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Particle Swarm Optimization for Computing Nash and Stackelberg Equilibria in Energy Markets

Operations Research Forum 2020年第3期1卷 1-23页

作者： Vrahatis, Michael N. Kontogiorgos, Panagiotis Papavassilopoulos, George P. Computational Intelligence Laboratory (CILab) Department of Mathematics University of Patras Patras GR-26110 Greece School of Electrical and Computer Engineering National Technical University of Athens Athens GR-15780 Greece

Interactions among stakeholders in deregulated markets lead to complex interdependent optimization problems. The present study is motivated by load control programs in energy markets and more precisely by using the power supply interruption as a tool for reducing consumers’ demand voluntarily, also known as voluntary load curtailment programs. The problem is formulated as a Stackelberg game, specifically, as a bilevel optimization problem that belongs to the mathematical programs with equilibrium constraints. In this game, a player that acts as leader determines the actions of the players that act as followers and play a Nash game among them through a subsidy program. The corresponding equilibria need to be found and the presence of nonconvex functions makes the use of metaheuristic algorithms attractive. An extension of particle swarm optimization is proposed for solving such problems based on the unified particle swarm optimization that is a variation of the plain particle swarm optimization algorithm. The proposed algorithm is tested by solving some examples of the formulated games in order to study its efficiency and the interactions between the stakeholders of the market. © 2020, Springer Nature Switzerland AG.

关键词： Bilevel programming Demand response Energy management system Energy market Management science Nash Operations research Particle swarm optimization Stackelberg

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Advantage of Machine Learning over Maximum Likelihood in Limited-Angle Low-Photon X-Ray Tomography

Advantage of Machine Learning over Maximum Likelihood in Lim...

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IS and T International Symposium on Electronic Imaging: Machine Learning for Scientific Imaging, MLSI 2022

作者： Guo, Zhen Song, Jung Ki Barbastathis, George Glinsky, Michael E. Vaughan, Courtenay T. Larson, Kurt W. Alpert, Bradley K. Levine, Zachary H. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Centre Singapore138002 Singapore Sandia National Laboratory AlbuquerqueNM87123 United States Applied and Computational Mathematics Division National Institute of Standards and Technology Boulder CO80305 United States Quantum Measurement Division National Institute of Standards and Technology GaithersburgMD20899 United States

Limited-angle X-ray tomography reconstruction is an ill-conditioned inverse problem in general. Especially when the projection angles are limited and the measurements are taken in a photon-limited condition, reconstructions from classical algorithms such as filtered backprojection may lose fidelity and acquire artifacts due to the missing-cone problem. To obtain satisfactory reconstruction results, prior assumptions, such as total variation minimization and nonlocal image similarity, are usually incorporated within the reconstruction algorithm. In this work, we introduce deep neural networks to determine and apply a prior distribution in the reconstruction process. Our neural networks learn the prior directly from synthetic training samples. The neural nets thus obtain a prior distribution that is specific to the class of objects we are interested in reconstructing. In particular, we used deep generative models with 3D convolutional layers and 3D attention layers which are trained on 3D synthetic integrated circuit (IC) data from a model dubbed CircuitFaker. We demonstrate that, when the projection angles and photon budgets are limited, the priors from our deep generative models can dramatically improve the IC reconstruction quality on synthetic data compared with maximum likelihood estimation. Training the deep generative models with synthetic IC data from CircuitFaker illustrates the capabilities of the learned prior from machine learning. We expect that if the process were reproduced with experimental data, the advantage of the machine learning would persist. The advantages of machine learning in limited angle X-ray tomography may further enable applications in low-photon nanoscale imaging. © 2022, Society for Imaging Science and Technology

关键词： Photons

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Exploiting the Intrinsic Neighborhood Semantic Structure for Domain Adaptation in EEG-based Emotion Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Yi Wang, Ze Song, Yu Jia, Ziyu Wang, Boyu Jung, Tzyy-Ping Wan, Feng Macau University of Science and Technology Macao Centre for Mathematical Sciences Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications Faculty of Innovation Engineering 999078 China Tianjin University of Technology School of Electrical Engineering and Automation Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin300384 China Chinese Academy of Sciences Beijing Key Laboratory of Brainnetome and Brain-Computer Interface and Brainnetome Center Institute of Automation Beijing100045 China Western University Department of Computer Science Brain Mind Institute LondonONN6A 3K7 Canada University of California at San Diego Swartz Center for Computational Neuroscience Institute for Neural Computation La Jolla CA92093 United States University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology China University of Macau Centre for Cognitive and Brain Sciences Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation 999078 China

Due to the inherent non-stationarity and individual differences present in electroencephalogram (EEG) signals, developing a generalizable model that performs well on new subjects is challenging in EEG-based emotion recognition. Most existing domain adaptation (DA) methods typically mitigate these discrepancies by aligning the marginal distributions of domain feature representations. However, when there is a significant difference in the class-conditional distribution between domain features and labels, the domain-invariant features learned by aligning marginal distributions may have limited discriminative ability for unlabeled target instances or even prove counterproductive. To address this issue, we propose a Neighborhood Semantic Aware Learning-based Dynamic Graph Attention Convolution (NSAL-DGAT) approach that learns target semantic information by considering the inter-domain semantic topological structure, thereby improving classifier adaptation for target instances. Specifically, the proposed NSAL framework is designed to capitalize on the insight that after domain feature alignment, some target samples and their neighboring source samples exhibit similar semantics. By leveraging the neighborhood topological structure, we extract and incorporate semantic target features to train a more transferable classifier. Besides, we implement an entropy weighting mechanism to emphasize representative target semantic information, encouraging target instances to prioritize high-confidence individuals within the source neighborhood. We have conducted extensive experiments on the public SEED dataset and our collected the Hearing-Impaired EEG Dataset (HIED). The experimental results underscore the efficacy of our proposed NSAL-DGAT approach, showcasing state-of-the-art accuracy in subject-dependent as well as subject-independent scenarios. The source code is available at https://***/YYingDL/NSAL-DGAT. © 2010-2012 IEEE.

关键词： Speech analysis

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Alpha (α) assumed rotations and shear strains for spatially isotropic polygonal Reissner-Mindlin plate elements (αARS-Poly)

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computers and Structures 2023年 274卷

作者： Nguyen, Son H. Nam, Nguyen N. Hoang, Tien-Dat Nguyen, Tan N. Nguyen-Thoi, T. Division of Computational Mathematics and Engineering Institute for Computational Science Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Civil Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Electrical and Electronic Engineering Phenikaa University Hanoi12116 Viet Nam Division of English Taught Mechanical Engineering Faculty of International Training Thai Nguyen University of Technology Thai Nguyen City Viet Nam Department of Architectural Engineering Sejong University 209 Neungdong-ro Gwangjin-gu Seoul05006 Korea Republic of Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Mechanical - Electrical and Computer Engineering School of Engineering and Technology Van Lang University Ho Chi Minh City Viet Nam

This paper proposes simple and efficient alpha assumed rotations and shear strains for polygonal plate elements, named αARS-Poly. In the αARS approach, an alternative assumption of the tangent rotations along element boundaries is applied by using the approximation of the rotations in Timoshenko's beam theory. Then, the quadratic term of this assumed field is linearly scaled up by adding an artificial positive scaling factor α>0. Through examination of the relative errors in the energy (s-) norm regarding α in numerical experiences, the value α=0.5 can be chosen as a general-fixed value that possibly achieves the optimal relative errors of the s-norm. The value α=0.5 seems to be not only mesh-independent but also problem-independent. The αARS-Poly element using α=0.5 passes all critical tests (spatially isotropic, zero-energy mode, and bending path tests) for a finite plate element which ensures the element orientation-independent property, solution stability, and free shear-locking in the thin plate limit. The implementation of the αARS-Poly element is straightforward through a unified form of the stiffness matrix for all arbitrary convex-shaped polygonal meshes. Numerical results show that the proposed element achieves high reliable and optimal results with uniform and excellent convergent rates in the static and free vibration analyses. © 2022 Elsevier Ltd

关键词： Finite element method

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"Unlocking Electrocatalytic Dynamics with Anti-Mxene Borides Monolayers for Nitrate Reduction"

SSRN

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SSRN 2024年

作者： Ho, Thi H. Nguyen, Quynh Anh T. Le, Bich-Tram Truong Kim, Seong-Gon Bui, Viet Q. Laboratory for Computational Physics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Mechanical - Electrical and Computer Engineering School of Technology Van Lang University Ho Chi Minh City Viet Nam Advanced Institute of Science and Technology The University of Danang 41 Le Duan Danang Viet Nam Department of Science and International Cooperation The University of Danang Danang551000 Viet Nam Department of Physics & Astronomy Center for Computational Sciences Mississippi State University MS39762 United States

In this comprehensive study, we explore the electrocatalytic behavior of novel two-dimensional (2D) monolayers composed of anti-MXene borides (TMB) towards the nitrate reduction reaction (NO3RR) using density functional theory (DFT). The analysis reveals that these TMB monolayers emerge as promising candidates for electrochemical NO3RR, characterized by their exceptional stability, advantageous selectivity, and effective activation properties. Notably, our results highlight the preferential progression of the NO3-to-N2 pathway over the NO3-to-NH3 pathway in these TMB systems, presenting a significant insight into the mechanism of nitrate reduction. Among the monolayers, MnB distinguishes itself with a limiting potential of 0.79 eV, underscoring its substantial potential for application in the electroreduction of nitrate. Additionally, this study sheds light on the broader applicability of these innovative two-dimensional anti-MXene TMB monolayers, suggesting their versatility as electrocatalysts in a variety of potential applications, thereby opening new avenues for future research and development in electrocatalysis. © 2024, The Authors. All rights reserved.

关键词： Electrocatalysis

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Towards a Quantum Algorithm for the Incompressible Nonlinear Navier-Stokes Equations

Towards a Quantum Algorithm for the Incompressible Nonlinear...

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Quantum Computing and engineering (QCE), IEEE International Conference on

作者： Muralikrishnan Gopalakrishnan Meena Yu Zhang Weiwen Jiang Youzuo Lin Stefanie Günther Xinfeng Gao National Center for Computational Sciences Oak Ridge National Laboratory Oak Ridge TN USA Theoretical Division Los Alamos National Laboratory Los Alamos NM USA Department of Electrical and Computer Engineering George Mason University Fairfax VA USA School of Data Science and Society University of North Carolina at Chapel Hill Chapel Hill NC USA Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore CA USA Department of Mechanical and Aerospace Engineering University of Virginia Charlottesville VA USA

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

In this work, we present novel concepts for quantum algorithms to solve transient, nonlinear partial differential equations (PDEs). The challenge lies in how to effectively represent, encode, process, and evolve the nonlinear system of PDEs on quantum computers. We will discuss the new techniques using the incompressible Navier-Stokes equations as an example, because it represents the fundamental nonlinear feature and yet removes certain complexity in physics, allowing us to focus on the design of quantum algorithms. Previous attempts solving nonlinear PDEs in quantum computation have often involved storing multiple copies of solutions or employing linearizations. Neither is practical due to exponential scaling with evolution time or insufficient solution accuracy. We propose a new framework based on matrix product states (MPSs) and matrix product operators (MPOs), in addition to the Krylov subspace methods. For example, the solution variables of the Navier-Stokes equations are represented by MPSs, and the linear and nonlinear terms are processed by MPOs. The time evolution of the operators is attained by a fast-forwarding algorithm using Krylov subspace methods. Furthermore, we discuss various techniques for efficient encoding of MPSs, measurement reduction for MPOs, and use of tensor operations to treat multi-variate, multi-physics characteristics of Navier-Stokes.

关键词： computers Quantum algorithm Tensors Fluids Partial differential equations Encoding Transient analysis Nonlinear systems Physics Navier-Stokes equations

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High-dimensional orbital angular momentum multiplexing nonlinear holography

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Advanced Photonics 2021年第1期3卷 40-46页

作者： Xinyuan Fang Haocheng Yang Wenzhe Yao Tianxin Wang Yong Zhang Min Gu Min Xiaoa Nanjing University College of Engineering and Applied SciencesSchool of PhysicsNational Laboratory of Solid State MicrostructuresNanjingChina University of Shanghai for Science and Technology School of Optical-Electrical and Computer EngineeringCentre for Artificial-Intelligence NanophotonicsShanghaiChina University of Arkansas Department of PhysicsFayettevilleArkansasUnited States

Nonlinear holography has been identified as a vital platform for optical multiplexing holography because of the appearance of new optical ***,due to nonlinear wave coupling in nonlinear optical processes,the nonlinear harmonic field is coupled with the input field,laying a fundamental barrier to independent control of the interacting fields for *** propose and experimentally demonstrate high-dimensional orbital angular momentum(OAM)multiplexing nonlinear holography to overcome this *** dividing the wavefront of the fundamental wave into different orthogonal OAM channels,multiple OAM and polarization-dependent holographic images in both the fundamental wave and second-harmonic wave have been reconstructed independently in the spatial frequency domain through a type-II second harmonic generation ***,this method can be easily extended to cascadedχ2 nonlinear optical processes for multiplexing in more wavelength channels,leading to potential applications in multicasting in optical communications,multiwavelength display,multidimensional optical storage,anticounterfeiting,and optical encryption.

关键词： orbital angular momentum nonlinear holography second harmonic generation multiplexing holography

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Top-k contrast order-preserving pattern mining

arXiv

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arXiv 2023年

作者： Wu, Youxi Meng, Yufei Li, Yan Guo, Lei Zhu, Xingquan Fournier-Viger, Philippe Wu, Xindong School of Artificial Intelligence Hebei University of Technology Tianjin300401 China Shijiazhuang University of Applied Technology Shijiazhuang050081 China School of Economics and Management Hebei University of Technology Tianjin300401 China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300401 China Department of Computer & Electrical Engineering and Computer Science Florida Atlantic University FL33431 United States Shenzhen University Shenzhen China Research Center for Knowledge Engineering The Zhejiang Lab Hangzhou311121 China

Recently, order-preserving pattern (OPP) mining, a new sequential pattern mining method, has been proposed to mine frequent relative orders in a time series. Although frequent relative orders can be used as features to classify a time series, the mined patterns do not reflect the differences between two classes of time series well. To effectively discover the differences between time series, this paper addresses the top-k contrast OPP (COPP) mining and proposes a COPP-Miner algorithm to discover the top-k contrast patterns as features for time series classification, avoiding the problem of improper parameter setting. COPP-Miner is composed of three parts: extreme point extraction to reduce the length of the original time series, forward mining, and reverse mining to discover COPPs. Forward mining contains three steps: group pattern fusion strategy to generate candidate patterns, the support rate calculation method to efficiently calculate the support of a pattern, and two pruning strategies to further prune candidate patterns. Reverse mining uses one pruning strategy to prune candidate patterns and consists of applying the same process as forward mining. Experimental results validate the efficiency of the proposed algorithm and show that top-k COPPs can be used as features to obtain a better classification performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Time series

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Learning A Coarse-to-Fine Diffusion Transformer for Image Restoration

arXiv

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arXiv 2023年

作者： Wang, Liyan Yang, Qinyu Wang, Cong Wang, Wei Pan, Jinshan Su, Zhixun School of Mathematical Sciences Dalian University of Technology Dalian China The Key Laboratory for Computational Mathematics and Data Intelligence of Liaoning Province The Liaoning Center for Applied Mathematics China The Department of Computing The Hong Kong Polytechnic University Hong Kong Dalian University of Technology Dalian China The School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

Recent years have witnessed the remarkable performance of diffusion models in various vision tasks. However, for image restoration that aims to recover clear images with sharper details from given degraded observations, diffusion-based methods may fail to recover promising results due to inaccurate noise estimation. Moreover, simple constraining noises cannot effectively learn complex degradation information, which subsequently hinders the model capacity. To solve the above problems, we propose a coarse-to-fine diffusion Transformer (C2F-DFT) for image restoration. Specifically, our C2F-DFT contains diffusion self-attention (DFSA) and diffusion feed-forward network (DFN) within a new coarse-to-fine training scheme. The DFSA and DFN respectively capture the long-range diffusion dependencies and learn hierarchy diffusion representation to facilitate better restoration. In the coarse training stage, our C2FDFT estimates noises and then generates the final clean image by a sampling algorithm. To further improve the restoration quality, we propose a simple yet effective fine training scheme. It first exploits the coarse-trained diffusion model with fixed steps to generate restoration results, which then would be constrained with corresponding ground-truth ones to optimize the models to remedy the unsatisfactory results affected by inaccurate noise estimation. Extensive experiments show that C2F-DFT significantly outperforms diffusion-based restoration method IR-SDE and achieves competitive performance compared with Transformer-based state-of-the-art methods on 3 tasks, including image deraining, image deblurring, and real image denoising. Code is available at https://***/wlydlut/C2F-DFT. Copyright © 2023, The Authors. All rights reserved.

关键词： Image reconstruction

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Validating a Chaos Based PRBG Under Different Chaotic Maps

Validating a Chaos Based PRBG Under Different Chaotic Maps

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Panhellenic Conference on Electronics and Telecommunications (PACET)

作者： Lazaros Moysis Marcin Lawnik Christos Volos Murilo S. Baptista George F. Fragulis Sotirios K. Goudos Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Physics Department Laboratory of Nonlinear Systems - Circuits & Complexity Aristotle University of Thessaloniki Thessaloniki Greece Department of Mechanical Engineering University of Western Macedonia Kozani Greece Department of Mathematics Applications and Methods for Artificial Intelligence Faculty of Applied Mathematics Silesian University of Technology Gliwice Poland Department of Physics Institute for Complex Systems and Mathematical Biology SUPA University of Aberdeen Aberdeen UK Physics Department ELEDIA@AUTH Aristotle University of Thessaloniki Thessaloniki Greece

Chaos-based random bit generators are abundantly used in chaos-based encryption and security applications, as a fast, deterministic source of randomness, to perform actions like permutation and substitution. The chaotic map used as a source of randomness though may heavily affect the bit generator’s statistical randomness, key space, and speed. Thus, any given bit generator may not be equally efficient for all the chaotic maps used as a seed. Motivated by this, the present work considers a bit generator that utilizes a common modulo-based hashing technique, and studies its performance under a plethora of different chaotic maps. For each map, the generator is evaluated with respect to randomness, key space, and operations per bit ratio. This analysis can serve as a guideline for random bit generator usage in future chaos-based encryption designs.

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