Haojie Lian¹, Jiaqi Wang¹, Leilei Chen^2,*, Shengze Li³, Ruochen Cao⁴, Qingyuan Hu⁵, Peiyun Zhao¹

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1143-1163, 2024, DOI:10.32604/cmes.2024.048549 - 16 April 2024

Abstract This paper presents a novel framework aimed at quantifying uncertainties associated with the 3D reconstruction of smoke from 2D images. This approach reconstructs color and density fields from 2D images using Neural Radiance Field (NeRF) and improves image quality using frequency regularization. The NeRF model is obtained via joint training of multiple artificial neural networks, whereby the expectation and standard deviation of density fields and RGB values can be evaluated for each pixel. In addition, customized physics-informed neural network (PINN) with residual blocks and two-layer activation functions are utilized to input the density fields of More >

Mousumi Basu¹, Chitralekha Jena², Baseem Khan^3,4,*, Ahmed Ali⁴

Energy Engineering, Vol.121, No.4, pp. 849-867, 2024, DOI:10.32604/ee.2024.043294 - 26 March 2024

Abstract In the restructured electricity market, microgrid (MG), with the incorporation of smart grid technologies, distributed energy resources (DERs), a pumped-storage-hydraulic (PSH) unit, and a demand response program (DRP), is a smarter and more reliable electricity provider. DER consists of gas turbines and renewable energy sources such as photovoltaic systems and wind turbines. Better bidding strategies, prepared by MG operators, decrease the electricity cost and emissions from upstream grid and conventional and renewable energy sources (RES). But it is inefficient due to the very high sporadic characteristics of RES and the very high outage rate. To More >

Zaihe Yang^1,*, Shuling Wang¹, Runhang Zhu¹, Jiao Cui², Ji Su², Liling Chen³

Energy Engineering, Vol.121, No.3, pp. 807-820, 2024, DOI:10.32604/ee.2023.028167 - 27 February 2024

Abstract To address the scheduling problem involving energy storage systems and uncertain energy, we propose a method based on multi-stage robust optimization. This approach aims to regulate the energy storage system by using a multi-stage robust optimal control method, which helps overcome the limitations of traditional methods in terms of time scale. The goal is to effectively utilize the energy storage power station system to address issues caused by unpredictable variations in environmental energy and fluctuating load throughout the day. To achieve this, a mathematical model is constructed to represent uncertain energy sources such as photovoltaic More >

Abidhan Bardhan^1,*, Raushan Kumar Singh², Mohammed Alatiyyah³, Sulaiman Abdullah Alateyah^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1521-1555, 2024, DOI:10.32604/cmes.2023.044467 - 29 January 2024

Abstract This research proposes a highly effective soft computing paradigm for estimating the compressive strength (CS) of metakaolin-contained cemented materials. The proposed approach is a combination of an enhanced grey wolf optimizer (EGWO) and an extreme learning machine (ELM). EGWO is an augmented form of the classic grey wolf optimizer (GWO). Compared to standard GWO, EGWO has a better hunting mechanism and produces an optimal performance. The EGWO was used to optimize the ELM structure and a hybrid model, ELM-EGWO, was built. To train and validate the proposed ELM-EGWO model, a sum of 361 experimental results… More >

Fangyi Li^*, Dachang Zhu^*, Huimin Shi

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1981-1999, 2024, DOI:10.32604/cmes.2023.031332 - 29 January 2024

Abstract In time-variant reliability problems, there are a lot of uncertain variables from different sources. Therefore, it is important to consider these uncertainties in engineering. In addition, time-variant reliability problems typically involve a complex multilevel nested optimization problem, which can result in an enormous amount of computation. To this end, this paper studies the time-variant reliability evaluation of structures with stochastic and bounded uncertainties using a mixed probability and convex set model. In this method, the stochastic process of a limit-state function with mixed uncertain parameters is first discretized and then converted into a time-independent reliability More >

Zhihui Xu¹, Shuwen Shang², Yuntong Pu³, Xiaoyan Su^2,*, Hong Qian², Xiaolei Pan²

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2597-2617, 2024, DOI:10.32604/cmes.2023.031247 - 15 December 2023

Abstract Cognitive Reliability and Error Analysis Method (CREAM) is widely used in human reliability analysis (HRA). It defines nine common performance conditions (CPCs), which represent the factors that may affect human reliability and are used to modify the cognitive failure probability (CFP). However, the levels of CPCs are usually determined by domain experts, which may be subjective and uncertain. What’s more, the classic CREAM assumes that the CPCs are independent, which is unrealistic. Ignoring the dependence among CPCs will result in repeated calculations of the influence of the CPCs on CFP and lead to unreasonable reliability More > Graphic Abstract

R. Saravanan^1,*, R. Muthaiah¹, A. Rajesh²

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2339-2356, 2024, DOI:10.32604/cmes.2023.030898 - 15 December 2023

Abstract This study develops an Enhanced Threshold Based Energy Detection approach (ETBED) for spectrum sensing in a cognitive radio network. The threshold identification method is implemented in the received signal at the secondary user based on the square law. The proposed method is implemented with the signal transmission of multiple outputs-orthogonal frequency division multiplexing. Additionally, the proposed method is considered the dynamic detection threshold adjustments and energy identification spectrum sensing technique in cognitive radio systems. In the dynamic threshold, the signal ratio-based threshold is fixed. The threshold is computed by considering the Modified Black Widow Optimization… More > Graphic Abstract

Yongqiu Liu¹, Shaohui Zhong^2,*, Nasreen Kausar³, Chunwei Zhang^4,*, Ardashir Mohammadzadeh⁴, Dragan Pamucar^5,6

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 793-812, 2024, DOI:10.32604/cmes.2023.028175 - 22 September 2023

Abstract In this paper, a stable and adaptive sliding mode control (SMC) method for induction motors is introduced. Determining the parameters of this system has been one of the existing challenges. To solve this challenge, a new self-tuning type-2 fuzzy neural network calculates and updates the control system parameters with a fast mechanism. According to the dynamic changes of the system, in addition to the parameters of the SMC, the parameters of the type-2 fuzzy neural network are also updated online. The conditions for guaranteeing the convergence and stability of the control system are provided. In More >

Feng Yang¹, Zhong Wu^2,*, Xiaoyan Teng¹

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 719-738, 2024, DOI:10.32604/cmes.2023.028699 - 22 September 2023

Abstract The low efficiency and high cost of fresh agricultural product terminal distribution directly restrict the operation of the entire supply network. To reduce costs and optimize the distribution network, we construct a mixed integer programming model that comprehensively considers to minimize fixed, transportation, fresh-keeping, time, carbon emissions, and performance incentive costs. We analyzed the performance of traditional rider distribution and robot distribution modes in detail. In addition, the uncertainty of the actual market demand poses a huge threat to the stability of the terminal distribution network. In order to resist uncertain interference, we further extend More > Graphic Abstract

Liang Zhang^1,*, Zigang He¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.2, pp. 1-1, 2023, DOI:10.32604/icces.2023.09713

Abstract The different elastic properties of tension and compression are obvious in many engineering materials, especially new materials. Materials with this characteristic, such as graphite, ceramics, and composite materials, are called bi-modulus materials. Their mechanical properties such as Young’s modulus have randomness in tension and compression due to different porosity, microstructure, etc. To calibrate the mechanical properties of bi-modulus materials by bridging FEM simulation results and scarce experimental data, the paper presents a data-fusion computational method. The FEM simulation is implemented based on Parametric Variational Principle (PVP), while the experimental result is obtained by Digital Image… More >