• Journal of Multimedia Information System
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• v.3 no.4
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• pp.119-128
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• 2016

This study aims to suggest a new algorithm for detecting two-wheelers on road that have various shapes according to the viewing angle for vision based intelligent vehicles. This article describes a new approach to two-wheelers detection algorithm riding on people based on modified Histogram of Oriented Gradients (HOG) using correlation coefficient (CC). The CC between two local area variables, in which one is the person riding a bike and other is its background, can represent correlation relation. First, we extract edge vectors using HOG which includes gradient information and differential magnitude as cell based. And then, the value, which is calculated by the CC between the area of each cell and one of two-wheelers, can be extracted as the weighting factor in process for normalizing the modified HOG cell. This paper applied the Adaboost algorithm to make a strong classification from weak classification. In this experiment, we can get the result that the detection rate of the proposed method is higher than that of the traditional method.

• Journal of Multimedia Information System
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• v.2 no.4
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• pp.303-310
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• 2015

Vulnerable road users such as bike, motorcycle, small automobiles, and etc. are easily attacked or threatened with bigger vehicles than them. So this paper suggests a new approach two-wheelers detection system riding on people based on modified histogram of oriented gradients (HOGs) which is weighted by curvature and local correlation coefficient. This correlation coefficient between two variables, in which one is the person riding a bike and other is its background, can represent correlation relation. First, we extract edge vectors using the curvature of Gaussian and Histogram of Oriented Gradients (HOG) which includes gradient information and differential magnitude as cell based. And then, the value, which is calculated by the correlation coefficient between the area of each cell and one of bike, can be used as the weighting factor in process for normalizing the HOG cell. This paper applied the Adaboost algorithm to make a strong classification from weak classification. The experimental results validate the effectiveness of our proposed algorithm show higher than that of the traditional method and under challenging, such as various two-wheeler postures, complex background, and even conclusion.

• The KSFM Journal of Fluid Machinery
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• v.13 no.6
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• pp.42-50
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• 2010

The present analysis deals with a numerical procedure for optimizing the shape of elliptical dimples in a cooling channel. The three-dimensional Reynolds-averaged Navier-Stokes (RANS) analysis is employed in conjunction with the SST model for predictions of the turbulent flow and the heat transfer. Three non-dimensional geometric design variables, such as the ellipse dimple diameter ratio, ratio of the dimple depth to the average diameter, and ratio of the distance between dimples to the pitch are considered in the optimization. Twenty-one experimental points within design space are selected by Latin Hypercube Sampling. Each objective function values at these points are evaluated by RANS analysis and producing optimal point using surrogate model. The linear combination of heat transfer coefficient and friction loss related terms with a weighting factor is defined as the objective function. The results show that the optimized elliptical dimple shape improves considerably the heat transfer performance than the circular dimple shape.

• Transactions of the Korean Society of Automotive Engineers
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• v.3 no.6
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• pp.96-111
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• 1995

A bellows is a component installed in the automobile exhaust system to reduce the impact from an engine. It's stiffness has a great influence on the natural frequency of the system. Therefore, it must be designed to keep the specified stiffness that requires in the system. This study present the finite element analysis of U-typed bellows using a curved conical frustum element and the shape optimal design with specified stiffness. The finite element analysis is verified by comparing with the experimental results. In the shape optimal design, the weight is considered as the cost function. The specified stiffness from the system design is transformed to equality constraints. The formulation has inequality constraints imposed on the fatigue limit, the natural frequencies, the buckling load and the manufacturing conditions. A procedure for shape optimization adopts a thickness, a corrugation radius, and a length of annular plate as optimal design variables. The external loading conditions include the axial and lateral loads with a boundary condition fixed at an end of the bellows. The recursive quadratic programming algorithm is selected to solve the problem. The result are compared with the existing bellows, and the characteristics of the bellows is investigated through the optimal design process. The optimized shape of the bellows are expected to give quite a good guideline to the practical design.

• 대한예방의학회:학술대회논문집
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• 1994.02b
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• pp.180-187
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• 1994

A slightly modified version of the Quality-of-Life after Myocardial Infarction (QLMI) questionnaire developed by Oldridge and colleagues was applied in a self-administered mode to patients with suspected acute myocardial infarction (AMI) in a randomized controlled trial of secondary prevention. Acceptability of the questionnaire was good, with 93% of responders answering all items. Factor analysis suggested three quality-of-life (QL) dimensions which we called 'emotional', 'physical' and 'social'. These differed somewhat from the dimensions proposed by Oldtidge and colleagues. However, a sensitivity analysis showed relative invariance of results to weighting schemes. Scores on our three dimensions were responsive to differences between the treatment groups, and demonstrated construct validity based on associations between the measured QL and variables expected to affect QL. We conclude that the QLMI questionnaire has good potential as an instrument for assessing QL in post-AMI patients and that it can be successfully self-administered.

• Asian Journal of Innovation and Policy
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• v.10 no.2
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• pp.212-235
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• 2021

In spite of the increasing popularity of the Ricardian model for the study of the impact of climate change on agriculture, there has been few attempts to examine the role of interregional spillovers in this framework and all of them rely on geographical proximity-based weighting schemes. We remedy to this gap by focusing on the spatial externalities of surface water flow used for irrigation purposes and demonstrate that farmland value, the usual dependent variable used in the Ricardian framework, is a function of the climate variables experienced locally and in the upstream locations. This novel approach is tested empirically on a spatial panel model estimated across the counties of the Southwest USA over 1997-2012. This region is one of the driest in the country, hence its agriculture relies heavily on irrigated surface water. The results highlight how the weather conditions in upstream counties significantly affect downstream agriculture, thus the actual impact of climate change on agriculture and subsequent adaptation policies cannot overlook the streamflow network anymore.

• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.177-191
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• 2022

This paper addresses the use of machine learning methods for causal estimation of treatment effects from observational data. Even though conducting randomized experimental trials is a gold standard to reveal potential causal relationships, observational study is another rich source for investigation of exposure effects, for example, in the research of comparative effectiveness and safety of treatments, where the causal effect can be identified if covariates contain all confounding variables. In this context, statistical regression models for the expected outcome and the probability of treatment are often imposed, which can be combined in a clever way to yield more efficient and robust causal estimators. Recently, targeted maximum likelihood estimation and causal random forest is proposed and extensively studied for the use of data-adaptive regression in estimation of causal inference parameters. Machine learning methods are a natural choice in these settings to improve the quality of the final estimate of the treatment effect. We explore how we can adapt the design and training of several machine learning algorithms for causal inference and study their finite-sample performance through simulation experiments under various scenarios. Application to the percutaneous coronary intervention (PCI) data shows that these adaptations can improve simple linear regression-based methods.

• Journal of Korean Society for Atmospheric Environment
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• v.28 no.1
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• pp.22-38
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• 2012

Adaptation of climate change is necessary to avoid unexpected impacts of climate change caused by human activities. Vulnerability refers to the degree to which system cannot cope with impacts of climate change, encompassing physical, social and economic aspects. Therefore the quantification of climate change impacts and its vulnerability is needed to identify vulnerable regions and to setup the proper strategies for adaptation. In this study, climate change vulnerability is defined as a function of climate exposure, sensitivity, and adaptive capacity. Also, we identified regions vulnerable to ozone due to climate change in Korea using developed proxy variables of vulnerability of regional level. 18 proxy variables are selected through delphi survey to assess vulnerability over human health sector for ozone concentration change due to climate change. Also, we estimate the weighting score of proxy variables from delphi survey. The results showed that the local regions with higher vulnerability index in the sector of human health are Seoul and Daegu, whereas regions with lower one are Jeollanam-do, Gyeonggi-do, Gwangju, Busan, Daejeon, and Gangwon-do. The regions of high level vulnerability are mainly caused by their high ozone exposure. We also assessed future vulnerability according to the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A2, A1FI, A1T, A1B, B2, and B1 scenarios in 2020s, 2050s and 2100s. The results showed that vulnerability increased in all scenarios due to increased ozone concentrations. Especially vulnerability index is increased by approximately 2 times in A1FI scenarios in the 2020s. This study could support regionally adjusted adaptation polices and the quantitative background of policy priority as providing the information on the regional vulnerability of ozone due to climate change in Korea.

• Korean Journal of Remote Sensing
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• v.30 no.5
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• pp.687-701
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• 2014

Sea ice is generally accepted as an important factor to understand the process of earth climate changes and is the basis of earth system models for analysis and prediction of the climate changes. To continuously monitor sea ice changes at kilometer scale, it is demanded to create more accurate grid data from the current, limited sea ice data. In this paper we described a downscaling method for Advanced Microwave Scanning Radiometer 2 (AMSR2) Sea Ice Concentration (SIC) from 10 km to 1 km resolution using a weighting scheme of sea ice days ratio derived from Moderate Resolution Imaging Spectroradiometer (MODIS) sea ice cover product that has a high correlation with the SIC. In a case study for Okhotsk Sea, the sea ice areas of both data (before and after downscaling) were identical, and the monthly means and standard deviations of SIC exhibited almost the same values. Also, Empirical Orthogonal Function (EOF) analyses showed that three kinds of SIC data (ERA-Interim, original AMSR2, and downscaled AMSR2) had very similar principal components for spatial and temporal variations. Our method can apply to downscaling of other continuous variables in the form of ratio such as percentage and can contribute to monitoring small-scale changes of sea ice by providing finer SIC data.

• Journal of Radiation Protection and Research
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• v.33 no.4
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• pp.127-134
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• 2008

Potential economic impact of a hypothetical severe accident at a nuclear power plant(Uljin units 3/4) was estimated by applying the Delphi method, which is based on the expert judgements and opinions, in the process of quantifying uncertain factors. For the purpose of this study, it is assumed that the radioactive plume directs the inland direction. Since the economic risk can be divided into direct costs and indirect effects and more uncertainties are involved in the latter, the direct costs were estimated first and the indirect effects were then estimated by applying a weighting factor to the direct cost. The Delphi method however subjects to risk of distortion or discrimination of variables because of the human behavior pattern. A mathematical approach based on the Bayesian inferences was employed for data processing to improve the Delphi results. For this task, a model for data processing was developed. One-dimensional Monte Carlo Analysis was applied to get a distribution of values of the weighting factor. The mean and median values of the weighting factor for the indirect effects appeared to be 2.59 and 2.08, respectively. These values are higher than the value suggested by OECD/NEA, 1.25. Some factors such as small territory and public attitude sensitive to radiation could affect the judgement of panel. Then the parameters of the model for estimating the direct costs were classified as U- and V-types, and two-dimensional Monte Carlo analysis was applied to quantify the overall economic risk. The resulting median of the overall economic risk was about 3.9% of the gross domestic products(GDP) of Korea in 2006. When the cost of electricity loss, the highest direct cost, was not taken into account, the overall economic risk was reduced to 2.2% of GDP. This assessment can be used as a reference for justifying the radiological emergency planning and preparedness.