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RISS 인기검색어
- 검색키워드
- 저자 : Guanyu Hu (검색결과 5 건)
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Investigation of touchdown point mismatch during installation for catenary risers
Huang, Chaojun,Hu, Guanyu,Yin, Fengjie Techno-Press 2018 Ocean systems engineering Vol.8 No.3
Meeting the touchdown point (TDP) target box is one of the challenges during catenary riser installation, especially for deep water or ultra-deep water riser systems. TDP location mismatch compared to the design can result in variation of riser configuration, additional hang-off misalignment, and extra bending loads going into the hang-off porch. A good understanding of the key installation parameters can help to minimize this mismatch, and ensure that the riser global response meets the design criteria. This paper focuses on investigating the potential factors that may affect the touchdown point location, and addressing the challenges both in the design stage and during installation campaign. Conventionally, the vessel offset and current are the most critical factors which may affect the TDP movement during installation. With the offshore exploration going deeper and deeper in the sea (up to 10,000ft), other sources such as the seabed slope and seabed soil stiffness are playing an important role as well. The impacts of potential sources are quantified through case studies for steel catenary riser (SCR) and lazy wave steel catenary riser (LWSCR) in deep water application. Investigations through both theoretical study and numerical validation are carried out. Furthermore, design recommendations are provided during execution phase for the TDP mismatch condition to ensure the integrity of the riser system.
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Yang Liu,Guanyu Hu,Lei Cao,Xiaojing Wang,Ming-Hui Chen 한국통계학회 2019 Journal of the Korean Statistical Society Vol.48 No.4
Nowadays, Bayesian methods are routinely used for estimating parameters of item response theory (IRT) models. However, the marginal likelihoods are still rarely used for comparing IRT models due to their complexity and a relatively high dimension of the model parameters. In this paper, we review Monte Carlo (MC) methods developed in the literature in recent years and provide a detailed development of how these methods are applied to the IRT models. In particular, we focus on the ‘‘best possible’’ implementation of these MC methods for the IRT models. These MC methods are used to compute the marginal likelihoods under the one-parameter IRT model with the logistic link (1PL model) and the two-parameter logistic IRT model (2PL model) for a real English Examination dataset. We further use the widely applicable information criterion (WAIC) and deviance information criterion (DIC) to compare the 1PL model and the 2PL model. The 2PL model is favored by all of these three Bayesian model comparison criteria for the English Examination data.
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Ma, Zhihua,Xue, Yishu,Hu, Guanyu The Korean Statistical Society 2019 Communications for statistical applications and me Vol.26 No.1
Income distribution is a major concern in economic theory. In regional economics, it is often of interest to compare income distributions in different regions. Traditional methods often compare the income inequality of different regions by assuming parametric forms of the income distributions, or using summary statistics like the Gini coefficient. In this paper, we propose a nonparametric procedure to test for heterogeneity in income distributions among different regions, and a K-means clustering procedure for clustering income distributions based on energy distance. In simulation studies, it is shown that the energy distance based method has competitive results with other common methods in hypothesis testing, and the energy distance based clustering method performs well in the clustering problem. The proposed approaches are applied in analyzing data from China Health and Nutrition Survey 2011. The results indicate that there are significant differences among income distributions of the 12 provinces in the dataset. After applying a 4-means clustering algorithm, we obtained the clustering results of the income distributions in the 12 provinces.
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Aeroengine performance degradation prediction method considering operating conditions
Bang-Cheng Zhang,Shuo Gao,Zhong Zheng,Guanyu Hu 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.9
It is significant to predict the performance degradation of complex electromechanical systems. Among the existing performance degradation prediction models, belief rule base (BRB) is a model that deal with quantitative data and qualitative information with uncertainty. However, when analyzing dynamic systems where observable indicators change frequently over time and working conditions, the traditional belief rule base (BRB) can not adapt to frequent changes in working conditions, such as the prediction of aeroengine performance degradation considering working condition. For the sake of settling this problem, this paper puts forward a new hidden belief rule base (HBRB) prediction method, in which the performance of aeroengines is regarded as hidden behavior, and operating conditions are used as observable indicators of the HBRB model to describe the hidden behavior to solve the problem of performance degradation prediction under different times and operating conditions. The performance degradation prediction case study of turbofan aeroengine simulation experiments proves the advantages of HBRB model, and the results testify the effectiveness and practicability of this method. Furthermore, it is compared with other advanced forecasting methods. The results testify this model can generate better predictions in aspects of accuracy and interpretability.
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