• Journal of Digital Convergence
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• v.17 no.5
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• pp.327-332
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• 2019

The purpose of this study is to derive the differentiation and improvement measures of Kakao T through a usability evaluation centered on the service. In-depth interviews were conducted by first identifying the status of the mobility O2O service and reconfiguring the usability principles of the Honeycomb model, focusing on the functions and interfaces. First, there are problems driven from the imbalance in supply and demand as a mobility service, but it is likely to be improved through the addition of future features, carpooling. Second, problems were elicited for not recognizing the need for incorporated functions of Kakao T in the presence of existing functional-specific applications. Improvements derived based on this study are expected to help increase the convenience of users of mobility O2O services.

• Journal of Korean Society on Water Environment
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• v.27 no.5
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• pp.699-709
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• 2011

The uses of multi-dimensional hydrodynamic and water quality models are increasing to support a sustainable management of large dam reservoirs in Korea. Any modeling study requires selection of a proper spatial dimension of the model based on the characteristics of spatial variability of concerned simulation variables. For example, a laterally averaged two-dimensional (2D) model, which has been widely used in many large dam reservoirs in Korea, assumes that the lateral variations of hydrodynamic and water quality variables are negligible. However, there has been limited studies to give a justification of the assumption. The objectives of this study were to present the characteristics of spatial variations of water quality variables through intensive field monitoring in Daechung Reservoir, and provide information on a proper spatial dimension for different water quality parameters. The monitoring results showed that the lateral variations of water temperature are marginal, but those of DO, pH, and conductivity could be significant depending on the hydrological conditions and local algal biomass. In particular, the phytoplankton (Chl-a) and nutrient concentrations showed a significant lateral variation at R2 (Daejeongri) during low flow periods in 2008 possibly because of slow lateral mixing of tributary inflow from So-oak Stream and wind driven patchiness.

• Journal of the Korean Institute of Gas
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• v.24 no.6
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• pp.91-97
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• 2020

Lithium-ion batteries (LIBs) have a longer lifespan, higher energy density, and lower self-discharge rates than other batteries, therefore, they are preferred as an Energy Storage System (ESS). However, during years 2017-2019, 28 ESS fire accidents occurred in Korea, and accurate capacity estimation of LIB is essential to ensure safety and reliability during operations. In this study, data-driven modeling that predicts capacity changes according to the charging cycle of LIB was conducted, and developed models were compared their performance for the selection of the optimal machine learning model, which includes the Decision Tree, Ensemble Learning Method, Support Vector Regression, and Gaussian Process Regression (GPR). For model training, lithium battery test data provided by NASA was used, and GPR showed the best prediction performance. Based on this study, we will develop an enhanced LIB capacity prediction and remaining useful life estimation model through additional data training, and improve the performance of anomaly detection and monitoring during operations, enabling safe and stable ESS operations.

• Journal of the Korean Society of Systems Engineering
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• v.16 no.2
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• pp.97-109
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• 2020

On March 11, 2011, an earthquake followed by a tsunami caused an extended station blackout (SBO) at the Fukushima Dai-ichi NPP Units. The accident was initiated by a total loss of both onsite and offsite electrical power resulting in the loss of the ultimate heat sink for several days, and a consequent core melt in some units where proper mitigation strategies could not be implemented in a timely fashion. To enhance the plant's coping capability, the Diverse and Flexible Strategies (FLEX) were proposed to append the Emergency Operation Procedures (EOPs) by relying on portable equipment as an additional line of defense. To assess the success window of FLEX strategies, all sources of uncertainties need to be considered, using a physics-based model or system code. This necessitates conducting a large number of simulations to reflect all potential variations in initial, boundary, and design conditions as well as thermophysical properties, empirical models, and scenario uncertainties. Alternatively, data-driven models may provide a fast tool to predict the success window of FLEX strategies given the underlying uncertainties. This paper explores the applicability of Artificial Intelligence (AI) to identify the success window of FLEX strategy for extended SBO. The developed model can be trained and validated using data produced by the lumped parameter thermal-hydraulic code, MARS-KS, as best estimate system code loosely coupled with Dakota for uncertainty quantification. A Systems Engineering (SE) approach is used to plan and manage the process of using AI to predict the success window of FLEX strategies under extended SBO conditions.

• Journal of Korea Trade
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• v.23 no.7
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• pp.96-112
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• 2019

Purpose - "Trade Facilitation" aims the easier flow of trade across borders, driven not only by effective customs administration, the efficiency of appropriate authorities, but also by telecommunications, the quality of infrastructures and competent logistics. Facilitating trade will help lower trade development costs as well as improve economic development and enhance economic benefits for emerging economies at a time when imports and exports are sent in and out across borders several times in the form of intermediate and final products. Not only that, globalization is being accelerated, which in turn increases competitiveness and this makes logistics one of the key factors when it comes to international trade. Highly efficient logistics services promote product movement, ensure product safety and delivery speed, and reduce trade costs between countries. The purpose of this study is, by using the LPI indices based on gravity model estimates, to analyze the impact of each LPI component on trade with the 20 biggest exporting countries of Northeast Asian countries-Korea, Japan, and China-which account for 19.05% of global exports. Design/methodology - Also, this study statistically analyzes the impact of trade on Northeast Asian countries' top 20 exporting countries, using the LPI indices relevant to Trade Facilitation based on the gravity model estimates. Findings - As a result, it was turned out that the distance, GDP, and the LPI components have relevant impact on the trade exports of all three countries but demonstrated little relation to the demographic perspective. Originality/value - The study also found we can increase the trade volume by improving three countries' trade partners' LPI indices since Korea, Japan, and China share most of their 20 biggest trade partners.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.75-93
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• 2022

Machine learning (ML) data-driven meta-model is proposed as a surrogate model to reduce the excessive computational cost of the physics-based model and facilitate the real-time prediction of a nuclear power plant's transient response. To forecast the transient response three machine learning (ML) meta-models based on recurrent neural networks (RNNs); specifically, Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and a sequence combination of Convolutional Neural Network (CNN) and LSTM are developed. The chosen accident scenario is a control element assembly withdrawal at power concurrent with the Loss Of Offsite Power (LOOP). The transient response was obtained using the best estimate thermal hydraulics code, MARS-KS, and cross-validated against the Design and control document (DCD). DAKOTA software is loosely coupled with MARS-KS code via a python interface to perform the Best Estimate Plus Uncertainty Quantification (BEPU) analysis and generate a time series database of the system response to train, test and validate the ML meta-models. Key uncertain parameters identified as required by the CASU methodology were propagated using the non-parametric Monte-Carlo (MC) random propagation and Latin Hypercube Sampling technique until a statistically significant database (181 samples) as required by Wilk's fifth order is achieved with 95% probability and 95% confidence level. The three ML RNN models were built and optimized with the help of the Talos tool and demonstrated excellent performance in forecasting the most probable NPP transient response. This research was guided by the Systems Engineering (SE) approach for the systematic and efficient planning and execution of the research.

• Structural Engineering and Mechanics
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• v.83 no.4
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• pp.515-535
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• 2022

The strength models for fiber-reinforced polymer (FRP)-confined normal strength concrete (NC) cylinders available in the literature have been suggested based on small databases using limited variables of such structural members portraying less accuracy. The artificial neural network (ANN) is an advanced technique for precisely predicting the response of composite structures by considering a large number of parameters. The main objective of the present investigation is to develop an ANN model for the axial strength of FRP-confined NC cylinders using various parameters to give the highest accuracy of the predictions. To secure this aim, a large experimental database of 313 FRP-confined NC cylinders has been constructed from previous research investigations. An evaluation of 33 different empirical strength models has been performed using various statistical parameters (root mean squared error RMSE, mean absolute error MAE, and coefficient of determination R²) over the developed database. Then, a new ANN model using the Group Method of Data Handling (GMDH) has been proposed based on the experimental database that portrayed the highest performance as compared with the previous models with R²=0.92, RMSE=0.27, and MAE=0.33. Therefore, the suggested ANN model can accurately capture the axial strength of FRP-confined NC cylinders that can be used for the further analysis and design of such members in the construction industry.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.1
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• pp.10-21
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• 2023

For various target tracking applications, it is well known that the Kalman filter is the optimal estimator(in the minimum mean-square sense) to predict and estimate the state(position and/or velocity) of linear dynamical systems driven by Gaussian stochastic noise. In the case of nonlinear systems, Extended Kalman filter(EKF) and/or Unscented Kalman filter(UKF) are widely used, which can be viewed as approximations of the(linear) Kalman filter in the sense of the conditional expectation. However, to implement EKF and UKF, the exact dynamical model information and the statistical information of noise are still required. In this paper, we propose the recurrent neural-network based Kalman filter, where its Kalman gain is obtained via the proposed GRU-LSTM based neural-network framework that does not need the precise model information as well as the noise covariance information. By the proposed neural-network based Kalman filter, the state estimation performance is enhanced in terms of the tracking error, which is verified through various linear and nonlinear tracking problems with incomplete model and statistical covariance information.

• Journal of the Korea Society of Computer and Information
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• v.14 no.1
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• pp.191-203
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• 2009

To provide dynamic services for users in ubiquitous computing environments by considering context in real-time, in our previous work we proposed Metaservice concept, the description specification and the process for building a Metaservice library. However, our previous process generates separated models - UML, OWL, OWL-S based models - from each step, so it did not provide the established method for translation between models. Moreover, it premises aid of experts in various ontology languages, ontology editing tools and the proposed Metaservice specification. In this paper, we design the translation process from domain ontology in OWL to Metaservice Library in OWL-S and develop a visual tool in order to enable non-experts to generate consistent models and to construct a Metaservice library. The purpose of the Metaservice Library translation process is to maintain consistency in all models and to automatically generate OWL-S code for Metaservice library by integrating existing OWL model and Metaservice model.

• Journal of the Korean Regional Science Association
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• v.39 no.4
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• pp.73-90
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• 2023

This research examines the impact of robotics integration on job dynamics in the U.S. manufacturing sector, adding to the critical dialogue on technological evolution and the future of jobs. Anchored in the task-model framework, the study hypothesizes that robotic integration exerts differential influences on diverse occupational clusters, each identified by their unique task-specific attributes. An in-depth examination was undertaken to elucidate the interplay between robotic integration and the occupation clusters. Employing a multilevel growth curve model, our empirical investigation tracked employment dynamics from 2012 to 2022 across 52 U.S. regions, covering 307 manufacturing occupations. The findings suggest a pronounced job decline within occupations necessitating manual dexterity. Nonetheless, the evidence does not conclusively support that the extent of robotics integration exacerbates this trend. These findings imply that the employment shifts in the U.S. manufacturing sector are predominantly driven by long-standing trends of deindustrialization and functional specialization, rather than by the recent diffusion of robotic technologies.