Yang Li^1,2, Jianjun Zhao², Xiaolong Yang², He Wang^1,*, Yuyan Wang¹

Energy Engineering, Vol.121, No.5, pp. 1263-1289, 2024, DOI:10.32604/ee.2024.046784

Abstract Distributed photovoltaic (PV) is one of the important power sources for building a new power system with new energy as the main body. The rapid development of distributed PV has brought new challenges to the operation of distribution networks. In order to improve the absorption ability of large-scale distributed PV access to the distribution network, the AC/DC hybrid distribution network is constructed based on flexible interconnection technology, and a coordinated scheduling strategy model of hydrogen energy storage (HS) and distributed PV is established. Firstly, the mathematical model of distributed PV and HS system is established, and a comprehensive energy storage… More >

Yu Zhang^*, Lianmin Li, Zhongxiang Liu, Yuhu Wu

Energy Engineering, Vol.121, No.5, pp. 1209-1221, 2024, DOI:10.32604/ee.2024.046141

Abstract With the development of renewable energy technologies such as photovoltaics and wind power, it has become a research hotspot to improve the consumption rate of new energy and reduce energy costs through the deployment of energy storage. To solve the problem of the interests of different subjects in the operation of the energy storage power stations (ESS) and the integrated energy multi-microgrid alliance (IEMA), this paper proposes the optimization operation method of the energy storage power station and the IEMA based on the Stackelberg game. In the upper layer, ESS optimizes charging and discharging decisions through a dynamic pricing mechanism.… More > Graphic Abstract

Aimin Gao¹, Xiaobo Cui^2,*, Guoqiang Yu¹, Jianjun Shu¹, Tianhai Zhang¹

Energy Engineering, Vol.121, No.5, pp. 1347-1361, 2024, DOI:10.32604/ee.2023.045805

Abstract Gas turbines play core roles in clean energy supply and the construction of comprehensive energy systems. The control performance of primary frequency modulation of gas turbines has a great impact on the frequency control of the power grid. However, there are some control difficulties in the primary frequency modulation control of gas turbines, such as the coupling effect of the fuel control loop and speed control loop, slow tracking speed, and so on. To relieve the abovementioned difficulties, a control strategy based on the desired dynamic equation proportional integral (DDE-PI) is proposed in this paper. Based on the parameter stability… More >

Sharaf J. Malebary^*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1301-1317, 2024, DOI:10.32604/cmc.2024.047917

Abstract Brain tumors are a pressing public health concern, characterized by their high mortality and morbidity rates. Nevertheless, the manual segmentation of brain tumors remains a laborious and error-prone task, necessitating the development of more precise and efficient methodologies. To address this formidable challenge, we propose an advanced approach for segmenting brain tumor Magnetic Resonance Imaging (MRI) images that harnesses the formidable capabilities of deep learning and convolutional neural networks (CNNs). While CNN-based methods have displayed promise in the realm of brain tumor segmentation, the intricate nature of these tumors, marked by irregular shapes, varying sizes, uneven distribution, and limited available… More >

Yuejiao Wang, Zhong Ma^*, Chaojie Yang, Yu Yang, Lu Wei

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 819-836, 2024, DOI:10.32604/cmc.2024.047108

Abstract The quantization algorithm compresses the original network by reducing the numerical bit width of the model, which improves the computation speed. Because different layers have different redundancy and sensitivity to data bit width. Reducing the data bit width will result in a loss of accuracy. Therefore, it is difficult to determine the optimal bit width for different parts of the network with guaranteed accuracy. Mixed precision quantization can effectively reduce the amount of computation while keeping the model accuracy basically unchanged. In this paper, a hardware-aware mixed precision quantization strategy optimal assignment algorithm adapted to low bit width is proposed,… More >

EFFAT ALEMZADEH¹, LEILA ALLAHQOLI², AFROOZ MAZIDIMORADI³, ESMAT ALEMZADEH^1,4, FAHIMEH GHASEMI^4,5, HAMID SALEHINIYA⁶, IBRAHIM ALKATOUT^7,*

Oncology Research, Vol.32, No.5, pp. 831-847, 2024, DOI:10.32604/or.2024.031006

Abstract Ovarian cancer is among the most lethal gynecological cancers, primarily due to the lack of specific symptoms leading to an advanced-stage diagnosis and resistance to chemotherapy. Drug resistance (DR) poses the most significant challenge in treating patients with existing drugs. The Food and Drug Administration (FDA) has recently approved three new therapeutic drugs, including two poly (ADP-ribose) polymerase (PARP) inhibitors (olaparib and niraparib) and one vascular endothelial growth factor (VEGF) inhibitor (bevacizumab) for maintenance therapy. However, resistance to these new drugs has emerged. Therefore, understanding the mechanisms of DR and exploring new approaches to overcome them is crucial for effective… More >

T. Aghalya, R. Tamizharasi^*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 431-460, 2024, DOI:10.32604/cmes.2024.046635

Abstract In this study, the characteristics of heat transfer on an unsteady magnetohydrodynamic (MHD) Casson nanofluid over an exponentially accelerated vertical porous plate with rotating effects were investigated. The flow was driven by the combined effects of the magnetic field, heat radiation, heat source/sink and chemical reaction. Copper oxide () and titanium oxide () are acknowledged as nanoparticle materials. The nondimensional governing equations were subjected to the Laplace transformation technique to derive closed-form solutions. Graphical representations are provided to analyze how changes in physical parameters, such as the magnetic field, heat radiation, heat source/sink and chemical reaction, affect the velocity, temperature… More >

Hong Huang¹, Xingxing Zhang^1,*, Ye Lu¹, Ze Li¹, Shaohua Zhou²

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3929-3951, 2024, DOI:10.32604/cmc.2024.047918

Abstract While encryption technology safeguards the security of network communications, malicious traffic also uses encryption protocols to obscure its malicious behavior. To address the issues of traditional machine learning methods relying on expert experience and the insufficient representation capabilities of existing deep learning methods for encrypted malicious traffic, we propose an encrypted malicious traffic classification method that integrates global semantic features with local spatiotemporal features, called BERT-based Spatio-Temporal Features Network (BSTFNet). At the packet-level granularity, the model captures the global semantic features of packets through the attention mechanism of the Bidirectional Encoder Representations from Transformers (BERT) model. At the byte-level granularity,… More >

Bin Li^*, Linghui Kong, Xiangyi Zhang, Bochuo Kou, Hui Yu, Bowen Liu

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3267-3282, 2024, DOI:10.32604/cmc.2024.047193

Abstract The automatic collection of power grid situation information, along with real-time multimedia interaction between the front and back ends during the accident handling process, has generated a massive amount of power grid data. While wireless communication offers a convenient channel for grid terminal access and data transmission, it is important to note that the bandwidth of wireless communication is limited. Additionally, the broadcast nature of wireless transmission raises concerns about the potential for unauthorized eavesdropping during data transmission. To address these challenges and achieve reliable, secure, and real-time transmission of power grid data, an intelligent security transmission strategy with sensor-transmission-computing… More >

Ying Su¹, Morgan C. Wang¹, Shuai Liu^2,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3529-3549, 2024, DOI:10.32604/cmc.2024.047189

Abstract Long-term time series forecasting stands as a crucial research domain within the realm of automated machine learning (AutoML). At present, forecasting, whether rooted in machine learning or statistical learning, typically relies on expert input and necessitates substantial manual involvement. This manual effort spans model development, feature engineering, hyper-parameter tuning, and the intricate construction of time series models. The complexity of these tasks renders complete automation unfeasible, as they inherently demand human intervention at multiple junctures. To surmount these challenges, this article proposes leveraging Long Short-Term Memory, which is the variant of Recurrent Neural Networks, harnessing memory cells and gating mechanisms… More >