Zhongguo Tang¹, Haijin Zhuo¹, Beian Li¹, Xiaotao Ma², Siyu Zhao², Kai Tong^2,*

Structural Durability & Health Monitoring, Vol.17, No.5, pp. 407-431, 2023, DOI:10.32604/sdhm.2023.026033

Abstract Corrosion can be very harmful to the service life and several properties of reinforced concrete structures. The metal magnetic memory (MMM) method, as a newly developed spontaneous magnetic flux leakage (SMFL) non-destructive testing (NDT) technique, is considered a potentially viable method for detecting corrosion damage in reinforced concrete members. To this end, in this paper, the indoor electrochemical method was employed to accelerate the corrosion of outsourced concrete specimens with different steel bar diameters, and the normal components BB_z and its gradient of the SMFL fields on the specimen surfaces were investigated based on the metal magnetic memory (MMM) method.… More >

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

Journal on Artificial Intelligence, Vol.5, pp. 1-14, 2023, DOI:10.32604/jai.2023.039786

Abstract Autonomous agents can explore the environment around them when equipped with advanced hardware and software systems that help intelligent agents minimize collisions. These systems are developed under the term Artificial Intelligence (AI) safety. AI safety is essential to provide reliable service to consumers in various fields such as military, education, healthcare, and automotive. This paper presents the design of an AI safety algorithm for safe autonomous navigation using Reinforcement Learning (RL). Machine Learning Agents Toolkit (ML-Agents) was used to train the agent with a proximal policy optimizer algorithm with an intrinsic curiosity module (PPO + ICM). This training aims to improve AI… More >

Wei Zhang¹, Bin Ji², Ping He¹, Nanqin Wang¹, Yuwei Wang¹, Mengzhe Zhang^2,*

Energy Engineering, Vol.120, No.9, pp. 2177-2192, 2023, DOI:10.32604/ee.2023.026504

Abstract Power flow calculation is the basis of power grid planning and many system analysis tasks require convergent power flow conditions. To address the unsolvable power flow problem caused by the reactive power imbalance, a method for adjusting reactive power flow convergence based on deep reinforcement learning is proposed. The deep reinforcement learning method takes switching parallel reactive compensation as the action space and sets the reward value based on the power flow convergence and reactive power adjustment. For the non-convergence power flow, the 500 kV nodes with reactive power compensation devices on the low-voltage side are converted into PV nodes… More >

Rundong Yang^1,*, 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 >

Wenjing Xia^1,*, 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

Lei Yin¹, Chang Sun², Ming Gao³, Yadong Fang⁴, Ming Li¹, Fengyu Zhou^1,*

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 >

Alaa Omran Almagrabi^1,*, 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 >

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 >

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 >

You Lu^1,2,#,*, Linqian Cui^1,2,#,*, Yunzhe Wang^1,2, Jiacheng Sun^1,2, 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 >