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A self-competitive mutation strategy for Differential Evolution algorithms with applications to Proportional–Integral–Derivative controllers and Automatic Voltage Regulator systems

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Decision Analytics Journal 2023年 7卷

作者： Ghasemi, Mojtaba Rahimnejad, Abolfazl Gil, Milad Akbari, Ebrahim Gadsden, S. Andrew Department of Electronics and Electrical Engineering Shiraz University of Technology Shiraz Iran Department of Mechanical Engineering McMaster University Hamilton Canada Department of Electrical and Computer Engineering Babol Noshirvani University of Technology Babol Iran Department of Mathematics Faculty of Science University of Hradec Králové Czech Republic

The Differential Evolution (DE) algorithm is a powerful and simple optimizer for solving various optimization problems. Based on the literature, DE has shown suitable performance in exploring search spaces and locating global optimums. However, it is typically slow in extracting the problem solution. In this paper, the exploration ability of the DE algorithm is augmented with a competitive control parameter ω based on the value of the objective function of the mutating members. A new mutation strategy is introduced, subtracting weaker members from superior weaker ones. The proposed DE algorithm, which is referred to as the self-competitive DE, has been employed for solving real-world optimization problems. Several DE algorithms are enhanced with the proposed parameter ω, and the efficiencies of the resulting enhanced algorithms are tested. Furthermore, the optimal Proportional–Integral–Derivative (PID) controller tuning for an Automatic Voltage Regulator (AVR) system is used to investigate the effectiveness of the proposed strategy in solving real-world optimization problems. Simulation results demonstrate a good performance of the proposed parameter ω over several other well-known DE algorithms. © 2023 The Authors

关键词： Automatic Voltage Regulator Differential Evolution Optimization Proportional–Integral–Derivative Real parameter test functions Self-competitive mutation

Enhancing Epileptic Seizure Detection with EEG Feature Embeddings

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arXiv 2023年

作者： Zarei, Arman Zhu, Bingzhao Shoaran, Mahsa Institute of Electrical and Micro Engineering Neuro-X Institute EPFL Lausanne Switzerland Department of Computer Science University of Maryland College ParkMD United States Department of Applied and Engineering Physics Cornell University IthacaNY United States

Epilepsy is one of the most prevalent brain disorders that disrupts the lives of millions worldwide. For patients with drug-resistant seizures, there exist implantable devices capable of monitoring neural activity, promptly triggering neurostimulation to regulate seizures, or alerting patients of potential episodes. Next-generation seizure detection systems heavily rely on high-accuracy machine learning-based classifiers to detect the seizure onset. Here, we propose to enhance the seizure detection performance by learning informative embeddings of the EEG signal. We empirically demonstrate, for the first time, that converting raw EEG signals to appropriate embeddings can significantly boost the performance of seizure detection algorithms. Importantly, we show that embedding features, which converts the raw EEG into an alternative representation, is beneficial for various machine learning models such as Logistic Regression, Multi-Layer Perceptron, Support Vector Machines, and Gradient Boosted Trees. The experiments were conducted on the CHB-MIT scalp EEG dataset. With the proposed EEG feature embeddings, we achieve significant improvements in sensitivity, specificity, and AUC score across multiple models. By employing this approach alongside an SVM classifier, we were able to attain state-of-the-art classification performance with a sensitivity of 100% and specificity of 99%, setting a new benchmark in the field. Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

Achieving Logarithmic Regret via Hints in Online Learning of Noisy LQR Systems

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Achieving Logarithmic Regret via Hints in Online Learning of...

IEEE Conference on Decision and Control

作者： Mohammad Akbari Bahman Gharesifard Tamas Linder Department of Mathematics and Statistics Queen’s University Kingston ON Canada Department of Electrical and Computer Engineering University of California Los Angeles CA USA

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

We consider the problem of online adaptive control of a linear-quadratic system, where the true system transition parameters (matrices A and B) are unknown. The objective is to design and analyze algorithms that generate control policies with sublinear "regret", defined as the difference between the cumulative cost of the policies generated by the algorithm and the cumulative cost of the optimal policy. Recent studies show that when the system parameters are fully unknown, for any algorithm that only uses data from the past system trajectory, there is a choice of system parameters such that the algorithm at best achieves a square root regret, providing a hard fundamental limit on the achievable regret in general. However, it is known that (poly)-logarithmic regret is achievable when only matrix A or only matrix B is unknown. We prove a result, encompassing both of these scenarios, showing that (poly)logarithmic regret is achievable when both of these matrices are unknown, but a hint about them is given to the learner over time. 1

关键词： Costs System dynamics Perturbation methods Trajectory Noise measurement Adaptive control

Survival Prediction of Heart Failure Patients using Stacked Ensemble Machine Learning Algorithm 7

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Survival Prediction of Heart Failure Patients using Stacked ...

7th IEEE International Women in engineering (WIE) Conference on electrical and computer engineering, WIECON-ECE 2021

作者： Mehedi Zaman, S.M. Qureshi, Wasay Mahmood Raihan, Md. Mohsin Sarker Shams, Abdullah Bin Sultana, Sharmin Islamic University of Technology Department of Electrical and Electronic Engineering Gazipur1704 Bangladesh Khulna University of Engineering & Technology Department of Biomedical Engineering Khulna9203 Bangladesh University of Toronto Department of Electrical & Computer Engineering TorontoM5S 3G4 Canada Inland Norway University of Applied Sciences Agricultural Sciences and Biotechnology Department of Applied Ecology Holsetgata 31 Hamar2318 Norway

ISBN: (纸本)9781665478496

Heart failure (HF) is the leading cause of global death from chronic diseases. Data mining using machine learning (ML) converts massive volumes of raw data created by healthcare institutions into meaningful information that can aid in making predictions and crucial decisions. After an HF, collecting and analyzing follow-up data from patients is critical to monitor their health recovery. The aim of this study is to use ML and predict the survival possibility of patients after HF based on the follow-up data. Three supervised classifiers i.e., Random Forest (RF), XGBoost (XGB), and Decision Tree (DT) have been used in our study. Moreover, we proposed to design a supervised stacked ensemble learning model that can achieve a prediction accuracy, precision, recall, and F1 score of 99.98%, 100%, 99.98%, and 99.98%, respectively. The ensemble structure has three base learners (DT, RF & XGB) and one meta learner (RF). The integration of multiple algorithms boosted the prediction performance, which is a significant improvement from the other contemporary studies. Our results demonstrate that Ensemble ML can be a powerful intervention tool to predict accurately, beforehand, the recovery status of a chronic HF patient and prevent potential fatalities. © 2021 IEEE.

关键词： Decision trees

Convergent regularization in inverse problems and linear plug-and-play denoisers

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arXiv 2023年

作者： Hauptmann, Andreas Mukherjee, Subhadip Schönlieb, Carola-Bibiane Sherry, Ferdia The Research Unit of Mathematical Sciences University of Oulu Finland The Department of Computer Science University College London United Kingdom The Dept. of Electronics and Electrical Communication Engineering IIT Kharagpur India The Dept. of Applied Mathematics and Theoretical Physics University of Cambridge United Kingdom

Plug-and-play (PnP) denoising is a popular iterative framework for solving imaging inverse problems using off-the-shelf image denoisers. Their empirical success has motivated a line of research that seeks to understand the convergence of PnP iterates under various assumptions on the denoiser. While a significant amount of research has gone into establishing the convergence of the PnP iteration for different regularity conditions on the denoisers, not much is known about the asymptotic properties of the converged solution as the noise level in the measurement tends to zero, i.e., whether PnP methods are provably convergent regularization schemes under reasonable assumptions on the denoiser. This paper serves two purposes: first, we provide an overview of the classical regularization theory in inverse problems and survey a few notable recent data-driven methods that are provably convergent regularization schemes. We then continue to discuss PnP algorithms and their established convergence guarantees. Subsequently, we consider PnP algorithms with linear denoisers and propose a novel spectral filtering technique to control the strength of regularization arising from the denoiser. Further, by relating the implicit regularization of the denoiser to an explicit regularization functional, we rigorously show that PnP with linear denoisers leads to a convergent regularization scheme. More specifically, we prove that in the limit as the noise vanishes, the PnP reconstruction converges to the minimizer of a regularization potential subject to the solution satisfying the noiseless operator equation. The theoretical analysis is corroborated by numerical experiments for the classical inverse problem of tomographic image reconstruction. Copyright © 2023, The Authors. All rights reserved.

关键词： Inverse problems

Splitting Guarantees for Prophet Inequalities via Nonlinear Systems

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arXiv 2024年

作者： Brustle, Johannes Perez-Salazar, Sebastian Verdugo, Victor Department of Computer and Systems Sciences Sapienza University of Rome Italy Department of Computational Applied Mathematics and Operations Research Rice University United States Institute for Mathematical and Computational Engineering Pontificia Universidad Católica de Chile Chile Department of Industrial and Systems Engineering Pontificia Universidad Católica de Chile Chile

The prophet inequality is one of the cornerstone problems in optimal stopping theory and has become a crucial tool for designing sequential algorithms in Bayesian settings. In the i.i.d. k-selection prophet inequality problem, we sequentially observe n non-negative random values sampled from a known distribution. Each time, a decision is made to accept or reject the value, and under the constraint of accepting at most k. For k = 1, Hill and Kertz [Ann. Probab. 1982] provided an upper bound on the worst-case approximation ratio that was later matched by an algorithm of Correa et al. [Math. Oper. Res. 2021]. The worst-case tight approximation ratio for k = 1 is computed by studying a differential equation that naturally appears when analyzing the optimal dynamic programming policy. A similar result for k > 1 has remained elusive. In this work, we introduce a nonlinear system of differential equations for the i.i.d. k-selection prophet inequality that generalizes Hill and Kertz's equation when k = 1. Our nonlinear system is defined by k constants that determine its functional structure, and their summation provides a lower bound on the optimal policy's asymptotic approximation ratio for the i.i.d. k-selection prophet inequality. To obtain this result, we introduce for every k an infinite-dimensional linear programming formulation that fully characterizes the worst-case tight approximation ratio of the k-selection prophet inequality problem for every n, and then we follow a dual-fitting approach to link with our nonlinear system for sufficiently large values of n. As a corollary, we use our provable lower bounds to establish a tight approximation ratio for the stochastic sequential assignment problem in the i.i.d. non-negative regime. Copyright © 2024, The Authors. All rights reserved.

关键词： Nonlinear systems

Predicting the Parabolic Growth Rate Constant for High-Temperature Oxidation of Steels Using Machine Learning Models

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SSRN

SSRN 2023年

作者： Aghaeian, Soroush Nourouzi, Farshid Sloof, Wim G. Mol, Arjan J.M.C. Böttger, Amarante J. Department of Materials Science and Engineering Faculty of Mechanical Maritime and Materials Delft University of Technology Mekelweg 2 Delft2628 CD Netherlands Department of Electrical Sustainable Energy Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Mekelweg 4 Delft2628 CD Netherlands

Predicting the high-temperature oxidation kinetics of different steel grades is a complex challenge that has not been fully addressed yet. In this work, a data analytics approach is presented to predict the parabolic growth rate constant (kp) of high-temperature oxidation of steels using published data. Experimental data are collected for more than 70 different steel grades exposed to temperatures between 500 and 1200 C in air. © 2023, The Authors. All rights reserved.

关键词： Forecasting

Trainable and Explainable Simplicial Map Neural Networks

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arXiv 2023年

作者： Paluzo-Hidalgo, Eduardo Gonzalez-Diaz, Rocio Gutiérrez-Naranjo, Miguel A. Department of Quantitative Methods Universidad Loyola Andalucía Campus Sevilla Dos Hermanas Seville Spain Department of Applied Mathematics I School of engineering University of Seville Seville Spain Department of Computer Science and Artificial Intelligence School of Engineering University of Seville Seville Spain

Simplicial map neural networks (SMNNs) are topology-based neural networks with interesting properties such as universal approximation ability and robustness to adversarial examples under appropriate conditions. However, SMNNs present some bottlenecks for their possible application in high-dimensional datasets. First, SMNNs have precomputed fixed weight and no SMNN training process has been defined so far, so they lack generalization ability. Second, SMNNs require the construction of a convex polytope surrounding the input dataset. In this paper, we overcome these issues by proposing an SMNN training procedure based on a support subset of the given dataset and replacing the construction of the convex polytope by a method based on projections to a hypersphere. In addition, the explainability capacity of SMNNs and effective implementation are also newly introduced in this paper. © 2023, CC BY.

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High-Temperature Strong Nonreciprocal Thermal Radiation from Semiconductors

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arXiv 2025年

作者： Nabavi, Bardia Ghalekohneh, Sina Jafari Shayegan, Komron J. Tervo, Eric J. Atwater, Harry Zhao, Bo Department of Mechanical and Aerospace Engineering University of Houston HoustonTX77204 United States Thomas J. Watson Laboratory of Applied Physics California Institute of Technology PasadenaCA91125 United States Department of Electrical Engineering California Institute of Technology PasadenaCA91125 United States Department of Electrical and Computer Engineering University of Wisconsin-Madison MadisonWI53706 United States Department of Mechanical Engineering University of Wisconsin-Madison MadisonWI53706 United States

Nonreciprocal thermal emitters that break the conventional Kirchhoff's law allow independent control of emissivity and absorptivity and promise exciting new functionalities in controlling heat flow for thermal and energy applications. In enabling some of these applications, nonreciprocal thermal emitters will unavoidably need to serve as hot emitters. Leveraging magneto-optical effects, degenerate semiconductors have been demonstrated as a promising optical material platform for nonreciprocal thermal radiation. However, existing modeling and experimental efforts are limited to near room temperature ( © 2025, CC BY.

关键词： Temperature distribution