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Junyong Zhou,Xuefei Shi,Liwen Zhang,Zuo Sun 국제구조공학회 2019 Structural Engineering and Mechanics, An Int'l Jou Vol.70 No.2
The replacement of damaged components is an important task for long-span bridges. Conventional strategy for component replacement is to close the bridge to traffic, so that the influence of the surrounding environment is reduced to a minimum extent. However, complete traffic interruption would bring substantial economic losses and negative social influence nowadays. This paper investigates traffic control technologies without interruption for component replacement of long-span bridges. A numerical procedure of traffic control technologies is proposed incorporating traffic microsimulation and site-specific data, which is then implemented through a case study of cable replacement of a long-span cable-stayed bridge. Results indicate traffic load effects on the bridge are lower than the design values under current low daily traffic volume, and therefore cable replacement could be conducted without traffic control. However, considering a possible medium or high level of daily traffic volume, traffic load effects of girder bending moment and cable force nearest to the replaced cable become larger than the design level. This indicates a potential risk of failure, and traffic control should be implemented. Parametric studies show that speed control does not decrease but increase the load effects, and flow control using lane closure is not effectual. However, weight control and gap control are very effective to mitigate traffic load effects, and it is recommended to employ a weight control with gross vehicle weight no more than 65 t or/and a gap control with minimum vehicle gap no less than 40 m for the cable replacement of the case bridge.
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Diagnosis of the Status of the ROK–U.S. Alliance and Suggestions for Policy Direction
Junyong Park 한국국방연구원 2023 The Korean Journal of Defense Analysis Vol.35 No.3
In late April this year, President Yoon visited the United States as a national guest. He emphasized the ROK–U.S. alliance and mentioned the word “alliance” 28 times during his speech to Congress. He explained how the alliance was formed and how it has developed so far. He also suggested a blueprint for the alliance and showed us a future direction and vision. His speech was touching and heart-fluttering since the alliance made a miracle of freedom and prosperity in South Korea. Nothing in the world, however, is perfect. Everything has its shortcomings and complements. From this perspective, this paper is for diagnosis of the current status of the ROK–U.S. alliance and suggestions for policy direction. Although the appearance of the alliance looks good, there are a couple of problems inside. This paper will examine various issues based on the alliance theories such as the security–autonomy exchange, balancing and bandwagoning theory, and alliance dilemma. The issues will be the transition of wartime operational control, integrated deterrence and Indo–Pacific strategy, and controversy over the effectiveness of extended deterrence. The paper will analyze whether the current government’s stance on the issues is helpful for national interests and suggest how we can improve policy directions way ahead. First, efforts for early transfer of wartime operational control are needed. Second, South Korea should expand its role as a global pivotal country since it openly exclaimed that in its Indo–Pacific Strategy of freedom, peace, and prosperity. Last but not least, South Korea should also strengthen its independent deterrence and response capabilities.
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Extreme value modeling of structural load effects with non-identical distribution using clustering
Junyong Zhou,Xin Ruan,Xuefei Shi,Chudong Pan 국제구조공학회 2020 Structural Engineering and Mechanics, An Int'l Jou Vol.74 No.1
The common practice to predict the characteristic structural load effects (LEs) in long reference periods is to employ the extreme value theory (EVT) for building limit distributions. However, most applications ignore that LEs are driven by multiple loading events and thus do not have the identical distribution, a prerequisite for EVT. In this study, we propose the composite extreme value modeling approach using clustering to (a) cluster initial blended samples into finite identical distributed subsamples using the finite mixture model, expectation-maximization algorithm, and the Akaike information criterion; (b) combine limit distributions of subsamples into a composite prediction equation using the generalized Pareto distribution based on a joint threshold. The proposed approach was validated both through numerical examples with known solutions and engineering applications of bridge traffic LEs on a long-span bridge. The results indicate that a joint threshold largely benefits the composite extreme value modeling, many appropriate tail approaching models can be used, and the equation form is simply the sum of the weighted models. In numerical examples, the proposed approach using clustering generated accurate extrema prediction of any reference period compared with the known solutions, whereas the common practice of employing EVT without clustering on the mixture data showed large deviations. Real-world bridge traffic LEs are driven by multi-events and present multipeak distributions, and the proposed approach is more capable of capturing the tendency of tailed LEs than the conventional approach. The proposed approach is expected to have wide applications to general problems such as samples that are driven by multiple events and that do not have the identical distribution.
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Regularization based Multi-Output Regression Model Tree
JunYong Jeong,Chi-Hyuck Jun 한국경영과학회 2017 한국경영과학회 학술대회논문집 Vol.2017 No.4
Multi-output regression has become an emerging problem in data-mining and machine learning. We propose a multi-output regression model tree to obtain both accuracy and interpretation. Each leaf of proposed model tree contains sparse linear models that exploit the relationship between response variables. We present an efficient splitting rule based on residual analysis. Experiments on several dataset identify the performance of the proposed model over benchmark methods.
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Convolved Gaussian process for multi-step ahead time series forecasting
JunYong Jeong(정준용),Chi-Hyuck Jun(전치혁) 한국경영과학회 2016 한국경영과학회 학술대회논문집 Vol.2016 No.4
Machine learning models have been important methods in nonlinear time series forecasting. Multistep ahead forecasting using machine learning models has practical applications, but it is a difficult problem because of increased uncertainty and lack of information. We propose Convolved Gaussian process with multi-task learning strategy to exploit the stochastic dependencies over future values. Our experiment on the reduced series of NN3 competition showed that our method was promising approach for multi-step ahead forecasting.
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Regularization based Multi-Output Regression Model Tree
JunYong Jeong,Chi-Hyuck Jun 대한산업공학회 2017 대한산업공학회 춘계학술대회논문집 Vol.2017 No.4
Multi-output regression has become an emerging problem in data-mining and machine learning. We propose a multi-output regression model tree to obtain both accuracy and interpretation. Each leaf of proposed model tree contains sparse linear models that exploit the relationship between response variables. We present an efficient splitting rule based on residual analysis. Experiments on several dataset identify the performance of the proposed model over benchmark methods.
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Junyong Kim 글로벌지식마케팅경영학회 2014 Global Marketing Conference Vol.2014 No.7
For decades, marketing practitioners and scholars have envisioned that environmental characteristics of market offerings will become an important consumer concern (Grant 2007; Henion 1981; Kassarjian 1971; Kotler 2011; Ottmann 2011) and a myriad of consumer surveys indicated that consumers are willing to change their purchase and consumption habits for a better environment (Banikarim 2010; Ferguson and Goldman 2010; Imkamp 2000; Laroche, Bergeron and Barbaro-Forleo 2001). However, consumers’ actual adoption of environmental market offerings has been far below what had been expected and predicted so far (Connolly et al. 2006; Davis 1993; do Pa?o and Varej?o 2010; Kilbournes 1998; Horne 2009). To bridge the gap between consumers’ stated interests in and attitudes regarding adoption of eco-friendly market offerings and their actual adoption of eco-friendly market offerings, marketers need an accurate understanding of: 1) what determines consumers’ willingness and propensity to adopt eco-friendly market offerings and consumption behaviors, 2) how those individual, social, and marketing determinants of eco-friendly consumer behaviors are interrelated with each other, 3) where consumers stand currently in terms of those determinants, and 4) what needs to be done to remove the bottlenecks in the adoption and diffusion processes. To provide answers to above four aspects of eco-friendly consumer behaviors, this paper attempts to advance a more theoretically-based and comprehensive model of eco-friendly consumer behavior than extant models by incorporating recent findings of research on determinants of pro-environmental consumer behaviors and integrating various relevant theories of attitude-behavior links including the value-belief-norm theory, the theory of reasoned action, and the elaboration likelihood model. The structural validity and generalizability of the proposed model is tested based on two sets of survey data collected from consumers in the U.S. Midwest region and a metropolitan area surrounding Seoul, the capital city of Korea. The findings of the empirical study demonstrate that while the proposed model explains eco-friendly consumer behaviors by both the U.S. and Korean consumers, the relative importance of the predictors of eco-friendly consumer behaviors vary between the two groups of consumers. The analysis of the data also reveals that consumers in the two groups differ significantly from each other on many variables which are included as direct and indirect determinants of eco-friendly consumer behaviors in the proposed model. Together, the findings provide interesting and practical implications for strategies to facilitate consumer adoption of eco-friendly market offerings.
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