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  • Open Access

    ARTICLE

    Realization of Internet of Things Smart Appliances

    Jia‐Shing Sheua, I‐Chen Chenb, Yi‐Syong Liaoa

    Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 395-404, 2019, DOI:10.31209/2019.100000101

    Abstract This study proposed a household energy state monitoring system (HESMS) and a household energy load monitoring system (HELMS) for monitoring smart appliances. The HESMS applies reinforcement learning to receive changes in the external environment and the state of an electrical appliance, determines if the electrical appliance should be turned on, and controls the signals sent to the HELMS according to these decisions. The HELMS implements an ON/OFF control mechanism for household appliances according to the control signals and the power consumption state. The proposed systems are based on the wireless communication network and can monitor household appliances’ energy usage, control… More >

  • Open Access

    ARTICLE

    Auxiliary Diagnosis Based on the Knowledge Graph of TCM Syndrome

    Yonghong Xie1, 3, Liangyuan Hu1, 3, Xingxing Chen2, 3, Jim Feng4, Dezheng Zhang1, 3, *

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 481-494, 2020, DOI:10.32604/cmc.2020.010297

    Abstract As one of the most valuable assets in China, traditional medicine has a long history and contains pieces of knowledge. The diagnosis and treatment of Traditional Chinese Medicine (TCM) has benefited from the natural language processing technology. This paper proposes a knowledge-based syndrome reasoning method in computerassisted diagnosis. This method is based on the established knowledge graph of TCM and this paper introduces the reinforcement learning algorithm to mine the hidden relationship among the entities and obtain the reasoning path. According to this reasoning path, we could infer the path from the symptoms to the syndrome and get all possibilities… More >

  • Open Access

    ARTICLE

    A Novel Beam Search to Improve Neural Machine Translation for English-Chinese

    Xinyue Lin1, Jin Liu1, *, Jianming Zhang2, Se-Jung Lim3

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 387-404, 2020, DOI:10.32604/cmc.2020.010984

    Abstract Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, overcoming the weaknesses of conventional phrase-based translation systems. Although NMT based systems have gained their popularity in commercial translation applications, there is still plenty of room for improvement. Being the most popular search algorithm in NMT, beam search is vital to the translation result. However, traditional beam search can produce duplicate or missing translation due to its target sequence selection strategy. Aiming to alleviate this problem, this paper proposed neural machine translation improvements based on a novel beam search evaluation function. And we use reinforcement learning to train… More >

  • Open Access

    ARTICLE

    Survey on the Application of Deep Reinforcement Learning in Image Processing

    Wei Fang1, 2, 3, ∗, Lin Pang1, Weinan Yi1

    Journal on Artificial Intelligence, Vol.2, No.1, pp. 39-58, 2020, DOI:10.32604/jai.2020.09789

    Abstract In recent years, with the rapid development of human society, more and more complex tasks have emerged that require deep learning to automatically extract abstract feature representations from a large amount of data, and use reinforcement learning to learn the best strategy to complete the task. Through the combination of deep learning and reinforcement learning, end-to-end input and output can be achieved, and substantial breakthroughs have been made in many planning and decision-making systems with infinite states, such as games, in particular, AlphaGo, robotics, natural language processing, dialogue systems, machine translation, and computer vision. In this paper we have summarized… More >

  • Open Access

    ARTICLE

    A Multi-Agent System for Environmental Monitoring Using Boolean Networks and Reinforcement Learning

    Hanzhong Zheng1, Dejie Shi2,*

    Journal of Cyber Security, Vol.2, No.2, pp. 85-96, 2020, DOI:10.32604/jcs.2020.010086

    Abstract Distributed wireless sensor networks have been shown to be effective for environmental monitoring tasks, in which multiple sensors are deployed in a wide range of the environments to collect information or monitor a particular event, Wireless sensor networks, consisting of a large number of interacting sensors, have been successful in a variety of applications where they are able to share information using different transmission protocols through the communication network. However, the irregular and dynamic environment requires traditional wireless sensor networks to have frequent communications to exchange the most recent information, which can easily generate high communication cost through the collaborative… More >

  • Open Access

    ARTICLE

    Study of the Superficial Modification of Sisal Fibres with Lignin, and Its Use As a Reinforcement Agent in Cementitious Composites

    Plínio B. Mundim1, Rondinele A. R. Ferreira1, Leila A. C. Motta1, Mariana A. Henrique2, Daniel Pasquini2,*

    Journal of Renewable Materials, Vol.8, No.8, pp. 891-903, 2020, DOI:10.32604/jrm.2020.010655

    Abstract The objective of this work was to evaluate different superficial treatments of sisal fibres employing lignin, and their use as a reinforcement agent in cementitious composites. The treatments consisted of superficially impregnating sisal fibres (S) with organosolv lignin (LO), organosolv lignin and glutaraldehyde (LOG), Kraft lignin (LK) and Kraft lignin and glutaraldehyde (LKG). The fibre modifications were verified by FTIR-ATR and SEM analyzes, and the presence of lignin on the surface of the fibres was evidenced, confirming the effectiveness of the treatments. The mechanical, thermal (by TGA) and water absorption properties of the fibres before and after the modifications were… More >

  • Open Access

    ARTICLE

    A DRL-Based Container Placement Scheme with Auxiliary Tasks

    Ningcheng Yuan1, Chao Jia2, *, Jizhao Lu3, Shaoyong Guo1, Wencui Li3, Xuesong Qiu1, Lei Shi4

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1657-1671, 2020, DOI:10.32604/cmc.2020.09840

    Abstract Container is an emerging virtualization technology and widely adopted in the cloud to provide services because of its lightweight, flexible, isolated and highly portable properties. Cloud services are often instantiated as clusters of interconnected containers. Due to the stochastic service arrival and complicated cloud environment, it is challenging to achieve an optimal container placement (CP) scheme. We propose to leverage Deep Reinforcement Learning (DRL) for solving CP problem, which is able to learn from experience interacting with the environment and does not rely on mathematical model or prior knowledge. However, applying DRL method directly dose not lead to a satisfying… More >

  • Open Access

    ARTICLE

    Resource Allocation and Power Control Policy for Device-toDevice Communication Using Multi-Agent Reinforcement Learning

    Yifei Wei1, *, Yinxiang Qu1, Min Zhao1, Lianping Zhang2, F. Richard Yu3

    CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1515-1532, 2020, DOI:10.32604/cmc.2020.09130

    Abstract Device-to-Device (D2D) communication is a promising technology that can reduce the burden on cellular networks while increasing network capacity. In this paper, we focus on the channel resource allocation and power control to improve the system resource utilization and network throughput. Firstly, we treat each D2D pair as an independent agent. Each agent makes decisions based on the local channel states information observed by itself. The multi-agent Reinforcement Learning (RL) algorithm is proposed for our multi-user system. We assume that the D2D pair do not possess any information on the availability and quality of the resource block to be selected,… More >

  • Open Access

    ARTICLE

    Corrosion Performance of Stainless Steel Reinforcement in the Concrete Prepared with Seawater and Coral Waste and Its Ecological Effects

    Xingguo Feng1,2,3, Yiji Zhang1, Xiangyu Lu1,*, Yiwen Xu1, Leyuan Zhang1, Chao Zhu1, Tong Wu1, Yashi Yang4, Xuhui Zhao5

    Journal of Renewable Materials, Vol.8, No.5, pp. 513-534, 2020, DOI:10.32604/jrm.2020.09549

    Abstract Durability and ecological effects of the stainless steel reinforced coral waste concrete were compared with those of the carbon steel reinforced ordinary concrete. The results showed that the corrosion current densities of the stainless steel in the coral waste concrete were less than one-tenth of those of the carbon steel in the same grade ordinary concrete. The stainless steel in the seawater coral waste concrete maintained passivation even after more than two years of immersion in 3.5% NaCl solution, while the carbon steel counterparts in the ordinary concrete were seriously corroded under the same condition. Simultaneously, the corrosion current density… More >

  • Open Access

    ARTICLE

    Experimental Study on the Influence Mechanism of Carbon Fiber/Epoxy Composite Reinforcement and Matrix on Its Fire Performance

    Lei Zhang1, Haiyan Wang1,*, Junpeng Zhang1, Zhi Wang2, Zuohui Xu1, Xinyu Gao1

    Journal of Renewable Materials, Vol.8, No.3, pp. 219-237, 2020, DOI:10.32604/jrm.2020.09096

    Abstract The effects of the number of layers, the arrangement of carbon fiber (CF) tow and the epoxy resin (ER) matrix on the fire performance of carbon fiber/epoxy composites (CFEC) were studied by a variety of experimental methods. The results show that the number of layers of CF tow has influence on the combustion characteristics and fire propagation of the composites. The arrangement of CF tow has influence on flame propagation rate and high temperature mechanicalproperties. The mechanism of the influence of the number of layers of CF tow on the composite is mainly due to the different thermal capacity of… More >

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