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Disassembly Sequence Planning:A Survey

IEEE/CAA Journal of Automatica Sinica 2021年第7期8卷 1308-1324页

作者： Xiwang Guo MengChu Zhou Abdullah Abusorrah Fahad Alsokhiry Khaled Sedraoui the College of Computer and Communication Engineering Liaoning Shihua UniversityFushun 113001China the Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA the Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz UniversityJeddah 21481Saudi Arabia the Department of Electrical and Computer Engineering Faculty of Engineeringand the Center of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz UniversityJeddah 21481Saudi Arabia

It is well-recognized that obsolete or discarded products can cause serious environmental pollution if they are poorly be *** contain reusable resource that can be recycled and used to generate desired economic ***,performing their efficient disassembly is highly important in green manufacturing and sustainable economic *** typical examples are electronic appliances and electromechanical/mechanical *** paper presents a survey on the state of the art of disassembly sequence *** can help new researchers or decision makers to search for the right solution for optimal disassembly *** reviews the disassembly theory and methods that are applied for the processing,repair,and maintenance of obsolete/discarded *** paper discusses the recent progress of disassembly sequencing planning in four major aspects:product disassembly modeling methods,mathematical programming methods,artificial intelligence methods,and uncertainty *** survey should stimulate readers to be engaged in the research,development and applications of disassembly and remanufacturing methodologies in the Industry 4.0 era.

关键词： Artificial intelligence(AI) disassembly sequence planning(DSP) intelligent optimization modeling methods multiobjective optimization single-objective optimization uncertainty.

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Position Control of Flexible Joint Carts Using Adaptive Generalized Dynamics Inversion

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computers, Materials & Continua 2022年第3期70卷 4691-4705页

作者： Ibrahim M.Mehedi Mohd Heidir Mohd Shah Soon Xin Ng Abdulah Jeza Aljohani Mohammed El-Hajjar Muhammad Moinuddin Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah21589Saudi Arabia Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz UniversityJeddah21589Saudi Arabia Next Generation Wireless Research Group University of SouthamptonUK

This paper presents the design and implementation of Adaptive Generalized Dynamic Inversion(AGDI)to track the position of a Linear Flexible Joint Cart(LFJC)system along with vibration suppression of the flexible *** proposed AGDI control law will be comprised of two control *** baseline(continuous)control law is based on principle of conventional GDI approach and is established by prescribing the constraint dynamics of controlled state variables that reflect the control *** control law is realized by inverting the prescribed dynamics using dynamically scaledMoore-Penrose generalized *** boost the robust attributes against system nonlinearities,parametric uncertainties and external perturbations,a discontinuous control law will be augmented which is based on the concept of sliding mode *** discontinuous control law,the sliding mode gain is made adaptive in order to achieve improved tracking performance and chattering *** closed-loop stability of resultant control law is established by introducing a positive define Lyapunov candidate function such that semi-global asymptotic attitude tracking of LFJC system is *** computer simulations followed by experimental investigation will be performed on Quanser’s LFJC system to authenticate the feasibility of proposed control approach for its application to real world problems.

关键词： Adaptive control generalized dynamic inversion moore-penrose generalized inverse sliding mode control lyapunov stability semi-global asymptotic stability

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Adaptive Mixture Model Reduction based on the Composite Transportation Dissimilarity 26

Adaptive Mixture Model Reduction based on the Composite Tran...

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26th International Conference on Information Fusion, FUSION 2023

作者： D'Ortenzio, Alessandro Manes, Costanzo De Iuliis, Vittorio Orguner, Umut University of l'Aquila Department of Information Engineering Computer Science and Mathematics Italy University of l'Aquila Center of Excellence EX-EMERGE Italy Middle East Technical University Department of Electrical and Electronics Engineering Turkey

ISBN: (纸本)9798890344854

Providing efficient yet accurate statistical models is a challenging problem in many applications. When elementary models are not sufficiently descriptive, mixtures of densities can be used. A complexity management issue arises when mixture models are employed: the number of components should be a trade-off between the complexity and the accuracy of the model. However, in general, it is not obvious how to determine the right number of mixture components for a specific application. In a previous work, theoretical foundations to address such a topic have been laid, grounded on the use of the Composite Transportation Dissimilarity between mixtures, and a preliminary criterion to manage the complexity of a mixture model has been proposed. In this paper, additional theoretical insights are provided that allow to formulate a novel adaptive mixture reduction algorithm. Numerical tests show that in most cases the new algorithm constitutes a significant improvement over the previous one. © 2023 International Society of Information Fusion.

关键词： Economic and social effects

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Online Detection of Water Contamination Under Concept Drift

arXiv

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arXiv 2025年

作者： Li, Jin Malialis, Kleanthis Vrachimis, Stelios G. Polycarpou, Marios M. KIOS Research Innovation Center of Excellence Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

Water Distribution Networks (WDNs) are vital infrastructures, and contamination poses serious public health risks. Harmful substances can interact with disinfectants like chlorine, making chlorine monitoring essential for detecting contaminants. However, chlorine sensors often become unreliable and require frequent calibration. This study introduces the Dual-Threshold Anomaly and Drift Detection (AD&DD) method, an unsupervised approach combining a dual-threshold drift detection mechanism with an LSTM-based Variational Autoencoder (LSTM-VAE) for real-time contamination detection. Tested on two realistic WDNs, AD&DD effectively identifies anomalies with sensor offsets as concept drift, and outperforms other methods. A proposed decentralized architecture enables accurate contamination detection and localization by deploying AD&DD on selected nodes. © 2025, CC BY.

关键词： Health risks

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Deadlock-free Supervisor Design for Robotic Manufacturing Cells With Uncontrollable and Unobservable Events

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IEEE/CAA Journal of Automatica Sinica 2021年第3期8卷 597-605页

作者： Bo Huang MengChu Zhou Cong Wang Abdullah Abusorrah Yusuf Al-Turki School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA the Center of Research Excellence in Renewable Energy and Power Systems and the Department of Electrical and Computer Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia

In this paper,a deadlock prevention policy for robotic manufacturing cells with uncontrollable and unobservable events is proposed based on a Petri net ***,a Petri net for the deadlock control of such systems is *** admissible markings and first-met inadmissible markings(FIMs)are ***,place invariants are designed via an integer linear program(ILP)to survive all admissible markings and prohibit all FIMs,keeping the underlying system from reaching deadlocks,livelocks,bad markings,and the markings that may evolve into them by firing uncontrollable *** also ensures that the obtained deadlock-free supervisor does not observe any unobservable *** addition,the supervisor is guaranteed to be admissible and structurally minimal in terms of both control places and added *** condition under which the supervisor is maximally permissive in behavior is ***,experimental results with the proposed method and existing ones are given to show its effectiveness.

关键词： Deadlock prevention Petri nets robotic manufacturing cells structure-minimized supervisor supervisory control uncontrollability unobservability

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Unsupervised Incremental Learning with Dual Concept Drift Detection for Identifying Anomalous Sequences

Unsupervised Incremental Learning with Dual Concept Drift De...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Jin Li Kleanthis Malialis Christos G. Panayiotou Marios M. Polycarpou Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In the contemporary digital landscape, the continuous generation of extensive streaming data across diverse domains has become pervasive. Yet, a significant portion of this data remains unlabeled, posing a challenge in identifying infrequent events such as anomalies. This challenge is further amplified in non-stationary environments, where the performance of models can degrade over time due to concept drift. To address these challenges, this paper introduces a new method referred to as VAE4AS (Variational Autoencoder for Anomalous Sequences). VAE4AS integrates incremental learning with dual drift detection mechanisms, employing both a statistical test and a distance-based test. The anomaly detection is facilitated by a Variational Autoencoder. To demonstrate the effectiveness of VAE4AS, a comprehensive experimental study is conducted using real-world and synthetic datasets characterized by anomalous rates below 10% and recurrent drift. The results show that the proposed method surpasses both robust baselines and state-of-the-art techniques, providing compelling evidence for their efficacy in effectively addressing some of the challenges associated with anomalous sequence detection in non-stationary streaming data.

关键词： Incremental learning Concept drift Neural networks Streams Anomaly detection Synthetic data

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Wirelessly Enabled Beam Steering MSF Antenna for Biomedical Imaging Applications

Wirelessly Enabled Beam Steering MSF Antenna for Biomedical ...

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2023 URSI International Symposium on Electromagnetic Theory, EMTS 2023

作者： Bilal, Asif Quddious, Abdul Darvazehban, Amin Rezaeieh, Sasan Ahdi Iezekiel, Stavros Antoniades, Marco A. EMPHASIS Research Centre University of Cyprus Nicosia Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus KIOS Research Center for Excellence University of Cyprus Nicosia Cyprus TU Dresden Dresden Germany School of Information Technology and Electrical Engineering The University of Queensland Brisbane Australia Department of Electrical Computer & Biomedical Engineering Toronto Metropolitan University TorontoON Canada

ISBN: (纸本)9798350399288

In this work, a wirelessly enabled beam steering metasurface (MSF) antenna for bio-imaging is proposed. The design consists of multiple layers, the top layer consists of an MSF layer, while the bottom layer consists of two layers, one that contains the feeding antennas and the second consists of the feeding network. In the feeding network of the MSF antenna, an RF energy harvesting (EH) system has been incorporated that allows dynamic switching to achieve the desired beam steering required for torso scanning. In the designed EH system, a receiving antenna and rectifying circuit harvests the low power received RF signal at three different frequencies in the 2.4 GHz ISM band. In order to avoid coupling between the EH system and MSF antenna, the former operates in the 2.4 GHz ISM band, while the latter operates in the 900 MHz ISM band. The proposed approach has potential to be used in biomedical imaging applications and to provide an alternative to complex biasing networks required to reconfigure devices. © 2023 IEEE.

关键词： Energy harvesting

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Optimizing the Energy Scheduling and Assessing Profitability of BESSs in Buildings Considering Battery Degradation

Optimizing the Energy Scheduling and Assessing Profitability...

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International Conference on Smart Energy Systems and Technologies (SEST)

作者： Lysandros Tziovani Lenos Hadjidemetriou Stelios Timotheou Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus

ISBN: (数字)9798350386493

ISBN: (纸本)9798350386509

Battery energy storage systems (BESSs) are an emerging technology that can be integrated into buildings equipped with photovoltaic systems to reduce the electricity cost for prosumers in the face of volatile electricity prices. This work develops an optimization scheme for the energy scheduling of a BESS integrated in a residential building with photovoltaic systems considering a variable electricity pricing scheme and battery degradation. The proposed scheme is formulated as a linear program that can be solved fast and reliably over long-term time horizons, enabling its usage for both planning and operating strategies. Utilizing the developed optimization model, a financial analysis is performed to assess the long-term BESS profitability by calculating the net present value (NPV) and internal rate of return (IRR) using real data from a residential building. Simulation results demonstrate the increment of the NPV and IRR using the proposed optimization scheme compared to the corresponding scheme that ignores BESS degradation. The results also highlight that the proper selection of the penalty cost in cycle-based battery degradation cost models is critical for the enhancement of the battery profitability.

关键词： Degradation Costs Profitability Electricity Simulation Buildings Batteries Planning Reliability Optimization

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Experimental Investigation of Inverter-Battery System Efficiency for Energy Scheduling

Experimental Investigation of Inverter-Battery System Effici...

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

ISBN: (数字)9798350390421

ISBN: (纸本)9798350390438

Battery energy storage systems (BESSs) are widely used in energy management applications to maximize arbitrage profits. The consideration of the power losses from both battery and inverter components in optimization schemes is vital to ensure the economic BESSs scheduling. Towards this direction, this work develops two optimization models that consider the combined inverter-battery efficiency. The first model represents the non-linear efficiency curve of the inverter using a piecewise linear approximation, resulting in mixed-integer linear programming formulations. The second model approximates the nonlinear efficiency curve using a constant term, resulting in linear programming formulations which can be fast and reliably solved. Moreover, an experimental investigation is performed to derive the efficiency curve of a real inverter-battery system, where the parameters of the two models are identified. Simulation results highlight the necessity to consider the efficiencies from both battery and inverter components to reduce modelling inaccuracies and hence to maximize profits. The results also show the capability of the linear programming model to yield high-quality solutions with an approximation error of less than 1%.

关键词： Simulation Optimization models Piecewise linear approximation Europe Linear programming Inverters Batteries Smart grids Reliability Optimization

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Synergising Human-like Responses and Machine Intelligence for Planning in Disaster Response

arXiv

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arXiv 2024年

作者： Papaioannou, Savvas Kolios, Panayiotis Panayiotou, Christos G. Polycarpou, Marios M. KIOS Research and Innovation Center of Excellence Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

In the rapidly changing environments of disaster response, planning and decision-making for autonomous agents involve complex and interdependent choices. Although recent advancements have improved traditional artificial intelligence (AI) approaches, they often struggle in such settings, particularly when applied to agents operating outside their well-defined training parameters. To address these challenges, we propose an attention-based cognitive architecture inspired by Dual Process Theory (DPT). This framework integrates, in an online fashion, rapid yet heuristic (human-like) responses (System 1) with the slow but optimized planning capabilities of machine intelligence (System 2). We illustrate how a supervisory controller can dynamically determine in real-time the engagement of either system to optimize mission objectives by assessing their performance across a number of distinct attributes. Evaluated for trajectory planning in dynamic environments, our framework demonstrates that this synergistic integration effectively manages complex tasks by optimizing multiple mission objectives. © 2024, CC BY.

关键词： Disasters

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