Yukai Li^1,*, Ruixue Zhang¹, Yongfeng Ni¹, Hongkai Qiu¹, Yuning Zhang², Chunming Liu²

Energy Engineering, Vol.122, No.2, pp. 681-707, 2025, DOI:10.32604/ee.2024.058981 - 31 January 2025

Abstract Aiming at the problems of increasing uncertainty of low-carbon generation energy in active distribution network (ADN) and the difficulty of security assessment of distribution network, this paper proposes a two-phase scheduling model for flexible resources in ADN based on probabilistic risk perception. First, a full-cycle probabilistic trend sequence is constructed based on the source-load historical data, and in the day-ahead scheduling phase, the response interval of the flexibility resources on the load and storage side is optimized based on the probabilistic trend, with the probability of the security boundary as the security constraint, and with… More >

Xueqian Wang^*, Hongsheng Su

Energy Engineering, Vol.122, No.2, pp. 667-680, 2025, DOI:10.32604/ee.2024.058102 - 31 January 2025

Abstract As the proportion of new energy increases, the traditional cumulant method (CM) produces significant errors when performing probabilistic load flow (PLF) calculations with large-scale wind power integrated. Considering the wind speed correlation, a multi-scenario PLF calculation method that combines random sampling and segmented discrete wind farm power was proposed. Firstly, based on constructing discrete scenes of wind farms, the Nataf transform is used to handle the correlation between wind speeds. Then, the random sampling method determines the output probability of discrete wind power scenarios when wind speed exhibits correlation. Finally, the PLF calculation results of More >

Jeng-Shyang Pan^1,2, Wenda Li¹, Shu-Chuan Chu^1,*, Xiao Sui¹, Junzo Watada³

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3033-3062, 2024, DOI:10.32604/cmc.2024.056310 - 18 November 2024

Abstract The growth optimizer (GO) is an innovative and robust metaheuristic optimization algorithm designed to simulate the learning and reflective processes experienced by individuals as they mature within the social environment. However, the original GO algorithm is constrained by two significant limitations: slow convergence and high memory requirements. This restricts its application to large-scale and complex problems. To address these problems, this paper proposes an innovative enhanced growth optimizer (eGO). In contrast to conventional population-based optimization algorithms, the eGO algorithm utilizes a probabilistic model, designated as the virtual population, which is capable of accurately replicating the… More >

Hongsheng Su, Shilin Song^*, Xingsheng Wang

Energy Engineering, Vol.121, No.11, pp. 3289-3303, 2024, DOI:10.32604/ee.2024.054643 - 21 October 2024

Abstract This paper introduces the Particle Swarm Optimization (PSO) algorithm to enhance the Latin Hypercube Sampling (LHS) process. The key objective is to mitigate the issues of lengthy computation times and low computational accuracy typically encountered when applying Monte Carlo Simulation (MCS) to LHS for probabilistic trend calculations. The PSO method optimizes sample distribution, enhances global search capabilities, and significantly boosts computational efficiency. To validate its effectiveness, the proposed method was applied to IEEE34 and IEEE-118 node systems containing wind power. The performance was then compared with Latin Hypercubic Important Sampling (LHIS), which integrates significant sampling More >

Talal H. Noor^*

Computer Systems Science and Engineering, Vol.48, No.5, pp. 1273-1299, 2024, DOI:10.32604/csse.2024.052510 - 13 September 2024

Abstract In recent years, container-based cloud virtualization solutions have emerged to mitigate the performance gap between non-virtualized and virtualized physical resources. However, there is a noticeable absence of techniques for predicting microservice performance in current research, which impacts cloud service users’ ability to determine when to provision or de-provision microservices. Predicting microservice performance poses challenges due to overheads associated with actions such as variations in processing time caused by resource contention, which potentially leads to user confusion. In this paper, we propose, develop, and validate a probabilistic architecture named Microservice Performance Diagnosis and Prediction (MPDP). MPDP… More >

Borja Bordel Sánchez^1,*, Ramón Alcarria², Tomás Robles¹

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 631-654, 2024, DOI:10.32604/cmes.2024.050349 - 20 August 2024

Abstract Future 6G communications are envisioned to enable a large catalogue of pioneering applications. These will range from networked Cyber-Physical Systems to edge computing devices, establishing real-time feedback control loops critical for managing Industry 5.0 deployments, digital agriculture systems, and essential infrastructures. The provision of extensive machine-type communications through 6G will render many of these innovative systems autonomous and unsupervised. While full automation will enhance industrial efficiency significantly, it concurrently introduces new cyber risks and vulnerabilities. In particular, unattended systems are highly susceptible to trust issues: malicious nodes and false information can be easily introduced into… More >

Chunhong Zeng, Kang Lu, Zhiqin He^*, Qinmu Wu

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1441-1456, 2024, DOI:10.32604/cmc.2024.051551 - 18 July 2024

Abstract Personalized gait curves are generated to enhance patient adaptability to gait trajectories used for passive training in the early stage of rehabilitation for hemiplegic patients. The article utilizes the random forest algorithm to construct a gait parameter model, which maps the relationship between parameters such as height, weight, age, gender, and gait speed, achieving prediction of key points on the gait curve. To enhance prediction accuracy, an attention mechanism is introduced into the algorithm to focus more on the main features. Meanwhile, to ensure high similarity between the reconstructed gait curve and the normal one, More >

Chengcheng Fan^1,2,*, Zhiruo Fang³

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4925-4943, 2024, DOI:10.32604/cmc.2024.049710 - 20 June 2024

Abstract Anchor-free object-detection methods achieve a significant advancement in field of computer vision, particularly in the realm of real-time inferences. However, in remote sensing object detection, anchor-free methods often lack of capability in separating the foreground and background. This paper proposes an anchor-free method named probability-enhanced anchor-free detector (ProEnDet) for remote sensing object detection. First, a weighted bidirectional feature pyramid is used for feature extraction. Second, we introduce probability enhancement to strengthen the classification of the object’s foreground and background. The detector uses the logarithm likelihood as the final score to improve the classification of the More >

Zibin Mao¹, Qinghai Zhao^1,2,*, Liang Zhang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 757-792, 2024, DOI:10.32604/cmes.2024.048016 - 16 April 2024

Abstract This paper proposes a multi-material topology optimization method based on the hybrid reliability of the probability-ellipsoid model with stress constraint for the stochastic uncertainty and epistemic uncertainty of mechanical loads in optimization design. The probabilistic model is combined with the ellipsoidal model to describe the uncertainty of mechanical loads. The topology optimization formula is combined with the ordered solid isotropic material with penalization (ordered-SIMP) multi-material interpolation model. The stresses of all elements are integrated into a global stress measurement that approximates the maximum stress using the normalized p-norm function. Furthermore, the sequential optimization and reliability assessment… More >

Zihao Song, Yan Zhou^*, Wei Cheng, Futai Liang, Chenhao Zhang

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3349-3376, 2024, DOI:10.32604/cmc.2024.047034 - 26 March 2024

Abstract The frequent missing values in radar-derived time-series tracks of aerial targets (RTT-AT) lead to significant challenges in subsequent data-driven tasks. However, the majority of imputation research focuses on random missing (RM) that differs significantly from common missing patterns of RTT-AT. The method for solving the RM may experience performance degradation or failure when applied to RTT-AT imputation. Conventional autoregressive deep learning methods are prone to error accumulation and long-term dependency loss. In this paper, a non-autoregressive imputation model that addresses the issue of missing value imputation for two common missing patterns in RTT-AT is proposed.… More >