Igor Grešovnik^1,2, Tadej Kodelja¹, Robert Vertnik^2,3, Bojan Senčič^3,2,3, Božidar Šarler^1,2,4

CMC-Computers, Materials & Continua, Vol.30, No.1, pp. 19-38, 2012, DOI:10.3970/cmc.2012.030.019

Abstract Artificial neural networks (ANNs) are employed as an alternative to physical modeling for calculation of the relations between the production path process parameters (melting of scrap steel and alloying, continuous casting, hydrogen removal, reheating, rolling, and cooling on a cooling bed) and the final product mechanical properties (elongation, tensile strength, yield stress, hardness after rolling, necking) of steel semi products. They provide a much faster technique of response evaluation complementary to physical modeling. The Štore Steel company process path for production of steel bars is used as an example for demonstrating the approach. The applied ANN is of a multilayer… More >

Jin Wang^1,2, Chunwei Ju², Yu Gao², Arun Kumar Sangaiah³, Gwang-jun Kim^4,*

CMC-Computers, Materials & Continua, Vol.56, No.3, pp. 433-446, 2018, DOI: 10.3970/cmc.2018.04132

Abstract Wireless Sensor Networks (WSNs) are large-scale and high-density networks that typically have coverage area overlap. In addition, a random deployment of sensor nodes cannot fully guarantee coverage of the sensing area, which leads to coverage holes in WSNs. Thus, coverage control plays an important role in WSNs. To alleviate unnecessary energy wastage and improve network performance, we consider both energy efficiency and coverage rate for WSNs. In this paper, we present a novel coverage control algorithm based on Particle Swarm Optimization (PSO). Firstly, the sensor nodes are randomly deployed in a target area and remain static after deployment. Then, the… More >

Hongbin Zhang^{1, 2}, Yuzi Yi^{1, *}, Junshe Wang¹, Ning Cao^{3, *}, Qiang Duan⁴

CMC-Computers, Materials & Continua, Vol.56, No.3, pp. 381-399, 2018, DOI: 10.3970/cmc.2018.03787

Abstract Network security situation awareness is an important foundation for network security management, which presents the target system security status by analyzing existing or potential cyber threats in the target system. In network offense and defense, the network security state of the target system will be affected by both offensive and defensive strategies. According to this feature, this paper proposes a network security situation awareness method using stochastic game in cloud computing environment, uses the utility of both sides of the game to quantify the network security situation value. This method analyzes the nodes based on the network security state of… More >

Yueyue Li^1,*, Zhong Huang¹, Yugang Ma², Guangjun Wen¹

CMC-Computers, Materials & Continua, Vol.56, No.1, pp. 35-46, 2018, DOI: 10.3970/cmc.2018.03712

Abstract As a fundamental operation in ad hoc networks, broadcast could achieve efficient message propagations. Particularl y in the cognitive radio ad hoc network where unlicensed users have different sets of available channels, broadcasts are carried out on multiple channels. Accordingly, channel selection and collision avoidance are challenging issues to balance the efficiency against the reliability of broadcasting. In this paper, an anti-collision selective broadcast protocol, called acSB, is proposed. A channel selection algorithm based on limited neighbor information is considered to maximize success rates of transmissions once the sender and receiver have the same channel. Moreover, an anti-collision scheme is… More >

Jae-In Kim¹, Hyoseon Jang², Jeong-Hee Ahn³, Kilho Eom⁴, Sungsoo Na⁵

CMC-Computers, Materials & Continua, Vol.9, No.2, pp. 137-152, 2009, DOI:10.3970/cmc.2009.009.137

Abstract Protein dynamics is essential for gaining insight into biological functions of proteins. Although protein dynamics is well delineated by molecular model, the molecular model is computationally prohibited for simulating large protein structures. In this work, we provide the three-dimensional coarser-grained anisotropic model (CGAM), which is based on model reduction applicable to large protein structures. It is shown that CGAM achieves the fast computation on low-frequency modes, quantitatively comparable to original structural model such as elastic network model (ENM). This indicates that the CGAM by model reduction method enable us to understand the functional motion of large proteins with remarkable computational… More >

Jin Yang¹, Tao Li¹, Gang Liang^1,*, Wenbo He², Yue Zhao³

CMES-Computer Modeling in Engineering & Sciences, Vol.120, No.1, pp. 1-23, 2019, DOI:10.32604/cmes.2019.04727

Abstract Deep Learning presents a critical capability to be geared into environments being constantly changed and ongoing learning dynamic, which is especially relevant in Network Intrusion Detection. In this paper, as enlightened by the theory of Deep Learning Neural Networks, Hierarchy Distributed-Agents Model for Network Risk Evaluation, a newly developed model, is proposed. The architecture taken on by the distributed-agents model are given, as well as the approach of analyzing network intrusion detection using Deep Learning, the mechanism of sharing hyper-parameters to improve the efficiency of learning is presented, and the hierarchical evaluative framework for Network Risk Evaluation of the proposed… More >

Lanlan Rui¹, Yabin Qin^1,*, Biyao Li¹, Zhipeng Gao¹

CMES-Computer Modeling in Engineering & Sciences, Vol.118, No.2, pp. 291-315, 2019, DOI:10.31614/cmes.2018.04240

Abstract Maintenance is an important aspect in the lifecycle of communication network devices. Prevalent problems in the maintenance of communication networks include inconvenient data carrying and sub-optimal scheduling of work orders, which significantly restrict the efficiency of maintenance work. Moreover, most maintenance systems are still based on cloud architectures that slow down data transfer. With a focus on the completion time, quality, and load balancing of maintenance work, we propose in this paper a learning-based virus evolutionary genetic algorithm with multiple quality-of-service (QoS) constraints to implement intelligent scheduling in an edge network. The algorithm maintains the diversity of the population and… More >

Abdulkadir Karaci^1,*, Hasbi Yaprak², Osman Ozkaraca³, Ilhami Demir⁴, Osman Simsek⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.118, No.1, pp. 207-228, 2019, DOI:10.31614/cmes.2019.04216

Abstract In this study, deep-neural-network (DNN)- and artificial-neural-network (ANN)-based models along with regression models have been developed to estimate the pressure, bending and elongation values of ground-brick (GB)-added mortar samples. This study is aimed at utilizing GB as a mineral additive in concrete in the ratios 0.0%, 2.5%, 5.0%, 7.5%, 10.0%, 12.5% and 15.0%. In this study, 756 mortar samples were produced for 84 different series and were cured in tap water (W), 5% sodium sulphate solution (SS5) and 5% ammonium nitrate solution (AN5) for 7 days, 28 days, 90 days and 180 days. The developed DNN models have three inputs… More >

Qianxin Wang^1,2,3, Chao Hu^1,2,*, Ya Mao^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.3, pp. 509-525, 2018, DOI:10.31614/cmes.2018.04098

Abstract Designing the optimal distribution of Global Navigation Satellite System (GNSS) ground stations is crucial for determining the satellite orbit, satellite clock and Earth Rotation Parameters (ERP) at a desired precision using a limited number of stations. In this work, a new criterion for the optimal GNSS station distribution for orbit and ERP determination is proposed, named the minimum Orbit and ERP Dilution of Precision Factor (OEDOP) criterion. To quickly identify the specific station locations for the optimal station distribution on a map, a method for the rapid determination of the selected station locations is developed, which is based on the… More >

Shengbiao Wu^1,2,3, Weihua Cao^1,3,*, Can Liu^1,3, Min Wu^1,3

CMES-Computer Modeling in Engineering & Sciences, Vol.116, No.3, pp. 433-453, 2018, DOI: 10.31614/cmes.2018.03309

Abstract A computer-aided tuning method that combines T-S fuzzy neural network (T-S FNN) and offers improved space mapping (SM) is presented in this study. This method consists of three main aspects. First, the coupling matrix is effectively extracted under the influence of phase shift and cavity loss after the initial tuning. Second, the surrogate model is realized by using a T-S FNN based on subspace clustering. Third, the mapping relationship between the actual and the surrogate models is established by the improved space mapping algorithm, and the optimal position of the tuning screws are found by updating the input and output… More >