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  • Open Access

    REVIEW

    Market Operation of Energy Storage System in Smart Grid: A Review

    Li Deng1, Jiafei Huan1, Wei Wang1, Weitao Zhang1, Liangbin Xie2, Lun Dong2, Jingrong Guo2, Zhongping Li2, Yuan Huang2,*, Yue Xiang2

    Energy Engineering, Vol.121, No.6, pp. 1403-1437, 2024, DOI:10.32604/ee.2024.046393

    Abstract As a flexible resource, energy storage plays an increasingly significant role in stabilizing and supporting the power system, while providing auxiliary services. Still, the current high demand for energy storage contrasts with the fuzzy lack of market-oriented mechanisms for energy storage, the principle of market-oriented operation has not been embodied, and there is no unified and systematic analytical framework for the business model. However, the dispatch management model of energy storage in actual power system operation is not clear. Still, the specific scheduling process and energy storage strategy on the source-load-network side could be more… More >

  • Open Access

    ARTICLE

    A Data-Oriented Method to Optimize Hydraulic Fracturing Parameters of Tight Sandstone Reservoirs

    Zhengrong Chen*, Mao Jiang, Chuanzhi Ai, Jianshu Wu, Xin Xie

    Energy Engineering, Vol.121, No.6, pp. 1657-1669, 2024, DOI:10.32604/ee.2024.030222

    Abstract Based on the actual data collected from the tight sandstone development zone, correlation analysis using the Spearman method was conducted to determine the main factors influencing the gas production rate of tight sandstone fracturing. An integrated model combining geological engineering and numerical simulation of fracture propagation and production was completed. Based on data analysis, the hydraulic fracture parameters were optimized to develop a differentiated fracturing treatment adjustment plan. The results indicate that the influence of geological and engineering factors in the X1 and X2 development zones in the study area differs significantly. Therefore, it is… More >

  • Open Access

    ARTICLE

    Contrast Normalization Strategies in Brain Tumor Imaging: From Preprocessing to Classification

    Samar M. Alqhtani1, Toufique A. Soomro2,*, Faisal Bin Ubaid3, Ahmed Ali4, Muhammad Irfan5, Abdullah A. Asiri6

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1539-1562, 2024, DOI:10.32604/cmes.2024.051475

    Abstract Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various countries. Magnetic resonance imaging (MRI) and computed tomography (CT) are utilized to capture brain images. MRI plays a crucial role in the diagnosis of brain tumors and the examination of other brain disorders. Typically, manual assessment of MRI images by radiologists or experts is performed to identify brain tumors and abnormalities in the early stages for timely intervention. However, early diagnosis of brain tumors is intricate, necessitating the use of computerized methods. This research introduces an innovative approach for… More > Graphic Abstract

    Contrast Normalization Strategies in Brain Tumor Imaging: From Preprocessing to Classification

  • 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

    Sleep Posture Classification Using RGB and Thermal Cameras Based on Deep Learning Model

    Awais Khan1, Chomyong Kim2, Jung-Yeon Kim2, Ahsan Aziz1, Yunyoung Nam3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1729-1755, 2024, DOI:10.32604/cmes.2024.049618

    Abstract Sleep posture surveillance is crucial for patient comfort, yet current systems face difficulties in providing comprehensive studies due to the obstruction caused by blankets. Precise posture assessment remains challenging because of the complex nature of the human body and variations in sleep patterns. Consequently, this study introduces an innovative method utilizing RGB and thermal cameras for comprehensive posture classification, thereby enhancing the analysis of body position and comfort. This method begins by capturing a dataset of sleep postures in the form of videos using RGB and thermal cameras, which depict six commonly adopted postures: supine,… More > Graphic Abstract

    Sleep Posture Classification Using RGB and Thermal Cameras Based on Deep Learning Model

  • Open Access

    ARTICLE

    Predicting the Mechanical Behavior of a Bioinspired Nanocomposite through Machine Learning

    Xingzi Yang1, Wei Gao2, Xiaodu Wang1, Xiaowei Zeng1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1299-1313, 2024, DOI:10.32604/cmes.2024.049371

    Abstract The bioinspired nacre or bone structure represents a remarkable example of tough, strong, lightweight, and multifunctional structures in biological materials that can be an inspiration to design bioinspired high-performance materials. The bioinspired structure consists of hard grains and soft material interfaces. While the material interface has a very low volume percentage, its property has the ability to determine the bulk material response. Machine learning technology nowadays is widely used in material science. A machine learning model was utilized to predict the material response based on the material interface properties in a bioinspired nanocomposite. This model More >

  • Open Access

    ARTICLE

    A Hybrid Approach for Predicting the Remaining Useful Life of Bearings Based on the RReliefF Algorithm and Extreme Learning Machine

    Sen-Hui Wang1,2,*, Xi Kang1, Cheng Wang1, Tian-Bing Ma1, Xiang He2, Ke Yang2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1405-1427, 2024, DOI:10.32604/cmes.2024.049281

    Abstract Accurately predicting the remaining useful life (RUL) of bearings in mining rotating equipment is vital for mining enterprises. This research aims to distinguish the features associated with the RUL of bearings and propose a prediction model based on these selected features. This study proposes a hybrid predictive model to assess the RUL of rolling element bearings. The proposed model begins with the pre-processing of bearing vibration signals to reconstruct sixty time-domain features. The hybrid model selects relevant features from the sixty time-domain features of the vibration signal by adopting the RReliefF feature selection algorithm. Subsequently,… More >

  • Open Access

    ARTICLE

    2P3FL: A Novel Approach for Privacy Preserving in Financial Sectors Using Flower Federated Learning

    Sandeep Dasari, Rajesh Kaluri*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 2035-2051, 2024, DOI:10.32604/cmes.2024.049152

    Abstract The increasing data pool in finance sectors forces machine learning (ML) to step into new complications. Banking data has significant financial implications and is confidential. Combining users data from several organizations for various banking services may result in various intrusions and privacy leakages. As a result, this study employs federated learning (FL) using a flower paradigm to preserve each organization’s privacy while collaborating to build a robust shared global model. However, diverse data distributions in the collaborative training process might result in inadequate model learning and a lack of privacy. To address this issue, the… More > Graphic Abstract

    2P3FL: A Novel Approach for Privacy Preserving in Financial Sectors Using Flower Federated Learning

  • Open Access

    ARTICLE

    Dynamic Characteristics of Functionally Graded Timoshenko Beams by Improved Differential Quadrature Method

    Xiaojun Huang1, Liaojun Zhang2,*, Hanbo Cui1, Gaoxing Hu1

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1647-1668, 2024, DOI:10.32604/cmes.2024.049124

    Abstract This study proposes an effective method to enhance the accuracy of the Differential Quadrature Method (DQM) for calculating the dynamic characteristics of functionally graded beams by improving the form of discrete node distribution. Firstly, based on the first-order shear deformation theory, the governing equation of free vibration of a functionally graded beam is transformed into the eigenvalue problem of ordinary differential equations with respect to beam axial displacement, transverse displacement, and cross-sectional rotation angle by considering the effects of shear deformation and rotational inertia of the beam cross-section. Then, ignoring the shear deformation of the… More >

  • Open Access

    REVIEW

    Progress in Mechanical Modeling of Implantable Flexible Neural Probes

    Xiaoli You1,2,3,, Ruiyu Bai1,2,3,4,, Kai Xue1,2,3, Zimo Zhang1,2,3, Minghao Wang5, Xuanqi Wang1,2,3, Jiahao Wang1,2,3, Jinku Guo1,2, Qiang Shen3, Honglong Chang3, Xu Long6,*, Bowen Ji1,2,3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1205-1231, 2024, DOI:10.32604/cmes.2024.049047

    Abstract Implanted neural probes can detect weak discharges of neurons in the brain by piercing soft brain tissue, thus as important tools for brain science research, as well as diagnosis and treatment of brain diseases. However, the rigid neural probes, such as Utah arrays, Michigan probes, and metal microfilament electrodes, are mechanically unmatched with brain tissue and are prone to rejection and glial scarring after implantation, which leads to a significant degradation in the signal quality with the implantation time. In recent years, flexible neural electrodes are rapidly developed with less damage to biological tissues, excellent… More >

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