Xiaojuan Wang¹, Zikui Lu^1,*, Siyuan Sun², Jingyue Wang¹, Luona Song³, Merveille Nicolas⁴

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2055-2071, 2023, DOI:10.32604/cmc.2023.031750

Abstract With the development of the Industrial Internet of Things (IIoT), end devices (EDs) are equipped with more functions to capture information. Therefore, a large amount of data is generated at the edge of the network and needs to be processed. However, no matter whether these computing tasks are offloaded to traditional central clusters or mobile edge computing (MEC) devices, the data is short of security and may be changed during transmission. In view of this challenge, this paper proposes a trusted task offloading optimization scheme that can offer low latency and high bandwidth services for IIoT with data security. Blockchain… More >

Arnaud Nicollin¹, Xinjun Li¹, Pierre Girods¹, Antonio Pizzi^1,2,*, Yann Rogaume¹

Journal of Renewable Materials, Vol.1, No.4, pp. 311-316, 2013, DOI:10.7569/JRM.2013.634124

Abstract Experimental investigations have been carried out on the potential use of tannin-furfuryl alcohol resin for biobased composites using vegetal fi ber reinforcement. Results showed that a mixture containing 54% furfuryl alcohol, 45% modifi ed quebracho extract and 0.9% paratoluenesulfonic acid as a catalyst yields a resin that can be used with a nonwoven fl ax fi ber mat to manufacture lightweight composites with good mechanical properties and a very short curing time with a regular hot press. The panels made were tested for tensile and fl exural modulus and strength, water resistance and thermo-degradation. More >

D. Puglia^*, R. Petrucci, E. Fortunati, F. Luzi, J.M. Kenny, L. Torre

Journal of Renewable Materials, Vol.2, No.1, pp. 66-76, 2014, DOI:10.7569/JRM.2013.634134

Abstract Posidonia Oceanica waste was used as reinforcement in a polyethylene matrix and the obtained composites were characterised by a tensile test and morphological analysis. The fi brous material derived from P. Oceanica wastes was characterised by morphological, thermal and chemical analysis, and a subsequent treatment with sodium hydroxide (NaOH) at different weight content (2, 5 and 10 wt%) was considered as an optimised method for surface modifi cation of pristine fi bres (PO). The TGA analysis and morphological investigation of the treated fi bres selected the 5 wt% of NaOH as the best treatment. Matrix compatibilisation with maleic anhydride grafted… More >

Chao Zhu¹, Lei Wang¹, Dai Pan¹, Zifei Wang², Tao Wang², Licheng Wang^2,*, Chengjin Ye³

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 599-609, 2023, DOI:10.32604/cmes.2022.021052

Abstract In this paper, a model free volt/var control (VVC) algorithm is developed by using deep reinforcement learning (DRL). We transform the VVC problem of distribution networks into the network framework of PPO algorithm, in order to avoid directly solving a large-scale nonlinear optimization problem. We select photovoltaic inverters as agents to adjust system voltage in a distribution network, taking the reactive power output of inverters as action variables. An appropriate reward function is designed to guide the interaction between photovoltaic inverters and the distribution network environment. OPENDSS is used to output system node voltage and network loss. This method realizes… More >

Fatih Aydemir¹, Aydin Cetin^2,*

Computer Systems Science and Engineering, Vol.45, No.1, pp. 215-230, 2023, DOI:10.32604/csse.2023.031116

Abstract Dynamic area coverage with small unmanned aerial vehicle (UAV) systems is one of the major research topics due to limited payloads and the difficulty of decentralized decision-making process. Collaborative behavior of a group of UAVs in an unknown environment is another hard problem to be solved. In this paper, we propose a method for decentralized execution of multi-UAVs for dynamic area coverage problems. The proposed decentralized decision-making dynamic area coverage (DDMDAC) method utilizes reinforcement learning (RL) where each UAV is represented by an intelligent agent that learns policies to create collaborative behaviors in partially observable environment. Intelligent agents increase their… More >

V. A. Vijayakumar^*, J. Shanthini, S. Karthik, K. Srihari

Computer Systems Science and Engineering, Vol.45, No.1, pp. 659-669, 2023, DOI:10.32604/csse.2023.028400

Abstract The autonomous driving aims at ensuring the vehicle to effectively sense the environment and use proper strategies to navigate the vehicle without the interventions of humans. Hence, there exist a prediction of the background scenes and that leads to discontinuity between the predicted and planned outputs. An optimal prediction engine is required that suitably reads the background objects and make optimal decisions. In this paper, the author(s) develop an autonomous model for vehicle driving using ensemble model for large Sport Utility Vehicles (SUVs) that uses three different modules involving (a) recognition model, (b) planning model and (c) prediction model. The… More >

Lei Yin, Yijie Huang^*, Yanfei Dang, Qing Wang

Journal of Renewable Materials, Vol.11, No.1, pp. 209-231, 2023, DOI:10.32604/jrm.2023.020406

Abstract

This study investigates the bond between seawater scoria aggregate concrete (SSAC) and stainless reinforcement (SR) through a series of pull-out tests. A total of 39 specimens, considering five experimental parameters—concrete type (SSAC, ordinary concrete (OC) and seawater coral aggregate concrete (SCAC)), reinforcement type (SR, ordinary reinforcement (OR)), bond length (3, 5 and 8 times bar diameter), concrete strength (C25 and C30) and concrete cover thickness (42 and 67 mm)—were prepared. The typical bond properties (failure pattern, bond strength, bond-slip curves and bond stress distribution, etc.) of seawater scoria aggregate concrete-stainless reinforcement (SSAC-SR) specimen were systematically studied. Generally, the failure pattern… More > Graphic Abstract

Dianen Liang¹, Zhenhao Ding¹, Qilin Yan¹, Redžo Hasanagić², Leila Fathi³, Zi Yang¹, Longhao Li¹, Jianbo Wang¹, Houhua Luo¹, Qian Wang¹, Demiao Chu^1,*

Journal of Renewable Materials, Vol.11, No.1, pp. 435-451, 2023, DOI:10.32604/jrm.2022.023214

Abstract This study aims to improve the value of fast-growing wood and extend the heat-treated wood utilization using inorganic calcium carbonate (CaCO₃) crystals via an in-situ synthesis method. CaCl₂ and Na₂CO₃ solutions with a concentration ratio of 1:1 were successively introduced into the thermally modified poplar wood obtained by steam heat treatment (HT) at 200°C for 1.5 and 3 h, resulting in the in-situ synthesis of CaCO₃ crystals inside the heat-treated wood. The filling effect was best at the concentration of 1.2 mol/L. CaCO₃ was uniformly distributed in the cell cavities of the heat-treated wood, and some of the crystals were… More > Graphic Abstract

Salem Alkhalaf^*, Fahad Alturise

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6393-6405, 2022, DOI:10.32604/cmc.2022.031698

Abstract Software-defined network (SDN) is a new form of network architecture that has programmability, ease of use, centralized control, and protocol independence. It has received high attention since its birth. With SDN network architecture, network management becomes more efficient, and programmable interfaces make network operations more flexible and can meet the different needs of various users. The mainstream communication protocol of SDN is OpenFlow, which contains a Match Field in the flow table structure of the protocol, which matches the content of the packet header of the data received by the switch, and completes the corresponding actions according to the matching… More >

Eman K. Elsayed¹, Asmaa K. Elsayed^2,*, Kamal A. Eldahshan³

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5103-5120, 2022, DOI:10.32604/cmc.2022.030803

Abstract Industry 4.0 production environments and smart manufacturing systems integrate both the physical and decision-making aspects of manufacturing operations into autonomous and decentralized systems. One of the key aspects of these systems is a production planning, specifically, Scheduling operations on the machines. To cope with this problem, this paper proposed a Deep Reinforcement Learning with an Actor-Critic algorithm (DRLAC). We model the Job-Shop Scheduling Problem (JSSP) as a Markov Decision Process (MDP), represent the state of a JSSP as simple Graph Isomorphism Networks (GIN) to extract nodes features during scheduling, and derive the policy of optimal scheduling which guides the included… More >