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RISS 인기검색어
- 검색키워드
- 저자 : Heping Zhu (검색결과 4 건)
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Yansong Zhu,Jun Wu,Wenzhuang Lu,Dunwen Zuo,Heping Xiao,Dawei Cao,Tae Jo Ko 한국정밀공학회 2022 International Journal of Precision Engineering and Vol.9 No.1
In this study, a two-dimensional model according to the microcutting mechanism of abrasive particle was developed to demonstrate the mechanics of surface formation of the abrasive air jet polishing (AAJP) of aluminum alloy using amino thermosetting plastic (ATP) abrasive. It is shown that due to the characteristics of medium hardness and angular shape of ATP particle, the impacted surface can be generated by particle sliding, ploughing, microcutting, and indentation with the impinging angle increasing from 0º to 90º. Moreover, the effects of particle impacting on the surface microstructural characteristics, especially residual stress, have been analyzed. It has been found that, compared with particle ploughing and sliding processes, particle microcutting and indentation processes have an obvious effect on the surface residual stress; furthermore, particle microcutting process that can cause the impacted surface with high material deformation and ductility is more beneficial for improving the compressive residual stress than particle indentation process. The results of the study are expected to be applied to improve the fatigue performance of integral and large aircraft structures made of aluminum alloy or other metal materials.
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Why do we test multiple traits in genetic association studies?
Wensheng Zhu,,Heping Zhang 한국통계학회 2009 Journal of the Korean Statistical Society Vol.38 No.1
In studies of complex disorders such as nicotine dependence, it is common that researchers assess multiple variables related to a disorder as well as other disorders that are potentially correlated with the primary disorder of interest. In this work, we refer to those variables and disorders broadly as multiple traits. The multiple traits may or may not have a common causal genetic variant. Intuitively, it may be more powerful to accommodate multiple traits in genetic traits, but the analysis of multiple traits is generally more complicated than the analysis of a single trait. Furthermore, it is not well documented as to how much power we may potentially gain by considering multiple traits. Our aim is to enhance our understanding on this important and practical issue. We considered a variety of correlation structures between traits and the disease locus. To focus on the effect of accommodating multiple traits, we examined genetic models that are relatively simple so that we can pinpoint the factors affecting the power. We conducted simulation studies to explore the performance of testing multiple traits simultaneously and the performance of testing a single trait at a time in family-based association studies. Our simulation results demonstrated that the performance of testing multiple traits simultaneously is better than that of testing each trait individually for almost models considered. We also found that the power of association tests varies among the underlying models. The advantage of conducting a multiple traits test is minimized when some traits are influenced by the gene only through other traits; and it is maximized when there are causal relations between the traits and the gene, and among the traits themselves or when there are extraneous traits.
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Rejoinder: Why do we test multiple traits in genetic association studies?
Wensheng Zhu,,Heping Zhang 한국통계학회 2009 Journal of the Korean Statistical Society Vol.38 No.1
This is a rejoinder to the discussions of ``Why do we test multiple traits in genetic association studies
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Guo Zhao,Shulin Li,Wanqing Zuo,Haoran Song,Heping Zhu,Wenjie Hu 전력전자학회 2023 JOURNAL OF POWER ELECTRONICS Vol.23 No.9
To address the problem where the different operating conditions of hydropower units have a large influence on the parameters of the trend prediction model of the operating condition indicators, a support vector regression machine prediction model based on parameter adaptation is proposed in this paper. First, the Aquila optimizer (AO) is improved, and a sine chaotic map is introduced to influence the population initialization process. An improved adaptive weight factor is used to balance the local search and global search capabilities. Second, according to the power and the head, the operating conditions of the unit are refined into several typical sets of operating conditions. On this basis, an SVR model is established using the improved AO search algorithm proposed in this paper, and the prediction parameters under each of the operating condition are optimized to establish the data of the operating conditions and optimal parameters. Then a neural network is used to fit the working condition and the optimal prediction parameters. In addition, the nonlinear function mapping of the complex relationship between the two is constructed. Finally, the constructed mapping relationship is added to the traditional SVR, and an adaptive SVR prediction model suitable for changes in the working conditions of hydropower units is realized. Simulation results show that when compared to the traditional SVR prediction model, the adaptive SVR prediction model designed in this paper can automatically adjust the prediction parameters according to changes in the working conditions and achieve the goal of maintaining optimal prediction performance under different working conditions. In addition, it has the ability to accurately predict the development trend of the unit operating state index within a certain time scale.
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