Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (667)
  • Open Access

    ARTICLE

    Reinforcement Effect of Recycled CFRP on Cement-Based Composites: With a Comparison to Commercial Carbon Fiber Powder

    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 - 05 June 2024

    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 >

  • Open Access

    ARTICLE

    Dynamic Economic Scheduling with Self-Adaptive Uncertainty in Distribution Network Based on Deep Reinforcement Learning

    Guanfu Wang1, Yudie Sun1, Jinling Li2,3,*, Yu Jiang1, Chunhui Li1, Huanan Yu2,3, He Wang2,3, Shiqiang Li2,3

    Energy Engineering, Vol.121, No.6, pp. 1671-1695, 2024, DOI:10.32604/ee.2024.047794 - 21 May 2024

    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 >

  • Open Access

    ARTICLE

    Intelligent Power Grid Load Transferring Based on Safe Action-Correction Reinforcement Learning

    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 - 21 May 2024

    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 >

  • Open Access

    ARTICLE

    Proactive Caching at the Wireless Edge: A Novel Predictive User Popularity-Aware Approach

    Yunye Wan1, Peng Chen2, Yunni Xia1,*, Yong Ma3, Dongge Zhu4, Xu Wang5, Hui Liu6, Weiling Li7, Xianhua Niu2, Lei Xu8, Yumin Dong9

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1997-2017, 2024, DOI:10.32604/cmes.2024.048723 - 20 May 2024

    Abstract Mobile Edge Computing (MEC) is a promising technology that provides on-demand computing and efficient storage services as close to end users as possible. In an MEC environment, servers are deployed closer to mobile terminals to exploit storage infrastructure, improve content delivery efficiency, and enhance user experience. However, due to the limited capacity of edge servers, it remains a significant challenge to meet the changing, time-varying, and customized needs for highly diversified content of users. Recently, techniques for caching content at the edge are becoming popular for addressing the above challenges. It is capable of filling… More >

  • Open Access

    ARTICLE

    Performance Evaluation of Multi-Agent Reinforcement Learning Algorithms

    Abdulghani M. Abdulghani, Mokhles M. Abdulghani, Wilbur L. Walters, Khalid H. Abed*

    Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 337-352, 2024, DOI:10.32604/iasc.2024.047017 - 21 May 2024

    Abstract Multi-Agent Reinforcement Learning (MARL) has proven to be successful in cooperative assignments. MARL is used to investigate how autonomous agents with the same interests can connect and act in one team. MARL cooperation scenarios are explored in recreational cooperative augmented reality environments, as well as real-world scenarios in robotics. In this paper, we explore the realm of MARL and its potential applications in cooperative assignments. Our focus is on developing a multi-agent system that can collaborate to attack or defend against enemies and achieve victory with minimal damage. To accomplish this, we utilize the StarCraft… More >

  • Open Access

    ARTICLE

    Trading in Fast-Changing Markets with Meta-Reinforcement Learning

    Yutong Tian1, Minghan Gao2, Qiang Gao1,*, Xiao-Hong Peng3

    Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 175-188, 2024, DOI:10.32604/iasc.2024.042762 - 21 May 2024

    Abstract How to find an effective trading policy is still an open question mainly due to the nonlinear and non-stationary dynamics in a financial market. Deep reinforcement learning, which has recently been used to develop trading strategies by automatically extracting complex features from a large amount of data, is struggling to deal with fast-changing markets due to sample inefficiency. This paper applies the meta-reinforcement learning method to tackle the trading challenges faced by conventional reinforcement learning (RL) approaches in non-stationary markets for the first time. In our work, the history trading data is divided into multiple… More >

  • Open Access

    ARTICLE

    QoS Routing Optimization Based on Deep Reinforcement Learning in SDN

    Yu Song1, Xusheng Qian2, Nan Zhang3, Wei Wang2, Ao Xiong1,*

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3007-3021, 2024, DOI:10.32604/cmc.2024.051217 - 15 May 2024

    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 >

  • Open Access

    ARTICLE

    Enhancing Relational Triple Extraction in Specific Domains: Semantic Enhancement and Synergy of Large Language Models and Small Pre-Trained Language Models

    Jiakai Li, Jianpeng Hu*, Geng Zhang

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2481-2503, 2024, DOI:10.32604/cmc.2024.050005 - 15 May 2024

    Abstract In the process of constructing domain-specific knowledge graphs, the task of relational triple extraction plays a critical role in transforming unstructured text into structured information. Existing relational triple extraction models face multiple challenges when processing domain-specific data, including insufficient utilization of semantic interaction information between entities and relations, difficulties in handling challenging samples, and the scarcity of domain-specific datasets. To address these issues, our study introduces three innovative components: Relation semantic enhancement, data augmentation, and a voting strategy, all designed to significantly improve the model’s performance in tackling domain-specific relational triple extraction tasks. We first… More >

  • Open Access

    ARTICLE

    Research on Performance Optimization of Spark Distributed Computing Platform

    Qinlu He1,*, Fan Zhang1, Genqing Bian1, Weiqi Zhang1, Zhen Li2

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2833-2850, 2024, DOI:10.32604/cmc.2024.046807 - 15 May 2024

    Abstract Spark, a distributed computing platform, has rapidly developed in the field of big data. Its in-memory computing feature reduces disk read overhead and shortens data processing time, making it have broad application prospects in large-scale computing applications such as machine learning and image processing. However, the performance of the Spark platform still needs to be improved. When a large number of tasks are processed simultaneously, Spark’s cache replacement mechanism cannot identify high-value data partitions, resulting in memory resources not being fully utilized and affecting the performance of the Spark platform. To address the problem that… More >

  • Open Access

    ARTICLE

    A Study on the Role of Tourism in Enhancing Personal Mental Health in the Post-Epidemic Era

    Ruiqin Tian*, Yue Feng, Lingqi Zhan

    International Journal of Mental Health Promotion, Vol.26, No.4, pp. 325-334, 2024, DOI:10.32604/ijmhp.2023.042827 - 04 May 2024

    Abstract With the advent of the post-epidemic era, a great wave of tourism has been ushered in everywhere. The relationship between tourism and mental health has become a hot topic in society. This paper investigates the enhancement of people’s mental health after tourism through social survey. Using Hangzhou as the sample collection site, this paper conducted a study on the role of tourism in enhancing personal mental health through descriptive analysis, factor analysis and structural equation modeling, and further specifically analyzed the role of mediating variables. The results showed that: (1) The purpose of tourism is… More >

Displaying 61-70 on page 7 of 667. Per Page