• Design & Manufacturing
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• v.13 no.3
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• pp.6-10
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• 2019

In this study, a study was carried out that could effectively produce a heat dissipation effect on plastic materials. Using carbon nanotube (CNT), aluminum powder and plastic, the material properties were tested in 2 cases of compounding ratio. The test sample mold was designed and constructed prior to the experiment. The experiments include tensile strength, elongation rate, flexural strength, flexural elasticity rate, eye-jaw impact strength, gravity and thermal conductivity. Results from 60% and 70% mixture of aluminium to plastic were tested, and a 10% less combined result was a relatively good property. For research purposes, the heat dissipation effect and light weighting obtained a good measure when the combined amount of Al was 60%.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.20 no.9
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• pp.791-796
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• 2010

Installing noise barriers is the most common method for reducing the highway traffic noise to the road side residential area. After the report about edge potential concept of a noise barrier, various types of noise reducing devices(NRDs) called "noise reducers" have been suggested for getting more shielding effect on the top of highway noise barriers. But, it has been doubtful about effect of the NRDs in field because there was no appropriate and unified method to estimate the acoustic performance by using field measurement of the NRDs in Korea. In this study, the authors have considered to setup a practical method to test and estimate the acoustic performance of NRDs. For eliminating the noise reduction effect of the NRDs height itself, the source and measuring points are adjusted as highly as the NRDs height. For the frequency weighting in the estimation of the NRDs effect, the highway noise spectra were measured at asphalt and concrete road side and then averaged for a unit spectral parameter.

• Structural Engineering and Mechanics
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• v.10 no.6
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• pp.517-532
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• 2000

A direct output feedback control scheme was recently proposed by the authors for single-story building structures resting on flexible soil body. In this paper, the control scheme is extended to mitigate the seismic responses of multi-story buildings. Soil-structure interaction is taken into account in two parts: input at the soil-structure interface/foundation and control algorithm. The former reflects the effect on ground motions and is monitored in real time with accelerometers at foundation. The latter includes the effect on the dynamic characteristics of structures, which is formulated by modifying the classical linear quadratic regulator based on the fundamental mode shape of the soil-structure system. Numerical result on the study of a $\frac{1}{4}$-scale three-story structure, supported by a viscoelastic half-space of soil mass, have demonstrated that the proposed algorithm is robust and very effective in suppressing the earthquake-induced vibration in building structures even supported on a flexible soil mass. Parametric studies are performed to understand how soil damping and flexibility affect the effectiveness of active tendon control. The selection of weighting matrix and effect of soil property uncertainty are investigated in detail for practical applications.

• 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.

• The Korean Journal of Applied Statistics
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• v.37 no.2
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• pp.225-237
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• 2024

In the analysis of competing risks data, some of covariates may not be fully observed for some subjects. In such cases, excluding subjects with missing covariate values from the analysis may result in biased estimates and loss of efficiency. In this paper, we studied multiple imputation and the augmented inverse probability weighting method for regression parameter estimation in the cause-specific proportional hazards model with missing covariates. The performance of estimators obtained from multiple imputation and the augmented inverse probability weighting method is evaluated by simulation studies, which show that those methods perform well. Multiple imputation and the augmented inverse probability weighting method were applied to investigate significant risk factors for the risk of death from breast cancer and from other causes for breast cancer data with missing values for tumor size obtained from the Prostate, Lung, Colorectal, and Ovarian Cancer Screen Trial Study. Under the cause-specific proportional hazards model, the methods show that race, marital status, stage, grade, and tumor size are significant risk factors for breast cancer mortality, and stage has the greatest effect on increasing the risk of breast cancer death. Age at diagnosis and tumor size have significant effects on increasing the risk of other-cause death.

• Journal of Energy Engineering
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• v.15 no.1 s.45
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• pp.74-81
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• 2006

In this study, on the basis of an analytic hierarchy process (AHP) method and through a questionnaire on subjective preference and importance, various power supply systems were comprehensively compared with multiple decision criteria such as environmental, social, healthy, and economic viewpoints and then overall priority was assessed. When a decision-making problem is modelled by a hierarchy structure, the AHP method is regarded as a useful tool for extracting subjective opinions via the aforementioned questionnaire. Here, the overall preferences were obtained by linearly aggregating weighting vector and preference matrix. The energy systems such as nuclear, coal, and LNG power plants were selected because they took share over 90% of domestic electricity supply in Korea. Furthermore, wind power and photovoltaic solar systems were included as representative renewable energy systems in Korea. According to the results of this demonstration study, the following comprehensive comparison indicators were yielded: 1) weighting factors for 4 types of main criteria as well as for 11 types of sub-criteria; 2) preference valuation for 7 types of energy systems under consideration; 3) overall score for each energy systems.

• Journal of Wetlands Research
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• v.18 no.2
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• pp.148-153
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• 2016

Hydraulic and hydrological flood routing methods are commonly used to analyze temporal and spatial flood influences of flood wave through a river reach. Hydrological flood routing method has relatively more simple and reasonable performance accuracy compared to the hydraulic method. Storage constant used in Muskingum method widely applied in hydrological flood routing is very similar to the travel time. Focusing on this point, in this study, we estimate the travel time from HEC-RAS results to estimate storage constant, and develop a non-linear regression equation for the travel time using reach length, channel slope, and discharge. The estimated flow by Muskingum model with storage constant of nonlinear equation is compared with the flow calculated by applying the HEC-RAS 1-D unsteady flow simulation. In addition, this study examines the effect on the weighting factor changes and interval reach divisions; peak discharge increases with the bigger weighting factor, and RMSE decreases with the fragmented division.

• Journal of the Korean Geotechnical Society
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• v.28 no.10
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• pp.89-96
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• 2012

In this study landslide causal factors which are considered to have the same effect in assessment techniques are categorized and their impact on landslides is analyzed to acquire reasonable weighting factors in the landslide hazard. Results are compared to those of the Assessment Chart developed by National Institute for Disaster Prevention (NIDP) and the adequacy and proper portion for landslide causal factors are considered. The Artificial Neural Network (ANN) method applied to 28 landslide areas is incorporated to evaluate the reasonable rating. Results show that the following items in the Chart are necessary to modify their portions in order to implement the precise assessment results: 1) Estimated damage; 2) Tension crack; 3) Existence of valley.

• Journal of computational fluids engineering
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• v.22 no.1
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• pp.37-42
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• 2017

Infrared signals are frequently used to detect objects exposed to wide variety of environmental conditions. Detection by infrared signature is accomplished by distinguishing objects by using the IR radiant contrast between objects and the background. There are several methods of estimating the IR radiant contrast. The inverse distance weighting method, which is one of the IR radiant contrast estimation method using the effect of distance from objects, is known to be an effective way to analyze radiant contrast for complex backgrounds. However this method has a disadvantage of requiring a long calculation time. In this study we propose a statistical method of estimating the IR radiant contrast by using randomly selected pixels of arbitrary number among background pixels to reduce calculation time. Some measured IR images in MWIR and LWIR regions are used to test the applicability of the method proposed and we found that the proposed method is very effective in determining the IR radiant contrast showing very rapid estimation with minar accuracy loss.

• Phonetics and Speech Sciences
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• v.14 no.3
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• pp.1-9
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• 2022

The current study examined English-speaking adult learners' production and perception of L2 Korean stops (/t/ or /t'/ or /t^h/) to investigate whether the two modalities are linked in utilizing voice onset time (VOT) and fundamental frequency (F0) for the L2 sound distinction and how the learners' L2 proficiency mediates the relationship. Twenty-two English-speaking learners of Korean living in Seoul participated in the word-reading task of producing stop-initial words and the identification task of labelling CV stimuli synthesized to vary VOT and F0. Using logistic mixed-effects regression models, we quantified group- and individual-level weights of the VOT and F0 cues in differentiating the tense-lax, lax-aspirated, and tense-aspirated stops in Korean. The results showed that the learners as a group relied on VOT more than F0 both in production and perception (except the tense-lax pair), reflecting the dominant role of VOT in their L1 stop distinction. Individual-level analyses further revealed that the learners' L2 proficiency was related to their use of F0 in L2 production and their use of VOT in L2 perception. With this effect of L2 proficiency controlled in the partial correlation tests, we found a significant correlation between production and perception in using VOT and F0 for the lax-aspirated stop contrast. However, the same correlation was absent for the other stop pairs. We discuss a contrast-specific role of acoustic cues to address the non-uniform patterns of the production-perception link in the L2 sound learning context.