Haider H. Al-Mahmood^1,*, Saad N. Alsaad²

Intelligent Automation & Soft Computing, Vol.40, pp. 53-74, 2025, DOI:10.32604/iasc.2024.059691 - 10 January 2025

Abstract In secure communications, lightweight encryption has become crucial, particularly for resource-constrained applications such as embedded devices, wireless sensor networks, and the Internet of Things (IoT). As these systems proliferate, cryptographic approaches that provide robust security while minimizing computing overhead, energy consumption, and memory usage are becoming increasingly essential. This study examines lightweight encryption techniques utilizing chaotic maps to ensure secure data transmission. Two algorithms are proposed, both employing the Logistic map; the first approach utilizes two logistic chaotic maps, while the second algorithm employs a single logistic chaotic map. Algorithm 1, including a two-stage mechanism… More >

Xunguang Yan^1,2, Wenrui Wang¹, Fanglin Lu¹, Hongyong Fan³, Bo Wu¹, Jianfeng Yu^1,*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1439-1458, 2025, DOI:10.32604/cmc.2024.057797 - 03 January 2025

Abstract To maintain the reliability of power systems, routine inspections using drones equipped with advanced object detection algorithms are essential for preempting power-related issues. The increasing resolution of drone-captured images has posed a challenge for traditional target detection methods, especially in identifying small objects in high-resolution images. This study presents an enhanced object detection algorithm based on the Faster Region-based Convolutional Neural Network (Faster R-CNN) framework, specifically tailored for detecting small-scale electrical components like insulators, shock hammers, and screws in transmission line. The algorithm features an improved backbone network for Faster R-CNN, which significantly boosts the More >

Yuzhe Ma, Jiye Zhang^*, Jiawei Shi

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.12, pp. 2843-2865, 2024, DOI:10.32604/fdmp.2024.055205 - 23 December 2024

Abstract The large number and dense layout of rail fastening can significantly affect the aerodynamic performance of trains. Utilizing the Improved Delayed Detached Eddy Simulation (IDDES) approach based on the SST (Shear Stress Transport) k-ω turbulent model, this study evaluates the effects of the rail fastening system on the aerodynamic force, slipstream and train wake under crosswind conditions. The results indicate that in such conditions, compared to the model without rails, the rail and the fastening system reduce the drag force coefficient of the train by 1.69%, while the lateral force coefficients increase by 1.16% and… More >

Kristine Meile^1,*, Martins Romanovskis^1,2, Thomas Nicol³, Neil Hindle³, Aivars Zhurinsh¹

Journal of Renewable Materials, Vol.12, No.12, pp. 2135-2152, 2024, DOI:10.32604/jrm.2024.056775 - 20 December 2024

Abstract Fast pyrolysis of pre-treated birch wood in a super-heated steam environment produces a condensate rich in anhydrosugars. With the objective to obtain several product streams from this condensate, the possibility of extracting additional chemical species is explored, thus promoting the development of a pyrolysis-based biorefinery. In this work, the extraction and recovery of pyrolytic phenols from birch wood pyrolysis condensate was studied using ion exchange resins. With an aim to achieve effective phenol recovery, while obtaining high purity levoglucosan, basic ion exchange resins, both in OH⁻ and Cl⁻ form, as well as polystyrene-divinyl resins without functional… More > Graphic Abstract

Yasunori Okano^1,*, Tsuyoshi Miyamoto¹, Sadik Dost²

The International Conference on Computational & Experimental Engineering and Sciences, Vol.31, No.2, pp. 1-1, 2024, DOI:10.32604/icces.2024.011685

Abstract We have developed a machine learning model, called Hybrid-PINNs (Physics Informed Neural Networks), and applied for fast predictions of transport structures (flow and thermal fields) in the silicon (Si) melt during the Czochralski (Cz) bulk single crystal growth. Si bulk single crystals are mostly grown by the Cz method. For the growth of high-quality Si crystals with this method, it is essential to understand and control these transport structures in the melt. Since the direct observation of such transport fields in the melt during growth is usually impossible, numerical simulations provide a powerful tool for… More >

Weiru Wang¹, Yulong Cao^1,*, Yanxu Wang¹, Jiale You¹, Guangnan Zhang¹, Yu Xiao²

Energy Engineering, Vol.121, No.12, pp. 3801-3823, 2024, DOI:10.32604/ee.2024.055398 - 22 November 2024

Abstract To ensure frequency stability in power systems with high wind penetration, the doubly-fed induction generator (DFIG) is often used with the frequency fast response control (FFRC) to participate in frequency response. However, a certain output power suppression amount (OPSA) is generated during frequency support, resulting in the frequency modulation (FM) capability of DFIG not being fully utilised, and the system’s unbalanced power will be increased during speed recovery, resulting in a second frequency drop (SFD) in the system. Firstly, the frequency response characteristics of the power system with DFIG containing FFRC are analysed. Then, based… More >

Farida Asriani^1,2, Azhari Azhari^1,*, Wahyono Wahyono¹

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3079-3096, 2024, DOI:10.32604/cmc.2024.058193 - 18 November 2024

Abstract Incredible progress has been made in human action recognition (HAR), significantly impacting computer vision applications in sports analytics. However, identifying dynamic and complex movements in sports like badminton remains challenging due to the need for precise recognition accuracy and better management of complex motion patterns. Deep learning techniques like convolutional neural networks (CNNs), long short-term memory (LSTM), and graph convolutional networks (GCNs) improve recognition in large datasets, while the traditional machine learning methods like SVM (support vector machines), RF (random forest), and LR (logistic regression), combined with handcrafted features and ensemble approaches, perform well but… More >

Trong Thua Huynh^*, Hoang Thanh Nguyen, Du Thang Phu

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2281-2298, 2024, DOI:10.32604/cmc.2024.057954 - 18 November 2024

Abstract In recent years, early detection and warning of fires have posed a significant challenge to environmental protection and human safety. Deep learning models such as Faster R-CNN (Faster Region based Convolutional Neural Network), YOLO (You Only Look Once), and their variants have demonstrated superiority in quickly detecting objects from images and videos, creating new opportunities to enhance automatic and efficient fire detection. The YOLO model, especially newer versions like YOLOv10, stands out for its fast processing capability, making it suitable for low-latency applications. However, when applied to real-world datasets, the accuracy of fire prediction is… More >

Hao Yu^1,*, Mengcheng Huang¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.2, pp. 1-1, 2024, DOI:10.32604/icces.2024.011120

Abstract Understanding the competitive adsorption behavior of CO₂ and CH₄ in shale nanopores is crucial for enhancing the recovery of shale gas and sequestration of CO₂, which is determined by both the inherent characteristics of the molecules and external environmental factors such as pore size, temperature, and partial pressures of CO₂ and CH₄. While the competitive adsorption behavior of CO₂/CH₄ has been analyzed by previous studies, a comprehensive understanding from the perspective of molecular kinetic theory and the efficient calculation for competitive adsorption behavior considering various geological situations is still challenging, limited by the huge computation cost of classical… More >

Fujian Zhang¹, Ziyang Wang¹, Xiang Gao¹, Zhongqiang Zhang^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.29, No.2, pp. 1-1, 2024, DOI:10.32604/icces.2024.011736

Abstract Spontaneous droplet transport has broad application prospects in fields such as water collection and microfluidic chips. Despite extensive research in this area, droplet self-transport is still limited by issues such as slow transport velocity, short distance, and poor integrity. Here, a novel cross-hatch textured cone (CHTC) with multistage microchannels and circular grooves is proposed to realize ultrafast directional long-distance self-transport of multi-scale droplets. The CHTC triggers two modes of fluid transport: Droplet transport by Laplace pressure difference and capillary suction pressure-induced fluid transfer in microchannels on cone surfaces. By leveraging the coupling effect of the… More >