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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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GENERALIZED BAYESIAN NASH EQUILIBRIUM WITH CONTINUOUS TYPE AND ACTION SPACES

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SIAM Journal on Optimization 2025年第2期35卷 789-817页

作者： Tao, Yuan Xu, Huifu Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Hong Kong

A Bayesian game is a strategic decision-making model where each player's type parameter characterizing its own objective is private information: Each player knows its own type but not its rivals' types, and a Bayesian Nash equilibrium is an outcome of this game where each player makes a strategic optimal decision according to its own type under the Nash conjecture. In this paper, we advance the literature by considering a generalized Bayesian game where each player's action space depends on its own type parameter and its rivals' actions. This reflects the fact that in practical applications, a player's feasible action is often related to its own type (e.g., marginal cost) and the rivals' actions (e.g., common resource constraints in a competitive market). Under some moderate conditions, we demonstrate the existence of a continuous generalized Bayesian Nash equilibrium (GBNE) and the uniqueness of such an equilibrium when each player's action space is only dependent on its type. In the case that each player's action space also depends on rivals' actions, we give a simple example to show that the uniqueness of the GBNE is not guaranteed under standard monotone conditions. To compute an approximate GBNE, we restrict each player's response function to the space of polynomial functions of its type parameter and subsequently convert the GBNE model to a stochastic generalized Nash equilibrium model. To justify the approximation, we discuss the convergence of the approximation scheme. Some preliminary numerical test results show that the approximation scheme works well. Copyright © by SIAM. Unauthorized reproduction of this article is prohibited.

关键词： Stochastic models

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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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Integrating Potential Pronunciations for Enhanced Mispronunciation Detection and Diagnosis Ability in LLMs

Integrating Potential Pronunciations for Enhanced Mispronunc...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wu, Minglin Xu, Jing Chen, Xueyuan Meng, Helen Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9798350368741

Large Language Models (LLMs) have exhibited significant potentials across various tasks. However, how to leverage the power of LLMs in the mispronunciation detection and diagnosis (MDD) task is still under-explored. In this paper, we propose a PP-ATP model, which integrates potential pronunciations covering common mispronunciations into the prompt part of LLMs, to enhance the MDD ability of LLMs in second language (L2) English. Specifically, the proposed PP-ATP model is composed of an audio encoder, an LLM decoder, and an adapter. Taking speech representations from the audio encoder as the audio prompt and reference sentence with canonical and potential pronunciations as text prompt, the LLM decoder is adapted to predict the actual pronunciation in the given L2 speech. Experiments show that our PP-ATP model achieves new state-of-the-art (SOTA) performance in MDD on CU-CHLOE corpus, confirming the effectiveness of potential pronunciation integration. © 2025 IEEE.

关键词： L2 English large language model mispronunciation detection and diagnosis potential pronunciations

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Single-Loop Variance-Reduced Stochastic Algorithm for Nonconvex-Concave Minimax Optimization

Single-Loop Variance-Reduced Stochastic Algorithm for Noncon...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Jiang, Xia Zhu, Linglingzhi Zheng, Taoli So, Anthony Man-Cho Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9798350368741

Nonconvex-concave (NC-C) finite-sum minimax problems have broad applications in decentralized optimization and various machine learning tasks. However, the nonsmooth nature of NC-C problems makes it challenging to design effective variance reduction techniques. Existing vanilla stochastic algorithms using uniform samples for gradient estimation often exhibit slow convergence rates and require bounded variance assumptions. In this paper, we develop a novel probabilistic variance reduction updating scheme and propose a single-loop algorithm called the probabilistic variance-reduced smoothed gradient descent-ascent (PVR-SGDA) algorithm. The proposed algorithm achieves an iteration complexity of O(ϵ−4), surpassing the best-known rates of stochastic algorithms for NC-C minimax problems and matching the performance of the best deterministic algorithms in this context. Finally, we demonstrate the effectiveness of the proposed algorithm through numerical simulations. © 2025 IEEE.

关键词： nonconvex-concave minimax optimization single-loop stochastic algorithm variance reduction

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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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