• The Korean Journal of Emergency Medical Services
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• v.27 no.1
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• pp.127-137
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• 2023

Purpose: This study aimed to analyze the treatment outcomes according to the socioeconomic factor of patients who visited the emergency room. Methods: This study conducted frequency analysis, percentage analysis, and Fisher's exact test analysis method, using the R 4.1.2 program based on the 2019 data from the Korea Health Panel. Results: Among the treatment results of 1,648 patients, 392 patients were hospitalized or transferred to other hospitals, 845 were discharged after treatment, 224 were discharged, and 7 died. The Fisher's exact test of treatment outcomes and socioeconomic factors was not statistically significant for status of the worker and employment relationship, but was significant for the housing, household, economic activity, and insurance types, and marital status and education. Conclusion: The results of this study indicate that it is necessary to conduct follow up studies on socioeconomic factors to provide basic data that can contribute to fairness and equity in the health care field.

• Journal of Korean society of Dental Hygiene
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• v.22 no.6
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• pp.503-512
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• 2022

Objectives: This study aimed to study the association between unmet dental treatment and depression in the dental area of the elderly. Methods: The data was from the 7th period of the National Health and Nutrition Examination Survey. Multiple logistic regression analysis was performed to evaluate the relationship between depression and unmet dental treatment when confounding factors such as income quintile and smoking were considered. Statistical software, SAS 9.4 version was used. Results: After correcting all confounding factors, the analysis showed that the experience of unmet dental treatment was 2.73 times more likely among depressed men and 2.52 times more likely among depressed women (p<0.05). Conclusions: The results of this study suggested that we should consider that depression in the elderly can affect unmet dental treatment regardless of gender.

• Journal of Korean Public Health Nursing
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• v.37 no.1
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• pp.125-135
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• 2023

Purpose: The number of patients suffering from depression is rapidly increasing worldwide, and by 2030, it is expected to pose a severe social and economic burden. Reports suggest that approximately 30% of subjects with symptoms of depression do not attempt treatment. Therefore, predicting the characteristics of subjects with depressive symptoms who have not even attempted counseling treatment is essential to increase the participation rate for such treatment. This study intends to predict the participation rates for psychological counseling treatment for depression among subjects with depressive symptoms. Methods: This study used data from the 2021 Korea Community Health Survey (KCHS). Data analysis was carried out using a decision tree to design a model that predicted participation in psychological counseling for depression. Results: The results showed that subjects aged 65 to 74 had difficulty understanding the explanations of medical staff even though they did not have cognitive impairment. Only 11.1% of this group received psychological counseling, which was the lowest rate among the various age groups. Among the subjects, 62.4% of those aged 19-44 or 45-64, who had suicidal thoughts and attempted suicide, received psychological counseling and this was the highest rate among the age groups surveyed. Conclusion: The identification of people showing depressive symptoms is crucial for encouraging them to undertake treatment. Also, proper depression-oriented medical services should be developed and implemented for people with depressive symptoms who exhibit a blind spot towards attempting treatment.

• Journal of the Korean Society for Heat Treatment
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• v.36 no.6
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• pp.396-401
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• 2023

Aus-tempering heat treatment is suitable for thin and small-sized in precision parts. However, the heat treatment process relies on the experience and skill of the operator, making it challenging to produce precision parts due to the cold forging process. The aims of this study is to explore suitable machine learning models using data from the aus-tempering heat treatment process and analyze the factors that significantly impact the mechanic properties (e.g. hardness). As a result, the study analyzed, from a machine learning perspective, how hardness prediction varies based on the quenching temperature, carbon (C), and copper (Cu) contents.

• Journal of Satellite, Information and Communications
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• v.10 no.4
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• pp.83-86
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• 2015

In this paper, we propose a real-tim measurement management system UI development linked the Water treatment facilities broadband Convergence Network. The sensor and the image data received by the server develop a program to interact with Web through water treatment facilities broadband convergence network. So, Separately develop UI capable of independently operating. Building a web server for remote monitoring of the transmission sensor and the image data. And Monitoring and control is possible the sensor data and image data through the Web-based UI. We can grasp the current state such as measurement time, concentration and depth of interface through the proposed real-time measurement management system UI development liked the water treatment facilities broadband convergence network. So, we can check in whether the normal operation of water treatment facilities and whether the casualties such as fire and security. As well as real time to see the information at a glance due to UI development can be raal-time monitoring of real-time measurement management system.

• Journal of Korean Society of Water and Wastewater
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• v.21 no.5
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• pp.601-607
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• 2007

In order to maintain constant residual chlorine in sedimentation basin, It is necessary to develop real time prediction model of residual chlorine considering water treatment plant data such as water qualities, weather, and plant operation conditions. Based on the operation data acquired from K water treatment plant, prediction models of residual chlorine in sediment basin were accomplished. The input parameters applied in the models were water temperature, turbidity, pH, conductivity, flow rate, alkalinity and pre-chlorination dosage. The multiple regression models were established with linear and non-linear model with 5,448 data set. The corelation coefficient (R) for the linear and non-linear model were 0.39 and 0.374, respectively. It shows low correlation coefficient, that is, these multiple regression models can not represent the residual chlorine with the input parameters which varies independently with time changes related to weather condition. Artificial neural network models are applied with three different conditions. Input parameters are consisted of water quality data observed in water treatment process based on the structure of auto-regressive model type, considering a time lag. The artificial neural network models have better ability to predict residual chlorine at sediment basin than conventional linear and nonlinear multi-regression models. The determination coefficients of each model in verification process were shown as 0.742, 0.754, and 0.869, respectively. Consequently, comparing the results of each model, neural network can simulate the residual chlorine in sedimentation basin better than mathematical regression models in terms of prediction performance. This results are expected to contribute into automation control of water treatment processes.

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2003.11a
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• pp.141-148
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• 2003

A data collection of a liquid radioactive waste treatment process of a research organization became necessary while developing the RAWMIS(Radioactive Waste Management Integration System) which it can generate personal history management for efficient management of a waste, documents, all kinds of statistics. This paper introduces an input and output application program design to do to database with data in the results and a stream process of a treatment that analyzed the waste occurrence present situation and data by treatment process. Data on the actual treatment process that is not limited experiment improve by a document, human traces, saving of material resources and improve with efficiency of tracking about a radioactive waste and a process and give help to radioactive waste material valance and inventory study.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.6
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• pp.217-224
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• 2015

In this paper, I describe a system for remote monitoring of devices Android-based sensor data recorded during the operation of the ballast water treatment system. The proposed remote monitoring system is Designed and implemented when a problem occurs in the ballast water treatment system installed in the vessel, provide information if you are unable to determine the cause in the field or engineer dispatch request of external. System developed is composed of a server holding the sensor data information collected from the vessel and a mobile device that is not bound to time and place. The transmission of sensor data between mobile devices and server, and is implemented by TCP/IP to transmit information securely. System is composed of request to the server from the mobile device after the user inputs the input values, to transmit the sensor data. The device provides the information of the sensor with a high level of importance to the user. Through the remote monitoring sensor information of the ballast water treatment system, the system is made to predict the failure of the sensor.

• The Korean Journal of Applied Statistics
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• v.36 no.4
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• pp.323-335
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• 2023

In causal analysis of high dimensional data, it is important to reduce the dimension of covariates and transform them appropriately to control confounders that affect treatment and potential outcomes. The augmented inverse probability weighting (AIPW) method is mainly used for estimation of average treatment effect (ATE). AIPW estimator can be obtained by using estimated propensity score and outcome model. ATE estimator can be inconsistent or have large asymptotic variance when using estimated propensity score and outcome model obtained by parametric methods that includes all covariates, especially for high dimensional data. For this reason, an ATE estimation using an appropriate dimension reduction method and semiparametric model for high dimensional data is attracting attention. Semiparametric method or sparse sufficient dimensionality reduction method can be uesd for dimension reduction for the estimation of propensity score and outcome model. Recently, another method has been proposed that does not use propensity score and outcome regression. After reducing dimension of covariates, ATE estimation can be performed using matching. Among the studies on ATE estimation methods for high dimensional data, four recently proposed studies will be introduced, and how to interpret the estimated ATE will be discussed.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.75-75
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• 2022

Traditional agriculture mostly focused on activity in the field, but current agriculture faces problems such as reduction of agricultural inputs, labor shortage and so on. Accordingly, traditional agricultural experiments generally considered the simple treatment effects, but current agricultural experiments need to consider the several and complicate treatment effects. To analyze such several and complicate treatment effects, data collection has the first priority. Remote sensing is a quite effective tool to collect information in agriculture, and recent easier availability of UAVs (Unmanned Aerial Vehicles) enhances the effectiveness. LAI (Leaf Area Index) is one of the most important information for evaluating the condition of crop growth. In this study, we utilized UAV with multispectral camera to evaluate plant-based LAI of sweetcorn in a small-scale field experiment and discussed the feasibility of a new experimental design to analyze the several and complicate treatment effects. The plant-based SR measured by UAV showed the highest correlation coefficient with LAI measured by a canopy analyzer in 2018 and 2019. Application of linear mix model showed that plant-based SR data had higher detection power due to its huge number of data although SR was inferior to evaluate LAI than the canopy analyzer. The distribution of plant-based data also statistically revealed the border effect in treatment plots in the traditional experimental design. These results suggest that remote sensing with UAVs has the advantage even in a small-scale experimental plot and has a possibility to provide a new experimental design if combined with various analytical applications such as plant size, shape, and color.