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  • Open Access

    ARTICLE

    A Spacecraft Equipment Layout Optimization Method for Diverse and Competitive Design

    Wei Cong, Yong Zhao*, Bingxiao Du*, Senlin Huo, Xianqi Chen

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 621-654, 2023, DOI:10.32604/cmes.2023.025143

    Abstract The spacecraft equipment layout optimization design (SELOD) problems with complicated performance constraints and diversity are studied in this paper. The previous literature uses the gradient-based algorithm to obtain optimized non-overlap layout schemes from randomly initialized cases effectively. However, these local optimal solutions are too difficult to jump out of their current relative geometry relationships, significantly limiting their further improvement in performance indicators. Therefore, considering the geometric diversity of layout schemes is put forward to alleviate this limitation. First, similarity measures, including modified cosine similarity and gaussian kernel function similarity, are introduced into the layout optimization process. Then the optimization produces… More >

  • Open Access

    ARTICLE

    Oversampling Methods Combined Clustering and Data Cleaning for Imbalanced Network Data

    Yang Yang1,*, Qian Zhao1, Linna Ruan2, Zhipeng Gao1, Yonghua Huo3, Xuesong Qiu1

    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 1139-1155, 2020, DOI:10.32604/iasc.2020.011705

    Abstract In network anomaly detection, network traffic data are often imbalanced, that is, certain classes of network traffic data have a large sample data volume while other classes have few, resulting in reduced overall network traffic anomaly detection on a minority class of samples. For imbalanced data, researchers have proposed the use of oversampling techniques to balance data sets; in particular, an oversampling method called the SMOTE provides a simple and effective solution for balancing data sets. However, current oversampling methods suffer from the generation of noisy samples and poor information quality. Hence, this study proposes an oversampling method for imbalanced… More >

  • Open Access

    ARTICLE

    Estimation of the Stress-Strength Reliability for Exponentiated Pareto Distribution Using Median and Ranked Set Sampling Methods

    Amer Ibrahim Al-Omari1, *, Ibrahim M. Almanjahie2, Amal S. Hassan3, Heba F. Nagy4

    CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 835-857, 2020, DOI:10.32604/cmc.2020.10944

    Abstract In reliability analysis, the stress-strength model is often used to describe the life of a component which has a random strength (X) and is subjected to a random stress (Y). In this paper, we considered the problem of estimating the reliability R=P [Y<X] when the distributions of both stress and strength are independent and follow exponentiated Pareto distribution. The maximum likelihood estimator of the stress strength reliability is calculated under simple random sample, ranked set sampling and median ranked set sampling methods. Four different reliability estimators under median ranked set sampling are derived. Two estimators are obtained when both strength… More >

  • Open Access

    ARTICLE

    The Volatility of High-Yield Bonds Using Mixed Data Sampling Methods

    Maojun Zhang1,2, Jiajin Yao1, Zhonghang Xia3, Jiangxia Nan1,*, Cuiqing Zhang1

    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1233-1244, 2019, DOI:10.32604/cmc.2019.06118

    Abstract It is well known that economic policy uncertainty prompts the volatility of the high-yield bond market. However, the correlation between economic policy uncertainty and volatility of high-yield bonds is still not clear. In this paper, we employ GARCH-MIDAS models to investigate their correlation with US economic policy uncertainty index and S&P high-yield bond index. The empirical studies show that mixed volatility models can effectively capture the realized volatility of high-yield bonds, and economic policy uncertainty and macroeconomic factors have significant effects on the long-term component of high-yield bonds volatility. More >

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