• Water Engineering Research
• /
• v.4 no.1
• /
• pp.19-30
• /
• 2003

TMDL development and implementation have great potential fur use in efforts to improve water quality management, but the TMDL approach still has several difficulties to overcome in terms of cost, time requirements, and suitable methodologies. A well-defined prioritization approach for identifying watersheds of concern among several tar-get locations that would benefit from TMDL development and implementation, based on a simple screening approach, could be a major step in solving some of these difficulties. Therefore, a web-based decision support system (DSS) was developed to help identify areas within watersheds that might be priority areas for TMDL development. The DSS includes a graphical user interface based on the HTML protocol, hydrological models, databases, and geographic information system (GIS) capabilities. The DSS has a hydrological model that can estimate non-point source pollution loading based on over 30 years of daily direct runoff using the curve number method and pollutant event mean concentration data. The DSS provides comprehensive output analysis tools using charts and tables, and also provides probability analysis and best management practice cost estimation. In conclusion, the DSS is a simple, affordable tool for the preliminary study of TMDL development via the Internet, and the DSS web site can also be used as an information web server for education related to TMDL.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.37 no.5
• /
• pp.293-302
• /
• 2019

In mountain accident events, it is important for the search team commander to determine the search area in order to secure the Golden Time. Within this period, assistance and treatment to the concerned individual will most likely prevent further injuries and harm. This paper proposes a method to determine the search priority area based on missing persons behavior and missing persons incidents statistics. GIS (Geographic Information System) and MCDM (Multi Criteria Decision Making) are integrated by applying WLC (Weighted Linear Combination) techniques. Missing persons were classified into five types, and their behavioral characteristics were analyzed to extract seven geographic analysis factors. Next, index values were set up for each missing person and element according to the behavioral characteristics, and the raster data generated by multiplying the weight of each element are superimposed to define models to select search priority areas, where each weight is calculated from the AHP (Analytical Hierarchy Process) through a pairwise comparison method obtained from search operation experts. Finally, the model generated in this study was applied to a missing person case through a virtual missing scenario, the priority area was selected, and the behavioral characteristics and topographical characteristics of the missing persons were compared with the selected area. The resulting analysis results were verified by mountain rescue experts as 'appropriate' in terms of the behavior analysis, analysis factor extraction, experimental process, and results for the missing persons.

• Journal of the Korea Society of Computer and Information
• /
• v.17 no.6
• /
• pp.145-152
• /
• 2012

Due to the large sizes of streaming media, previous delivery techniques are not providing optimal performance. For this purpose, video proxy server is employed for reducing the bandwidth consumption, network congestion, and network traffic. This paper proposes a fast distributed network file system using state transition model in the media streaming system for efficient utilization of video proxy server. The proposed method is composed of three steps: step 1. Training process using state transition model, step 2. base and decision probability generation, and step 3. storing and deletion based on probability. In addition, storage space of video proxy server is divided into each segment area in order to store the segments efficiently and to avoid the fragmentation. The simulation results show that the proposed method performs better than other methods in terms of hit rate and number of deletion. Therefore, the proposed method provides the lowest user start-up latency and the highest bandwidth saving significantly.

• Journal of Korean Society of Forest Science
• /
• v.102 no.3
• /
• pp.415-419
• /
• 2013

To mitigate forest fire damage, it is needed to concentrate suppression resources on the fire having a high probability to become large in the initial stage. The objective of this study is to develop the large fire judgement model which can estimate large fire possibility index between the fire size and the related factors such as weather, terrain, and fuel. The results of logistic regression equation indicated that temperature, wind speed, continuous drought days, slope variance, forest area were related to the large fire possibility positively but elevation has negative relationship. This model may help decision-making about size of suppression resources, local residents evacuation and suppression priority.

• Journal of Korean Society for Quality Management
• /
• v.42 no.1
• /
• pp.81-89
• /
• 2014

Purpose: The purpose of this study is to propose Clinical Practice Guideline(CPG) model and Clinical Index(CI) for implementing CDSS in digital hospitals. Methods: This study uses EMR data at department of family practice in A hospital; 636 patients, 570 diseases (based on ICD 10-CM criteria), and 37,000 data related with labs and treatments. This study focuses on disease J342 which is the most high rate of incidence. Results: Using the suggested model, this study calculates frequency matrix and probability matrix to find out the correlation of diseases and labs. This study indicates the lab sets of Disease (J342) as CI for CPG. Conclusion: This study suggests CPG model including Lab-based, Disease-Based and Case-based modules. Through 6 level cased-based CPG model, especially, this study develops Clinical Index(CI) such as the Incidence Rate, Lab Rate, Disease Lab Rate, Disease confirmed by Lab.

• The Journal of Bigdata
• /
• v.8 no.1
• /
• pp.23-34
• /
• 2023

Nowadays, the Designated Driver Services employ dynamic pricing, which adapts in real-time based on nearby driver availability, service user volume, and current weather conditions during the user's request. The uncertain volatility is the main cause of price increases, leading to customer attrition and service refusal from driver. To make a good Designated Driver Services, development of a demand forecasting model is required. In this study, we propose developing a demand forecasting model using data from the Designated Driver Service by considering normal and peak periods, such as rush hour and rush day, as prior knowledge to enhance the model performance. We propose a new methodology called Time-Series with Conditional Probability(TSCP), which combines conditional probability and time-series models to enhance performance. Extensive experiments have been conducted with real Designated Driver Service data, and the result demonstrated that our method outperforms the existing time-series models such as SARIMA, Prophet. Therefore, our study can be considered for decision-making to facilitate proactive response in Designated Driver Services.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2005.11a
• /
• pp.241-250
• /
• 2005

New product development (NPD) is defined as the transformation of a market opportunity and a set of assumptions about product technology into a product available for sale. Managers charged with project selection decisions in the NPD process, such as go/no-go choices and specific resource allocation decisions, are faced with a complicated problem. Therefore, the ability to develop new successful products has identifies as a major determinant in sustaining a firm's competitive advantage. The purpose of this study is to develop a new evaluation model for NPD project selection in the high -tech industry using support vector machines (SYM). The evaluation model is developed through two phases. In the first phase, binary (go/no-go) classification prediction model, i.e. SVM for high-tech NPD project selection is developed. In the second phase. using the predicted output value of SVM, feasibility grade is calculated for the final NPD project decision making. In this study, the feasibility grades are also divided as three level grades. We assume that the frequency of NPD project cases is symmetrically determined according to the feasibility grades and misclassification errors are partially minimized by the multiple grades. However, the horizon of grade level can be changed by firms' NPD strategy. Our proposed feasibility grade method is more reasonable in NPD decision problems by considering particularly risk factor of NPD in viewpoints of future NPD success probability. In our empirical study using Korean NPD cases, the SVM significantly outperformed ANN and logistic regression as benchmark models in hit ratio. And the feasibility grades generated from the predicted output value of SVM showed that they can offer a useful guideline for NPD project selection.

• The Journal of the Acoustical Society of Korea
• /
• v.30 no.6
• /
• pp.324-329
• /
• 2011

In this paper, we propose a novel approach to improve the performance of a statistical model-based voice activity detection (VAD) which is based on the conditional maximum a posteriori (CMAP) with deviation. In our approach, the VAD decision rule is expressed as the geometric mean of likelihood ratios (LRs) based on adapted threshold according to the speech presence probability conditioned on both the speech activity decisions and spectral deviation in the pervious frame. Experimental results show that the proposed approach yields better results compared to the CMAP-based VAD using the LR test.

• Journal of the Korean Society of Propulsion Engineers
• /
• v.20 no.2
• /
• pp.56-66
• /
• 2016

Robustness is essential for model based FDI (Fault Detection and Isolation) and it is inevitable to have modeling errors and sensor signal noises during the process of FDI. This study suggests an improved method by applying NARX (Nonlinear Auto Regressive eXogenous) model and Kalman estimator in order to cope with problems caused by linear model errors and sensor signal noises in the process of fault diagnoses. Fault decision is made by the probability of the trend of gradually accumulated errors applying Fuzzy logic, which are robust to instantaneous sensor signal noises. Reliability of fault diagnosis is verified under various fault simulations.

• Journal of Families and Better Life
• /
• v.23 no.3 s.75
• /
• pp.217-230
• /
• 2005

The purpose of this study was to examine factors related to the purchase of cash-value life insurance of households. Based on human capital and bequest motive theories of the demand for life insurance, this study developed a conceptual model of the demand for life insurance of households. In addition, in order to capture the beneficiaries' preference and expected lifetime utility, expected future financial needs were included in the conceptual model. Using Heckit analysis, the model was estimated by two stages. The results supported that human capital, bequest motives and expected future financial needs were significant factors on both decision to have insurance and the mont of insurance. Specifically, if the household's head expected to have a higher potential in the future, the household was more likely to have insurance. If a household had dependents, the household was more likely to have insurance. As income or monthly expenditure increased, the probability of haying insurance and the amount of the insurance increased However, savings or social insurance were positively related to the purchase of insurance.