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  • Open Access

    ARTICLE

    Novel Static Security and Stability Control of Power Systems Based on Artificial Emotional Lazy Q-Learning

    Tao Bao*, Xiyuan Ma, Zhuohuan Li, Duotong Yang, Pengyu Wang, Changcheng Zhou

    Energy Engineering, Vol.121, No.6, pp. 1713-1737, 2024, DOI:10.32604/ee.2023.046150

    Abstract The stability problem of power grids has become increasingly serious in recent years as the size of novel power systems increases. In order to improve and ensure the stable operation of the novel power system, this study proposes an artificial emotional lazy Q-learning method, which combines artificial emotion, lazy learning, and reinforcement learning for static security and stability analysis of power systems. Moreover, this study compares the analysis results of the proposed method with those of the small disturbance method for a stand-alone power system and verifies that the proposed lazy Q-learning method is able to effectively screen useful data… More >

  • Open Access

    ARTICLE

    Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning Multi-Objective Grey Wolf Optimizer

    Hongliang Zhang1,2, Yi Chen1, Yuteng Zhang1, Gongjie Xu3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1459-1483, 2024, DOI:10.32604/cmes.2024.049756

    Abstract The distributed flexible job shop scheduling problem (DFJSP) has attracted great attention with the growth of the global manufacturing industry. General DFJSP research only considers machine constraints and ignores worker constraints. As one critical factor of production, effective utilization of worker resources can increase productivity. Meanwhile, energy consumption is a growing concern due to the increasingly serious environmental issues. Therefore, the distributed flexible job shop scheduling problem with dual resource constraints (DFJSP-DRC) for minimizing makespan and total energy consumption is studied in this paper. To solve the problem, we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based… More >

  • Open Access

    ARTICLE

    Traffic Control Based on Integrated Kalman Filtering and Adaptive Quantized Q-Learning Framework for Internet of Vehicles

    Othman S. Al-Heety1,*, Zahriladha Zakaria1,*, Ahmed Abu-Khadrah2, Mahamod Ismail3, Sarmad Nozad Mahmood4, Mohammed Mudhafar Shakir5, Sameer Alani6, Hussein Alsariera1

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2103-2127, 2024, DOI:10.32604/cmes.2023.029509

    Abstract Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the decision. In this article, these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things data. The framework integrates Kalman filtering and Q-learning. Unlike smoothing Kalman filtering, our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction error. Unlike traditional Q-learning, our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from high traffic on the road… More >

  • Open Access

    ARTICLE

    Social Engineering Attack-Defense Strategies Based on Reinforcement Learning

    Rundong Yang1,*, Kangfeng Zheng1, Xiujuan Wang2, Bin Wu1, Chunhua Wu1

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2153-2170, 2023, DOI:10.32604/csse.2023.038917

    Abstract Social engineering attacks are considered one of the most hazardous cyberattacks in cybersecurity, as human vulnerabilities are often the weakest link in the entire network. Such vulnerabilities are becoming increasingly susceptible to network security risks. Addressing the social engineering attack defense problem has been the focus of many studies. However, two main challenges hinder its successful resolution. Firstly, the vulnerabilities in social engineering attacks are unique due to multistage attacks, leading to incorrect social engineering defense strategies. Secondly, social engineering attacks are real-time, and the defense strategy algorithms based on gaming or reinforcement learning are too complex to make rapid… More >

  • Open Access

    ARTICLE

    Residential Energy Consumption Forecasting Based on Federated Reinforcement Learning with Data Privacy Protection

    You Lu1,2,#,*, Linqian Cui1,2,#,*, Yunzhe Wang1,2, Jiacheng Sun1,2, Lanhui Liu3

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 717-732, 2023, DOI:10.32604/cmes.2023.027032

    Abstract Most studies have conducted experiments on predicting energy consumption by integrating data for model training. However, the process of centralizing data can cause problems of data leakage. Meanwhile, many laws and regulations on data security and privacy have been enacted, making it difficult to centralize data, which can lead to a data silo problem. Thus, to train the model while maintaining user privacy, we adopt a federated learning framework. However, in all classical federated learning frameworks secure aggregation, the Federated Averaging (FedAvg) method is used to directly weight the model parameters on average, which may have an adverse effect on… More >

  • Open Access

    ARTICLE

    An Intelligent Admission Control Scheme for Dynamic Slice Handover Policy in 5G Network Slicing

    Ratih Hikmah Puspita1, Jehad Ali1,*, Byeong-hee Roh2

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4611-4631, 2023, DOI:10.32604/cmc.2023.033598

    Abstract 5G use cases, for example enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and an ultra-reliable low latency communication (URLLC), need a network architecture capable of sustaining stringent latency and bandwidth requirements; thus, it should be extremely flexible and dynamic. Slicing enables service providers to develop various network slice architectures. As users travel from one coverage region to another area, the call must be routed to a slice that meets the same or different expectations. This research aims to develop and evaluate an algorithm to make handover decisions appearing in 5G sliced networks. Rules of thumb which indicates the accuracy… More >

  • Open Access

    ARTICLE

    Bayes-Q-Learning Algorithm in Edge Computing for Waste Tracking

    D. Palanikkumar1, R. Ramesh Kumar2, Mehedi Masud3, Mrim M. Alnfiai4, Mohamed Abouhawwash5,6,*

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2425-2440, 2023, DOI:10.32604/iasc.2023.033879

    Abstract The major environmental hazard in this pandemic is the unhygienic disposal of medical waste. Medical wastage is not properly managed it will become a hazard to the environment and humans. Managing medical wastage is a major issue in the city, municipalities in the aspects of the environment, and logistics. An efficient supply chain with edge computing technology is used in managing medical waste. The supply chain operations include processing of waste collection, transportation, and disposal of waste. Many research works have been applied to improve the management of wastage. The main issues in the existing techniques are ineffective and expensive… More >

  • Open Access

    ARTICLE

    Reinforcement Learning-Based Handover Scheme with Neighbor Beacon Frame Transmission

    Youngjun Kim1, Taekook Kim2, Hyungoo Choi1, Jinwoo Park1, Yeunwoong Kyung3,*

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 193-204, 2023, DOI:10.32604/iasc.2023.032784

    Abstract Mobility support to change the connection from one access point (AP) to the next (i.e., handover) becomes one of the important issues in IEEE 802.11 wireless local area networks (WLANs). During handover, the channel scanning procedure, which aims to collect neighbor AP (NAP) information on all available channels, accounts for most of the delay time. To reduce the channel scanning procedure, a neighbor beacon frame transmission scheme (N-BTS) was proposed for a seamless handover. N-BTS can provide a seamless handover by removing the channel scanning procedure. However, N-BTS always requires operating overhead even if there are few mobile stations (MSs)… More >

  • Open Access

    ARTICLE

    Q-Learning-Based Pesticide Contamination Prediction in Vegetables and Fruits

    Kandasamy Sellamuthu*, Vishnu Kumar Kaliappan

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 715-736, 2023, DOI:10.32604/csse.2023.029017

    Abstract Pesticides have become more necessary in modern agricultural production. However, these pesticides have an unforeseeable long-term impact on people's wellbeing as well as the ecosystem. Due to a shortage of basic pesticide exposure awareness, farmers typically utilize pesticides extremely close to harvesting. Pesticide residues within foods, particularly fruits as well as veggies, are a significant issue among farmers, merchants, and particularly consumers. The residual concentrations were far lower than these maximal allowable limits, with only a few surpassing the restrictions for such pesticides in food. There is an obligation to provide a warning about this amount of pesticide use in… More >

  • Open Access

    ARTICLE

    QL-CBR Hybrid Approach for Adapting Context-Aware Services

    Somia Belaidouni1,2, Moeiz Miraoui3,4,*, Chakib Tadj1

    Computer Systems Science and Engineering, Vol.43, No.3, pp. 1085-1098, 2022, DOI:10.32604/csse.2022.024056

    Abstract A context-aware service in a smart environment aims to supply services according to user situational information, which changes dynamically. Most existing context-aware systems provide context-aware services based on supervised algorithms. Reinforcement algorithms are another type of machine-learning algorithm that have been shown to be useful in dynamic environments through trial-and-error interactions. They also have the ability to build excellent self-adaptive systems. In this study, we aim to incorporate reinforcement algorithms (Q-learning) into a context-aware system to provide relevant services based on a user’s dynamic context. To accelerate the convergence of reinforcement learning (RL) algorithms and provide the correct services in… More >

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