisfaction of the electrical load, reducing the costs, showing the advantages as well of EVs' integration in the VPP under analysis.

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