• Journal of Institute of Control, Robotics and Systems
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• v.12 no.12
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• pp.1215-1219
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• 2006

Scheduling plays an important role in the process management in terms of providing profit-maximizing operation sequence of multiple orders and estimating completion times of them. In order to takes its full potential, varying conditions should be properly reflected in computing the schedule. The adjustment of scheduling decisions has to be made frequently in response to the occurrence of variations. It is often challenging because their model has to be adjusted and their solutions have to be computed within short time period. This paper employs Model Predictive Control(MPC) principles for updating the process condition in the scheduling model. The solutions of the resulting problems considering variations are computed using parametric programming techniques. The key advantage of the proposed framework is that repetition of solving similar programming problems with decreasing dimensionis avoided and all potential schedules are obtained before the execution of the actual processes. Therefore, the proposed framework contributes to constructing a robust decision-support tool in the face of varying environment. An example is solved to illustrate the potential of the proposed framework with remarks on potential wide applications.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.5
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• pp.2393-2393
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• 2014

• The Journal of Information Systems
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• v.30 no.2
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• pp.29-56
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• 2021

Purpose Cyber deviance of adolescents has become a serious social problem. With a widespread use of smartphones, incidents of cyber deviance have increased in Korea and both quantitative and qualitative damages such as suicide and depression are increasing. Research has been conducted to understand diverse factors that explain adolescents' delinquency in cyber space. However, most previous studies have focused on a single theory or perspective. Therefore, this study aims to comprehensively analyze motivations of juvenile cyber deviance and to develop a predictive model for delinquent adolescents by integrating four different theories on cyber deviance. Design/methodology/approach By using data from Korean Children & Youth Panel Survey 2010, this study extracts 27 potential factors for cyber deivance based on four background theories including general strain, social learning, social bonding, and routine activity theories. Then this study employs econometric analysis to empirically assess the impact of potential factors and utilizes a machine learning approach to predict the likelihood of cyber deviance by adolescents. Findings This study found that general strain factors as well as social learning factors have positive effects on cyber deviance. Routine activity-related factors such as real-life delinquent behaviors and online activities also positively influence the likelihood of cyber diviance. On the other hand, social bonding factors such as community commitment and attachment to community lessen the likelihood of cyber deviance while social factors related to school activities are found to have positive impacts on cyber deviance. This study also found a predictive model using a deep learning algorithm indicates the highest prediction performance. This study contributes to the prevention of cyber deviance of teenagers in practice by understanding motivations for adolescents' delinquency and predicting potential cyber deviants.

• Journal of the Korea Safety Management & Science
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• v.25 no.4
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• pp.141-151
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• 2023

Recently, due to the expansion of the logistics industry, demand for logistics automation equipment is increasing. The modern logistics industry is a high-tech industry that combines various technologies. In general, as various technologies are grafted, the complexity of the system increases, and the occurrence rate of defects and failures also increases. As such, it is time for a predictive maintenance model specialized for logistics automation equipment. In this paper, in order to secure the operational safety and reliability of the parcel loading system, a predictive maintenance platform was implemented based on the Naive Bayes-LSTM(Long Short Term Memory) model. The predictive maintenance platform presented in this paper works by collecting data and receiving data based on a RabbitMQ, loading data in an InMemory method using a Redis, and managing snapshot DB in real time. Also, in this paper, as a verification of the Naive Bayes-LSTM predictive maintenance platform, the function of measuring the time for data collection/storage/processing and determining outliers/normal values was confirmed. The predictive maintenance platform can contribute to securing reliability and safety by identifying potential failures and defects that may occur in the operation of the parcel loading system in the future.

• Journal of The Geomorphological Association of Korea
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• v.26 no.2
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• pp.1-14
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• 2019

This study analyzed the relativity between block stream and talus distributions by employing a likelihood ratio approach. Possible distribution sites for each debris slope landform were extracted by applying a spatial integration model, in which we combined fuzzy set model, Bayesian predictive model, and logistic regression model. Moreover, to verify model performance, a success rate curve was prepared by cross-validation. The results showed that elevation, slope, curvature, topographic wetness index, geology, soil drainage, and soil depth were closely related to the debris slope landform sites. In addition, all spatial integration models displayed an accuracy of over 90%. The accuracy of the distribution potential area map of the block stream was highest in the logistic regression model (93.79%). Eventually, the accuracy of the distribution potential area map of the talus was also highest in the logistic regression model (97.02%). We expect that the present results will provide essential data and propose methodologies to improve the performance of efficient and systematic micro-landform studies. Moreover, our research will potentially help to enhance field research and topographic resource management.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1443-1448
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• 2003

Model Predictive Control (MPC) has recently found wide acceptance in industrial applications, but its potential has been much impounded by linear models due to the lack of a similarly accepted nonlinear modelling or data based technique. The authors have recently developed a new method for obtaining Volterra kernels of up to third order by use of pseudorandom M-sequence. By use of this method, nonparametric NMPC is derived in discrete-time using multi-dimensional convolution between plant data and Volterra kernel measurements. This approach is applied to an industrial polymerisation process using Volterra kernels of up to the third order. Results show that the nonparametric approach is very efficient and effective and considerably outperforms existing methods, while retaining the original data-based spirit and characteristics of linear MPC.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.630-644
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• 2021

Educational data analysis is attracting increasing attention with the rise of the big data industry. The amounts and types of learning data available are increasing steadily, and the information technology required to analyze these data continues to develop. The early identification of potential dropout students is very important; education is important in terms of social movement and social achievement. Here, we analyze educational data and generate predictive models for student dropout using logistic regression, a decision tree, a naïve Bayes method, and a multilayer perceptron. The multilayer perceptron model using independent variables selected via the variance analysis showed better performance than the other models. In addition, we experimentally found that not only grades but also extracurricular activities were important in terms of preventing student dropout.

• Korean Journal of Remote Sensing
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• v.23 no.4
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• pp.287-295
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• 2007

A mineral potential map showing the distributions of potential areas for exploration of undiscovered mineral deposits is a kind of predictive thematic maps. For any predictive thematic maps to show reasonably significant prediction results, validation information on prediction capability should be provided in addition to spatial locations of high potential areas. The objective of this paper is to apply prediction rate curves to the estimation of prediction probability of future discovery. A case study for Au-Ag mineral potential mapping using geochemical data sets is carried out to illustrate procedures for estimating prediction probability and for an interpretation. Through the case study, quantitative information including prediction rates and probability obtained by prediction rate curves was found to be very important for the interpretation of prediction results. It is expected that such quantitative validation information would be effectively used as basic information for cost analysis of exploration and environmental impact assessment.

• Korean Journal of Adult Nursing
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• v.27 no.5
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• pp.559-571
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• 2015

Purpose: This study is to determine the predictive validity of the St. Thomas Risk Assessment Tool in Falling Elderly Inpatients (STRATIFY) for inpatients' fall risk. Methods: A literature search was performed to identify all studies published between 1946 and 2014 from periodicals indexed in Ovid Medline, Embase, CINAHL, KoreaMed, NDSL and other databases, using the following key words; 'fall', 'fall risk assessment', 'fall screening', 'mobility scale', and 'risk assessment tool'. The QUADAS-II was applied to assess the internal validity of the diagnostic studies. Fourteen studies were analyzed using meta-analysis with MetaDisc 1.4. Results: The predictive validity of STRATIFY was as follows; pooled sensitivity .75 (95% CI: 0.72~0.78), pooled specificity .69 (95% CI: 0.69~0.70) respectively. In addition, the pooled sensitivity in the study that targets only the over 65 years of age was .89 (95% CI: 0.85~0.93). Conclusion: The STRATIFY's predictive validity for fall risk is at a moderate level. Although there is a limit to interpret the results for heterogeneity between the literature, STRATIFY is an appropriate tool to apply to hospitalized patients of the elderly at a potential risk of accidental fall in a hospital.

• Quality Improvement in Health Care
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• v.22 no.2
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• pp.27-40
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• 2016

Purpose: Globally, falls are a major public health problem. The study aimed to evaluate the predictive validity of the Timed Up and Go test (TUGT) as a screening tool for fall risk. Methods: An electronic search was performed Medline, EMBASE, CINAHL, Cochran Library, KoreaMed and the National Digital Science Library and other databases, using the following keywords: 'fall', 'fall risk assessment', 'fall screening', 'mobility scale', and 'risk assessment tool'. The QUADAS-II was applied to assess the internal validity of the diagnostic studies. Thirteen studies were analyzed using meta-analysis with MetaDisc 1.4. Results: The selected 13 studies reporting predictive validity of TUGT of fall risks were meta-analyzed with a sample size of 1004 with high methodological quality. Overall predictive validity of TGUT was as follows. The pooled sensitivity 0.72 (95% confidence interval [CI]: 0.67-0.77), pooled specificity 0.58 (95% CI: 0.54-0.63) and sROC AUC was 0.75 respectively. Heterogeneity among studies was a moderate level in sensitivity. Conclusion: The TGUT's predictive validity for fall risk is at a moderate level. Although there is a limit to interpret the results for heterogeneity between the literature, TGUT is an appropriate tool to apply to all patients at a potential risk of accidental fall in a hospital or long-term care facility.