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33rd British Machine Vision Conference Proceedings, BMVC 2022

作者： Ha, Heonseok Hwang, Uiwon Jang, Jaehee Bae, Ho Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of Department of Cyber Security Ewha Womans University Seoul Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul Korea Republic of

A membership inference attack (MIA) identifies if an instance was included in the victim model's train dataset. Without an appropriate defense mechanism, MIA can result in serious privacy breaches. Although several methods have been proposed to protect membership privacy in discriminative models, research into generative adversarial networks (GANs), remains insufficient despite their vulnerability to MIAs. In this study, we propose a membership privacy-preserving GAN (MP-GAN), which plays an additional adversarial game for membership privacy between an auxiliary membership inference network M and a GAN. M seeks to find out whether an instance belongs to the reference or train dataset, whereas the generator and discriminator of the GAN attempt to deceive M. Our theoretical analysis results demonstrate that the MP-GAN improves membership privacy by not learning sample-specific features. We perform extensive empirical evaluations to show that the MP-GAN can successfully defend against MIAs under advantageous scenarios to the attacker (for example, white-box access to networks and small training dataset size). Furthermore, we demonstrate that the MP-GAN has several advantages over other privacy-preserving GAN training techniques. © 2022. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

关键词： Generative adversarial networks

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Performance Analysis of Economic Model Predictive Control on a Solar-Powered Direct Contact Membrane Distillation System

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IFAC-PapersOnLine 2022年第33期55卷 13-19页

作者： Hichem Bessafa Ghania Khodja Chakir Messaoud Taous-Meriem Laleg-Kirati Electrical and Computer Engineering Program. Computer Electrical and Mathematical Science and Engineering (CEMSE) division. King Abdullah University of Science and Technology (KAUST) Ecole Polytechnique of Algiers Algeria National Institute for Research in Digital Science and Technology (INRIA) Paris-Saclay France

This paper proposes an economic model predictive control (EMPC) design for a Direct Contact Membrane Distillation powered by a solar collector system which aims at enhancing its economical performances. A differential algebraic equations-based model is used for the design of the EMPC control. Moreover, a nonlinear observer is developed for the estimation of the unmeasured state. A neural network is proposed to predict the unknown solar irradiance for future horizon where a solar model provides temperature predictions. The proposed control design has been validated in simulation using data provided by a partial differential equation-based model mimicking the real plant.

关键词： Direct contact membrane distillation Economic model predictive control differential algebraic equations nonlinear observer Long-short term memory neural network

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Reconfiguration of Power Distribution Systems in the Presence of Voltage-Dependent Loads

Reconfiguration of Power Distribution Systems in the Presenc...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Meisam Mahdavi Konrad Schmitt Stephen Bayne Manohar Chamana Department of Electrical Engineering University of Jaén Linares Jaén Spain Electrical and Computer Engineering Department Texas Tech University Lubbock TX USA Renewable Energy Program Texas Tech University Lubbock TX USA

Load power can be changed by voltage fluctuation of network buses, in which reconfiguring the topology of distribution systems impacts the magnitude of bus voltages. Only a few papers have considered voltage-dependent loads in their reconfiguration models but introduced nonlinear formulations or highly approximated linear approaches. While nonlinear models can be solved by metaheuristic algorithms without guaranteeing the optimality of solutions or may be implemented by commercial nonlinear solvers, they require intensive and time-consuming computations. Moreover, linearized models are highly approximated by piecewise linear functions with many unknown parameters. Thus, this paper presents the effective design of the reconfiguration problems, including voltage dependency of loads, which can be solved using commercial linear solvers. The proposed models are precise enough to find accurate results for the reconfiguration problem and are adequately fast to converge to optimal solutions. The results show that the proposed solutions not only reduce active power losses significantly but increase the minimum voltage of the system effectively.

关键词： Voltage fluctuations Fluctuations Network topology Computational modeling Metaheuristics Power distribution Distribution networks

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Safe CoR: A Dual-Expert Approach to Integrating Imitation Learning and Safe Reinforcement Learning Using Constraint Rewards

arXiv

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arXiv 2024年

作者： Kwon, Hyeokjin Lee, Gunmin Lee, Junseo Oh, Songhwai Interdisciplinary Program in Artificial Intelligence ASRI Seoul National University Seoul08826 Korea Republic of Department of Electrical and Computer Engineering ASRI Seoul National University Seoul08826 Korea Republic of

In the realm of autonomous agents, ensuring safety and reliability in complex and dynamic environments remains a paramount challenge. Safe reinforcement learning addresses these concerns by introducing safety constraints, but still faces challenges in navigating intricate environments such as complex driving situations. To overcome these challenges, we present the safe constraint reward (Safe CoR) framework, a novel method that utilizes two types of expert demonstrations—reward expert demonstrations focusing on performance optimization and safe expert demonstrations prioritizing safety. By exploiting a constraint reward (CoR), our framework guides the agent to balance performance goals of reward sum with safety constraints. We test the proposed framework in diverse environments, including the safety gym, metadrive, and the real-world Jackal platform. Our proposed framework enhances the performance of algorithms by 39% and reduces constraint violations by 88% on the real-world Jackal platform, demonstrating the framework’s efficacy. Through this innovative approach, we expect significant advancements in real-world performance, leading to transformative effects in the realm of safe and reliable autonomous agents. Copyright © 2024, The Authors. All rights reserved.

关键词： Demonstrations

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Magnetoelastic coupling driven magnon gap in a honeycomb antiferromagnet

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Physical Review B 2024年第10期110卷 104419页

作者： Jeongheon Choe Andrey Baydin Gaihua Ye Bowen Ma Cynthia Nnokwe Martin Rodriguez-Vega David Lujan Swati Chaudhary Fuyang Tay Jiaming He Hiroyuki Nojiri Jianshi Zhou Gregory A. Fiete Junichiro Kono Rui He Xiaoqin Li Department of Physics Center of Complex Quantum Systems Center for Dynamics and Control of Materials Department of Electrical and Computer Engineering Smalley–Curl Institute Department of Physics Applied Physics Graduate Program Smalley–Curl Institute Department of Mechanical Engineering Institute for Materials Research Department of Physics and Astronomy Department of Materials Science and NanoEngineering

Cobalt titanate, CoTiO3, is a honeycomb antiferromagnet recently confirmed experimentally to host Dirac magnons, topological spin-orbit excitons, and chiral phonons. Here, we investigate a magnon gap at the zone center which calls for a refined spin Hamiltonian. We propose a microscopic model for the magnon gap and attribute it to a lattice-distortion (phonon)-induced higher-order spin interaction. Strong magnetoelastic coupling in CoTiO3 is also evident in Raman spectra, in which the magnetic order exerts a stronger influence on phonons corresponding to in-plane ionic motions than those with out-of-plane motions. We further examine the evolution of the zone-center magnons in a high magnetic field up to 18.5 T via THz absorption spectroscopy measurements. Based on this field dependence, we propose a spin Hamiltonian that not only agrees with magnon dispersion measured by inelastic neutron scattering but also includes fewer exchange constants and a realistic anisotropy term. Our work highlights the broad implications of magnetoelastic coupling in the study of topologically protected bosonic excitations.

关键词： Magnetism Magnons Topological materials Raman spectroscopy Terahertz spectroscopy

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Fuzzy Art: Pattern Recognition of Wifi Detected Occupancy in Commercial Buildings

Fuzzy Art: Pattern Recognition of Wifi Detected Occupancy in...

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Annual Conference of the Canadian Society of Civil engineering, CSCE 2021

作者： Simma, Krishna Chaitanya Jagadeesh Caudell, Thomas P. Bogus, Susan M. Mammoli, Andrea Department of Civil Construction and Environmental Engineering University of New Mexico AlbuquerqueNMMSC01 1070 United States Department of Electrical and Computer Engineering University of New Mexico AlbuquerqueNMMSC01 1070 United States Customer Technologies Program Electric Power Research Institute 3420 Hillview Ave Palo AltoCA94304 United States

ISBN: (纸本)9789811905025

Research that predicts occupancy patterns in commercial buildings has gained in significance ever since the influence of occupants on building energy consumption became evident. Studies have employed a variety of sensory systems to collect the occupancy data and understand human behaviors throughout buildings. However, establishing a dedicated sensor network to collect occupancy data can become expensive for building owners. In this context, obtaining occupancy data from an existing WiFi network could eliminate the cost concerns. Data within the WiFi routers provide sufficient information for accurate estimates of occupancy. To estimate occupancy levels, this work proposes to learn and recognize WiFi connection using an Adaptive Resonance Theory (ART) artificial neural network. A detailed understanding of occupancy patterns using the WiFi data is helpful for developing heating and cooling schedules that optimize HVAC energy consumption. For this study, occupancy data was collected over a 17-week semester at the University of New Mexico using existing WiFi routers located in a large lecture hall used by multiple classes. This data was used to learn patterns of repetition using the neural network. The results show that if the 24-h occupancy profiles can be subdivided into smaller time segments defined by external schedules such as lecture start and end times or other constraints, significant patterns can be detected. A detailed understanding of these patterns can greatly facilitate occupancy load forecasting for effective building management (e.g. HVAC operation). © 2023, Canadian Society for Civil engineering.

关键词： Office buildings

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Enhancing Fault Diagnosis of Uncertain Grid-Connected Photovoltaic Systems using Deep GRU-based Bayesian optimization

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IFAC-PapersOnLine 2024年第4期58卷 449-454页

作者： Zahra Yahyaoui Mansour Hajji Majdi Mansouri Abdelmalek Kouadri Kais Bouzrara Hazem Nounou Research Unit Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kasserine 1200 Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Signals and Systems Laboratory Institute of Electrical and Electronics Engineering University M Hamed Bougara of Boumerdes Boumerdes Algeria Laboratory of Automatic Signal and Image Processing National Engineering School of Monastir Tunisia

The efficacy of photovoltaic systems is significantly impacted by electrical production losses attributed to faults. Ensuring the rapid and cost-effective restoration of system efficiency necessitates robust fault detection and diagnosis (FDD) procedures. This study introduces a novel interval-gated recurrent unit (I-GRU) based Bayesian optimization framework for FDD in grid-connected photovoltaic (GCPV) systems. The utilization of an interval-valued representation is proposed to address uncertainties inherent in the systems, the GRU is employed for fault classification, while the Bayesian algorithm optimizes its hyperparameters. Addressing uncertainties through the proposed approach enhances monitoring capabilities, mitigating computational and storage costs associated with sensor uncertainties. The effectiveness of the proposed approach for FDD in GCPV systems is demonstrated using experimental application.

关键词： Bayesian optimization fault detection fault diagnosis gated recurrent units grid-connected PV systems interval-data representation uncertainties

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Antithetic Multilevel Particle Filters

arXiv

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arXiv 2023年

作者： Jasra, Ajay Maama, Mohamed Ombao, Hernando Applied Mathematics and Computational Science Program United States Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

In this paper we consider the filtering of partially observed multi-dimensional diffusion processes that are observed regularly at discrete times. This is a challenging problem which requires the use of advanced numerical schemes based upon time-discretization of the diffusion process and then the application of particle filters. Perhaps the state-of-the-art method for moderate dimensional problems is the multilevel particle filter of [12]. This is a method that combines multilevel Monte Carlo and particle filters. The approach in that article is based intrinsically upon an Euler discretization method. We develop a new particle filter based upon the antithetic truncated Milstein scheme of [10]. We show that for a class of diffusion problems, for ϵ > 0 given, that the cost to produce a mean square error (MSE) in estimation of the filter, of O(ϵ2) is O(ϵ−2 log(ϵ)2). In the case of multidimensional diffusions with non-constant diffusion coefficient, the method of [12] has a cost of O(ϵ−2.5) to achieve the same MSE. We support our theory with numerical results in several examples. Copyright © 2023, The Authors. All rights reserved.

关键词： Mean square error

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Deformable Image Registration Using Vision Transformers for Cardiac Motion Estimation from Cine Cardiac MRI Images 1

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Functional Imaging and Modeling of the Heart - 12th International Conference, FIMH 2023, Proceedings

作者： Upendra, Roshan Reddy Simon, Richard Shontz, Suzanne M. Linte, Cristian A. Center for Imaging Science Rochester Institute of Technology RochesterNY United States Biomedical Engineering Rochester Institute of Technology RochesterNY United States Electrical Engineering and Computer Science University of Kansas LawrenceKS United States Bioengineering Program University of Kansas LawrenceKS United States Institute for Information Sciences University of Kansas LawrenceKS United States

ISBN: (数字)9783031353024

ISBN: (纸本)9783031353017

Accurate cardiac motion estimation is a crucial step in assessing the kinematic and contractile properties of the cardiac chambers, thereby directly quantifying the regional cardiac function, which plays an important role in understanding myocardial diseases and planning their treatment. Since the cine cardiac magnetic resonance imaging (MRI) provides dynamic, high-resolution 3D images of the heart that depict cardiac motion throughout the cardiac cycle, cardiac motion can be estimated by finding the optical flow representation between the consecutive 3D volumes from a 4D cine cardiac MRI dataset, thereby formulating it as an image registration problem. Therefore, we propose a hybrid convolutional neural network (CNN) and Vision Transformer (ViT) architecture for deformable image registration of 3D cine cardiac MRI images for consistent cardiac motion estimation. We compare the image registration results of our proposed method with those of the VoxelMorph CNN model and conventional B-spline free form deformation (FFD) non-rigid image registration algorithm. We conduct all our experiments on the open-source Automated Cardiac Diagnosis Challenge (ACDC) dataset. Our experiments show that the deformable image registration results obtained using the proposed method outperform the CNN model and the traditional FFD image registration method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image registration

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Effective Dimension Aware Fractional-Order Stochastic Gradient Descent for Convex Optimization Problems

arXiv

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arXiv 2025年

作者： Partohaghighi, Mohammad Marcia, Roummel Chen, YangQuan Electrical Engineering and Computer Science graduate program University of California Merced United States Department of Applied Mathematics University of California Merced MercedCA United States Lab Department of Mechanical Engineering School of Engineering University of California MercedCA95343 United States

Fractional-order stochastic gradient descent (FOSGD) leverages a fractional exponent to capture long-memory effects in optimization, yet its practical impact is often constrained by the difficulty of tuning and stabilizing this exponent. In this work, we introduce 2SED Fractional-Order Stochastic Gradient Descent (2SEDFOSGD), a novel method that synergistically combines the Two-Scale Effective Dimension (2SED) algorithm with FOSGD to automatically calibrate the fractional exponent in a data-driven manner. By continuously gauging model sensitivity and effective dimensionality, 2SED dynamically adjusts the exponent to curb erratic oscillations and enhance convergence rates. Theoretically, we demonstrate how this dimension-aware adaptation retains the benefits of fractional memory while averting the sluggish or unstable behaviors frequently observed in naive fractional SGD. Empirical evaluations across multiple benchmarks confirm that our 2SED-driven fractional exponent approach not only converges faster but also achieves more robust final performance, suggesting broad applicability for fractional-order methodologies in large-scale machine learning and related domains. © 2025, CC BY.

关键词： Convex optimization

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