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  • Open Access

    ARTICLE

    Intelligent Power Grid Load Transferring Based on Safe Action-Correction Reinforcement Learning

    Fuju Zhou*, Li Li, Tengfei Jia, Yongchang Yin, Aixiang Shi, Shengrong Xu

    Energy Engineering, Vol.121, No.6, pp. 1697-1711, 2024, DOI:10.32604/ee.2024.047680

    Abstract When a line failure occurs in a power grid, a load transfer is implemented to reconfigure the network by changing the states of tie-switches and load demands. Computation speed is one of the major performance indicators in power grid load transfer, as a fast load transfer model can greatly reduce the economic loss of post-fault power grids. In this study, a reinforcement learning method is developed based on a deep deterministic policy gradient. The tedious training process of the reinforcement learning model can be conducted offline, so the model shows satisfactory performance in real-time operation, More >

  • Open Access

    ARTICLE

    Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning Multi-Objective Grey Wolf Optimizer

    Hongliang Zhang1,2, Yi Chen1, Yuteng Zhang1, Gongjie Xu3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1459-1483, 2024, DOI:10.32604/cmes.2024.049756

    Abstract The distributed flexible job shop scheduling problem (DFJSP) has attracted great attention with the growth of the global manufacturing industry. General DFJSP research only considers machine constraints and ignores worker constraints. As one critical factor of production, effective utilization of worker resources can increase productivity. Meanwhile, energy consumption is a growing concern due to the increasingly serious environmental issues. Therefore, the distributed flexible job shop scheduling problem with dual resource constraints (DFJSP-DRC) for minimizing makespan and total energy consumption is studied in this paper. To solve the problem, we present a multi-objective mathematical model for… More >

  • Open Access

    ARTICLE

    Nonlinear Registration of Brain Magnetic Resonance Images with Cross Constraints of Intensity and Structure

    Han Zhou1,2, Hongtao Xu1,2, Xinyue Chang1,2, Wei Zhang1,2, Heng Dong1,2,*

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2295-2313, 2024, DOI:10.32604/cmc.2024.047754

    Abstract Many deep learning-based registration methods rely on a single-stream encoder-decoder network for computing deformation fields between 3D volumes. However, these methods often lack constraint information and overlook semantic consistency, limiting their performance. To address these issues, we present a novel approach for medical image registration called the Dual-VoxelMorph, featuring a dual-channel cross-constraint network. This innovative network utilizes both intensity and segmentation images, which share identical semantic information and feature representations. Two encoder-decoder structures calculate deformation fields for intensity and segmentation images, as generated by the dual-channel cross-constraint network. This design facilitates bidirectional communication between grayscale More >

  • Open Access

    REVIEW

    AI Fairness–From Machine Learning to Federated Learning

    Lalit Mohan Patnaik1,5, Wenfeng Wang2,3,4,5,6,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1203-1215, 2024, DOI:10.32604/cmes.2023.029451

    Abstract This article reviews the theory of fairness in AI–from machine learning to federated learning, where the constraints on precision AI fairness and perspective solutions are also discussed. For a reliable and quantitative evaluation of AI fairness, many associated concepts have been proposed, formulated and classified. However, the inexplicability of machine learning systems makes it almost impossible to include all necessary details in the modelling stage to ensure fairness. The privacy worries induce the data unfairness and hence, the biases in the datasets for evaluating AI fairness are unavoidable. The imbalance between algorithms’ utility and humanization More >

  • Open Access

    ARTICLE

    A Method of Integrating Length Constraints into Encoder-Decoder Transformer for Abstractive Text Summarization

    Ngoc-Khuong Nguyen1,2, Dac-Nhuong Le1, Viet-Ha Nguyen2, Anh-Cuong Le3,*

    Intelligent Automation & Soft Computing, Vol.38, No.1, pp. 1-18, 2023, DOI:10.32604/iasc.2023.037083

    Abstract Text summarization aims to generate a concise version of the original text. The longer the summary text is, the more detailed it will be from the original text, and this depends on the intended use. Therefore, the problem of generating summary texts with desired lengths is a vital task to put the research into practice. To solve this problem, in this paper, we propose a new method to integrate the desired length of the summarized text into the encoder-decoder model for the abstractive text summarization problem. This length parameter is integrated into the encoding phase More >

  • Open Access

    ARTICLE

    Adaptive H Filtering Algorithm for Train Positioning Based on Prior Combination Constraints

    Xiuhui Diao1, Pengfei Wang1,2,*, Weidong Li2, Xianwu Chu2, Yunming Wang2

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1795-1812, 2024, DOI:10.32604/cmes.2023.030008

    Abstract To solve the problem of data fusion for prior information such as track information and train status in train positioning, an adaptive H filtering algorithm with combination constraint is proposed, which fuses prior information with other sensor information in the form of constraints. Firstly, the train precise track constraint method of the train is proposed, and the plane position constraint and train motion state constraints are analysed. A model for combining prior information with constraints is established. Then an adaptive H filter with combination constraints is derived based on the adaptive adjustment method of the robustness More >

  • Open Access

    ARTICLE

    Binary Oriented Feature Selection for Valid Product Derivation in Software Product Line

    Muhammad Fezan Afzal1, Imran Khan1, Javed Rashid1,2,3, Mubbashar Saddique4,*, Heba G. Mohamed5

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3653-3670, 2023, DOI:10.32604/cmc.2023.041627

    Abstract Software Product Line (SPL) is a group of software-intensive systems that share common and variable resources for developing a particular system. The feature model is a tree-type structure used to manage SPL’s common and variable features with their different relations and problem of Crosstree Constraints (CTC). CTC problems exist in groups of common and variable features among the sub-tree of feature models more diverse in Internet of Things (IoT) devices because different Internet devices and protocols are communicated. Therefore, managing the CTC problem to achieve valid product configuration in IoT-based SPL is more complex, time-consuming,… More >

  • Open Access

    ARTICLE

    A New Partial Task Offloading Method in a Cooperation Mode under Multi-Constraints for Multi-UE

    Shengyao Sun1,2, Ying Du3, Jiajun Chen4, Xuan Zhang5, Jiwei Zhang6,*, Yiyi Xu7

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2879-2900, 2023, DOI:10.32604/cmc.2023.037483

    Abstract In Multi-access Edge Computing (MEC), to deal with multiple user equipment (UE)’s task offloading problem of parallel relationships under the multi-constraints, this paper proposes a cooperation partial task offloading method (named CPMM), aiming to reduce UE's energy and computation consumption, while meeting the task completion delay as much as possible. CPMM first studies the task offloading of single-UE and then considers the task offloading of multi-UE based on single-UE task offloading. CPMM uses the critical path algorithm to divide the modules into key and non-key modules. According to some constraints of UE-self when offloading tasks,… More >

  • Open Access

    ARTICLE

    Study on Flow Field Simulation at Transmission Towers in Loess Hilly Regions Based on Circular Boundary Constraints

    Yongxin Liu1, Huaiwei Cao2, Puyu Zhao2, Gang Yang1, Hua Yu1, Fuwei He3, Bo He2,*

    Energy Engineering, Vol.120, No.10, pp. 2417-2431, 2023, DOI:10.32604/ee.2023.029596

    Abstract When using high-voltage transmission lines for energy transmission in loess hilly regions, local extreme wind fields such as turbulence and high-speed cyclones occur from time to time, which can cause many kinds of mechanical and electrical failures, seriously affecting the reliable and stable energy transmission of the power grid. The existing research focuses on the wind field simulation of ideal micro-terrain and actual terrain with mostly single micro-terrain characteristics. Model boundary constraints and the influence of constrained boundaries are the main problems that need to be solved to accurately model and simulate complex flow fields.… More >

  • Open Access

    PROCEEDINGS

    Direct FE2 Method For Concurrent Multilevel Modeling of Piezoelectric Structures

    Leilei Chen2,3, Haozhi Li3,4, Lu Meng5, Pan Chen3, Pei Li1,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.26, No.4, pp. 1-2, 2023, DOI:10.32604/icces.2023.010584

    Abstract In this paper, a Direct FE2 method is proposed to simulate the electromechanical coupling problem of inhomogeneous materials. The theoretical foundation for the proposed method, downscaling and upscaling principles, is the same as that of the FE2 method. The two-level simulation in the Direct FE2 method may be addressed in an integrative framework where macroscopic and microscopic degrees of freedom (DOFs) are related by multipoint constraints (MPCs) [1]. This critical characteristic permits simple implementation in commercial FE software, eliminating the necessity for recurrent data transfer between two scales [2-4]. The capabilities of Direct FE2 are validated using More >

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