• Research in Community and Public Health Nursing
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• v.14 no.4
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• pp.587-597
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• 2003

Purpose: The purpose of this study was to estimate the demand and supply of visiting nursing services provided by health centers in urban area, aiming at strengthening infrastructure, which may improved the quality of life and health status of vulnerable population in the community. Methods: This study was conducted through nominal group discussion, focus group study. The demand and supply of visiting nursing were estimated by health economists based on the secondary analysis data from 25 health centers in Seoul. Result: Primary targets for the visiting nursing must be people who are homebound in the community. They can be classified into: a group of Level I: chronic patients who need visiting nursing care at least once a week: and a group of Level II: vulnerable families that need management periodically e. g. twice a month. Based on the estimation of demand for visiting nursing services in the community, the estimated supply required was $651{\sim}770$ visiting nurses including home health nurses in visiting nursing programs based on health centers in Seoul. Conclusions: The estimated demand and supply of visiting nursing are expected to provide basic data for establishing alternative policies on visiting nursing infrastructure that might be accomplished through demand-based visiting nursing programs by districts.

• Analytical Science and Technology
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• v.27 no.3
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• pp.153-160
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• 2014

While variety of cases of studies about asbestos analysis methods are released internationally, the results of Asbestos Containing Materials (ACM) according to differences in the method of the analysis is becoming an issue. In this study, homogeneity ensured ACM samples were analyzed by visual estimation method and point counting method, and the result cound be used not only to improve the reliability on asbestos analysis of the institutions and analysts but also to obtain the basic data of Polarizing Light Microscope (PLM) analysis by comparing and evaluating. Asbestos analysis were divided into qualitative and quantitative analysis method. The quantitative analysis was performed by visual estimation method and point counting method (total 400 points) of EPA 600-R-93-116 method by using PLM. Firstly, The following was the result of homogeneity of the samples by ANOVA (Analysis of variance) and the results were satisfied. The results of qualitative analysis showed that the samples were chrysotile and amosite, and about the results of quantitative analysis, asbestos concentration determined by point counting method tend to be lower than concentrations determined by visual estimation method and also, pont counting method was a little more complicated and time-consuming.

• Journal of Preventive Medicine and Public Health
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• v.24 no.1 s.33
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• pp.29-36
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• 1991

A multiple regression analysis using ordinary least square (OLS) is frequently used for the projection of health expenditure as well as for the identification of factors affecting health care costs. Data for the analysis often have mixed characteristics of time series and cross section. Parameters as a result of OLS estimation, in this case, are no longer the best linear unbiased estimators (BLUE) because the data do not satisfy basic assumptions of regression analysis. The study theoretically examined statistical problems induced when OLS estimation was applied with the time series cross section data. Then both the OLS regression and time series cross section regression (TSCS regression) were applied to the same empirical da. Finally, the difference in parameters between the two estimations were explained through residual analysis.

• Journal of Power Electronics
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• v.16 no.1
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• pp.217-226
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• 2016

A new method is proposed based on a hidden Markov model (HMM) to estimate and analyze battery states of health. Battery system health states are defined according to the relationship between internal resistance and lifetime of cells. The source data (terminal voltages and currents) can be obtained from vehicular battery models. A characteristic value extraction method is proposed for HMM. A recognition framework and testing datasets are built to test the estimation rates of different states. Test results show that the estimation rates achieved based on this method are above 90% under single conditions. The method achieves the same results under hybrid conditions. We can also use the HMMs that correspond to hybrid conditions to estimate the states under a single condition. Therefore, this method can achieve the purpose of the study in estimating battery life states. Only voltage and current are used in this method, thereby establishing its simplicity compared with other methods. The batteries can also be tested online, and the method can be used for online prediction.

• Journal of Korean Dental Science
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• v.15 no.1
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• pp.31-50
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• 2022

Purpose: This study was to investigate the types of imaging modalities, analytical methods for age estimation, and the age of the subjects in research on age estimation using dental radiography through a scoping review, and to investigate the overall trends in age estimation studies. Materials and Methods: A scoping review was designed according to the Arksey and O'Malley guidelines and the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) statement. Three electronic databases were used as search sources (Medline, Embase, and Cochrane Library). Studies were classified according to the three main components of the research question. "What are the imaging modalities, analytical methods, and target age in dental imaging-based age estimation studies?" Result: The final 198 studies were selected by two reviewers. The most common imaging modality used in studies was panoramic radiography (69.7%), and studies using cone-beam computed tomography have increased over time. Analytical methods for age estimation were 62.6% in studies based on tooth development and 26.3% in studies using pulp/tooth ratio. The subject age was 27.8% for children and 27.3% for adults. Studies conducted in all age groups comprised the smallest category (5.2%). Conclusion: Panoramic radiography has been the most used types of imaging modalities for age estimation, and the most common analytical method was analysis of tooth development. Most studies targeted specific ages, and very few involved all age groups. Dental age estimation studies should be carried out with appropriate consideration of the imaging modality that is analyzed, the methods that are used, and the age that is targeted.

• Journal of Environmental Health Sciences
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• v.21 no.1
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• pp.29-38
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• 1995

The purpose of this study is to improve the present methodology-for the estimation of optimal water supply from an impounding reservoir. The stochastic reservoir storage model presented in this paper is believed to be rational in that. the probability of reservoir depletion (return period) is to be calculated for the various monthly demands and storage capacities. The monthly flows are used to derive the reservoir storage capacity-monthly demand-probability curves at Dalcheon damsite and Hongcheon damsite in Han river basin.

• Journal of the Korean Data and Information Science Society
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• v.18 no.3
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• pp.817-826
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• 2007

In this paper, we derive the approximate maximum likelihood estimators of the scale parameter and the location parameter in a generalized extreme value distribution under multiply Type-II censoring by the approximate maximum likelihood estimation method. We compare the proposed estimators in the sense of the mean squared error for various censored samples.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.7
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• pp.667-675
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• 2012

A damage estimation method for support structure of offshore wind turbine using modal parameters is presented for effective structural health monitoring. Natural frequencies and mode shapes for a support structure with monopile of an offshore wind turbine were calculated considering soil condition and added mass. A neural network was learned based on training patterns generated by the changes of natural frequency and mode shape due to various damages. Natural frequencies and mode shapes for 10 prospective damage cases were input to the trained neural network for damage estimation. The identified damage locations and severities agreed reasonably well with the accurate damages. Multi-damage cases could also be successfully estimated. Enhancement of estimation result using another parameters as input to neural network will be carried out by further study. Proposed method could be applied to other type of support structure of offshore wind turbine for structural health monitoring.

• Journal of Power Electronics
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• v.16 no.1
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• pp.238-248
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• 2016

The state-of-charge (SOC) and state-of-health (SOH) estimation of batteries play important roles in managing batteries for automotive applications. However, an accurate state estimation of a battery is difficult to achieve because of certain factors, such as measurement noise, highly nonlinear characteristics, strong hysteresis phenomenon, and diffusion effect of batteries. In certain vehicular applications, such as idle stop-start systems (ISSs), significant errors in SOC/SOH estimation may lead to a failure in restarting a combustion engine after the shut-off period of the engine when the vehicle is at rest, such as at a traffic light. In this paper, a dual extended Kalman filter algorithm with a dynamic equivalent circuit model of a lead-acid battery is proposed to deal with this problem. The proposed algorithm adopts a battery model by taking into account the hysteresis phenomenon, diffusion effect, and parameter variations for accurate state estimations of the battery. The validity of the proposed algorithm is verified through experiments by using an absorbed glass mat valve-regulated lead-acid battery and a battery sensor cable for commercial ISS vehicles.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.733-741
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• 2018

Capacity estimation is indispensable to ensure the safety and reliability of lithium-ion batteries in electric vehicles (EVs). Therefore it's quite necessary to develop an effective on-board capacity estimation technique. Based on experiment, it's found constant current charge time (CCCT) and the capacity have a strong linear correlation when the capacity is more than 80% of its rated value, during which the battery is considered healthy. Thus this paper employs CCCT as the health indicator for on-board capacity estimation by means of relevance vector machine (RVM). As the ambient temperature (AT) dramatically influences the capacity fading, it is added to RVM input to improve the estimation accuracy. The estimations are compared with that via back-propagation neural network (BPNN). The experiments demonstrate that CCCT with AT is highly qualified for on-board capacity estimation of lithium-ion batteries via RVM as the results are more precise and reliable than that calculated by BPNN.