• Smart Structures and Systems
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• v.33 no.5
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• pp.325-332
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• 2024

This study proposes a novel long short-term memory (LSTM)-based approach for predicting carbonation depth, with the aim of enhancing the durability evaluation of concrete structures. Conventional carbonation depth prediction relies on statistical methodologies using carbonation influencing factors and in-situ carbonation depth data. However, applying in-situ data for predictive modeling faces challenges due to the lack of time-series data. To address this limitation, an LSTM-based carbonation depth prediction technique is proposed. First, training data are generated through random sampling from the distribution of carbonation velocity coefficients, which are calculated from in-situ carbonation depth data. Subsequently, a Bayesian theorem is applied to tailor the training data for each target bridge, which are depending on surrounding environmental conditions. Ultimately, the LSTM model predicts the time-dependent carbonation depth data for the target bridge. To examine the feasibility of this technique, a carbonation depth dataset from 3,960 in-situ bridges was used for training, and untrained time-series data from the Miho River bridge in the Republic of Korea were used for experimental validation. The results of the experimental validation demonstrate a significant reduction in prediction error from 8.19% to 1.75% compared with the conventional statistical method. Furthermore, the LSTM prediction result can be enhanced by sequentially updating the LSTM model using actual time-series measurement data.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.266-268
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• 2003

This is based on the AGENT-LUC model framework. Luangprabang Province has the largest percentage of shifting cultivation area in Laos PDR. The model simulates the spatial and temporal patterns of the shifting cultivation in the study area, using a GIS database while the total area of shifting cultivation is controlled by supply and demand balance of food. The model simulation period is from 1990 to 1999, at a spatial resolution of 500m. The results are evaluated using statistical data and remote sensing images. Through the validation, it is concluded that the trends simulated agrees to that of statistical data and the spatial and temporal patterns are also replicated satisfactorily.

• Journal of the Korean Data and Information Science Society
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• v.19 no.3
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• pp.789-800
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• 2008

Current credit evaluation models based on financial data make use of smoothing estimated default ratios which are transformed from each financial variable. In this work, some problems of the credit evaluation models developed by financial experts are discussed and we propose improved credit evaluation models based on the stepwise variable selection method and Box-Cox transformed data whose distribution is much skewed to the right. After comparing goodness-of-fit tests of these models, the validation of the credit evaluation models using statistical methods such as the stepwise variable selection method and Box-Cox transformation function is explained.

• International Journal of Highway Engineering
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• v.15 no.3
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• pp.93-101
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• 2013

PURPOSES : This study is to investigate the relationship of socioeconomic characteristics and road network structure with traffic growth patterns. The findings is to be used to tweak traffic forecast provided by traditional four step process using relevant socioeconomic and road network data. METHODS: Comprehensive statistical analysis is used to identify key explanatory variables using historical observations on traffic forecast, actual traffic counts and surrounding environments. Based on statistical results, a multiple regression model is developed to predict the effects of socioeconomic and road network attributes on traffic growth patterns. The validation of the proposed model is also performed using a different set of historical data. RESULTS : The statistical analysis results indicate that several socioeconomic characteristics and road network structure cleary affect the tendency of over- and under-estimation of road traffics. Among them, land use is a key factor which is revealed by a factor that traffic forecast for urban road tends to be under-estimated while rural road traffic prediction is generally over-estimated. The model application suggests that tweaking the traffic forecast using the proposed model can reduce the discrepancies between the predicted and actual traffic counts from 30.4% to 21.9%. CONCLUSIONS : Prediction of road traffic growth patterns based on surrounding socioeconomic and road network attributes can help develop the optimal strategy of road construction plan by enhancing reliability of traffic forecast as well as tendency of traffic growth.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.2 no.2
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• pp.81-89
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• 2001

Space systems are operating in a changing and uncertain space environment and are desired to have autonomous capability for long periods of time without frequent telecommunications from the ground station At the same time. requirements for new set of projects/systems calling for ""autonomous"" operations for long unattended periods of time are emerging. Since, by the nature of space systems, it is desired that they perform their mission flawlessly and also it is of extreme importance to have fault-tolerant sensor/actuator sub-systems for the purpose of validating science measurement data for the mission success. Technology innovations attendant on autonomous data validation and health monitoring are articulated for a growing class of autonomous operations of space systems. The greatest need is on focus research effort to the development of a new class of fault-tolerant space systems such as attitude actuators and sensors as well as validation of measurement data from scientific instruments. The characterization for the next step in evolving the existing control processes to an autonomous posture is to embed intelligence into actively control. modify parameters and select sensor/actuator subsystems based on statistical parameters of the measurement errors in real-time. This research focuses on the identification/demonstration of critical technology innovations that will be applied to Autonomous Spacecraft Health Monitoring/Data Validation Control Systems (ASHMDVCS). Systems (ASHMDVCS).

• Journal of the Korea Society of Computer and Information
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• v.24 no.1
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• pp.197-206
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• 2019

The purpose of this study is to develop and validate a model for diagnosing core competencies at the higher education level. Based on literature reviews, a potential model for core competencies at university was suggested. A tool for validating the model was composed of 24 items, which were delivered to 226 professors and administrative staffs, 730 students, and 134 graduates & external industrial experts. Five constructs (core competencies) were extracted from the data collected among professors and administrative staff responding to the importance of the items. The results of importance and performance surveys on core competencies with students were respectively 3.28 to 3.66 and 2.68 to 3.28 (4-point Likert scale). Statistical differences between importance level and performance level were found in all the sub-categories of core competencies. Borich priority determination formula and Locus for Focus Model were used for the determination of the priority of needs. Importance survey among graduates and external experts showed that the mean of each items ranged from 2.80 to 3.76 (4-point Likert scale). The overall results of the analyses suggest that the final model is appropriate for measuring the core competencies.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1319-1325
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• 2010

Quantile regression provides a more complete statistical analysis of the stochastic relationships among random variables. Sometimes quantile functions estimated at different orders can cross each other. We propose a new non-crossing quantile regression method applying support vector median regression to restricted regression quantile, restricted support vector quantile regression. The proposed method provides a satisfying solution to estimating non-crossing quantile functions when multiple quantiles for high dimensional data are needed. We also present the model selection method that employs cross validation techniques for choosing the parameters which aect the performance of the proposed method. One real example and a simulated example are provided to show the usefulness of the proposed method.

• The Korean journal of internal medicine
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• v.39 no.4
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• pp.625-639
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• 2024

Background/Aims: Intensive care unit (ICU) quality is largely determined by the mortality rate. Therefore, we aimed to develop and validate a novel prognostic model for predicting mortality in Korean ICUs, using national insurance claims data. Methods: Data were obtained from the health insurance claims database maintained by the Health Insurance Review and Assessment Service of South Korea. From patients who underwent the third ICU adequacy evaluation, 42,489 cases were enrolled and randomly divided into the derivation and validation cohorts. Using the models derived from the derivation cohort, we analyzed whether they accurately predicted death in the validation cohort. The models were verified using data from one general and two tertiary hospitals. Results: Two severity correction models were created from the derivation cohort data, by applying variables selected through statistical analysis, through clinical consensus, and from performing multiple logistic regression analysis. Model 1 included six categorical variables (age, sex, Charlson comorbidity index, ventilator use, hemodialysis or continuous renal replacement therapy, and vasopressor use). Model 2 additionally included presence/absence of ICU specialists and nursing grades. In external validation, the performance of models 1 and 2 for predicting in-hospital and ICU mortality was not inferior to that of pre-existing scoring systems. Conclusions: The novel and simple models could predict in-hospital and ICU mortality and were not inferior compared to the pre-existing scoring systems.

• Communications for Statistical Applications and Methods
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• v.27 no.3
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• pp.365-376
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• 2020

Interval-valued data, a type of symbolic data, is given as an interval in which the observation object is not a single value. It can also occur frequently in the process of aggregating large databases into a form that is easy to manage. Various regression methods for interval-valued data have been proposed relatively recently. In this paper, we introduce a nonparametric regression model using the kernel function and a nonlinear regression model for the interval-valued data. We also propose applying the local linear regression model, one of the nonparametric methods, to the interval-valued data. Simulations based on several distributions of the center point and the range are conducted using each of the methods presented in this paper. Various conditions confirm that the performance of the proposed local linear estimator is better than the others.

• Communications for Statistical Applications and Methods
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• v.29 no.1
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• pp.1-25
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• 2022

A modified Bagdonavičius-Nikulin chi-square goodness-of-fit is defined and studied. The lymphoma data is analyzed using the modified goodness-of-fit test statistic. Different non-Bayesian estimation methods under complete samples schemes are considered, discussed and compared such as the maximum likelihood least square estimation method, the Cramer-von Mises estimation method, the weighted least square estimation method, the left tail-Anderson Darling estimation method and the right tail Anderson Darling estimation method. Numerical simulation studies are performed for comparing these estimation methods. The potentiality of the new model is illustrated using three real data sets and compared with many other well-known generalizations.