Enhanced State-of-Charge Balancing Control for MMC-SCES Using Centralized Controllers and Adaptive Optimization by Qiluan Yang, Huangqing Xiao, Qionghai Zhu

“…The implementation of the reduced switching frequency voltage balancing algorithm reduced the average switching frequency by 94.54% while maintaining the maximum SOC difference below 0.50%. …”
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Transient Time Reduction in Time-Varying Digital Filters via Second-Order Section Optimization by Piotr Okoniewski, Jacek Piskorowski

“…By employing a numerical optimization approach, we selectively adjust the coefficients of a single SOS within a higher-order filter to minimize the transient period while maintaining overall stability. Using a sequential quadratic programming (SQP) algorithm, we determine a time-varying coefficient trajectory over a finite horizon, ensuring a rapid convergence to steady-state behavior. …”
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Minimum Error Integration Method for Quadrilateral Flat Plate Fitting in Steel Construction by Zhuoju Huang, Jiemin Ding

“…While traditional approaches that rely on empirical craftsmanship or least-squares fitting lack precision, this study proposes a minimum error integration method that integrates Lagrange interpolation-based error estimation with an adaptive step-size steepest descent algorithm to reduce fitting error. …”
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Crystal structure prediction based on diffusion model and graph network optimization by Tao Hong, Jiong Yang, Guixin Cao

“…In this work, we propose a crystal structure prediction method called DiffOA, which combines a diffusion model with an optimization algorithm based on GNNs. By generating atomic coordinates through a diffusion process and minimizing the formation energy through an optimization process, DiffOA effectively reduces the searching space required for crystal structure prediction while respecting both the minimum energy principle and the symmetry of crystal structures. …”
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An Optimization Framework for Allocating and Scheduling Multiple Tasks of Multiple Logistics Robots by Byoungho Choi, Minkyu Kim, Heungseob Kim

“…To address this, we propose a five-step optimization framework that reduces computation time while maintaining practical applicability. The first step calculates and stores distances and paths between product locations using the A* algorithm, enabling reuse in subsequent computations. …”
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Gradient-Informed Pareto-Based Multi-Objective Binary Topology Optimization by Francesco Lucchini, Riccardo Torchio, Piergiorgio Alotto

“…While WS remains a dominant method in TO, GPBTO represents a promising alternative for cases where Pareto-based solutions are desirable. …”
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Towards a Standard Benchmarking Framework for Domain Adaptation in Intelligent Fault Diagnosis by Mohammed M. Farag

“…Domain shift is a major problem facing contemporary data-based intelligent fault diagnosis (IFD) solutions. While domain adaptation (DA) methods have been proposed to address this issue, standardizing DA benchmarks has not received much attention. …”
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Effects of data transformation and model selection on feature importance in microbiome classification data by Zuzanna Karwowska, Oliver Aasmets, Estonian Biobank research team, Tomasz Kosciolek, Elin Org

“…Our findings suggest that while classification is robust across different transformations, the variation in feature selection necessitates caution when using machine learning for biomarker identification. …”
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Autonomous air combat decision making via graph neural networks and reinforcement learning by Lin Huo, Chudi Wang, Yue Han

“…The findings demonstrate that the proposed method can effectively adapt to large-scale air combat environments while achieving high win rates and rapid decision-making performance.…”
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Flexible hardware for teaching and research in renewable energy and off-grid microgrids by Cristian Escudero-Quintero, Juan Pablo Guzman-Rodriguez, Juan Pablo Villegas-Ceballos, Elkin Edilberto Henao-Bravo, Daniel Gonzalez-Montoya

“…The platform enables real-time energy management using FPPT algorithms while providing an intuitive graphical interface for data visualization and system monitoring. …”
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Nonlinear dynamics of the media addiction model using the fractal-fractional derivative technique by Saima Rashid, Rehana Ashraf, Ebenezer Bonyah

“…The Atangana-Baleanu FF differential operators are recently defined in FF differential operators that are applied to characterize the SMA model’s mathematical algorithm. We investigated the numerical behaviour of the SMA in three ways: (i) changing the fractional-order α as well as the fractal-dimension ℘; (ii) changing α while keeping ℘ constant; and (iii) changing ℘ while keeping α constant. …”
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Design of Nonlinear PID and FOPID Controllers for Electronic Throttle Valve Plate’s Position by Mohamed Jasim Mohamed, Luay Thamir Rasheed

“…A metaheuristic optimization technique, namely, the grey wolf optimization (GWO) algorithm, is introduced for optimizing the controllers’ parameters while minimizing the integral of the cube time square error (IT^3SE) cost function. …”
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Distributed Target Detection with Coherent Fusion in Tracking Based on Phase Prediction by Aoya Wang, Jing Lu, Shenghua Zhou, Linhai Wang

“…In order to obtain the superiority of coherent processing while overcoming the real implementation difficulties of a coherent framework, this paper studies a distributed coherent detection algorithm for fusion detection. …”
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AlphaRouter: Bridging the Gap Between Reinforcement Learning and Optimization for Vehicle Routing with Monte Carlo Tree Searches by Won-Jun Kim, Junho Jeong, Taeyeong Kim, Kichun Lee

“…However, an inherent gap exists between computationally driven DRL and optimization-based heuristics. While a DRL algorithm for a certain problem is able to solve several similar problem instances, traditional optimization algorithms focus on optimizing solutions to one specific problem instance. …”
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Evaluating Sparse Inertial Measurement Unit Configurations for Inferring Treadmill Running Motion by Mackenzie N. Pitts, Megan R. Ebers, Cristine E. Agresta, Katherine M. Steele

“…Inertial measurement units (IMUs) are used to analyze running performance. While leveraging one sensor to estimate kinematic and kinetic variables is common, sparsity limits the number of digital biomarkers that can be evaluated. …”
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Optimizing Q-Learning for Automated Cavity Filter Tuning: Leveraging PCA and Neural Networks by Aghanim Amina, Otman Oulhaj, Oukaira Aziz, Lasri Rafik

“…One of the main challenges in this process lies in the nonlinear relationship between screw positions and the filter’s frequency response, making conventional tuning methods difficult and inefficient. Additionally, while intelligent algorithms can assist in tuning, they often require large volumes of simulated data, leading to high computational costs. …”
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A Dynamic Framework for Community-Facility Siting with Inter-Community Competition by Sisi Zhu, Haoying Han, Anran Dai

“…This study proposes a bi-objective multi-scenario dynamic model (BOMSDM) for community-facility siting, which aims to maximize service efficiency and social equity while considering variable facility numbers and inter-community competition. …”
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Trading in the Quantum Era: optimizing Bitcoin gains and energy costs by Simona-Vasilica Oprea, Adela Bâra, Cristian Bucur, Bogdan-George Tudorică, Niculae Oprea

“…This paper presents an in-depth analysis of a Quantum-inspired Multi-objective Optimization Algorithm (QMOA) applied to a unique problem: maximizing trading profits while minimizing energy costs. …”
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Occupant-Centric Visual Comfort Assessment and Optimization of Passive Solar Façade Shading Systems through View-Based Analysis of Daylight Glare Probability, View Content, and Spa... by Martin Ivanov, Jun Sato

“…Two experimental studies validate the approach: the first systematically evaluates multiple predefined shading patterns to identify optimal characteristics, while the second demonstrates that algorithmic optimization of highly irregular shading configurations can simultaneously improve multiple visual comfort metrics, significantly outperforming regular shading patterns in terms of glare reduction, view preservation, and spatial frequency performance.…”
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Bayesian Optimized GridDehazeNet for Adaptive Haze Removal in Real-World Applications by Sungkwan Youm

“…Haze removal is essential for improving image visibility in applications like autonomous vehicles and surveillance. While models like GridDehazeNet enhance dehazing performance, our research emphasizes that selecting appropriate image data and application methods is even more crucial. …”
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