• Proceedings of the SAREK Conference
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• 2009.06a
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• pp.148-153
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• 2009

This paper considers the age of air in a ventilated space with multiple supply inlets. The local mean age of air at a point from one supply inlet is different from those from the other supply inlets. It is the purpose of the present paper to investigate theoretically the relations between the LMA's from each supply and overall combined LMA whether or not to trace the origins of supply air. Transient concentration distributions are calculated with a step-up injection of tracer gas at each supply inlet, and at both inlets simultaneously. The steady state concentration with a continuous tracer injection at a supply inlet works as a weighting factor for the corresponding LMA in calculating the average overall LMA from multiple inlets.

• Journal of the Korean Society of Safety
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• v.24 no.2
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• pp.100-104
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• 2009

In this study, the space gamma dose rates in the apartments structured with concrete were measured in accordance with construction year. In addition, the environmental radiation rates coming from the subway platforms and the road tunnels were analyzed in the equivalent dose by multiplying the absorbed dose with the radiation weighting factors. The space gamma dose rates measured in apartments were higher than those of outdoor which was $0.08{\sim}0.11uSv/h$ in the natural conditions. Especially, the older construction year is, the higher becomes space gamma dose rate. The average gamma dose rates in the subway platforms were measured. In the case of Busan and Daegu subway, the earlier the opening year is, the higher becomes dose rate. However, the dose rates of Seoul subway Lines were high overall, regardless of opening year. Seoul subway Line 6 showed the highest value of 0.21uSv/h. The gamma dose rate in road tunnels was higher than one of the outdoor and increased with opening year like as apartment. In dose rate comparison of the concrete structures with the outdoor, therefore, the space gamma dose rate of indoor is higher than one of the outdoor and the older structures have a higher dose rate.

• Journal of Power Electronics
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• v.8 no.4
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• pp.354-362
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• 2008

This paper presents neural load torque compensation method which is composed of a deadbeat load torque observer and gains compensation by a parameter estimator. As a result, the response of the PMSM (permanent magnet synchronous motor) obtains better precision position control. To reduce the noise effect, the post-filter is implemented by a MA (moving average) process. The parameter compensator with an RLSM (recursive least square method) parameter estimator is adopted to increase the performance of the load torque observer and main controller. The parameter estimator is combined with a high performance neural load torque observer to resolve problems. The neural network is trained in online phases and it is composed by a feed forward recall and error back-propagation training. During normal operation, the input-output response is sampled and the weighting value is trained multi-times by the error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. As a result, the proposed control system has a robust and precise system against load torque and parameter variation. Stability and usefulness are verified by computer simulation and experiment.

• 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.

• The KSFM Journal of Fluid Machinery
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• v.18 no.2
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• pp.67-72
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• 2015

The purpose of this study is to achieve energy saving effect of inverter driving multistage centrifugal pump. For determining the operation points in the pump system, the system curves should be obtained experimentally. To get the system curves, three pumps combined in parallel and one pump operated with different rotational speeds. But for variable speed pump system, energy saving rates can not be evaluated from operation efficiencies. That is why operation efficiencies, system curves, duty cycles, and input powers of the pump system were measured by the constructed experimental apparatus. The duty cycle segmented into different flow rates and weighting the average value for each segment by the interval time. The system was operated with two different periods. The mean duty cycles were collected from apartment and found that the system operated at 40% and at 50% or below capacity. Measured energy saving rate was 58.16%. Estimating method of energy saving rate could be more effective operation index than that of operation efficiency.

• Journal of Soil and Groundwater Environment
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• v.22 no.6
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• pp.37-47
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• 2017

Climate change has often resulted in severe droughts in a rice-farming season (i.e., April to June), and the large amount of water resources were needed to cope with droughts during the season. Therefore, the subsurface dam, which is able to store groundwater resources in the alluvium aquifer, has been considered to be an alternative for securing more groundwater resources. In this study, suitable sites assessment criteria for agricultural subsurface dam using analytic hierarchy process (AHP) were established for adequate drought management. Moreover, the criteria were applied to the existing five agricultural subsurface dams to verify their applicability of groundwater supply for each subsurface dam. The assessment criteria were divided into three major categories (geology, hydrology and business condition) and classified to 12 individual sub-categories with weighting. From the assessment, Ian subsurface dam and Wooil subsurface dam were identified as the best and the worst suitable site, respectively, and this result was in accordance with the average amount of annual groundwater supply by each subsurface dam during the period of 2011-2017.

• Journal of the Earthquake Engineering Society of Korea
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• v.23 no.6
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• pp.301-309
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• 2019

A methodology to assess seismic fragility of a nuclear power plant (NPP) using a conditional mean spectrum is proposed as an alternative to using a uniform hazard response spectrum. Rather than the single-scenario conditional mean spectrum, which is the conventional conditional mean spectrum based on a single scenario, a multi-scenario conditional mean spectrum is proposed for the case in which no single scenario is dominant. The multi-scenario conditional mean spectrum is defined as the weighted average of different conditional mean spectra, each one of which corresponds to an individual scenario. The weighting factors for scenarios are obtained from a deaggregation of seismic hazards. As a validation example, a seismic fragility assessment of an NPP containment structure is performed using a uniform hazard response spectrum and different single-scenario conditional mean spectra and multi-scenario conditional mean spectra. In the example, the number of scenarios primarily influences the median capacity of the evaluated structure. Meanwhile, the control frequency, a key parameter of a conditional mean spectrum, plays an important role in reducing logarithmic standard deviation of the corresponding fragility curves and corresponding high confidence of low probability of failure (HCLPF) capacity.

• Journal of Powder Materials
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• v.25 no.6
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• pp.501-506
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• 2018

With the recent remarkable improvements in the average speeds of contemporary trains, a necessity has arisen for the development of new friction modifiers to improve adhesion characteristics at the wheel-rail interface. The friction modifier must be designed to reduce slippage or sliding of the trains' wheels on the rails under conditions of rapid acceleration or braking without excessive rolling contact wear. In this study, a novel composite material consisting of metal, ceramic, and polymer is proposed as a friction modifier to improve adhesion between wheels and rails. A blend of Al-6Cu-0.5Mg metallic powder, $Al_2O_3$ ceramic powder, and Bakelite-based polymer in various weight-fractions is hot-pressed at $150^{\circ}C$ to form a bulk composite material. Variation in the adhesion coefficient is evaluated using a high-speed wheel-rail friction tester, with and without application of the composite friction modifier, under both dry and wet conditions. The effect of varying the weighting fractions of metal and ceramic friction powders is detailed in the paper.

• Structural Monitoring and Maintenance
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• v.9 no.1
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• pp.59-80
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• 2022

In the present study, the objective is to detect the structural damages using the responses obtained from the sensors at the optimal location under uncertainty conditions. Reducing the error rate in damage detection process due to responses' noise is an important goal in this study. In the proposed algorithm for optimal sensor placement, the noise of responses recorded from the sensors is initially reduced using the principal component analysis. Afterward, the optimal sensor placement is obtained by the damage detection equation based sensitivity analysis. The sensors are placed on degrees of freedom corresponding to the minimum error rate in structural damage detection through this procedure. The efficiency of the proposed method is studied on a truss bridge, a space dome, a double-layer grid as well as a three-story experimental frame structure and the results are compared. Moreover, the performance of the suggested method is compared with three other algorithms of Average Driving Point Residue (ADPR), Effective Independence (EI) method, and a mass weighting version of EI. In the examples, young's modulus, density, and cross-sectional areas of the elements are considered as uncertainty parameters. Ultimately, the results have demonstrated that the presented algorithm under uncertainty conditions represents a high accuracy to obtain the optimal sensor placement in the structures.

• 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.