Yu Song¹, Xusheng Qian², Nan Zhang³, Wei Wang², Ao Xiong^1,*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3007-3021, 2024, DOI:10.32604/cmc.2024.051217

Abstract To enhance the efficiency and expediency of issuing e-licenses within the power sector, we must confront the challenge of managing the surging demand for data traffic. Within this realm, the network imposes stringent Quality of Service (QoS) requirements, revealing the inadequacies of traditional routing allocation mechanisms in accommodating such extensive data flows. In response to the imperative of handling a substantial influx of data requests promptly and alleviating the constraints of existing technologies and network congestion, we present an architecture for QoS routing optimization with in Software Defined Network (SDN), leveraging deep reinforcement learning. This… More >

Yongjiang Zhao, Haoyi Zhong, Chang Cyoon Lim^*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 449-471, 2024, DOI:10.32604/cmc.2024.048771

Abstract This paper examines the difficulties of managing distributed power systems, notably due to the increasing use of renewable energy sources, and focuses on voltage control challenges exacerbated by their variable nature in modern power grids. To tackle the unique challenges of voltage control in distributed renewable energy networks, researchers are increasingly turning towards multi-agent reinforcement learning (MARL). However, MARL raises safety concerns due to the unpredictability in agent actions during their exploration phase. This unpredictability can lead to unsafe control measures. To mitigate these safety concerns in MARL-based voltage control, our study introduces a novel… 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… More >

Yutao Hu¹, Yuntao Zhao^1,*, Yongxin Feng², Xiangyu Ma¹

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 509-530, 2024, DOI:10.32604/cmc.2024.042216

Abstract In the face of the increasingly severe Botnet problem on the Internet, how to effectively detect Botnet traffic in real-time has become a critical problem. Although the existing deep Q network (DQN) algorithm in Deep reinforcement learning can solve the problem of real-time updating, its prediction results are always higher than the actual results. In Botnet traffic detection, although it performs well in the training set, the accuracy rate of predicting traffic is as high as%; however, in the test set, its accuracy has declined, and it is impossible to adjust its prediction strategy on… More >

Yong Ma¹, Han Zhao², Kunyin Guo^3,*, Yunni Xia^3,*, Xu Wang⁴, Xianhua Niu⁵, Dongge Zhu⁶, Yumin Dong⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 907-927, 2024, DOI:10.32604/cmes.2024.048759

Abstract Mobile Edge Computing (MEC) is a technology designed for the on-demand provisioning of computing and storage services, strategically positioned close to users. In the MEC environment, frequently accessed content can be deployed and cached on edge servers to optimize the efficiency of content delivery, ultimately enhancing the quality of the user experience. However, due to the typical placement of edge devices and nodes at the network’s periphery, these components may face various potential fault tolerance challenges, including network instability, device failures, and resource constraints. Considering the dynamic nature of MEC, making high-quality content caching decisions… More >

Tezara Cionita^1,*, Mohammad Hazim Mohamad Hamdan², Januar Parlaungan Siregar^3,4,*, Deni Fajar Fitriyana⁵, Ramli Junid⁶, Wong Ling Shing⁷, Jamiluddin Jaafar⁸, Agustinus Purna Irawan⁹, Teuku Rihayat¹⁰, Rifky Ismail¹¹, Athanasius Priharyoto Bayuseno¹¹, Emilianus Jehadus¹²

Journal of Renewable Materials, Vol.12, No.3, pp. 457-483, 2024, DOI:10.32604/jrm.2024.045814

Abstract Recent decades have seen a substantial increase in interest in research on natural fibres that is aligned with sustainable development goals (SDGs). Due to their renewable resources and biodegradability, natural fiber-reinforced composites have been investigated as a sustainable alternative to synthetic materials to reduce the usage of hazardous waste and environmental pollution. Among the natural fibre, jute fibre obtained from a bast plant has an increasing trend in the application, especially as a reinforcement material. Numerous research works have been performed on jute fibre with regard to reinforced thermoset and thermoplastic composites. Nevertheless, current demands More >

Joseph O. Dirisu^1,*, Imhade P. Okokpujie^2,3,*, Olufunmilayo O. Joseph¹, Sunday O. Oyedepo¹, Oluwasegun Falodun⁴, Lagouge K. Tartibu³, Firdaussi D. Shehu¹

Journal of Renewable Materials, Vol.12, No.3, pp. 485-511, 2024, DOI:10.32604/jrm.2024.045188

Abstract Research into converting waste into viable eco-friendly products has gained global concern. Using natural fibres and pulverized metallic waste becomes necessary to reduce noxious environmental emissions due to indiscriminately occupying the land. This study reviews the literature in the broad area of green composites in search of materials that can be used in automotive brake pads. Materials made by biocomposite, rather than fossil fuels, will be favoured. A database containing the tribo-mechanical performance of numerous potential components for the future green composite was established using the technical details of bio-polymers and natural reinforcements. The development… More > Graphic Abstract

Jiangang Lu, Ruifeng Zhao^*, Zhiwen Yu, Yue Dai, Kaiwen Zeng

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4453-4469, 2024, DOI:10.32604/cmc.2024.048020

Abstract Time synchronization (TS) is crucial for ensuring the secure and reliable functioning of the distribution power Internet of Things (IoT). Multi-clock source time synchronization (MTS) has significant advantages of high reliability and accuracy but still faces challenges such as optimization of the multi-clock source selection and the clock source weight calculation at different timescales, and the coupling of synchronization latency jitter and pulse phase difference. In this paper, the multi-timescale MTS model is conducted, and the reinforcement learning (RL) and analytic hierarchy process (AHP)-based multi-timescale MTS algorithm is designed to improve the weighted summation of More >

Sepehr Soltani¹, Ehsan Ghafourian², Reza Salehi³, Diego Martín^3,*, Milad Vahidi⁴

Intelligent Automation & Soft Computing, Vol.39, No.1, pp. 93-108, 2024, DOI:10.32604/iasc.2024.042693

Abstract For many years, researchers have explored power allocation (PA) algorithms driven by models in wireless networks where multiple-user communications with interference are present. Nowadays, data-driven machine learning methods have become quite popular in analyzing wireless communication systems, which among them deep reinforcement learning (DRL) has a significant role in solving optimization issues under certain constraints. To this purpose, in this paper, we investigate the PA problem in a -user multiple access channels (MAC), where transmitters (e.g., mobile users) aim to send an independent message to a common receiver (e.g., base station) through wireless channels. To… More >

Gang Niu^1,2, Zhaoyang Jin², Wei Zhang^3,*, Yiqun Huang³

Structural Durability & Health Monitoring, Vol.18, No.2, pp. 161-179, 2024, DOI:10.32604/sdhm.2023.044580

Abstract Amid urbanization and the continuous expansion of transportation networks, the necessity for tunnel construction and maintenance has become paramount. Addressing this need requires the investigation of efficient, economical, and robust tunnel reinforcement techniques. This paper explores fiber reinforced polymer (FRP) and steel fiber reinforced concrete (SFRC) technologies, which have emerged as viable solutions for enhancing tunnel structures. FRP is celebrated for its lightweight and high-strength attributes, effectively augmenting load-bearing capacity and seismic resistance, while SFRC’s notable crack resistance and longevity potentially enhance the performance of tunnel segments. Nonetheless, current research predominantly focuses on experimental analysis,… More > Graphic Abstract