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  • Open Access

    ARTICLE

    Enhancing Image Description Generation through Deep Reinforcement Learning: Fusing Multiple Visual Features and Reward Mechanisms

    Yan Li, Qiyuan Wang*, Kaidi Jia

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2469-2489, 2024, DOI:10.32604/cmc.2024.047822

    Abstract Image description task is the intersection of computer vision and natural language processing, and it has important prospects, including helping computers understand images and obtaining information for the visually impaired. This study presents an innovative approach employing deep reinforcement learning to enhance the accuracy of natural language descriptions of images. Our method focuses on refining the reward function in deep reinforcement learning, facilitating the generation of precise descriptions by aligning visual and textual features more closely. Our approach comprises three key architectures. Firstly, it utilizes Residual Network 101 (ResNet-101) and Faster Region-based Convolutional Neural Network (Faster R-CNN) to extract average… More >

  • Open Access

    ARTICLE

    Simulation of Corrosion-Induced Cracking of Reinforced Concrete Based on Fracture Phase Field Method

    Xiaozhou Xia1, Changsheng Qin1, Guangda Lu2, Xin Gu1,*, Qing Zhang1

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2257-2276, 2024, DOI:10.32604/cmes.2023.031238

    Abstract Accurate simulation of the cracking process caused by rust expansion of reinforced concrete (RC) structures plays an intuitive role in revealing the corrosion-induced failure mechanism. Considering the quasi-brittle fracture of concrete, the fracture phase field driven by the compressive-shear term is constructed and added to the traditional brittle fracture phase field model. The rationality of the proposed model is verified by a mixed fracture example under a shear displacement load. Then, the extended fracture phase model is applied to simulate the corrosion-induced cracking process of RC. The cracking patterns caused by non-uniform corrosion expansion are discussed for RC specimens with… More >

  • Open Access

    ARTICLE

    Numerical Simulation of Surrounding Rock Deformation and Grouting Reinforcement of Cross-Fault Tunnel under Different Excavation Methods

    Duan Zhu1,2, Zhende Zhu1,2, Cong Zhang1,2,*, Lun Dai1,2, Baotian Wang1,2

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2445-2470, 2024, DOI:10.32604/cmes.2023.030847

    Abstract Tunnel construction is susceptible to accidents such as loosening, deformation, collapse, and water inrush, especially under complex geological conditions like dense fault areas. These accidents can cause instability and damage to the tunnel. As a result, it is essential to conduct research on tunnel construction and grouting reinforcement technology in fault fracture zones to address these issues and ensure the safety of tunnel excavation projects. This study utilized the Xianglushan cross-fault tunnel to conduct a comprehensive analysis on the construction, support, and reinforcement of a tunnel crossing a fault fracture zone using the three-dimensional finite element numerical method. The study… More >

  • Open Access

    ARTICLE

    Research on the Application of Reinforcement Learning Model in Vocational Education System

    Fei Xue*

    Journal on Artificial Intelligence, Vol.5, pp. 131-143, 2023, DOI:10.32604/jai.2023.046293

    Abstract Vocational education can effectively improve the vocational skills of employees, improve people’s traditional concept of vocational education, and focus on the training of vocational skills for students by using new educational methods and concepts, so that they can master key vocational skills and develop key abilities. In this paper, three different learning models, Deep Knowledge Tracing (DKT), Dynamic Key-Value Memory Networks (DKVMN) and Double Deep Q-network (DDQN), are used to evaluate the indicators in the vocational education system. On the one hand, the influence of learning degree on the performance of the model is compared, on the other hand, the… More >

  • Open Access

    ARTICLE

    Multi-Versus Optimization with Deep Reinforcement Learning Enabled Affect Analysis on Arabic Corpus

    Mesfer Al Duhayyim1,*, Badriyya B. Al-onazi2, Jaber S. Alzahrani3, Hussain Alshahrani4, Mohamed Ahmed Elfaki4, Abdullah Mohamed5, Ishfaq Yaseen6, Gouse Pasha Mohammed6, Mohammed Rizwanullah6, Abu Sarwar Zamani6

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 3049-3065, 2023, DOI:10.32604/csse.2023.033836

    Abstract Sentiment analysis (SA) of the Arabic language becomes important despite scarce annotated corpora and confined sources. Arabic affect Analysis has become an active research zone nowadays. But still, the Arabic language lags behind adequate language sources for enabling the SA tasks. Thus, Arabic still faces challenges in natural language processing (NLP) tasks because of its structure complexities, history, and distinct cultures. It has gained lesser effort than the other languages. This paper developed a Multi-versus Optimization with Deep Reinforcement Learning Enabled Affect Analysis (MVODRL-AA) on Arabic Corpus. The presented MVODRL-AA model majorly concentrates on identifying and classifying effects or emotions… More >

  • Open Access

    ARTICLE

    An Intelligent Algorithm for Solving Weapon-Target Assignment Problem: DDPG-DNPE Algorithm

    Tengda Li, Gang Wang, Qiang Fu*, Xiangke Guo, Minrui Zhao, Xiangyu Liu

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3499-3522, 2023, DOI:10.32604/cmc.2023.041253

    Abstract Aiming at the problems of traditional dynamic weapon-target assignment algorithms in command decision-making, such as large computational amount, slow solution speed, and low calculation accuracy, combined with deep reinforcement learning theory, an improved Deep Deterministic Policy Gradient algorithm with dual noise and prioritized experience replay is proposed, which uses a double noise mechanism to expand the search range of the action, and introduces a priority experience playback mechanism to effectively achieve data utilization. Finally, the algorithm is simulated and validated on the ground-to-air countermeasures digital battlefield. The results of the experiment show that, under the framework of the deep neural… More >

  • Open Access

    ARTICLE

    Multi-Agent Deep Reinforcement Learning for Efficient Computation Offloading in Mobile Edge Computing

    Tianzhe Jiao, Xiaoyue Feng, Chaopeng Guo, Dongqi Wang, Jie Song*

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3585-3603, 2023, DOI:10.32604/cmc.2023.040068

    Abstract Mobile-edge computing (MEC) is a promising technology for the fifth-generation (5G) and sixth-generation (6G) architectures, which provides resourceful computing capabilities for Internet of Things (IoT) devices, such as virtual reality, mobile devices, and smart cities. In general, these IoT applications always bring higher energy consumption than traditional applications, which are usually energy-constrained. To provide persistent energy, many references have studied the offloading problem to save energy consumption. However, the dynamic environment dramatically increases the optimization difficulty of the offloading decision. In this paper, we aim to minimize the energy consumption of the entire MEC system under the latency constraint by… More >

  • Open Access

    ARTICLE

    Role Dynamic Allocation of Human-Robot Cooperation Based on Reinforcement Learning in an Installation of Curtain Wall

    Zhiguang Liu1, Shilin Wang1, Jian Zhao1,*, Jianhong Hao2, Fei Yu3

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 473-487, 2024, DOI:10.32604/cmes.2023.029729

    Abstract A real-time adaptive roles allocation method based on reinforcement learning is proposed to improve human-robot cooperation performance for a curtain wall installation task. This method breaks the traditional idea that the robot is regarded as the follower or only adjusts the leader and the follower in cooperation. In this paper, a self-learning method is proposed which can dynamically adapt and continuously adjust the initiative weight of the robot according to the change of the task. Firstly, the physical human-robot cooperation model, including the role factor is built. Then, a reinforcement learning model that can adjust the role factor in real… More > Graphic Abstract

    Role Dynamic Allocation of Human-Robot Cooperation Based on Reinforcement Learning in an Installation of Curtain Wall

  • Open Access

    ARTICLE

    Dynamic Security SFC Branching Path Selection Using Deep Reinforcement Learning

    Shuangxing Deng, Man Li*, Huachun Zhou

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2919-2939, 2023, DOI:10.32604/iasc.2023.039985

    Abstract Security service function chaining (SFC) based on software-defined networking (SDN) and network function virtualization (NFV) technology allows traffic to be forwarded sequentially among different security service functions to achieve a combination of security functions. Security SFC can be deployed according to requirements, but the current SFC is not flexible enough and lacks an effective feedback mechanism. The SFC is not traffic aware and the changes of traffic may cause the previously deployed security SFC to be invalid. How to establish a closed-loop mechanism to enhance the adaptive capability of the security SFC to malicious traffic has become an important issue.… More >

  • Open Access

    ARTICLE

    Quantitative Detection of Corrosion State of Concrete Internal Reinforcement Based on Metal Magnetic Memory

    Zhongguo Tang1, Haijin Zhuo1, Beian Li1, Xiaotao Ma2, Siyu Zhao2, Kai Tong2,*

    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 BBz and its gradient of the SMFL fields on the specimen surfaces were investigated based on the metal magnetic memory (MMM) method.… More >

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