Guangfei Jia^*, Jinqiu Yang, Hanwen Liang

Structural Durability & Health Monitoring, Vol.19, No.4, pp. 1057-1072, 2025, DOI:10.32604/sdhm.2025.061805 - 30 June 2025

Abstract Considering the noise problem of the acquisition signals from mechanical transmission systems, a novel denoising method is proposed that combines Variational Mode Decomposition (VMD) with wavelet thresholding. The key innovation of this method lies in the optimization of VMD parameters K and using the improved Horned Lizard Optimization Algorithm (IHLOA). An inertia weight parameter is introduced into the random walk strategy of HLOA, and the related formula is improved. The acquisition signal can be adaptively decomposed into some Intrinsic Mode Functions (IMFs), and the high-noise IMFs are identified based on a correlation coefficient-variance method. Further noise… More > Graphic Abstract

Jiaqing Wang¹, Yuming Shen^2,*, Xuli Wang², Jiayin Xu²

Energy Engineering, Vol.122, No.7, pp. 2751-2766, 2025, DOI:10.32604/ee.2025.064317 - 27 June 2025

Abstract To adapt to the uncertainty of new energy, increase new energy consumption, and reduce carbon emissions, a high-voltage distribution network energy storage planning model based on robustness-oriented planning and distributed new energy consumption is proposed. Firstly, the spatio-temporal correlation of large-scale wind-photovoltaic energy is modeled based on the Vine Copula model, and the spatial correlation of the generated wind-photovoltaic power generation is corrected to get the spatio-temporal correlation of wind-photovoltaic power generation scenarios. Finally, considering the subsequent development of new energy on demand for high-voltage distribution network peaking margin and the economy of the system More >

Liang Ai¹, Mingyue Li², Lumin Chen¹, Yihua Gao², Yi Sun¹, Yue Wu¹, Fuping Qian^1,*, Jinli Lu², Naijin Huang³

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.5, pp. 1171-1190, 2025, DOI:10.32604/fdmp.2025.055985 - 30 May 2025

Abstract In this study, Computational Fluid Dynamics (CFD) together with a component transport model are exploited to investigate the influence of dimensionless parameters, involving the height of the rectifier grid and the installation height of the first catalyst layer, on the flow field and the overall denitration efficiency of a cement kiln’s SCR (Selective catalytic reduction) denitrification reactor. It is shown that accurate numerical results can be obtained by fitting the particle size distribution function to the actual cement kiln fly ash and implementing a non-uniform particle inlet boundary condition. The relative error between denitration More >

Yuxin Yan, Huirui Wang, Jingyi Wen, Zerong Lan, Liang Wang^*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3059-3073, 2025, DOI:10.32604/cmc.2024.057949 - 16 April 2025

Abstract The development of artificial intelligence (AI) technologies creates a great chance for the iteration of railway monitoring. This paper proposes a comprehensive method for railway utility pole detection. The framework of this paper on railway systems consists of two parts: point cloud preprocessing and railway utility pole detection. This method overcomes the challenges of dynamic environment adaptability, reliance on lighting conditions, sensitivity to weather and environmental conditions, and visual occlusion issues present in 2D images and videos, which utilize mobile LiDAR (Laser Radar) acquisition devices to obtain point cloud data. Due to factors such as… More >

Elaine Yi-Ling Wu^*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 1185-1214, 2025, DOI:10.32604/cmes.2025.064636 - 11 April 2025

Abstract Hybrid renewable energy systems (HRES) offer cost-effectiveness, low-emission power solutions, and reduced dependence on fossil fuels. However, the renewable energy allocation problem remains challenging due to complex system interactions and multiple operational constraints. This study develops a novel Multi-Neighborhood Enhanced Harris Hawks Optimization (MNEHHO) algorithm to address the allocation of HRES components. The proposed approach integrates key technical parameters, including charge-discharge efficiency, storage device configurations, and renewable energy fraction. We formulate a comprehensive mathematical model that simultaneously minimizes levelized energy costs and pollutant emissions while maintaining system reliability. The MNEHHO algorithm employs multiple neighborhood structures… More >

Noveela Iftikhar¹, Mujeeb Ur Rehman¹, Mumtaz Ali Shah², Mohammed J. F. Alenazi³, Jehad Ali^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 639-671, 2025, DOI:10.32604/cmes.2025.062788 - 11 April 2025

Abstract Intrusion attempts against Internet of Things (IoT) devices have significantly increased in the last few years. These devices are now easy targets for hackers because of their built-in security flaws. Combining a Self-Organizing Map (SOM) hybrid anomaly detection system for dimensionality reduction with the inherited nature of clustering and Extreme Gradient Boosting (XGBoost) for multi-class classification can improve network traffic intrusion detection. The proposed model is evaluated on the NSL-KDD dataset. The hybrid approach outperforms the baseline line models, Multilayer perceptron model, and SOM-KNN (k-nearest neighbors) model in precision, recall, and F1-score, highlighting the proposed More >

Xingxu Zhu^1,*, Shiye Wang¹, Gangui Yan¹, Junhui Li¹, Hongda Dong², Chenggang Li²

Energy Engineering, Vol.122, No.4, pp. 1469-1489, 2025, DOI:10.32604/ee.2025.059956 - 31 March 2025

Abstract To address the excessive complexity of monthly scheduling and the impact of uncertain net load on the chargeable energy of storage, a reduced time-period monthly scheduling model for thermal generators and energy storage, incorporating daily minimum chargeable energy constraints, was developed. Firstly, considering the variations in the frequency of unit start-ups and shutdowns under different levels of net load fluctuation, a method was proposed to reduce decision time periods for unit start-up and shut-down operations. This approach, based on the characteristics of net load fluctuations, minimizes the decision variables of units, thereby simplifying the monthly… More >

Surajudeen Sikiru^1,*, Yusuf Olanrewaju Busari^2,3, John Oluwadamilola Olutoki⁴, Mohd Muzamir Mahat¹, Sanusi Yekinni Kolawole⁵

Journal of Polymer Materials, Vol.42, No.1, pp. 1-31, 2025, DOI:10.32604/jpm.2025.058936 - 27 March 2025

Abstract Photocatalysis is a crucial technique for environmental cleanup and renewable energy generation. Polymer-based photocatalysts have attracted interest due to their adaptability, adjustable chemical characteristics, and enhanced light absorption efficiency. Unlike traditional inorganic photocatalysts, we can optimize polymeric systems to enhance photocatalytic efficiency and yield significant advantages in environmental remediation and energy conversion applications. This study talks about the latest developments in polymer-based photocatalysts and how important they are for cleaning water, breaking down pollutants, and making renewable energy through processes like hydrogen production and CO₂ reduction. These materials are proficient in degrading harmful pollutants such as… More >

Tianpeng Li^1,*, Yujun Duan², Qianghu Ji³

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.3, pp. 623-644, 2025, DOI:10.32604/fdmp.2024.053862 - 01 April 2025

Abstract A centrifugal pump with a specific speed n_s = 67 is considered in this study to investigate the impact of blade cutting (at the outlet edge) on the fluid-induced noise, while keeping all the other geometric parameters unchanged. The required unsteady numerical calculations are conducted by applying the RNG k-ε turbulence model with the volute dipole being used as the sound source. The results indicate that the internal pressure energy of the centrifugal pump essentially depends on the blade passing frequency and its low-frequency harmonic frequency. Moreover, the pressure pulsation distribution directly affects the noise caused More >

Ishaani Priyadarshini^*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 635-659, 2025, DOI:10.32604/cmc.2025.061062 - 26 March 2025

Abstract Predictive maintenance plays a crucial role in preventing equipment failures and minimizing operational downtime in modern industries. However, traditional predictive maintenance methods often face challenges in adapting to diverse industrial environments and ensuring the transparency and fairness of their predictions. This paper presents a novel predictive maintenance framework that integrates deep learning and optimization techniques while addressing key ethical considerations, such as transparency, fairness, and explainability, in artificial intelligence driven decision-making. The framework employs an Autoencoder for feature reduction, a Convolutional Neural Network for pattern recognition, and a Long Short-Term Memory network for temporal analysis.… More >