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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Yosra Ben Fadhel Khawla Kachroudi Achraf Derbel Hedi Trabelsi Nabil Derbel University of Tunis El Manar Higher Institute of Medical Technologies Tunisia Departement of Computer Systems Eng. Mediterranean Institute of Technology South Mediterranean University Tunisia University of Sfax National Engineering School of Sfax Control & Energy Management Laboratory Tunisia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

In this study, we propose a novel nanorobot model for the early detection of Alzheimer's disease. As a first step, we explored the polymorphism of the TREM2 gene's exons to assess its association with Alzheimer's. Our case-control study involved 124 Tunisian participants-64 Alzheimer's patients and 60 age-matched controls, averaging 67 years. Each participant underwent neurological evaluation, neuropsychological testing, brain imaging, and molecular analysis. Using Polymerase Chain Reaction (PCR) and sequencing techniques, we identified the rs2234253 variant in exon 2 and a novel intronic variant in intron 4, both of which were significantly correlated with Alzheimer's risk. Based on these genetic findings, we designed a nanorobot capable of detecting early biomarkers of Alzheimer's disease, such as amyloid plaques and tau tangles. The nanorobots are approximately 100 nm in size, enabling them to navigate the blood-brain barrier and specifically target regions of the brain affected by Alzheimer's pathology. They are constructed from biocompatible materials, such as gold nanoparticles and silica, and functionalized with surface receptors that selectively bind to Alzheimer's-related biomarkers. Equipped with nanosensors, the nanorobots are designed to detect binding events and transmit data to an external monitoring system in real time, facilitating early diagnosis and continuous monitoring of disease progression. These technological advancements offer a promising approach to enhancing Alzheimer's detection and improving patient out-comes.

关键词： Pathology Sequential analysis Biomarkers Real-time systems Safety Polymers Alzheimer's disease Monitoring Silicon dioxide Testing

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A Wearable Eye-Tracking Approach for Early Autism Detection with Machine Learning: Unravelling Challeng.s and Opportunities

A Wearable Eye-Tracking Approach for Early Autism Detection ...

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International Joint Conference on Neural Networks (IJCNN)

作者： J. Lopez-Martinez Purificación Checa José Manuel Soto-Hidalgo Isaac Triguero Alberto Fernández Department of Computer Science and Artificial Intelligence University of Granada Spain Dept. of Educational and Evolutive Psychology University of Granada Spain Dept. of Comput. Eng. Automation and Robotics University of Granada Spain DaSCI Andalusian Institute in Data Science and Computational Intelligence School of Computer Science University of Nottingham Nottingham UK

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Early detection of Autism Spectrum Disorder (ASD) is crucial to facilitate timely interventions, improve outcomes, and enhance the quality of life for individuals on the spectrum. Artificial intelligence and machine learning have significantly advanced the study of ASD by enabling sophisticated analyses of complex behavioural data, providing more accurate and timely detection methods. Nevertheless, existing research is mostly focused on either screening manual methods via psychological surveys, and/or the application of functional Magnetic Resonance Imaging. In the former case, there is a limitation due the subjectivity of the procedure; whereas in the latter the high cost represents a barrier to widespread use in diagnosis. In consideration of the aforementioned factors, this research contributes significantly to the field by integrating commodity wearable technology with psycho-healthcare, employing eye-tracking capabilities as a novel, non-invasive approach for early ASD diagnosis. Our methodology stands out by offering a comprehensive, four-step process that includes sophisticated image preprocessing, innovative feature extraction, precise classification, and a novel multi-instance aggregation strategy. We show its potential via a compelling case study that attests to the feasibility and efficacy of this innovative paradigm. Additionally, the work underscores prevailing challeng.s in the field, stressing some factors such as the acquisition of extensive and diverse datasets to allow for a multi-modal approach and the application of Trustworthy Artificial Intelligence.

关键词： Surveys Autism Neural networks Psychology Machine learning Gaze tracking Manuals

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Improved Proximal Policy Optimization Algorithm for Multi-objective Disassembly Line Balancing Considering Hazardous Tasks

Improved Proximal Policy Optimization Algorithm for Multi-ob...

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IEEE International Conference on Networking, Sensing and control

作者： Zeyu Guo Xiwang Guo Jiacun Wang Shujin Qin Liang Qi Qi Kang Information and Control Engineering Liaoning Petrochemical University Fushun China Computer Science and Software Eng. Monmouth University Wes Long Branch New Jersey USA Economics and Management Shangqiu Normal University Shangqiu China Computer Science Department Shandong Technology University Qingdao China Electrical and Computer Engineering New Jersey Institute of Technology Newark USA

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

As disassembly lines evolve, keeping workers safe from all potential dangers has become paramount. This work proposes an improved approach, Proximal Policy Optimization for Disassembly Line(PPO-DL), to address Multi-objective Dis-assembly Line Balancing Problem(MDLBP) that integrates task switching time and hazardous tasks. The MDLBP model aims to optimize disassembly net profit, balance workstation utilization, and mitigate penalties from hazardous tasks. The proposed PPO-DL modifies the action space of PPO to accommodate MD LBP characteristics, ensuring efficient learning by restricting actions to valid ranges through action masking. Comparative experiments with PPO, Q-learning, and A2C demonstrate PPO-DL's superiority in finding near-optimal solutions across various scenarios.

关键词： Q-learning Switches Fatigue control systems Workstations Sensors Optimization

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Improved Dingo Optimization Algorithm for Human-robot Collaborative U-shaped Disassembly Line Balancing Problem

Improved Dingo Optimization Algorithm for Human-robot Collab...

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Cyber-Physical Social Intelligence (ICCSI), International Conference on

作者： Jialong Li XiWang Guo Jiacun Wang ShuJin Qin Liang Qi Qi Kang Information and Control Engineering Liaoning Petrochemical University Fushun China Computer Science and Software Eng. Monmouth University Wes Long Branch New Jersey USA Economics and Management Shangqiu Normal University Shangqiu China Computer Science and Technology Shandong University of Science and Technology Qingdao China Electrical and Computer Engineering New Jersey Institute of Technology Newark USA

ISBN: (数字)9798350376739

ISBN: (纸本)9798350376746

There is increasing attention on remanufacturing, with the primary focus in the industry on recovering reusable products to generate profit. In the process of optimizing remanufacturing, addressing the balance issue in the operation of the disassembly line is crucial. Therefore, this study proposes the use of an improved Dingo Optimization Algorithm (IDOA) and designs a mathematical model for human-robot collaboration to explore how to achieve higher profits under these conditions. Finally, the improved IDOA is experimentally compared with the commercial optimization solver CPLEX and another intelligent optimization method—the Battle Royale Optimization (BRO). The results demonstrate that this method offers high quality and efficiency.

关键词： Industries Human-robot interaction Collaboration Mathematical models Social intelligence Optimization

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Balancing Human-robot Collaborative Disassembly Line by Using Dingo Optimization Algorithm

Balancing Human-robot Collaborative Disassembly Line by Usin...

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IEEE International Conference on Human-Machine Systems

作者： Xiwang Guo Liangbo Zhou Meng.hu Zhou Weitian Wang Jiacun Wang Liang Qi Information and Control Engineering Liaoning Petrochemical University Fushun China Information and Control Liaoning Petrochemical University Fushun China Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA School of Computing Montclair State University Montclair USA Computer Science and Software Eng. Monmouth University West Long Branch NJ USA Computer Intelligent Sci. and Tech. Shandong University of Sci. and Tech. Qingdao China

ISBN: (数字)9798350315790

ISBN: (纸本)9798350315806

Disassembling and recycling scrapped products play important roles in effectively reducing environmental pollution and improving resource sustainability. A multi-product human-robot collaborative disassembly-line-balancing problem (MHDP) arises from doing so and represents an important yet to be solved in the remanufacturing field. This work explores how to use optimization algorithms to schedule tasks between humans and robots to maximize remanufacturing profit in human-robot collaboration contexts. Specifically, we investigate MHDP and develop a mixed integer programming model considering various system constraints. We propose a dingo optimization algorithm that simulates wild dogs’ social hierarchy and chasing processes to solve MHDP. We compare the proposed algorithm with an exact solution finder, i.e., IBM CPLEX and a well-known intelligent optimization method, i.e., Genetic Algorithm to show its competitive performance in terms of solution accuracy and efficiency.

关键词： Integer programming Schedules Pollution Human-machine systems Collaboration Optimization methods Recycling

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Universal Approximator Approach for Fuzzy Modeling in Thermoelectric Generator

Universal Approximator Approach for Fuzzy Modeling in Thermo...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Achraf Derbel Carlo Trigona Noura Baccar Nabil Derbel Departement of Computer Systems Eng. Mediterranean Institute of Technology South Mediterranean University Tunisia DIEEI Dipartimento di Ingegneria Elettrica Elettronica e Informatica Università degli Studi di Catania Italy Laboratory of Control & Energy Management ENIS National Engineering School of Sfax University of Sfax Tunisia

ISBN: (数字)9798350374131

ISBN: (纸本)9798350374148

This study explores the application of fuzzy systems as universal approximators for nonlinear functions, focusing on mono-variable and scalable to multi-variable functions. Ensuring error-free approximations through quadratic terms, our method-ology emphasizes reliability. Shifting to practicality, we investi-gate thermoelectric generators' dynamic response to temperature fluctuations. The research underscores fuzzy systems' precision in modeling TEG behavior, validating applicability and accuracy through diverse temperature scenarios.

关键词： Temperature distribution Temperature dependence Solid modeling Scalability Focusing Predictive models Solids

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Reinforcement Learning with Large Language Model for Hybrid Disassembly Lines in Remanufacturing Contexts

Reinforcement Learning with Large Language Model for Hybrid ...

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IEEE International Conference on Automation Science and eng.neering (CASE)

作者： Peng.Ji Xi Wang Guo Jiacun Wang Weitian Wang Shu Jin Qin Ying Tang Qi Kang College of Information and Control Engineering Liaoning Petrochemical University Fushun China Dept of Comp. Sci. and Soft. Eng. Monmouth University W. Long Branch NJ USA Collaborative Robotics and Smart Systems Laboratory (CRoSS Lab) Montclair State University Montclair NJ USA College of Economics and Management Shangqiu Normal University Shangqiu China Electrical and Computer Engineering Rowan University Glassboro New Jersey USA Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Large language models (LLM), ChatGPT is making substantial impact across various fields. This study for the first time presents a novel approach to the hybrid disassembly line balancing problem using LLM and reinforcement learning algorithms in remanufacturing contexts. The problem is divided into two sub-stages. LLM is innovatively used to capture a disassembly sequence well in the first stage, while reinforcement learning is utilized to address the problem in the second stage. Upon comparing the performance with and without LLM, the proposed approach significantly reduces the trial-and-error space and achieves faster convergence to achieve the desired solution. Future work of this study is also discussed.

关键词： computer aided software eng.neering Automation Large language models Reinforcement learning Chatbots Convergence

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Sensitivity Analysis for Uncertain Linear Systems: A Set-Membership Approach

SSRN

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SSRN 2023年

作者： Silvestre, Daniel Rosa, Paulo Hespanha, João P. Silvestre, Carlos Caparica2829-516 Portugal COPELABS The Lusófona University Portugal The Institute for Systems and Robotics Instituto Superior Técnico University of Lisbon Portugal Deimos Engenharia Lisbon Portugal The Department of Electrical and Computer Engineering The Faculty of Science and Technology The University of Macau China The Dept. of Electrical and Computer Eng. University of California Santa BarbaraCA93106-9560 United States

This paper deals with the problem of deciding which inputs or parameters in a dynamic model have the greatest impact on the state or output of a linear system. In over-parameterized models or Multi-Agent Systems (MAS), a sensitivity analysis tool would allow for reduced representations of the same system or to decide which agents should be monitored. The proposed approach consists in constructing the exact reachable set for a linear system (uncertain in the case of parameter sensitivity) and projecting onto the desired coordinates associated with the state or output in question. Given the exact representation, under mild assumptions, sensitivity values are optimal for the proposed functions, although others can be specified using the same sets. Illustrative examples are presented in order to provide insights on the proposed method. © 2023, The Authors. All rights reserved.

关键词： Sensitivity analysis

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Digital Map Generation Workflow Demonstrated on ZalaZONE Automotive Proving Ground Elements

Digital Map Generation Workflow Demonstrated on ZalaZONE Aut...

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IEEE Symposium on Network Operations and Management

作者： Árpád Somogyi Tamás Tettamanti Pál Varga Zsolt Szalay Dániel Baranyai Tamás Lovas Dept. of Photogrammetry and Geoinformatics Fac. of Civil Engineering Budapest University of Technology and Economics Budapest Hungary Systems and Control Laboratory Institute for Computer Science and Control of the Eötvös Loránd Research Network Budapest Hungary Dept. of Telecommunications and Media Informatics Fac. of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary Dept. of Automotive Technologies Fac. of Transportation Eng. and Vehicle Eng. Budapest University of Technology and Economics Budapest Hungary

One major goal of digital twin technology applied in the Architecture, eng.neering, and Construction (AEC) Industry is the mapping of roads and road environments with their associated information. Such digital twins can support cost-effective testing, simulations, and the production of mapping backgrounds for automated vehicles and generally for Cooperative, Connected and Automated Mobility (CCAM) developments. The efficient workflow that leads from data acquisition to finished products depends on a number of requirements and knowledge sets. The main contribution of the paper is a general methodology of creating HD-map from point cloud and image data set. Accordingly, the most widely used survey techniques are presented and the requirements for the objects to be derived are summarized. The paper then outlines a general implementation procedure as an expert knowledge through a real-life example, where the results obtained for the ZalaZONE Automotive Proving Ground are demonstrated.

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Predicting nanorobot shapes via generative models

arXiv

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arXiv 2021年

作者： Benjaminson, Emma Taylor, Rebecca E. Travers, Matthew Mechanical Eng Carnegie Mellon University PittsburghPA15213 United States Mechanical Eng. Biomedical Eng. Electrical & Computer Eng Carnegie Mellon University PittsburghPA15213 United States Robotics Institute Carnegie Mellon University PittsburghPA15213 United States

The field of DNA nanotechnology has made it possible to assemble, with high yields, different structures that have actionable properties. For example, researchers have created components that can be actuated, used to sense (e.g., changes in pH), or to store and release loads. An exciting next step is to combine these components into multifunctional nanorobots that could, potentially, perform complex tasks like swimming to a target location in the human body, detect an adverse reaction and then release a drug load to stop it. However, as we start to assemble more complex nanorobots, the yield of the desired nanorobot begins to decrease as the number of possible component combinations increases. Therefore, the ultimate goal of this work is to develop a predictive model to maximize yield. However, training predictive models typically requires a large dataset. For the nanorobots we are interested in assembling, this will be difficult to collect. This is because high-fidelity data, which allows us to exactly characterize the shape and size of individual structures, is very time-consuming to collect, whereas low-fidelity data is readily available but only captures bulk statistics for different processes. Therefore, this work combines low- and high-fidelity data to train a generative model using a two-step process. We first use a relatively small, high-fidelity dataset to train a generative model. Then at run time, the model takes low-fidelity data and uses it to approximate the high-fidelity content. We do this by biasing the model towards samples with specific properties as measured by low-fidelity data. In this work we bias our distribution towards a desired node degree of a graphical model that we take as a surrogate representation of the nanorobots that this work will ultimately focus on. We have not yet accumulated a high-fidelity dataset of nanorobots, so we leverage the MolGAN architecture [1] and the QM9 small molecule dataset [2-3] to demonstrate our approach. ©

关键词： controlled drug delivery

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