• Proceedings of the Korea Water Resources Association Conference
• /
• 2012.05a
• /
• pp.39-39
• /
• 2012

Data assimilation techniques have received growing attention due to their capability to improve prediction in various areas. Despite of their potentials, applicable software frameworks to probabilistic approaches and data assimilation are still limited because the most of hydrologic modelling software are based on a deterministic approach. In this study, we developed a hydrological modelling framework for sequential data assimilation, namely MPI-OHyMoS. MPI-OHyMoS allows user to develop his/her own element models and to easily build a total simulation system model for hydrological simulations. Unlike process-based modelling framework, this software framework benefits from its object-oriented feature to flexibly represent hydrological processes without any change of the main library. In this software framework, sequential data assimilation based on the particle filters is available for any hydrologic models considering various sources of uncertainty originated from input forcing, parameters and observations. The particle filters are a Bayesian learning process in which the propagation of all uncertainties is carried out by a suitable selection of randomly generated particles without any assumptions about the nature of the distributions. In MPI-OHyMoS, ensemble simulations are parallelized, which can take advantage of high performance computing (HPC) system. We applied this software framework for several catchments in Japan using a distributed hydrologic model. Uncertainty of model parameters and radar rainfall estimates is assessed simultaneously in sequential data assimilation.

• Journal of KIISE:Software and Applications
• /
• v.30 no.10
• /
• pp.983-991
• /
• 2003

The Web-based instruction on the internet provides lots of learners with the related information and knowledge beyond time and space. But in the Web-based instruction, there is a problem that the teaming process statuses for learners can be known only through an exam. This paper introduces a web monitoring method to check whether the learner has some problems in learning process and to be able to find out the students with the problems. In the method this paper proposes a learning time prediction model for predicting the proper next study time intervals based on the learner`s learning times and grades on Previous learning units. This method provides the educator with the learning Process statuses for learners. The Loaming prediction model for web-based monitoring can be used to stimulate learners to take the good teaming processes by sending automatically alerting messages if their real teaming times exceeds on his predicted learning time interval. The results of the estimation through case study on the web-based monitoring to use the teaming time prediction model show that most of on-line learners with Poor teaming process statuses get poor grades. In addition, the results show that learner`s poor habits keep going on without change.

• Journal of KIISE
• /
• v.42 no.3
• /
• pp.328-340
• /
• 2015

Software requirements are continuously changed for various reasons, consequently changes of software are inevitable. In the case of changes necessitated by changes in requirements, it is necessary to precisely predict the ripple effects of the changes for efficient management of the changes. This paper proposes the management method of traceability information, which can be applied in object-oriented development. Furthermore, we introduce the guidelines for prediction of the ripple effects of changes based on traceability information among artifacts composing a system. We identify traceability items for the essential artifacts which were composed of the object-oriented system, and define relationships among them. The purpose of the method proposed in this paper is to identify the scope of change precisely through the guidelines. These can then be used for tracing and analyzing the impact of the changes both the forward and backward looking, based on the relationships of traceability items.

• Physical Therapy Korea
• /
• v.28 no.3
• /
• pp.177-185
• /
• 2021

Background: Muscle undergoes change continuously with aging. Sarcopenia, in which muscle mass decrease with aging, is associated with various diseases, the risk of falling, and the deterioration of quality of life. Obesity and sarcopenia also have a synergy effect on the disease of the older adults. Objects: This study examined the risk factors for sarcopenia, sarcopenic obesity, and sarcopenia without obesity and developed prediction models. Methods: This machine-learning study used the 2008-2011 Korea National Health and Nutrition Examination Surveys in the analysis. After data curation, 5,563 older participants were selected, of whom 1,169 had sarcopenia, 538 had sarcopenic obesity, and 631 had sarcopenia without obesity; the remaining 4,394 were normal. Decision tree and random forest models were used to identify risk factors. Results: The risk factors for sarcopenia chosen by both methods were body mass index (BMI) and duration of moderate physical activity; those for sarcopenic obesity were sex, BMI, and duration of moderate physical activity; and those for sarcopenia without obesity were BMI and sex. The areas under the receiver operating characteristic curves of all prediction models exceeded 0.75. BMI could predict sarcopenia-related disease. Conclusion: Risk factors for sarcopenia-related diseases should be identified and programs for sarcopenia-related disease prevention should be developed. Data-mining research using population data should be conducted to enhance the effectiveness of early treatment for people with sarcopenia-related diseases through predictive models.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.1
• /
• pp.57-64
• /
• 2020

Recently, due to the proliferation of IoT sensors, the development of big data and artificial intelligence, time series prediction research on fine dust pollution is actively conducted. However, because the data representing fine dust contamination changes rapidly, traditional time series prediction methods do not provide a level of accuracy that can be used in the field. In this paper, we propose a method that reflects the classification results of environmental conditions through CNN when predicting micro dust contamination using LSTM. Although LSTM and CNN are independent, they are integrated into one network through the interface, so this method is easier to understand than the application LSTM. In the verification experiments of the proposed method using Beijing PM2.5 data, the prediction accuracy and predictive power for the timing of change were consistently improved in various experimental cases.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.27 no.2
• /
• pp.139-147
• /
• 2017

Acoustic performance of splitter silencers was investigated by using 3-dimensional commercial software and experiments. Flow resistivity of sound absorbing material was indirectly estimated by using an impedance tube setup and a curve fitting method. In addition the acoustic impedance of perforated plate was determined by an empirical formulation. Such properties have been used as input parameters in the commercial software. The prediction for a splitter silencer with 1000 mm length was compared with the experimental result. The numerical method is then applied to identify the effects of number of splitters, length of splitters, absorptive material density, and porosity of a perforated plate on the performance of the splitter silencers. As the number and length of splitter increases, the acoustic performance significantly increases. Although the increase of density of absorptive material also increase the acoustic performance, a change in the density over a certain level hardly affect it. The increase of porosity will enhance the performance especially at higher frequencies.

• Journal of Space Technology and Applications
• /
• v.1 no.1
• /
• pp.23-32
• /
• 2021

The high-level Space Situational Awareness (SSA) objective is to provide to the users dependable, accurate and timely information in order to support risk management on orbit and during re-entry and support safe and secure operation of space assets and related services. Therefore the risk assessment for the re-entry of space objects should be managed nationally. In this research, the Software for Re-Entry Prediction of space objects (SREP) was developed for national SSA system. In particular, the rate of change of the drag coefficient is estimated through a newly proposed Drag Scale Factor Estimation (DSFE), and is used for high-precision orbit propagator (HPOP) up to an altitude of 100 km to predict the re-entry time and position of the space object. The effectiveness of this re-entry prediction is shown through the re-entry time window and ground track of space objects falling in real events, Grace-1, Grace-2, Tiangong-1, and Chang Zheng-5B Rocket body. As a result, through analysis 12 hours before the final re-entry time, it is shown that the re-entry time window and crash time can be accurately predicted with an error of less than 20 minutes.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.3
• /
• pp.67-74
• /
• 2024

In this paper, we present a study aimed at analyzing how different rainfall measurement methods affect the performance of reservoir water level predictions. This work is particularly timely given the increasing emphasis on climate change and the sustainable management of water resources. To this end, we have employed rainfall data from ASOS, AWS, and Thiessen Network-based measures provided by the KMA Weather Data Service to train our neural network models for reservoir yield predictions. Our analysis, which encompasses 34 reservoirs in Jeollabuk-do Province, examines how each method contributes to enhancing prediction accuracy. The results reveal that models using rainfall data based on the Thiessen Network's area rainfall ratio yield the highest accuracy. This can be attributed to the method's accounting for precise distances between observation stations, offering a more accurate reflection of the actual rainfall across different regions. These findings underscore the importance of precise regional rainfall data in predicting reservoir yields. Additionally, the paper underscores the significance of meticulous rainfall measurement and data analysis, and discusses the prediction model's potential applications in agriculture, urban planning, and flood management.

• Journal of the Korea Convergence Society
• /
• v.8 no.11
• /
• pp.77-83
• /
• 2017

For efficient predictive analysis, self-healing research is needed that enables the system to recover autonomously by self-cognition and diagnosing system problems. However, software development does not provide formal contextual information analysis and appropriate presentation structure according to external situation. In this paper, we propose a prediction analysis method based on the change contents by applying the extraction rule to the functions that can act, data, and transaction based on the new Goal-scenario. We also evaluated how well the predictive analysis met through the performance indicators for achieving the requirements goal. Compared with the existing methods, the proposed method has a maximum 32.8% higher matching result through performance measurement, resulting in a 28.9% error rate and a 45.8% reduction in the change code. This shows that it can be processed into a serviceable form through rules, and it shows that performance can be expanded through predictive analysis of changes.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.37 no.7
• /
• pp.653-660
• /
• 2009

A helicopter rotor brake system stops or reduces the speed of the rotor by transforming the kinetic energy into the heat energy. The frictionally generated heat has a considerable effect on the frictional property of material itself and causes the change of the friction coefficient which may affect the breaking time significantly. In this paper, to take into account the effect of change of friction coefficient according to temperature on braking time, thermo-mechanically coupled analysis is carried out by commercial software ABAQUS. Further, simple theoretical equation is derived considering thermo-mechanical behaviors. The predicted braking times both from theoretical and numerical methods are compared and validity of proposed theoretical equation is investigated.