• KIPS Transactions on Computer and Communication Systems
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• 제5권9호
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• pp.211-216
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• 2016

Worldwide casualties caused by earthquakes, floods, fire or other disaster has been increasing. So many researchers are being actively done technical studies to ensure golden-time. In this paper if a disaster occurs, use the IoT technologies in order to secure golden-time and transmits the message after to find the user of the accident area first. When the previous job is finished, gradually finds a user of the surrounding area and transmits the message. For national emergency information, OPEN API of Korea Meteorological Administration was used. To collect detailed information on a relevant area in real time, this study established the system that connects and integrates Crowd Sensing technology with BLE (Bluetooth Low Energy) Beacon technology. Up to now, the CBS based on base station has been applied. However, this study designed and mapped DB in the integration of Beacon based user positioning and national administrative address system in order to estimate local users. In this experiment, the accuracy and speed of information dif6fusion algorithm were measured with a rise in the number of users. The experiments were conducted in a manner that increases the number of users by one thousand and was measured the accuracy and speed of the message spread transfer algorithm. Finally, became operational in less than one second in 20,000 users, it was confirmed that the notification message is sent.

• Journal of Korea Soil Environment Society
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• 제5권1호
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• pp.3-12
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• 2000

The multi-region model, to describe preferential flow, is an equation representing solute transport in soils by dividing soil into numerous pore groups and using the hydraulic properties of the soil. As the model partial differential equation (PDE) is solved numerically with finite difference methods. a modified equivalent partial differential equation(MEPDE) of the partial differential equation of the multi-region model is derived to analyze the accuracy and consistency of the solution of the model PDE and the Von Neumann method is used to analyze the stability of the finite difference scheme. The evaluation obtained from the MEPDE indicated that the finite difference scheme was found to be consistent with the model PDE and had the second order accuracy The stability analysis is performed to analyze the model PDE with the amplification ratio and the phase lag using the Von Neumann method. The amplification ratio of the finite difference scheme gave non-dissipative results with various Peclet numbers and yielded the most high values as the Peclet number was one. The phase lag showed that the frequency component of the finite difference scheme lagged the true solution. From the result of the stability analysis for the model PDE, it is analyzed that the model domain should be discretized in the range of Pe < 1.0 and Cr < 2.0 to obtain the more accurate solution.

• The Journal of Korean Medicine
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• 제41권2호
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• pp.58-79
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• 2020

Objectives: The purpose of this study is to predict the weight loss by applying machine learning using real-world clinical data from overweight and obese adults on weight loss program in 4 Korean Medicine obesity clinics. Methods: From January, 2017 to May, 2019, we collected data from overweight and obese adults (BMI≥23 kg/m2) who registered for a 3-month Gamitaeeumjowi-tang prescription program. Predictive analysis was conducted at the time of three prescriptions, and the expected reduced rate and reduced weight at the next order of prescription were predicted as binary classification (classification benchmark: highest quartile, median, lowest quartile). For the median, further analysis was conducted after using the variable selection method. The data set for each analysis was 25,988 in the first, 6,304 in the second, and 833 in the third. 5-fold cross validation was used to prevent overfitting. Results: Prediction accuracy was increased from 1^st to 2^nd and 3^rd analysis. After selecting the variables based on the median, artificial neural network showed the highest accuracy in 1^st (54.69%), 2^nd (73.52%), and 3^rd (81.88%) prediction analysis based on reduced rate. The prediction performance was additionally confirmed through AUC, Random Forest showed the highest in 1^st (0.640), 2^nd (0.816), and 3^rd (0.939) prediction analysis based on reduced weight. Conclusions: The prediction of weight loss by applying machine learning showed that the accuracy was improved by using the initial weight loss information. There is a possibility that it can be used to screen patients who need intensive intervention when expected weight loss is low.

• Transactions of the Korean Society of Mechanical Engineers A
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• 제35권10호
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• pp.1307-1313
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• 2011

The mechanical limited slip differential (LSD) in vehicles is being replaced by the electromagnetic LSD because of its fast response and better active control characteristics. The coil housing made of STS 304 is one of the most important parts in the solenoid assembly of the electromagnetic LSD. High geometrical accuracy is a prerequisite for the manufacture of such coil housings, but precision machining is difficult because of the use of STS 304 thin plate and the variance in machining variables. The aim of this study is to optimize the mean and variance of the shape accuracy in the coil housing by finding a robust solution for the machining process conditions. The mean and standard deviation of the jaw contact pressure, cutting speed, and feed rate are considered to be the major parameters for minimizing the geometrical mean and variance. The response surface model based on the second-order Taylor series is combined together to minimize the mean and variance of the shape accuracy of the coil housing.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• 제12권5호
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• pp.467-474
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• 2019

In this paper, we propose a novel method to improve FPS while maintaining the accuracy of YOLO v2 model in NVIDIA Jetson TX1. In general, in order to reduce the amount of computation, a conversion to an integer operation or reducing the depth of a network have been used. However, the accuracy of recognition can be deteriorated. So, we use methods to reduce computation and memory consumption through adjustment of the filter size and integrated computation of the network The first method is to replace the $3{\times}3$ filter with a $1{\times}1$ filter, which reduces the number of parameters to one-ninth. The second method is to reduce the amount of computation through CBR (Convolution-Add Bias-Relu) among the inference acceleration functions of TensorRT, and the last method is to reduce memory consumption by integrating repeated layers using TensorRT. For the simulation results, although the accuracy is decreased by 1% compared to the existing YOLO v2 model, the FPS has been improved from the existing 3.9 FPS to 11 FPS.

• Journal of the Korean Geotechnical Society
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• 제37권11호
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• pp.119-129
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• 2021

The process of constructing the DB in the current geotechnical information DB system needs a lot of human and time resource consumption. In addition, it causes accuracy problems frequently because the current input method is a person viewing the PDF and directly inputting the results. Therefore, this study proposes building an automatic digital DB using AI (artificial intelligence) of boring logs. In order to automatically construct DB for various boring log formats without exception, the boring log forms were classified using the deep learning model ResNet 34 for a total of 6 boring log forms. As a result, the overall accuracy was 99.7, and the ROC_AUC score was 1.0, which separated the boring log forms with very high performance. After that, the text in the PDF is automatically read using the robotic processing automation technique fine-tuned for each form. Furthermore, the general information, strata information, and standard penetration test information were extracted, separated, and saved in the same format provided by the geotechnical information DB system. Finally, the information in the boring log was automatically converted into a DB at a speed of 140 pages per second.

• Journal of Intelligence and Information Systems
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• 제24권2호
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• pp.111-124
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• 2018

This study aims to develop a classification model for predicting the occurrence of hyperlipidemia, one of the chronic diseases. Prior studies applying data mining techniques for predicting disease can be classified into a model design study for predicting cardiovascular disease and a study comparing disease prediction research results. In the case of foreign literatures, studies predicting cardiovascular disease were predominant in predicting disease using data mining techniques. Although domestic studies were not much different from those of foreign countries, studies focusing on hypertension and diabetes were mainly conducted. Since hypertension and diabetes as well as chronic diseases, hyperlipidemia, are also of high importance, this study selected hyperlipidemia as the disease to be analyzed. We also developed a model for predicting hyperlipidemia using SVM and meta learning algorithms, which are already known to have excellent predictive power. In order to achieve the purpose of this study, we used data set from Korea Health Panel 2012. The Korean Health Panel produces basic data on the level of health expenditure, health level and health behavior, and has conducted an annual survey since 2008. In this study, 1,088 patients with hyperlipidemia were randomly selected from the hospitalized, outpatient, emergency, and chronic disease data of the Korean Health Panel in 2012, and 1,088 nonpatients were also randomly extracted. A total of 2,176 people were selected for the study. Three methods were used to select input variables for predicting hyperlipidemia. First, stepwise method was performed using logistic regression. Among the 17 variables, the categorical variables(except for length of smoking) are expressed as dummy variables, which are assumed to be separate variables on the basis of the reference group, and these variables were analyzed. Six variables (age, BMI, education level, marital status, smoking status, gender) excluding income level and smoking period were selected based on significance level 0.1. Second, C4.5 as a decision tree algorithm is used. The significant input variables were age, smoking status, and education level. Finally, C4.5 as a decision tree algorithm is used. In SVM, the input variables selected by genetic algorithms consisted of 6 variables such as age, marital status, education level, economic activity, smoking period, and physical activity status, and the input variables selected by genetic algorithms in artificial neural network consist of 3 variables such as age, marital status, and education level. Based on the selected parameters, we compared SVM, meta learning algorithm and other prediction models for hyperlipidemia patients, and compared the classification performances using TP rate and precision. The main results of the analysis are as follows. First, the accuracy of the SVM was 88.4% and the accuracy of the artificial neural network was 86.7%. Second, the accuracy of classification models using the selected input variables through stepwise method was slightly higher than that of classification models using the whole variables. Third, the precision of artificial neural network was higher than that of SVM when only three variables as input variables were selected by decision trees. As a result of classification models based on the input variables selected through the genetic algorithm, classification accuracy of SVM was 88.5% and that of artificial neural network was 87.9%. Finally, this study indicated that stacking as the meta learning algorithm proposed in this study, has the best performance when it uses the predicted outputs of SVM and MLP as input variables of SVM, which is a meta classifier. The purpose of this study was to predict hyperlipidemia, one of the representative chronic diseases. To do this, we used SVM and meta-learning algorithms, which is known to have high accuracy. As a result, the accuracy of classification of hyperlipidemia in the stacking as a meta learner was higher than other meta-learning algorithms. However, the predictive performance of the meta-learning algorithm proposed in this study is the same as that of SVM with the best performance (88.6%) among the single models. The limitations of this study are as follows. First, various variable selection methods were tried, but most variables used in the study were categorical dummy variables. In the case with a large number of categorical variables, the results may be different if continuous variables are used because the model can be better suited to categorical variables such as decision trees than general models such as neural networks. Despite these limitations, this study has significance in predicting hyperlipidemia with hybrid models such as met learning algorithms which have not been studied previously. It can be said that the result of improving the model accuracy by applying various variable selection techniques is meaningful. In addition, it is expected that our proposed model will be effective for the prevention and management of hyperlipidemia.

• Journal of Korea Water Resources Association
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• 제42권12호
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• pp.1079-1089
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• 2009

The objective of this study is to develop the accurate, robust and high resolution two-dimensional numerical model that solves the computationally difficult hydraulic problems, including the wave front propagation over dry bed and abrupt change in bathymetry. The developed model in this study solves the conservative form of the two-dimensional shallow water equations using an unsplit finite volume scheme and HLLC approximate Riemann solvers to compute the interface fluxes. Bed-slope term is discretized by the divergence theorem in the framework of FVM for application of unsplit scheme. Accurate and stable SGM, in conjunction with the MUSCL which is second-order-accurate both in space and time, is adopted to balance with fluxes and source terms. The exact C-property is shown to be satisfied for balancing the fluxes and the source terms. Since the spurious oscillations in second-order schemes are inherent, an efficient slope limiting technique is used to supply TVD property. The accuracy, conservation property and application of developed model are verified by comparing numerical solution with analytical solution and experimental data through the simulations of one-dimensional dam break flow without bed slope, steady transcritical flow over a hump and two-dimensional dam break flow with a constriction.

• Journal of Korea Port Economic Association
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• 제37권4호
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• pp.103-126
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• 2021

The purpose of this study is described in detail as follows. First, I would like to define what digital transformation is in the maritime transport sector. Second, it is intended to derive success factors for digital transformation in the maritime transportation field by examining various preceding studies related to digital transformation. Finally, in order to derive priorities for the derived success factors, an AHP analysis model is built and an expert survey is conducted for practical experts in the maritime transportation field. Based on the survey results, we would like to provide guidelines on what factors should be considered first among the success factors of digital transformation in the maritime transportation sector. In this study, in order to derive the priority of success factors for digital transformation in the maritime transportation field, the hierarchical structure was divided into four high-level evaluation items(strategic factors, organizational culture and human factors, technology factors, and environmental factors) and 21 sub-evaluation items. A relative evaluation method of weighting items among AHP(Analytic Hierarchy Process) was applied. AHP analysis of 24 questionnaires with a consistency ratio of 0.1 or less in order to increase the accuracy of information among questionnaires collected through maritime transportation related university professors, research groups, shipping companies, container terminals, and experts engaged in shipping related IT companies was carried out. As a result of the analysis, the priority of the first-tier factors for the success factors of digital transformation in the maritime transport sector was shown in the order of strategic factors, organizational culture and human factors, technology factors, and environmental factors. In addition, when looking at the priorities of 21 detailed items, it was found that the development of new business models, the creation of an active future digital strategy, and the leadership of the chief digital officer were high.

• The Korean Journal of Nuclear Medicine Technology
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• 제16권2호
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• pp.131-134
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• 2012

Purpose : The labeling efficiency of radiopharmaceuticals in nuclear medicine is important in terms of accuracy and reliability of the examination. Usually $^{99m}Tc$-HMPAO used for brain SPECT scan is chemically unstable since lots of impurities are existing. Therefore, occurrence of loss of labeling efficiency is easy to appear. In this paper, labeling and use of $^{99m}Tc$-HMPAO should be helpful through experiments on factors affecting the labeling efficiency of $^{99m}Tc$-HMPAO. Materials and Methods : Domestic HMPAO vials (Dong-A) used for brain SPECT scan were tested. Domestic Samyeong Generator 55.5 GBq (1,500 mCi), TLC measurement sets (ITLC-SG, butanone, saline, TLC chamber) and radio-TLC scanner (Advantest, Bioscan) were used. In the first experiment, after eluting generator at 1, 8, 16, 24, 28 hours apart, each eluted $^{99m}Tc$-pertechnetate were labeled with HMPAO and the labeling efficiency was measured. In the second experiment, after eluting $^{99m}Tc$-pertechnetate from a generator, $^{99m}Tc$-pertechnetate was drawn at 0, 1, 3, 6 hours. And each drawn $^{99m}Tc$-pertechnetate were labeled with HMPAO for measuring labeling efficiency. In the third experiment, labeling efficiency was measured at 0, 0.5, 3, 5, 7 hours after labeling $^{99m}Tc$-HMPAO. Results : In the first experiment, measured values were appeared 95.05, 94.64, 94.94, 95.64, 96.76% in passing order of time. In the second experiment, measured values were appeared 94.38, 94.23, 93.26, 91.03% in passing order of time. In the third experiment, measured values were appeared 95.76, 94.17, 88.19, 83.6, 76.86% in passing order of time. Conclusion : In the first experiment of this paper, labeling efficiency of $^{99m}Tc$-HMPAO labeled with $^{99m}Tc$-pertechnetate eluted after 24 hours from first elution. Additional experiments will be needed to discuss for usability. In the second experiment, the labeling efficiency was slightly decreased in chronological order, but it was measured higher than 90%. Also, additional experiments will be needed to discuss for usability. In the third experiment, the labeling efficiency was decreased considerably. Especially, within 3 hours after the labeling is recommended to use $^{99m}Tc$-HMPAO