• Journal of Environmental Science International
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• v.18 no.11
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• pp.1271-1281
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• 2009

The heavy snowfall event over the eastern part of Seoul, Korea on Mar. 04, 2008 has been abruptly occurred after the frontal system with the heavy snowfall event had been past over the Korean peninsula on Mar. 03, 2008. Therefore, this heavy snowfall event couldn't be predicted well by any means of theoretical knowledges and models. After the cold front passed by, the cold air mass was flown over the peninsula immediately and became clear expectedly except the eastern part and southwestern part of peninsula with some large amount of snowfall. Even though the wide and intense massive cold anticyclone was expanded and enhanced by the lowest tropospheric baroclinicity over the Yellow Sea, but the intrusion and eastward movement of cold air to Seoul was too slow than normally predicted. Using the data of numerical model, satellite and radar images, three dimensional analysis Products(KLAPS : Korea Local Analysis and Prediction System) of the environmental conditions of this event such as temperature, equivalent potential temperature, wind, vertical circulation, divergence, moisture flux divergence and relative vorticity could be analyzed precisely. Through the analysis of this event, the formation and westward advection of lower cyclonic circulation with continuously horizontal movement of air into the eastern part of Seoul by the analyses of KLAPS fields have been affected by occurring the heavy snowfall event. As the predictability of abrupt snowfall event was very hard and dependent on not only the synoptic atmospheric circulation but also for mesoscale atmospheric circulation, the forecaster can be predicted well this event which may be occurred and developed within the very short time period using sequential satellite images and KLAPS products.

• Journal of Korea Multimedia Society
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• v.9 no.10
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• pp.1322-1331
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• 2006

Concurrency control is one of the important factors to maintain consistent conditions of a game because most participants of the game should be shared information to play the game through a distributed network system. replay delay times should establish in every event and the received event should be saved and performed simultaneously for Concurrency Control. However, it is not easy to practice the event with same speed in environment having various moving speed. Therefore, expansion have to be provided. In other words, one of the most important factors of a game's efficiency is the process of bandwidth and delay. the process of concurrency control method based on scalable prediction for Multi-platform games would minimize the loss rate of a event and then would improve the interaction capacity of a game. It also might get reliability between clients. This paper analyzes some problems in terms of a layout of a game that integrates a cable and a wireless system. In addition, this paper provides methods to expand bandwidth and delay that might be an obstacle of a On-line game and to ensure reliability between a server and a client.

• Journal of Korean Society on Water Environment
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• v.21 no.6
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• pp.617-623
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• 2005

Forestry and agricultural land uses constitute 85% of Korea and these land uses are typically mixed in many watersheds. Biological Oxygen Demand (BOD) concentration is a primary factor for managing water qualities of the water resources in Korea. BOD loadings from diffuse sources, however, not well monitored yet. This study aims to assess BOD loadings from diffuse sources and their affecting factors to conserve quality of water resources. Event Mean Concentration (EMC) of BOD was calculated based on the monitoring data of forty rainfall events at four agricultural-forestry watersheds. Exceedence cumulative probability of BOD EMCs were plotted to show agricultural activities in a watershed impacts on the magnitude of EMCs. Prediction equation for each rainfall event was proposed to estimate BOD EMCs: $EMC_{BOD}(mg/L)=EXP(0.413+0.0000001157{\times}$(discharged runoff volume in $m^3$)+0.018${\times}$(ratio of agricultural land use to total watershed area).

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.232-240
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• 2022

To fulfill user expectations, the rapid evolution of software techniques and approaches has necessitated reliable and flawless software operations. Aging prediction in the software under operation is becoming a basic and unavoidable requirement for ensuring the systems' availability, reliability, and operations. In this paper, an improved evolutionary computing-driven extreme learning scheme (ECD-ELM) has been suggested for object-oriented software aging prediction. To perform aging prediction, we employed a variety of metrics, including program size, McCube complexity metrics, Halstead metrics, runtime failure event metrics, and some unique aging-related metrics (ARM). In our suggested paradigm, extracting OOP software metrics is done after pre-processing, which includes outlier detection and normalization. This technique improved our proposed system's ability to deal with instances with unbalanced biases and metrics. Further, different dimensional reduction and feature selection algorithms such as principal component analysis (PCA), linear discriminant analysis (LDA), and T-Test analysis have been applied. We have suggested a single hidden layer multi-feed forward neural network (SL-MFNN) based ELM, where an adaptive genetic algorithm (AGA) has been applied to estimate the weight and bias parameters for ELM learning. Unlike the traditional neural networks model, the implementation of GA-based ELM with LDA feature selection has outperformed other aging prediction approaches in terms of prediction accuracy, precision, recall, and F-measure. The results affirm that the implementation of outlier detection, normalization of imbalanced metrics, LDA-based feature selection, and GA-based ELM can be the reliable solution for object-oriented software aging prediction.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.10a
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• pp.40-43
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• 2000

In this paper, we investigate how the average survival time of listed companies in the Korea Stock Exchange (KSE) are affected by changes in macro-economic environment and covariate vectors which show peculiar financial characteristics of each company. We also apply the survival analysis approach to the dichotomous firm failure prediction and the results show a similar pattern of forecasting performance using the existing dichotomous prediction techniques. These findings suggest that, when we consider a bankruptcy model under a certain economic event, the survival approach can be a useful alternative to the existing dichotomous prediction methods since the approach provides estimation of average survival time as well as simple binary prediction.

• ETRI Journal
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• v.43 no.6
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• pp.1058-1080
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• 2021

Crime is not a completely random event but rather shows a pattern in space and time. Capturing the dynamic nature of crime patterns is a challenging task. Crime prediction models that rely only on neighborhood influence and demographic features might not be able to capture the dynamics of crime patterns, as demographic data collection does not occur frequently and is static. This work proposes a novel approach for crime count and hotspot prediction to capture the dynamic nature of crime patterns using taxi data along with historical crime and demographic data. The proposed approach predicts crime events in spatial units and classifies each of them into a hotspot category based on the number of crime events. Four models are proposed, which consider different covariates to select a set of independent variables. The experimental results show that the proposed combined subset model (CSM), in which static and dynamic aspects of crime are combined by employing the taxi dataset, is more accurate than the other models presented in this study.

• Transactions of the Korean hydrogen and new energy society
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• v.31 no.1
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• pp.73-81
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• 2020

For the fault tree analysis (FTA) analysis of the packaged hydrogen filling station, the composition of the charging station was analyzed and the fault tree (FT) diagram was prepared. FT diagrams were created by dividing the causes of events into external factors and internal factors with the hydrogen event as the top event. The external factors include the effects of major disasters caused by natural disasters and external factors as OR gates. Internal factors are divided into tube tailer, compressor & storage tank, and dispenser, which are composed of mistakes in operation process and causes of accidents caused by parts leakage. In this study, the purpose was to improve the hydrogen station. The subjects of this study were domestic packaged hydrogen stations and FTA study was conducted based on the previous studies, failure mode & effect analysis (FMEA) and hazard & operability study (HAZOP). Top event as a hydrogen leaking event and constructed the flow of events based on the previous study. Refer to "Off shore and onshore reliability data 6th edition", "European Industry Reliability Data Bank", technique for human error rate prediction (THERP) for reliability data. We hope that this study will help to improve the safety and activation of the hydrogen station.

• Genomics & Informatics
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• v.17 no.4
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• pp.41.1-41.12
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• 2019

Survival analysis mainly deals with the time to event, including death, onset of disease, and bankruptcy. The common characteristic of survival analysis is that it contains "censored" data, in which the time to event cannot be completely observed, but instead represents the lower bound of the time to event. Only the occurrence of either time to event or censoring time is observed. Many traditional statistical methods have been effectively used for analyzing survival data with censored observations. However, with the development of high-throughput technologies for producing "omics" data, more advanced statistical methods, such as regularization, should be required to construct the predictive survival model with high-dimensional genomic data. Furthermore, machine learning approaches have been adapted for survival analysis, to fit nonlinear and complex interaction effects between predictors, and achieve more accurate prediction of individual survival probability. Presently, since most clinicians and medical researchers can easily assess statistical programs for analyzing survival data, a review article is helpful for understanding statistical methods used in survival analysis. We review traditional survival methods and regularization methods, with various penalty functions, for the analysis of high-dimensional genomics, and describe machine learning techniques that have been adapted to survival analysis.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.5
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• pp.101-108
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• 2017

Recently, the occurrence of landslides has been increasing over the years due to the extreme weather event. Developments of landslides monitoring technology that reduce damage caused by landslide are urgently needed. Therefore, in this study, a strain ratio sensor was developed to predict the ground behavior during the slope failure, and the change in surface ground displacement was observed as slope failed on the field model experiment. As a result, in the slope failure, the ground displacement process increases the risk of collapse as the inverse displacement approaches zero. It is closely related to the prediction of precursor. In all cases, increase in displacement and reverse speed of inverse displacement with time was observed during the slope failure, and it is very important event for monitoring collapse phenomenon of risky slopes. In the future, it can be used as disaster prevention technology to contribute in reduction of landslide damage and activation of measurement industry.

• Journal of Korea Water Resources Association
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• v.54 no.9
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• pp.667-680
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• 2021

We investigated changes in frost days and frost-free periods and to comparatively assess frost event prediction models developed using logistic regression (LR), random forest (RF), and long short-term memory (LSTM) networks. The meteorological variables for the model development were collected from the Suwon, Cheongju, and Gwangju stations for the period of 1973-2019 for spring (March - May) and fall (September - November). The developed models were then evaluated by Precision, Recall, and f-1 score and graphical evaluation methods such as AUC and reliability diagram. The results showed that significant decreases (significance level of 0.01) in the frequencies of frost days were at the three stations in both spring and fall. Overall, the evaluation metrics showed that the performance of RF was highest, while that of LSTM was lowest. Despite higher AUC values (above 0.9) were found at the three stations, reliability diagrams showed inconsistent reliability. A further study is suggested on the improvement of the predictability of both frost events and the first and last frost days by the frost event prediction models and reliability of the models. It would be beneficial to replicate this study at more stations in other regions.