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Resilence Control of DC Microgrid Integrated with Multi-Nanogrids Considering Connected-Islanded States

CSEE Journal of Power and Energy systems 2024年第2期10卷 786-796页

作者： Hossien Faraji Reza Hemmati Pierluigi Siano Department of Electrical Engineering Amirkabir University of TechnologyTehranIran Department of Electrical Engineering Kermanshah University of TechnologyKermanshahIran Department of Management&Innovation Systems University of Salernoand also with the Department of Electrical and Electronic Engineering ScienceUniversity of JohannesburgJohannesburg 2006South Africa

In this paper, a DC microgrid (DCMG) integrated with a set of nano-grids (NG) is studied. DCMG exchanges predetermined active and reactive power with the upstream network. DCMG and NGs are coordinately controlled and managed in such a way the exchanged P-Q power with external grid are kept on scheduled level following all events and operating conditions. The proposed control system, in addition to the ability of mutual support between DCMG and NGs, makes NGs support each other in critical situations. On the other hand, in all operating conditions, DCMG not only feeds three-phase loads with time-varying active and reactive power on the grid side but also injects constant active power into the grid. During events, NGs support each other, NGs support DCMG, and DCMG supports NGs. Such control strategies are realized by the proposed control method to increase resilience of the system. For these purposes, all resources and loads in DCMG and NGs are equipped with individual controllers. Then, a central control unit analyzes, monitors, and regularizes performance of individual controllers in DCMG and NGs. Nonlinear simulations show the proposed model can effectively control DCMG and NGs under normal and critical conditions.

关键词： Control system DC microgrid nanogrid resilience

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Modelling customer demand for mobile value-added services: non-stationary time series models or neural networks time series analysis?

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International Journal of Industrial and systems engineering 2023年第4期43卷 555-581页

作者： Vaghefzadeh, Mohammad Hossein Karimi, Behrooz Ahmadi, Abbas Industrial Engineering and Management Systems Faculty Amirkabir University of Technology Tehran Iran Department of Industrial Engineering and Management Systems Amirkabir University of Technology Tehran Iran

The present research applies two different modelling approaches to evaluate the historical demand for a special mobile value-added service (VAS) that is offered and delivered to airline customers. The first method is deterministic and includes non-stationary time series models that cover both mean and variance fluctuation, as well as seasonality effect, in the dataset. The second method is a metaheuristic approach in the form of artificial neural network time series analysis (ANN-TSA). These methods are used to evaluate the power of each category and to choose the best model based on appropriate criteria. The results show that non-stationary time series models outperform ANN-TSA, as indicated by the smaller number of errors in the simulation of the demand dataset. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Neural networks

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Incremental Reinforcement Learning via Performance Evaluation and Policy Perturbation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2253-2263页

作者： Deng, Guizhou Fu, Huiqiao Wang, Xinpeng Liu, Canghai Tang, Kaiqiang Chen, Chunlin Southwest University of Science and Technology Mechanical Engineering School of Manufacturing Science and Engineering Mianyang621000 China Nanjing University School of Management and Engineering Nanjing210093 China Nanjing University Department of Control and Systems Engineering School of Management and Engineering Nanjing210093 China

Rapid adaptation to the environment is the long-term task of reinforcement learning. However, reinforcement learning faces great challenges in dynamic environments, especially with continuous state-action spaces. In this article, we propose a systematic incremental reinforcement learning method via performance evaluation and policy perturbation (IRL-PEPP) to improve the adaptability of reinforcement learning algorithms in dynamic environments with continuous state-action spaces, which mainly includes three parts, i.e., performance evaluation, policy perturbation, and importance weighting. First, in performance evaluation, we apply the learned optimal policy to sample a few episodes in the original environment and use these samples to evaluate the policy applicability in the new environment. Then, in policy perturbation, the policy is perturbed according to the policy applicability to balance the tradeoff between exploration and exploitation in the new environment. Finally, importance weighting is applied to weight the information to speed up the adjustment process of the policy. Experimental results demonstrate the feasibility and efficiency of the proposed IRL-PEPP method for continuous control tasks in comparison with the existing state-of-the-art methods. © 2023 IEEE.

关键词： Reinforcement learning

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A GAN-based method for 3D lung tumor reconstruction boosted by a knowledge transfer approach

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Multimedia Tools and Applications 2023年第28期82卷 44359-44385页

作者： Rezaei, Seyed Reza Ahmadi, Abbas Department of Industrial Engineering and Management Systems Amirkabir University of Technology Tehran Iran

Three-dimensional (3D) image reconstruction of tumors has been one of the most effective techniques for accurately visualizing tumor structures and treatment with high resolution, which requires a set of two-dimensional medical images such as CT slices. In this paper we propose a novel method based on generative adversarial networks (GANs) for 3D lung tumor reconstruction by CT images. The proposed method consists of three stages: lung segmentation, tumor segmentation and 3D lung tumor reconstruction. Lung and tumor segmentation are performed using snake optimization and Gustafson-Kessel (GK) clustering. In the 3D reconstruction part first, features are extracted using the pre-trained VGG model from the tumors that detected in 2D CT slices. Then, a sequence of extracted features is fed into an LSTM to output compressed features. Finally, the compressed feature is used as input for GAN, where the generator is responsible for high-level reconstructing the 3D image of the lung tumor. The main novelty of this paper is the use of GAN to reconstruct a 3D lung tumor model for the first time, to the best of our knowledge. Also, we used knowledge transfer to extract features from 2D images to speed up the training process. The results obtained from the proposed model on the LUNA dataset showed better results than state of the art. According to HD and ED metrics, the proposed method has the lowest values of 3.02 and 1.06, respectively, as compared to those of other methods. The experimental results show that the proposed method performs better than previous similar methods and it is useful to help practitioners in the treatment process. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Tumors

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Two-stage robust optimization approach for enhanced community resilience under tornado hazards

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European Journal of Operational Research 2025年第3期325卷 525-540页

作者： Ansari, Mehdi Borrero, Juan S. González, Andrés D. American Airlines Fort WorthTX United States Department of Industrial and Management Systems Engineering University of South Florida United States School of Industrial and Systems Engineering University of Oklahoma United States

Catastrophic tornadoes cause severe damage and are a threat to human wellbeing, making it critical to determine mitigation strategies to reduce their impact. One such strategy, following recent research, is to retrofit existing structures. To this end, in this article we propose a model that considers a decision-maker (a government agency or a public–private consortium) who seeks to allocate resources to retrofit and recover wood-frame residential structures, to minimize the population dislocation due to an uncertain tornado. In the first stage the decision-maker selects the retrofitting strategies, and in the second stage the recovery decisions are made after observing the tornado. As tornado paths cannot be forecasted reliably, we take a worst-case approach to uncertainty where paths are modeled as arbitrary line segments on the plane. Under the assumption that an area is affected if it is sufficiently close to the tornado path, the problem is framed as a two-stage robust optimization problem with a mixed-integer non-linear uncertainty set. We solve this problem by using a decomposition column-and-constraint generation algorithm that solves a two-level integer problem at each iteration. This problem, in turn, is solved by a decomposition branch-and-cut method that exploits the geometry of the uncertainty set. To illustrate the model's applicability, we present a case study based on Joplin, Missouri. Our results show that there can be up to 20% reductions in worst-case population dislocation by investing $15 million in retrofitting and recovery and that our approach outperforms other retrofitting policies. © 2025 Elsevier B.V.

关键词： Retrofitting

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Innovative Mechanical Ventilation Control for Enhanced Indoor Air Quality and Energy Efficiency

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Energy engineering 2025年第3期122卷 861-883页

作者： Giovanni Miracco Francesco Nicoletti Vittorio Ferraro Dimitrios Kaliakatsos Department of Mechanical Energy and Management Engineering(DIMEG)University of CalabriaCosenza87036Italy Department of Computer ModellingElectronics and Systems Engineering(DIMES)University of CalabriaCosenza87036Italy

Indoor air quality(IAQ)is often overlooked,yet a poorly maintained environment can lead to significant health issues and reduced concentration and productivity in work or educational *** study presents an innovative control system for mechanical ventilation specifically designed for university classrooms,with the dual goal of enhancing IAQ and increasing energy *** classrooms with distinct construction characteristics were analyzed:one with exterior walls and windows,and the other completely *** each classroom,a model was developed using DesignBuilder software,which was calibrated with experimental data regarding CO_(2) concentration,temperature,and relative humidity *** proposed ventilation system operates based on CO_(2) concentration,relative humidity,and potential for free heating and *** addition,the analysis was conducted for other locations,demonstrating consistent energy savings across different climates and environments,always showing an annual reduction in energy *** demonstrate that mechanical ventilation,when integrated with heat recovery and free cooling strategies,significantly reduces energy consumption by up to 25%,while also maintaining optimal CO_(2) levels to enhance comfort and air *** findings emphasize the essential need for well-designed mechanical ventilation systems to ensure both psychophysical well-being and IAQ in enclosed spaces,particularly in environments intended for extended occupancy,such as ***,this approach has broad applicability,as it could be adapted to various building types,thereby contributing to sustainable energy management practices and promoting healthier indoor *** study serves as a model for future designs aiming to balance energy efficiency with indoor air quality,especially relevant in the post-COVID era,where the importance of indoor air quality has become more widely recognized.

关键词： Indoor air quality mechanical ventilation system innovative control system energy efficiency Energy-Plus simulation

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Change-point detection with deep learning:A review

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Frontiers of engineering management 2025年第1期12卷 154-176页

作者： Ruiyu XU Zheren SONG Jianguo WU Chao WANG Shiyu ZHOU Department of Industrial Engineering and Management Peking UniversityBeijing 100871China Department of Industrial and Systems Engineering University of IowaIowa CityIA 52242USA Department of Industrial and Systems Engineering University of Wisconsin-MadisonMadisonWI 53706USA

Recent advances in deep learning have led to the creation of various methods for change-point detection(CPD).These methods enhance the ability of CPD tech-niques to handle complex,high-dimensional data,making them more adaptable and less dependent on strict assump-tions about data *** methods have also demonstrated high accuracy and have been applied across various fields,including manufacturing,healthcare,activity monitoring,finance,and environmental *** review provides an overview of how these methods are applied,the data sets they use,and how their performance is *** also organizes techniques into supervised and unsupervised categories,citing key ***,we explore ongoing challenges and suggest directions for future research to improve interpretability,generalizability,and real-world implementation.

关键词： change-point detection deep learning supervised learning unsupervised learning time-series analysis

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Enhancing the effectiveness of output projection in wafer fabrication using an Industry 4.0 and XAI approach

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International Journal of Advanced Manufacturing Technology 2024年第1-2期134卷 113-125页

作者： Chen, Tin-Chih Toly Wang, Yu-Cheng Lin, Chi-Wei Department of Industrial Engineering and Management National Yang Ming Chiao Tung University University Road Hsinchu1001 Taiwan Department of Aeronautical Engineering Chaoyang University of Technology Taichung City Taiwan Department of Industrial Engineering and Systems Management Feng Chia University Taichung Taiwan

Industry 4.0 and deep learning methods have been widely used for output projection in wafer fabrication. Although such applications appeared to be effective, they were difficult to understand and/or improve. Therefore, in this study, an Industry 4.0 and explainable artificial intelligence (XAI) approach is proposed to enhance the interpretability of a deep learning application for output projection. First, a deep neural network (DNN) is constructed to estimate the output times of jobs based on real-time job, machine, and factory information collected using Industry 4.0 technologies. Then, the DNN is approximated by a random forest (RF) to generate interpretable decision rules. SHAP analysis is also performed to generate local explanations. The proposed methodology has been applied to a real case from a wafer fab. Through the synergy of Industry 4.0 and deep learning, the proposed methodology achieved a satisfactory output projection performance. The MAE was only about two wafer lots. Additionally, user communication was also facilitated since the output projection mechanism using deep learning has been explained using XAI techniques. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Industry 4.0

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RAYLEIGH QUOTIENT GRAPH NEURAL NETWORKS FOR GRAPH-LEVEL ANOMALY DETECTION 12

RAYLEIGH QUOTIENT GRAPH NEURAL NETWORKS FOR GRAPH-LEVEL ANOM...

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12th International Conference on Learning Representations, ICLR 2024

作者： Dong, Xiangyu Zhang, Xingyi Wang, Sibo Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong

Graph-level anomaly detection has gained significant attention as it finds applications in various domains, such as cancer diagnosis and enzyme prediction. However, existing methods fail to capture the spectral properties of graph anomalies, resulting in unexplainable framework design and unsatisfying performance. In this paper, we re-investigate the spectral differences between anomalous and normal graphs. Our main observation shows a significant disparity in the accumulated spectral energy between these two classes. Moreover, we prove that the accumulated spectral energy of the graph signal can be represented by its Rayleigh Quotient, indicating that the Rayleigh Quotient is a driving factor behind the anomalous properties of graphs. Motivated by this, we propose Rayleigh Quotient Graph Neural Network (RQGNN), the first spectral GNN that explores the inherent spectral features of anomalous graphs for graph-level anomaly detection. Specifically, we introduce a novel framework with two components: the Rayleigh Quotient learning component (RQL) and Chebyshev Wavelet GNN with RQ-pooling (CWGNN-RQ). RQL explicitly captures the Rayleigh Quotient of graphs and CWGNN-RQ implicitly explores the spectral space of graphs. Extensive experiments on 10 real-world datasets show that RQGNN outperforms the best rival by 6.74% in Macro-F1 score and 1.44% in AUC, demonstrating the effectiveness of our framework. Our code is available at https://***/xydong127/RQGNN. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Anomaly detection

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A Framework for Designing Zonal Architectures for In-Vehicle Networks: Balancing Communication Load and Wiring Length

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IEEE Transactions on Vehicular Technology 2024年第5期74卷 7940-7952页

作者： Lim, Jongwook Lee, Jongheon Hong, Yoo Suk Kang, Changmuk Seoul National University Department of Industrial Engineering Seoul Korea Republic of Incheon National University Department of Industrial and Management Engineering Incheon Korea Republic of Soongsil University Department of Industrial and Information Systems Engineering Seoul Korea Republic of

The electrical and electronic (E/E) architectures are a growing concern in the automotive industry, driven by the increasing prevalence of software-oriented features and technologies. As system complexity rises, many automotive companies are exploring new architectural strategies to manage this complexity effectively. In this research, we focused on the zonal E/E architectures as a future solution and proposed a framework for their design. The framework integrates the software and physical components that form the zonal architectures and analyzes the decisions that map the relationships between these elements. Its goal is to create zonal architectures that balance key performance metrics, such as communication load and wiring length. To achieve this, we formulated the zonal architecting problem (ZAP) based on the proposed framework and developed a column generation method to solve it. The framework and its mathematical model were applied to a use case involving advanced driver assistance systems (ADAS), demonstrating that our approach significantly improves both communication load and wiring length, thereby validating the effectiveness of the proposed framework. © 1967-2012 IEEE.

关键词： Automotive industry

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