Xingxu Zhu¹, Guiqing Zhao¹, Gangui Yan¹, Junhui Li^1,*, Hongda Dong², Chenggang Li²

Energy Engineering, Vol.122, No.5, pp. 1645-1668, 2025, DOI:10.32604/ee.2025.063192 - 25 April 2025

Abstract The participation of wind farms in the former energy market faces challenges such as power fluctuations and energy storage construction costs. To this end, this paper proposes a joint energy storage operation scheme for multiple wind farms based on a leasing model, which assists wind farms in bidding for participation in the former energy market through leasing services, thereby enhancing energy storage efficiency and maximizing economic benefits. In this paper, based on the Weibull probability distribution to portray the uncertainty of wind power, and considering the lifetime capacity loss caused by charging and discharging of… More > Graphic Abstract

Yoshihiro Kanno^1,*, Izuru Takewaki²

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 115-152, 2025, DOI:10.32604/cmes.2025.061551 - 11 April 2025

Abstract Response analysis of structures involving non-probabilistic uncertain parameters can be closely related to optimization. This paper provides a review on optimization-based methods for uncertainty analysis, with focusing attention on specific properties of adopted numerical optimization approaches. We collect and discuss the methods based on nonlinear programming, semidefinite programming, mixed-integer programming, mathematical programming with complementarity constraints, difference-of-convex programming, optimization methods using surrogate models and machine learning techniques, and metaheuristics. As a closely related topic, we also overview the methods for assessing structural robustness using non-probabilistic uncertainty modeling. We conclude the paper by drawing several remarks through More >

Anandhavalli Muniasamy^1,*, Ashwag Alasmari²

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 569-592, 2025, DOI:10.32604/cmes.2025.060484 - 11 April 2025

Abstract The effective and timely diagnosis and treatment of ocular diseases are key to the rapid recovery of patients. Today, the mass disease that needs attention in this context is cataracts. Although deep learning has significantly advanced the analysis of ocular disease images, there is a need for a probabilistic model to generate the distributions of potential outcomes and thus make decisions related to uncertainty quantification. Therefore, this study implements a Bayesian Convolutional Neural Networks (BCNN) model for predicting cataracts by assigning probability values to the predictions. It prepares convolutional neural network (CNN) and BCNN models. More > Graphic Abstract

Zhihui Feng¹, Jun Zhang¹, Jun Lu¹, Zhongdan Zhang¹, Wangwang Bai¹, Long Ma¹, Haonan Lu², Jie Lin^2,*

Energy Engineering, Vol.122, No.4, pp. 1531-1560, 2025, DOI:10.32604/ee.2025.060795 - 31 March 2025

Abstract In the background of the low-carbon transformation of the energy structure, the problem of operational uncertainty caused by the high proportion of renewable energy sources and diverse loads in the integrated energy systems (IES) is becoming increasingly obvious. In this case, to promote the low-carbon operation of IES and renewable energy consumption, and to improve the IES anti-interference ability, this paper proposes an IES scheduling strategy that considers CCS-P2G and concentrating solar power (CSP) station. Firstly, CSP station, gas hydrogen doping mode and variable hydrogen doping ratio mode are applied to IES, and combined with… More >

Can Wu^1,2, Wenyi Tang², Yunbo Rao^1,2,*, Yinjie Chen¹, Hui Ding², Shuzhen Zhu³, Yuanyuan Wang³

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1415-1434, 2025, DOI:10.32604/cmc.2025.059797 - 26 March 2025

Abstract Infrared unmanned aerial vehicle (UAV) target detection presents significant challenges due to the interplay between small targets and complex backgrounds. Traditional methods, while effective in controlled environments, often fail in scenarios involving long-range targets, high noise levels, or intricate backgrounds, highlighting the need for more robust approaches. To address these challenges, we propose a novel three-stage UAV segmentation framework that leverages uncertainty quantification to enhance target saliency. This framework incorporates a Bayesian convolutional neural network capable of generating both segmentation maps and probabilistic uncertainty maps. By utilizing uncertainty predictions, our method refines segmentation outcomes, achieving… More >

Guodong Yuan^1,*, Jin Xie²

Journal on Artificial Intelligence, Vol.7, pp. 1-16, 2025, DOI:10.32604/jai.2025.061437 - 14 March 2025

Abstract This paper addresses the problem of access efficiency in multi-robot systems to the monitoring area. A distributed algorithm for multi-robot continuous monitoring, based on the uncertainty of target points, is used to minimize the uncertainty and instantaneous idle time of all target points in the task domain, while maintaining a certain access frequency to the entire task domain at regular time intervals. During monitoring, the robot uses shared information to evaluate the cumulative uncertainty and idle time of the target points, and combines the update list collected from adjacent target points with a utility function More >

Anastasios Dounis^*, Ioannis Palaiothodoros, Anna Panagiotou

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 759-811, 2025, DOI:10.32604/cmes.2024.057888 - 17 December 2024

Abstract Accurate medical diagnosis, which involves identifying diseases based on patient symptoms, is often hindered by uncertainties in data interpretation and retrieval. Advanced fuzzy set theories have emerged as effective tools to address these challenges. In this paper, new mathematical approaches for handling uncertainty in medical diagnosis are introduced using q-rung orthopair fuzzy sets (q-ROFS) and interval-valued q-rung orthopair fuzzy sets (IVq-ROFS). Three aggregation operators are proposed in our methodologies: the q-ROF weighted averaging (q-ROFWA), the q-ROF weighted geometric (q-ROFWG), and the q-ROF weighted neutrality averaging (q-ROFWNA), which enhance decision-making under uncertainty. These operators are paired More > Graphic Abstract

Khuram Ali Khan¹, Saba Mubeen Ishfaq¹, Atiqe Ur Rahman², Salwa El-Morsy^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 501-530, 2025, DOI:10.32604/cmes.2024.057865 - 17 December 2024

Abstract Due to the numerous variables to take into account as well as the inherent ambiguity and uncertainty, evaluating educational institutions can be difficult. The concept of a possibility Pythagorean fuzzy hypersoft set (pPyFHSS) is more flexible in this regard than other theoretical fuzzy set-like models, even though some attempts have been made in the literature to address such uncertainties. This study investigates the elementary notions of pPyFHSS including its set-theoretic operations union, intersection, complement, OR- and AND-operations. Some results related to these operations are also modified for pPyFHSS. Additionally, the similarity measures between pPyFHSSs are More >

Pugazhenthi Sivasankar^1,*, Austin B. Probe², Tarek A. Elgohary¹

Digital Engineering and Digital Twin, Vol.2, pp. 169-205, 2024, DOI:10.32604/dedt.2024.052805 - 31 December 2024

Abstract In Space Situational Awareness (SSA), accurate and efficient uncertainty quantification and propagation are essential for various applications, such as conjunction analysis, track correlation, and orbit prediction. The propagation of the probability density function (PDF) in nonlinear systems results in non-Gaussian distributions, which are difficult to approximate. Furthermore, the computational cost of approximating the PDF increases exponentially with the number of random variables, a phenomenon known as the curse of dimensionality. To address these challenges, the Orthogonal Probability Approximation (OPA) method is presented for high-fidelity uncertainty propagation and PDF approximation in nonlinear dynamical systems. The method… More >

Peiyan Wang^1,*, Haoyu Wang¹, Minghui Liu², Fuchao Liu¹, Zhufeng Yue¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.3, pp. 1-1, 2024, DOI:10.32604/icces.2024.011318

Abstract The virtual test is supported by the physical test data, and a high-precision simulation model needs to be established to maximize the alignment between the simulation prediction results and the physical test data. It can replace other physical tests and achieve the goal of reducing the design cycle time and cost. However, due to the errors caused by the position and angle deviation of the strain gauge paste, as well as the sensitivity coefficient of the strain gauge and the wire, it is difficult for the simulation results to correspond to the test results in… More >