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  • Open Access

    ARTICLE

    A Fixed-Point Iterative Method for Discrete Tomography Reconstruction Based on Intelligent Optimization

    Luyao Yang1,#, Hao Chen2,#, Haocheng Yu1, Jin Qiu1,*, Shuxian Zhu1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 731-745, 2023, DOI:10.32604/cmes.2022.020656 - 24 August 2022

    Abstract Discrete Tomography (DT) is a technology that uses image projection to reconstruct images. Its reconstruction problem, especially the binary image (0–1 matrix) has attracted strong attention. In this study, a fixed point iterative method of integer programming based on intelligent optimization is proposed to optimize the reconstructed model. The solution process can be divided into two procedures. First, the DT problem is reformulated into a polyhedron judgment problem based on lattice basis reduction. Second, the fixed-point iterative method of Dang and Ye is used to judge whether an integer point exists in the polyhedron of More >

  • Open Access

    ARTICLE

    Improved Staggered Algorithm for Phase-Field Brittle Fracture with the Local Arc-Length Method

    Zhijian Wu, Li Guo*, Jun Hong

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 611-636, 2023, DOI:10.32604/cmes.2022.020694 - 24 August 2022

    Abstract The local arc-length method is employed to control the incremental loading procedure for phase-field brittle fracture modeling. An improved staggered algorithm with energy and damage iterative tolerance convergence criteria is developed based on the residuals of displacement and phase-field. The improved staggered solution scheme is implemented in the commercial software ABAQUS with user-defined element subroutines. The layered system of finite elements is utilized to solve the coupled elastic displacement and phase-field fracture problem. A one-element benchmark test compared with the analytical solution was conducted to validate the feasibility and accuracy of the developed method. Our More >

  • Open Access

    ARTICLE

    Research on Volt/Var Control of Distribution Networks Based on PPO Algorithm

    Chao Zhu1, Lei Wang1, Dai Pan1, Zifei Wang2, Tao Wang2, Licheng Wang2,*, Chengjin Ye3

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 599-609, 2023, DOI:10.32604/cmes.2022.021052 - 24 August 2022

    Abstract In this paper, a model free volt/var control (VVC) algorithm is developed by using deep reinforcement learning (DRL). We transform the VVC problem of distribution networks into the network framework of PPO algorithm, in order to avoid directly solving a large-scale nonlinear optimization problem. We select photovoltaic inverters as agents to adjust system voltage in a distribution network, taking the reactive power output of inverters as action variables. An appropriate reward function is designed to guide the interaction between photovoltaic inverters and the distribution network environment. OPENDSS is used to output system node voltage and More >

  • Open Access

    ARTICLE

    An Intelligent Optimization Method of Reinforcing Bar Cutting for Construction Site

    Zhaoxi Ma1, Qin Zhao1,*, Tianyou Cang1, Zongjian Li2, Yiyun Zhu1, Xinhong Hei3

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 637-655, 2023, DOI:10.32604/cmes.2022.021216 - 24 August 2022

    Abstract To meet the requirements of specifications, intelligent optimization of steel bar blanking can improve resource utilization and promote the intelligent development of sustainable construction. As one of the most important building materials in construction engineering, reinforcing bars (rebar) account for more than 30% of the cost in civil engineering. A significant amount of cutting waste is generated during the construction phase. Excessive cutting waste increases construction costs and generates a considerable amount of COCO2 emission. This study aimed to develop an optimization algorithm for steel bar blanking that can be used in the intelligent optimization of… More >

  • Open Access

    ARTICLE

    Interpreting Randomly Wired Graph Models for Chinese NER

    Jie Chen1, Jiabao Xu1, Xuefeng Xi1,*, Zhiming Cui1, Victor S. Sheng2

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 747-761, 2023, DOI:10.32604/cmes.2022.020771 - 24 August 2022

    Abstract Interpreting deep neural networks is of great importance to understand and verify deep models for natural language processing (NLP) tasks. However, most existing approaches only focus on improving the performance of models but ignore their interpretability. In this work, we propose a Randomly Wired Graph Neural Network (RWGNN) by using graph to model the structure of Neural Network, which could solve two major problems (word-boundary ambiguity and polysemy) of Chinese NER. Besides, we develop a pipeline to explain the RWGNN by using Saliency Map and Adversarial Attacks. Experimental results demonstrate that our approach can identify More >

  • Open Access

    ARTICLE

    Dynamic Mechanical Behavior and Numerical Simulation of an Ancient Underground Rock Mass under Impact Loading

    Baoping Zou*, Zhiping Liu, Weifeng Jin, Haonan Ding, Zhanyou Luo

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 517-539, 2023, DOI:10.32604/cmes.2022.020853 - 24 August 2022

    Abstract To study the dynamic mechanical properties of tuff under different environmental conditions, the tuff from an ancient quarry in Shepan Island was prepared. The impact damage to the rock was tested using a triaxial dynamic impact mechanical testing system (TDIMTS) with different ground stresses, temperatures, and groundwater pressures. The time-strain relationship, dynamic stress-strain relationship, energy dissipation law, energy-peak strain relationship, and the impact damage pattern of the tuff specimens under impact air pressures were investigated. The TDIMTS experiment on ancient underground rock mass under impact loading was also simulated using the finite element analysis software… More >

  • Open Access

    ARTICLE

    Metal Corrosion Rate Prediction of Small Samples Using an Ensemble Technique

    Yang Yang1,2,*, Pengfei Zheng3,4, Fanru Zeng5, Peng Xin6, Guoxi He1, Kexi Liao1

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 267-291, 2023, DOI:10.32604/cmes.2022.020220 - 24 August 2022

    Abstract Accurate prediction of the internal corrosion rates of oil and gas pipelines could be an effective way to prevent pipeline leaks. In this study, a proposed framework for predicting corrosion rates under a small sample of metal corrosion data in the laboratory was developed to provide a new perspective on how to solve the problem of pipeline corrosion under the condition of insufficient real samples. This approach employed the bagging algorithm to construct a strong learner by integrating several KNN learners. A total of 99 data were collected and split into training and test set More >

  • Open Access

    ARTICLE

    An Efficient Differential Evolution for Truss Sizing Optimization Using AdaBoost Classifier

    Tran-Hieu Nguyen*, Anh-Tuan Vu

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 429-458, 2023, DOI:10.32604/cmes.2022.020819 - 24 August 2022

    Abstract Design constraints verification is the most computationally expensive task in evolutionary structural optimization due to a large number of structural analyses that must be conducted. Building a surrogate model to approximate the behavior of structures instead of the exact structural analyses is a possible solution to tackle this problem. However, most existing surrogate models have been designed based on regression techniques. This paper proposes a novel method, called CaDE, which adopts a machine learning classification technique for enhancing the performance of the Differential Evolution (DE) optimization. The proposed method is separated into two stages. During… More >

  • Open Access

    ARTICLE

    Three-Stages Hyperspectral Image Compression Sensing with Band Selection

    Jingbo Zhang, Yanjun Zhang, Xingjuan Cai*, Liping Xie*

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 293-316, 2023, DOI:10.32604/cmes.2022.020426 - 24 August 2022

    Abstract Compressed sensing (CS), as an efficient data transmission method, has achieved great success in the field of data transmission such as image, video and text. It can robustly recover signals from fewer Measurements, effectively alleviating the bandwidth pressure during data transmission. However, CS has many shortcomings in the transmission of hyperspectral image (HSI) data. This work aims to consider the application of CS in the transmission of hyperspectral image (HSI) data, and provides a feasible research scheme for CS of HSI data. HSI has rich spectral information and spatial information in bands, which can reflect… More >

  • Open Access

    ARTICLE

    A Hybrid Regional Model for Predicting Ground Deformation Induced by Large-Section Tunnel Excavation

    Shengjun Deng1,2,3,*, Yang He1, Xiaonan Gong2, Jiajin Zhou2, Xiangdong Hu4

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 495-516, 2023, DOI:10.32604/cmes.2022.020386 - 24 August 2022

    Abstract Due to the large number of finite element mesh generated, it is difficult to use full-scale model to simulate largesection underground engineering, especially considering the coupling effect. A regional model is attempted to achieve this simulation. A variable boundary condition method for hybrid regional model is proposed to realize the numerical simulation of large-section tunnel construction. Accordingly, the balance of initial ground stress under asymmetric boundary conditions achieves by applying boundary conditions step by step with secondary development of Dynaflow scripts, which is the key issue of variable boundary condition method implementation. In this paper, More >

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