Lanze Zhang, Yijun Gu^*, Jingjie Peng

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1701-1731, 2024, DOI:10.32604/cmes.2023.045129

Abstract Graph Neural Networks (GNNs) play a significant role in tasks related to homophilic graphs. Traditional GNNs, based on the assumption of homophily, employ low-pass filters for neighboring nodes to achieve information aggregation and embedding. However, in heterophilic graphs, nodes from different categories often establish connections, while nodes of the same category are located further apart in the graph topology. This characteristic poses challenges to traditional GNNs, leading to issues of “distant node modeling deficiency” and “failure of the homophily assumption”. In response, this paper introduces the Spatial-Frequency domain Adaptive Heterophilic Graph Neural Networks (SFA-HGNN), which integrates adaptive embedding mechanisms for… More >

Yan Shi¹, Wenjie Li¹, Gongbo Fan^2,*, Luxi Zhang¹, Fengjiu Yang¹

Energy Engineering, Vol.121, No.2, pp. 461-482, 2024, DOI:10.32604/ee.2023.043835

Abstract Addressing the insufficiency in down-regulation leeway within integrated energy systems stemming from the erratic and volatile nature of wind and solar renewable energy generation, this study focuses on formulating a coordinated strategy involving the carbon capture unit of the integrated energy system and the resources on the load storage side. A scheduling model is devised that takes into account the confidence interval associated with renewable energy generation, with the overarching goal of optimizing the system for low-carbon operation. To begin with, an in-depth analysis is conducted on the temporal energy-shifting attributes and the low-carbon modulation mechanisms exhibited by the source-side… More >

Feng Zhao, Jinshuo Zhang^*, Xiaoqiang Chen, Ying Wang

Energy Engineering, Vol.121, No.2, pp. 339-358, 2024, DOI:10.32604/ee.2023.043082

Abstract In the DC microgrid, the lack of inertia and damping in power electronic converters results in poor stability of DC bus voltage and low inertia of the DC microgrid during fluctuations in load and photovoltaic power. To address this issue, the application of a virtual synchronous generator (VSG) in grid-connected inverters control is referenced and proposes a control strategy called the analogous virtual synchronous generator (AVSG) control strategy for the interface DC/DC converter of the battery in the microgrid. Besides, a flexible parameter adaptive control method is introduced to further enhance the inertial behavior of the AVSG control. Firstly, a… More >

L. Maria Michael Visuwasam^1,*, D. Paul Raj²

Computer Systems Science and Engineering, Vol.48, No.1, pp. 267-267, 2024, DOI:10.32604/csse.2023.047461

Abstract This article has no abstract. More >

Karim Ragui¹, Mengshuai Chen^1,2, Lin Chen^1,2,3,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.3, pp. 1-2, 2023, DOI:10.32604/icces.2023.010378

Abstract The ambiguous dynamics associated with heat and mass transfer of invading carbon dioxide in sub-critical and supercritical states, as well as the response of pore-scale resident fluids, play a key role in understanding CO₂ capture and storage (CCUS) and the corresponding phase equilibrium mechanisms. To this end, this paper reveals the transport mechanisms of invading supercritical carbon dioxide (sCO₂) in polluted micromodels using a variant of Lattice-Boltzmann Color Fluid model and descriptive experimental data. The breakthrough time is evaluated by characterizing the displacement velocity, the capillary to pressuredifference ratio, and the transient heat and mass diffusion at a series of… More >

Changtao Wang, Honghua Dai^*, Wenchuan Yang

Digital Engineering and Digital Twin, Vol.1, pp. 3-13, 2023, DOI:10.32604/dedt.2023.044210

Abstract A novel Adaptive Parallel Feedback-Accelerated Picard Iteration (AP-FAPI) method is proposed to meet the requirements of various aerospace missions for fast and accurate orbit propagation. The Parallel Feedback-Accelerated Picard Iteration (P-FAPI) method is an advanced iterative collocation method. With large-step computing and parallel acceleration, the P-FAPI method outperforms the traditional finite-difference-based methods, which require small-step and serial integration to ensure accuracy. Although efficient and accurate, the P-FAPI method suffers extensive trials in tuning method parameters, strongly influencing its performance. To overcome this problem, we propose the AP-FAPI method based on the relationship between the parameters and the convergence speed leveraging… More >

Xiulai Li^1,2,3,4, Jieren Cheng^1,3,*, Chengchun Ruan^1,3, Bin Zhang^1,3, Xiangyan Tang^1,3, Mengzhe Sun⁵

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3265-3288, 2023, DOI:10.32604/cmc.2023.045588

Abstract With the rising adoption of blockchain technology due to its decentralized, secure, and transparent features, ensuring its resilience against network threats, especially Distributed Denial of Service (DDoS) attacks, is crucial. This research addresses the vulnerability of blockchain systems to DDoS assaults, which undermine their core decentralized characteristics, posing threats to their security and reliability. We have devised a novel adaptive integration technique for the detection and identification of varied DDoS attacks. To ensure the robustness and validity of our approach, a dataset amalgamating multiple DDoS attacks was derived from the CIC-DDoS2019 dataset. Using this, our methodology was applied to detect… More >

Ting Zhao¹, Weiping Ding¹, Haibo Huang¹, Yudong Wu^1,2,*

Sound & Vibration, Vol.57, pp. 133-153, 2023, DOI:10.32604/sv.2023.044203

Abstract The deployment of vehicle micro-motors has witnessed an expansion owing to the progression in electrification and intelligent technologies. However, some micro-motors may exhibit design deficiencies, component wear, assembly errors, and other imperfections that may arise during the design or manufacturing phases. Consequently, these micro-motors might generate anomalous noises during their operation, consequently exerting a substantial adverse influence on the overall comfort of drivers and passengers. Automobile micro-motors exhibit a diverse array of structural variations, consequently leading to the manifestation of a multitude of distinctive auditory irregularities. To address the identification of diverse forms of abnormal noise, this research presents a… More > Graphic Abstract

Jinxi Guo¹, Kai Chen^1,2, Jiehui Liu¹, Yuhao Ma², Jie Wu^2,*, Yaochun Wu², Xiaofeng Xue³, Jianshen Li¹

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2619-2640, 2024, DOI:10.32604/cmes.2023.031360

Abstract Intelligent diagnosis driven by big data for mechanical fault is an important means to ensure the safe operation of equipment. In these methods, deep learning-based machinery fault diagnosis approaches have received increasing attention and achieved some results. It might lead to insufficient performance for using transfer learning alone and cause misclassification of target samples for domain bias when building deep models to learn domain-invariant features. To address the above problems, a deep discriminative adversarial domain adaptation neural network for the bearing fault diagnosis model is proposed (DDADAN). In this method, the raw vibration data are firstly converted into frequency domain… More >

Qixin Lan¹, Binqiang Chen^1,*, Bin Yao¹, Wangpeng He²

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2825-2844, 2024, DOI:10.32604/cmes.2023.030378

Abstract The wear of metal cutting tools will progressively rise as the cutting time goes on. Wearing heavily on the tool will generate significant noise and vibration, negatively impacting the accuracy of the forming and the surface integrity of the workpiece. Hence, during the cutting process, it is imperative to continually monitor the tool wear state and promptly replace any heavily worn tools to guarantee the quality of the cutting. The conventional tool wear monitoring models, which are based on machine learning, are specifically built for the intended cutting conditions. However, these models require retraining when the cutting conditions undergo any… More >