Zhuhong Chu¹, Jianxun Zhang^1,*, Chengdong Wang², Changhui Yang³

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1143-1161, 2024, DOI:10.32604/cmc.2024.052269

Abstract Rail fasteners are a crucial component of the railway transportation safety system. These fasteners, distinguished by their high length-to-width ratio, frequently encounter elevated failure rates, necessitating manual inspection and maintenance. Manual inspection not only consumes time but also poses the risk of potential oversights. With the advancement of deep learning technology in rail fasteners, challenges such as the complex background of rail fasteners and the similarity in their states are addressed. We have proposed an efficient and high-precision rail fastener detection algorithm, named YOLO-O2E (you only look once-O2E). Firstly, we propose the EFOV (Enhanced Field… More >

Tong Gan¹, Shuo Dong¹, Shiyou Wang¹, Jiaxin Li^2,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 643-660, 2024, DOI:10.32604/cmc.2024.051408

Abstract With the rapid development of urban rail transit, the existing track detection has some problems such as low efficiency and insufficient detection coverage, so an intelligent and automatic track detection method based on UAV is urgently needed to avoid major safety accidents. At the same time, the geographical distribution of IoT devices results in the inefficient use of the significant computing potential held by a large number of devices. As a result, the Dispersed Computing (DCOMP) architecture enables collaborative computing between devices in the Internet of Everything (IoE), promotes low-latency and efficient cross-wide applications, and… More >

Shiming Lin^1,2,3, Ling Ye², Yijie Zhuang¹, Lingyun Lu^2,*, Shaoqiu Zheng^2,*, Chenxi Huang¹, Ng Yin Kwee⁴

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 299-317, 2024, DOI:10.32604/cmc.2024.051379

Abstract In recent years, with the continuous development of deep learning and knowledge graph reasoning methods, more and more researchers have shown great interest in improving knowledge graph reasoning methods by inferring missing facts through reasoning. By searching paths on the knowledge graph and making fact and link predictions based on these paths, deep learning-based Reinforcement Learning (RL) agents can demonstrate good performance and interpretability. Therefore, deep reinforcement learning-based knowledge reasoning methods have rapidly emerged in recent years and have become a hot research topic. However, even in a small and fixed knowledge graph reasoning action… More >

Tengda Li, Gang Wang, Qiang Fu^*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2559-2586, 2024, DOI:10.32604/cmes.2024.052039

Abstract Aiming at the problems of low solution accuracy and high decision pressure when facing large-scale dynamic task allocation (DTA) and high-dimensional decision space with single agent, this paper combines the deep reinforcement learning (DRL) theory and an improved Multi-Agent Deep Deterministic Policy Gradient (MADDPG-D2) algorithm with a dual experience replay pool and a dual noise based on multi-agent architecture is proposed to improve the efficiency of DTA. The algorithm is based on the traditional Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, and considers the introduction of a double noise mechanism to increase the action exploration… More >

Ziqiao Zhou¹, Tianyang Zhou^1,*, Jinghao Xu², Junhu Zhu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2613-2634, 2024, DOI:10.32604/cmes.2023.028553

Abstract Intelligent penetration testing is of great significance for the improvement of the security of information systems, and the critical issue is the planning of penetration test paths. In view of the difficulty for attackers to obtain complete network information in realistic network scenarios, Reinforcement Learning (RL) is a promising solution to discover the optimal penetration path under incomplete information about the target network. Existing RL-based methods are challenged by the sizeable discrete action space, which leads to difficulties in the convergence. Moreover, most methods still rely on experts’ knowledge. To address these issues, this paper… More >

Mehrdad Shoeibi¹, Mohammad Mehdi Sharifi Nevisi², Reza Salehi³, Diego Martín^3,*, Zahra Halimi⁴, Sahba Baniasadi⁵

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3469-3493, 2024, DOI:10.32604/cmc.2024.049847

Abstract Hyperspectral (HS) image classification plays a crucial role in numerous areas including remote sensing (RS), agriculture, and the monitoring of the environment. Optimal band selection in HS images is crucial for improving the efficiency and accuracy of image classification. This process involves selecting the most informative spectral bands, which leads to a reduction in data volume. Focusing on these key bands also enhances the accuracy of classification algorithms, as redundant or irrelevant bands, which can introduce noise and lower model performance, are excluded. In this paper, we propose an approach for HS image classification using… More >

Shuming Sha^1,2, Naiwang Guo³, Wang Luo^1,2, Yong Zhang^1,2,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5105-5124, 2024, DOI:10.32604/cmc.2024.049584

Abstract This paper focuses on the scheduling problem of workflow tasks that exhibit interdependencies. Unlike independent batch tasks, workflows typically consist of multiple subtasks with intrinsic correlations and dependencies. It necessitates the distribution of various computational tasks to appropriate computing node resources in accordance with task dependencies to ensure the smooth completion of the entire workflow. Workflow scheduling must consider an array of factors, including task dependencies, availability of computational resources, and the schedulability of tasks. Therefore, this paper delves into the distributed graph database workflow task scheduling problem and proposes a workflow scheduling methodology based… More >

Hantao Huang, Zhifang Zhang^*, Zhenhua Wu, Yao Liu

Structural Durability & Health Monitoring, Vol.18, No.4, pp. 409-423, 2024, DOI:10.32604/sdhm.2024.048597

Abstract In this paper, recycled carbon fiber reinforced polymer (CFRP) mixture (CFRP-M, including recycled carbon fiber and powder) and refined recycled CFRP fiber (CFRP-F, mostly recycled carbon fiber) were added to cement to study the influence of addition on the flexural strength, compressive strength, and fluidity of cement-based materials. The recycled CFRP were prepared by mechanically processing the prepreg scraps generated during the manufacture of CFRP products. For comparison, commercial carbon fiber powder was also added in cement and the performance was compared to that of addition of recycled CFRP. The hydration products and strengthening mechanism… More >

Guanfu Wang¹, Yudie Sun¹, Jinling Li^2,3,*, Yu Jiang¹, Chunhui Li¹, Huanan Yu^2,3, He Wang^2,3, Shiqiang Li^2,3

Energy Engineering, Vol.121, No.6, pp. 1671-1695, 2024, DOI:10.32604/ee.2024.047794

Abstract Traditional optimal scheduling methods are limited to accurate physical models and parameter settings, which are difficult to adapt to the uncertainty of source and load, and there are problems such as the inability to make dynamic decisions continuously. This paper proposed a dynamic economic scheduling method for distribution networks based on deep reinforcement learning. Firstly, the economic scheduling model of the new energy distribution network is established considering the action characteristics of micro-gas turbines, and the dynamic scheduling model based on deep reinforcement learning is constructed for the new energy distribution network system with a More >

Fuju Zhou^*, Li Li, Tengfei Jia, Yongchang Yin, Aixiang Shi, Shengrong Xu

Energy Engineering, Vol.121, No.6, pp. 1697-1711, 2024, DOI:10.32604/ee.2024.047680

Abstract When a line failure occurs in a power grid, a load transfer is implemented to reconfigure the network by changing the states of tie-switches and load demands. Computation speed is one of the major performance indicators in power grid load transfer, as a fast load transfer model can greatly reduce the economic loss of post-fault power grids. In this study, a reinforcement learning method is developed based on a deep deterministic policy gradient. The tedious training process of the reinforcement learning model can be conducted offline, so the model shows satisfactory performance in real-time operation, More >