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  • Open Access

    ARTICLE

    An Exact Solution for Acoustic Simulation Based Transmission Loss Optimization of Double-Chamber Silencer

    Wael A. Altabey1,2,*

    Sound & Vibration, Vol.54, No.4, pp. 215-224, 2020, DOI:10.32604/sv.2020.011516

    Abstract The optimization of the acoustic silencer volume is very important to develop it and to get high-performance, the importance of the silencer was appeared in industrial field to eliminate the noise of the duct by efficient and economical method. The main goal of this research is to optimize the transmission loss (TL) by analytical method of the Double-Chamber Silencer (DCS), the TL has been selected as the main parameter in silencer because it does not based on the source or the termination impedances. First we calculated the power transmission coefficient (PTC) and the TL of an acoustic silencer, then used… More >

  • Open Access

    ARTICLE

    Robust Hybrid Artificial Fish Swarm Simulated Annealing Optimization Algorithm for Secured Free Scale Networks against Malicious Attacks

    Ganeshan Keerthana1,*, Panneerselvam Anandan2, Nandhagopal Nachimuthu3

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 903-917, 2021, DOI:10.32604/cmc.2020.012255

    Abstract Due to the recent proliferation of cyber-attacks, highly robust wireless sensor networks (WSN) become a critical issue as they survive node failures. Scale-free WSN is essential because they endure random attacks effectively. But they are susceptible to malicious attacks, which mainly targets particular significant nodes. Therefore, the robustness of the network becomes important for ensuring the network security. This paper presents a Robust Hybrid Artificial Fish Swarm Simulated Annealing Optimization (RHAFS-SA) Algorithm. It is introduced for improving the robust nature of free scale networks over malicious attacks (MA) with no change in degree distribution. The proposed RHAFS-SA is an enhanced… More >

  • Open Access

    ARTICLE

    Intelligent Tunicate Swarm-Optimization-Algorithm-Based Lightweight Security Mechanism in Internet of Health Things

    Gia Nhu Nguyen1,2, Nin Ho Le Viet1,2, Gyanendra Prasad Joshi3, Bhanu Shrestha4,*

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 551-562, 2021, DOI:10.32604/cmc.2020.012441

    Abstract Fog computing in the Internet of Health Things (IoHT) is promising owing to the increasing need for energy- and latency-optimized health sector provisioning. Additionally, clinical data (particularly, medical image data) are a delicate, highly protected resource that should be utilized in an effective and responsible manner to fulfil consumer needs. Herein, we propose an energy-effi- cient fog-based IoHT with a tunicate swarm-optimization-(TSO)-based lightweight Simon cipher to enhance the energy efficiency at the fog layer and the security of data stored at the cloud server. The proposed Simon cipher uses the TSO algorithm to select the optimal keys that will minimize… More >

  • Open Access

    ARTICLE

    Research on Distribution Network Full Cost-Benefit Optimization Considering Different Renewable Energy Penetration

    Tanzhong Fu1,2,3, Yu Xue1,*, Tancai Xia1, Wang Jing1, De Gejirifu1

    Energy Engineering, Vol.117, No.6, pp. 397-411, 2020, DOI:10.32604/EE.2020.011633

    Abstract To further study the impact of renewable energy penetration on the technical transformation of distribution networks. Based on the output power characteristics of wind power and photovoltaics, a renewable energy grid-connected capacity model and a distribution network full cost-benefit model were constructed. Based on this, to maximize the comprehensive income of the distribution network and the renewable energy penetration rate, to establish the technical reform optimization model and search for the optimal solution through the improved NSGA-II algorithm. Finally, the effectiveness of the proposed model was verified by setting up three scenarios of simultaneous wind power, grid-connected wind power, grid-connected… More >

  • Open Access

    ARTICLE

    SI Bitmap Index and Optimization for Membership Query

    Shu Gaoa,b,*, Zhen Wanga, Liangchen Chena

    Intelligent Automation & Soft Computing, Vol.25, No.4, pp. 683-689, 2019, DOI:10.31209/2018.100000061

    Abstract The explosive growth of data produced by internet of things has contributed to the abundance of data. Since then, efficient indexing and querying techniques for data retrieval has become a major challenge. Bitmap index and its extension techniques, which involve a bit sequence that represents a specified property and indicates the data items that satisfies this property, are well-known methods to improve processing time for complex and interactive queries on the read-mostly or append-only data. This paper proposes an improved bitmap index technique, named Sliced-Interval Bitmap Index (SI Bitmap Index), which is efficient in both space and response time for… More >

  • Open Access

    ARTICLE

    Reliability Analysis Based on Optimization Random Forest Model and MCMC

    Fan Yang1,2,3,*, Jianwei Ren1,2

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 801-814, 2020, DOI:10.32604/cmes.2020.08889

    Abstract Based on the rapid simulation of Markov Chain on samples in failure region, a novel method of reliability analysis combining Monte Carlo Markov Chain (MCMC) with random forest algorithm was proposed. Firstly, a series of samples distributing around limit state function are generated by MCMC. Then, the samples were taken as training data to establish the random forest model. Afterwards, Monte Carlo simulation was used to evaluate the failure probability. Finally, examples demonstrate the proposed method possesses higher computational efficiency and accuracy. More >

  • Open Access

    ARTICLE

    Forecasting Multi-Step Ahead Monthly Reference Evapotranspiration Using Hybrid Extreme Gradient Boosting with Grey Wolf Optimization Algorithm

    Xianghui Lu1, Junliang Fan2, Lifeng Wu1,*, Jianhua Dong3

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 699-723, 2020, DOI:10.32604/cmes.2020.011004

    Abstract It is important for regional water resources management to know the agricultural water consumption information several months in advance. Forecasting reference evapotranspiration (ET0) in the next few months is important for irrigation and reservoir management. Studies on forecasting of multiple-month ahead ET0 using machine learning models have not been reported yet. Besides, machine learning models such as the XGBoost model has multiple parameters that need to be tuned, and traditional methods can get stuck in a regional optimal solution and fail to obtain a global optimal solution. This study investigated the performance of the hybrid extreme gradient boosting (XGBoost) model… More >

  • Open Access

    ARTICLE

    An Effective Non-Commutative Encryption Approach with Optimized Genetic Algorithm for Ensuring Data Protection in Cloud Computing

    S. Jerald Nirmal Kumar1,*, S. Ravimaran2, M. M. Gowthul Alam3

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 671-697, 2020, DOI:10.32604/cmes.2020.09361

    Abstract Nowadays, succeeding safe communication and protection-sensitive data from unauthorized access above public networks are the main worries in cloud servers. Hence, to secure both data and keys ensuring secured data storage and access, our proposed work designs a Novel Quantum Key Distribution (QKD) relying upon a non-commutative encryption framework. It makes use of a Novel Quantum Key Distribution approach, which guarantees high level secured data transmission. Along with this, a shared secret is generated using Diffie Hellman (DH) to certify secured key generation at reduced time complexity. Moreover, a non-commutative approach is used, which effectively allows the users to store… More >

  • Open Access

    ARTICLE

    Hybridization of Fuzzy and Hard Semi-Supervised Clustering Algorithms Tuned with Ant Lion Optimizer Applied to Higgs Boson Search

    Soukaina Mjahed1,*, Khadija Bouzaachane1, Ahmad Taher Azar2,3, Salah El Hadaj1, Said Raghay1

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 459-494, 2020, DOI:10.32604/cmes.2020.010791

    Abstract This paper focuses on the unsupervised detection of the Higgs boson particle using the most informative features and variables which characterize the “Higgs machine learning challenge 2014” data set. This unsupervised detection goes in this paper analysis through 4 steps: (1) selection of the most informative features from the considered data; (2) definition of the number of clusters based on the elbow criterion. The experimental results showed that the optimal number of clusters that group the considered data in an unsupervised manner corresponds to 2 clusters; (3) proposition of a new approach for hybridization of both hard and fuzzy clustering… More >

  • Open Access

    ARTICLE

    On the Design and Optimization of a Clean and Efficient Combustion Mode for Internal Combustion Engines through a Computer NSGA-II Algorithm

    Xiaobin Shu, Miaomiao Ren*

    FDMP-Fluid Dynamics & Materials Processing, Vol.16, No.5, pp. 1019-1029, 2020, DOI:10.32604/fdmp.2020.09564

    Abstract In order to address typical problems due to the huge demand of oil for consumption in traditional internal combustion engines, a new more efficient combustion mode is proposed and studied in the framework of Computational Fluid Dynamics (CFD). Moreover, a Non-dominated Sorting Genetic Algorithm (NSGA-II) is applied to optimize the related parameters, namely, the engine methanol ratio, the fuel injection time, the initial temperature, the Exhaust Gas Re-Circulation (EGR) rate, and the initial pressure. The so-called Conventional Diesel Combustion (CDC), Homogeneous Charge Compression Ignition (HCCI) and the Reactivity Controlled Compression Ignition (RCCI) combustion modes are compared. The results show that… More >

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