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  • Open Access

    ARTICLE

    Effects of Filler-Asphalt Ratio on the Properties of Lignin and Polyester Fiber Reinforced SMPU/SBS Modified Asphalt Mortar

    Wenjing Xia1,*, JinHui Wang1, Tao Xu1, Nan Jiang2

    Journal of Renewable Materials, Vol.11, No.8, pp. 3387-3402, 2023, DOI:10.32604/jrm.2023.026971

    Abstract To understand the effects of filler-asphalt ratio on different properties of lignin and polyester fiber reinforced shape memory polyurethane (SMPU)/styrene butadiene styrene (SBS) composite modified asphalt mortar (PSAM), as well as to reveal the reinforcing and toughening mechanisms of lignin and polyester fibers on PSAM, SMPU, SBS and mineral powder were first utilized to prepare PSAM. Then the conventional, rheological and anticracking properties of lignin fiber reinforced PSAM (LFAM) and polyester fiber reinforced PSAM (PFAM) at different filler-asphalt ratios were characterized. Test results indicate that the shear strength, deformation resistance and viscosity are increased after adding 0.8wt% lignin fiber or… More > Graphic Abstract

    Effects of Filler-Asphalt Ratio on the Properties of Lignin and Polyester Fiber Reinforced SMPU/SBS Modified Asphalt Mortar

  • Open Access

    ARTICLE

    Hyper-Heuristic Task Scheduling Algorithm Based on Reinforcement Learning in Cloud Computing

    Lei Yin1, Chang Sun2, Ming Gao3, Yadong Fang4, Ming Li1, Fengyu Zhou1,*

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1587-1608, 2023, DOI:10.32604/iasc.2023.039380

    Abstract The solution strategy of the heuristic algorithm is pre-set and has good performance in the conventional cloud resource scheduling process. However, for complex and dynamic cloud service scheduling tasks, due to the difference in service attributes, the solution efficiency of a single strategy is low for such problems. In this paper, we presents a hyper-heuristic algorithm based on reinforcement learning (HHRL) to optimize the completion time of the task sequence. Firstly, In the reward table setting stage of HHRL, we introduce population diversity and integrate maximum time to comprehensively determine the task scheduling and the selection of low-level heuristic strategies.… More >

  • Open Access

    ARTICLE

    Network Learning-Enabled Sensor Association for Massive Internet of Things

    Alaa Omran Almagrabi1,*, Rashid Ali2, Daniyal Alghazzawi1, Bander A. Alzahrani1, Fahad M. Alotaibi1

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 843-853, 2023, DOI:10.32604/csse.2023.037652

    Abstract The massive Internet of Things (IoT) comprises different gateways (GW) covering a given region of a massive number of connected devices with sensors. In IoT networks, transmission interference is observed when different sensor devices (SD) try to send information to a single GW. This is mitigated by allotting various channels to adjoining GWs. Furthermore, SDs are permitted to associate with any GW in a network, naturally choosing the one with a higher received signal strength indicator (RSSI), regardless of whether it is the ideal choice for network execution. Finding an appropriate GW to optimize the performance of IoT systems is… More >

  • Open Access

    ARTICLE

    Implementation of Strangely Behaving Intelligent Agents to Determine Human Intervention During Reinforcement Learning

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

    Journal on Artificial Intelligence, Vol.4, No.4, pp. 261-277, 2022, DOI:10.32604/jai.2022.039703

    Abstract Intrinsic motivation helps autonomous exploring agents traverse a larger portion of their environments. However, simulations of different learning environments in previous research show that after millions of timesteps of successful training, an intrinsically motivated agent may learn to act in ways unintended by the designer. This potential for unintended actions of autonomous exploring agents poses threats to the environment and humans if operated in the real world. We investigated this topic by using Unity’s Machine Learning Agent Toolkit (ML-Agents) implementation of the Proximal Policy Optimization (PPO) algorithm with the Intrinsic Curiosity Module (ICM) to train autonomous exploring agents in three… More >

  • Open Access

    ARTICLE

    Reliable Scheduling Method for Sensitive Power Business Based on Deep Reinforcement Learning

    Shen Guo*, Jiaying Lin, Shuaitao Bai, Jichuan Zhang, Peng Wang

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1053-1066, 2023, DOI:10.32604/iasc.2023.038332

    Abstract The main function of the power communication business is to monitor, control and manage the power communication network to ensure normal and stable operation of the power communication network. Communication services related to dispatching data networks and the transmission of fault information or feeder automation have high requirements for delay. If processing time is prolonged, a power business cascade reaction may be triggered. In order to solve the above problems, this paper establishes an edge object-linked agent business deployment model for power communication network to unify the management of data collection, resource allocation and task scheduling within the system, realizes… 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

    Feature Selection with Deep Reinforcement Learning for Intrusion Detection System

    S. Priya1,*, K. Pradeep Mohan Kumar2

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3339-3353, 2023, DOI:10.32604/csse.2023.030630

    Abstract An intrusion detection system (IDS) becomes an important tool for ensuring security in the network. In recent times, machine learning (ML) and deep learning (DL) models can be applied for the identification of intrusions over the network effectively. To resolve the security issues, this paper presents a new Binary Butterfly Optimization algorithm based on Feature Selection with DRL technique, called BBOFS-DRL for intrusion detection. The proposed BBOFSDRL model mainly accomplishes the recognition of intrusions in the network. To attain this, the BBOFS-DRL model initially designs the BBOFS algorithm based on the traditional butterfly optimization algorithm (BOA) to elect feature subsets.… More >

  • Open Access

    ARTICLE

    Reinforcement Learning with an Ensemble of Binary Action Deep Q-Networks

    A. M. Hafiz1, M. Hassaballah2,3,*, Abdullah Alqahtani3, Shtwai Alsubai3, Mohamed Abdel Hameed4

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 2651-2666, 2023, DOI:10.32604/csse.2023.031720

    Abstract With the advent of Reinforcement Learning (RL) and its continuous progress, state-of-the-art RL systems have come up for many challenging and real-world tasks. Given the scope of this area, various techniques are found in the literature. One such notable technique, Multiple Deep Q-Network (DQN) based RL systems use multiple DQN-based-entities, which learn together and communicate with each other. The learning has to be distributed wisely among all entities in such a scheme and the inter-entity communication protocol has to be carefully designed. As more complex DQNs come to the fore, the overall complexity of these multi-entity systems has increased many… More >

  • Open Access

    ARTICLE

    Generation and Simulation of Basic Maneuver Action Library for 6-DOF Aircraft by Reinforcement Learning

    Jinlin Wang1, Jitao Teng3, Yang He1, Hongyu Yang1,*, Yulong Ji2,*, Zhikun Tang4, Ningwei Bai5

    Journal on Internet of Things, Vol.4, No.2, pp. 85-98, 2022, DOI:10.32604/jiot.2022.031043

    Abstract The development of modern air combat requires aircraft to have certain intelligent decision-making ability. In some of the existing solutions, the automatic control of aircraft is mostly composed of the upper mission decision and the lower control system. Although the underlying PID (Proportional Integral Derivative) based controller has a good performance in stable conditions, it lacks stability in complex environments. So, we need to design a new system for the problem of aircraft decision making. Studies have shown that the behavior of an aircraft can be viewed as a combination of several basic maneuvers. The establishment of aircraft basic motion… More >

  • Open Access

    ARTICLE

    Adaptive Cyber Defense Technique Based on Multiagent Reinforcement Learning Strategies

    Adel Alshamrani1,*, Abdullah Alshahrani2

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2757-2771, 2023, DOI:10.32604/iasc.2023.032835

    Abstract The static nature of cyber defense systems gives attackers a sufficient amount of time to explore and further exploit the vulnerabilities of information technology systems. In this paper, we investigate a problem where multiagent systems sensing and acting in an environment contribute to adaptive cyber defense. We present a learning strategy that enables multiple agents to learn optimal policies using multiagent reinforcement learning (MARL). Our proposed approach is inspired by the multiarmed bandits (MAB) learning technique for multiple agents to cooperate in decision making or to work independently. We study a MAB approach in which defenders visit a system multiple… More >

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