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  • Open Access

    ARTICLE

    Mixed Integer Robust Programming Model for Multimodal Fresh Agricultural Products Terminal Distribution Network Design

    Feng Yang1, Zhong Wu2,*, Xiaoyan Teng1

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

    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 the model to a robust… More > Graphic Abstract

    Mixed Integer Robust Programming Model for Multimodal Fresh Agricultural Products Terminal Distribution Network Design

  • Open Access

    PROCEEDINGS

    A Data-Fusion Method for Uncertainty Quantification of Mechanical Property of Bi-Modulus Materials: An Example of Graphite

    Liang Zhang1,*, 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 Correlation (DIC) technology. To deal… More >

  • Open Access

    ARTICLE

    Stochastic Programming for Hub Energy Management Considering Uncertainty Using Two-Point Estimate Method and Optimization Algorithm

    Ali S. Alghamdi1, Mohana Alanazi2, Abdulaziz Alanazi3, Yazeed Qasaymeh1,*, Muhammad Zubair1,4, Ahmed Bilal Awan5, M. G. B. Ashiq6

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2163-2192, 2023, DOI:10.32604/cmes.2023.029453

    Abstract To maximize energy profit with the participation of electricity, natural gas, and district heating networks in the day-ahead market, stochastic scheduling of energy hubs taking into account the uncertainty of photovoltaic and wind resources, has been carried out. This has been done using a new meta-heuristic algorithm, improved artificial rabbits optimization (IARO). In this study, the uncertainty of solar and wind energy sources is modeled using Hang’s two-point estimating method (TPEM). The IARO algorithm is applied to calculate the best capacity of hub energy equipment, such as solar and wind renewable energy sources, combined heat and power (CHP) systems, steam… More >

  • Open Access

    ARTICLE

    Distributed Robust Optimal Dispatch for the Microgrid Considering Output Correlation between Wind and Photovoltaic

    Ming Li1,*, Cairen Furifu1, Chengyang Ge2, Yunping Zheng1, Shunfu Lin2, Ronghui Liu2

    Energy Engineering, Vol.120, No.8, pp. 1775-1801, 2023, DOI:10.32604/ee.2023.027215

    Abstract As an effective carrier of integrated clean energy, the microgrid has attracted wide attention. The randomness of renewable energies such as wind and solar power output brings a significant cost and impact on the economics and reliability of microgrids. This paper proposes an optimization scheme based on the distributionally robust optimization (DRO) model for a microgrid considering solar-wind correlation. Firstly, scenarios of wind and solar power output scenarios are generated based on non-parametric kernel density estimation and the Frank-Copula function; then the generated scenario results are reduced by K-means clustering; finally, the probability confidence interval of scenario distribution is constrained… More >

  • Open Access

    ARTICLE

    A Three-Dimensional Model for the Formation Pressure in Wellbores under Uncertainty

    Jiawei Zhang*, Qing Wang, Hongchun Huang, Haige Wang, Guodong Ji, Meng Cui, Hongyuan Zhang

    FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.9, pp. 2305-2314, 2023, DOI:10.32604/fdmp.2023.026304

    Abstract Formation pressure is the key parameter for the analysis of wellbore safety. With increasing drilling depth, however, the behavior of this variable becomes increasingly complex. In this work, a 3D model of the formation pressure under uncertainty is presented. Moreover a relevant algorithm is elaborated. First, the logging data of regional key drilling wells are collected and a one-dimensional formation pressure profile along the well depth is determined. Then, a 3D model of regional formation pressure of the hierarchical group layer is defined by using the Kriging interpolation algorithm relying on a support vector machine (SVM) and the formation pressure… More >

  • Open Access

    ARTICLE

    Prediction of Uncertainty Estimation and Confidence Calibration Using Fully Convolutional Neural Network

    Karim Gasmi1,*, Lassaad Ben Ammar2,, Hmoud Elshammari4, Fadwa Yahya2

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2557-2573, 2023, DOI:10.32604/cmc.2023.033270

    Abstract Convolution neural networks (CNNs) have proven to be effective clinical imaging methods. This study highlighted some of the key issues within these systems. It is difficult to train these systems in a limited clinical image databases, and many publications present strategies including such learning algorithm. Furthermore, these patterns are known for making a highly reliable prognosis. In addition, normalization of volume and losses of dice have been used effectively to accelerate and stabilize the training. Furthermore, these systems are improperly regulated, resulting in more confident ratings for correct and incorrect classification, which are inaccurate and difficult to understand. This study… More >

  • Open Access

    ARTICLE

    Reliability-Based Topology Optimization of Fail-Safe Structures Using Moving Morphable Bars

    Xuan Wang1,2, Yuankun Shi2, Van-Nam Hoang3, Zeng Meng2,*, Kai Long4,*, Yuesheng Wang1

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 3173-3195, 2023, DOI:10.32604/cmes.2023.025501

    Abstract This paper proposes an effective reliability design optimization method for fail-safe topology optimization (FSTO) considering uncertainty based on the moving morphable bars method to establish the ideal balance between cost and robustness, reliability and structural safety. To this end, a performance measure approach (PMA)-based double-loop optimization algorithm is developed to minimize the relative volume percentage while achieving the reliability criterion. To ensure the compliance value of the worst failure case can better approximate the quantified design requirement, a p-norm constraint approach with correction parameter is introduced. Finally, the significance of accounting for uncertainty in the fail-safe design is illustrated by… More > Graphic Abstract

    Reliability-Based Topology Optimization of Fail-Safe Structures Using Moving Morphable Bars

  • Open Access

    ARTICLE

    Structural Interval Reliability Algorithm Based on Bernstein Polynomials and Evidence Theory

    Xu Zhang1, Jianchao Ni2, Juxi Hu3,*, Weisi Chen4

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1947-1960, 2023, DOI:10.32604/csse.2023.035118

    Abstract Structural reliability is an important method to measure the safety performance of structures under the influence of uncertain factors. Traditional structural reliability analysis methods often convert the limit state function to the polynomial form to measure whether the structure is invalid. The uncertain parameters mainly exist in the form of intervals. This method requires a lot of calculation and is often difficult to achieve efficiently. In order to solve this problem, this paper proposes an interval variable multivariate polynomial algorithm based on Bernstein polynomials and evidence theory to solve the structural reliability problem with cognitive uncertainty. Based on the non-probabilistic… More >

  • Open Access

    ARTICLE

    Optimizing Service Stipulation Uncertainty with Deep Reinforcement Learning for Internet Vehicle Systems

    Zulqar Nain1, B. Shahana2, Shehzad Ashraf Chaudhry3, P. Viswanathan4, M.S. Mekala1, Sung Won Kim1,*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5705-5721, 2023, DOI:10.32604/cmc.2023.033194

    Abstract Fog computing brings computational services near the network edge to meet the latency constraints of cyber-physical System (CPS) applications. Edge devices enable limited computational capacity and energy availability that hamper end user performance. We designed a novel performance measurement index to gauge a device’s resource capacity. This examination addresses the offloading mechanism issues, where the end user (EU) offloads a part of its workload to a nearby edge server (ES). Sometimes, the ES further offloads the workload to another ES or cloud server to achieve reliable performance because of limited resources (such as storage and computation). The manuscript aims to… More >

  • Open Access

    ARTICLE

    Energy Management of Networked Smart Railway Stations Considering Regenerative Braking, Energy Storage System, and Photovoltaic Units

    Saeed Akbari1, Seyed Saeed Fazel1,*, Hamed Hashemi-Dezaki2,3

    Energy Engineering, Vol.120, No.1, pp. 69-86, 2023, DOI:10.32604/ee.2022.024121

    Abstract The networking of microgrids has received significant attention in the form of a smart grid. In this paper, a set of smart railway stations, which is assumed as microgrids, is connected together. It has been tried to manage the energy exchanged between the networked microgrids to reduce received energy from the utility grid. Also, the operational costs of stations under various conditions decrease by applying the proposed method. The smart railway stations are studied in the presence of photovoltaic (PV) units, energy storage systems (ESSs), and regenerative braking strategies. Studying regenerative braking is one of the essential contributions. Moreover, the… More > Graphic Abstract

    Energy Management of Networked Smart Railway Stations Considering Regenerative Braking, Energy Storage System, and Photovoltaic Units

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