• KSCE Journal of Civil and Environmental Engineering Research
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• v.12 no.2
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• pp.123-130
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• 1992

We check up on whether the one-dimensional station precipitation model of Geogakakos and Bras is suitable to the storm model for Chonju station or not. The fundamental variables of the physically based model consists of the pressure at the cloud top, the hight-averaged updraft velocity(HAUV), and the inverse of the average diameter of the hydrometeors(ADH) at cloud base. And they are parameterized by input variables. The parameters are eastimated by the direct search algorithm of Hooke and Jeeves in this paper. The results show that HAUV and ADH are dominant factors to minimize root mean square error between the calculated and the observed rainfall. In this numerical analysis, the deviation between the calculated and the total observed rainfall is small, otherwise the gap for the time distribution is quite big.

• Journal of the Korean Applied Science and Technology
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• v.41 no.3
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• pp.673-684
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• 2024

This study aims to enhance the accuracy of severity classification by examining the usage patterns and characteristics of emergency department visits. It focuses on adult and elderly patients who visited a public hospital in Seoul. This descriptive study retrospectively reviewed the electronic medical records of patients who visited the emergency department of a public hospital between November and December 2023. The total number of participants was 1,033, with 46.4% (n=479) being elderly and 53.6% (n=554) being adults. The chief complaints of the participants were as follows: for the elderly, nervous system symptoms at 8.2% (n=85) and digestive symptoms at 7.5% (n=77) were the most common, while for adults, gastrointestinal symptoms at 11.0% (n=114) and trauma at 8.6% (n=89) were more prevalent. In the case of the elderly, patients classified as urgent accounted for the highest percentage at 23.9% (n=247), while for adults, non-emergency were more prevalent at 32.2% (n=333). The initial severity classification error rate for elderly patients in the urgent was 3.8%, indicating that the suitability of KTAS for elderly patients with high severity was low. To minimize severity classification errors and enhance KTAS accuracy, it's essential to address its current limitation of only classifying adults and children separately by developing a KTAS classification system that reflects the diverse characteristics of elderly patients.

• Journal of Korean Medicine Rehabilitation
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• v.20 no.4
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• pp.105-130
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• 2010

Objectives : The purpose of this study was to assess the statistical methods errors used in the journal of Oriental Rehabilitation Medicine(JORM) and to identify the types of errors in statistical analysis. Methods : We reviewed quantitative articles that were published in the JORM from January 2005 through October 2009. Those were not used by statistical analysis such as literature studies, case study, review articles were not included in this analysis. A total of 296 articles was reviewed. We evaluated the adequacy and the validity of the statistical techniques with our checklist established be modified Lee's checklist, and three statistical evaluators assessed together to minimize bias. Results : Of the 222 articles, 213 were used in inferential and descriptive statistics. Of those 80% of articles adopting descriptive and inferential statistics were detected having statistical errors. One articles used 1.7 statistical method unit generally. Most frequently employed statistics were student t-test, one way ANOVA. pearson correlation analysis, Mann-whitney U test, paired t-test, and chi-square test in their order. However, most frequent statistics having errors were similar in order. The most common statistic errors were as follow: 1. absence of normality test, 2. misuse between paired test and unpaired test, 3. wrong choice of repeated measures analysis without consideration of time variables, 4, increase of Type I error by using inappropriate multiple test, 5. inappropriate application of discrete or categorical data instead of continuous data in correlation analysis, 6. poor consideration of basic consumption in chi-square test, 7. confusion between frequency comparison and average comparison, 8. mentioning the statistical technique without using it. Conclusions : We found various mistake or misuses in the applications of statistical methodologies in the articles published in the JORM. Careful consideration of statistical use and review from the specialist of statistics are warranted for improving the quality of JORM.

• Journal of Korean Tunnelling and Underground Space Association
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• v.14 no.1
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• pp.37-53
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• 2012

There is considerable uncertainty in ground properties used in tunnel designs. In this study, a back analysis was performed to find optimal ground properties based on the artificial neural network facility of MATLAB program of using tunnel monitoring data. Total 81 data were constructed by changing elastic modulus and coefficient of lateral pressure which have great influence on tunnel convergence. A sensitivity analysis was conducted to establish an optimal training model by varying the number of hidden layers, the number of nodes, learning rate, and momentum. Meanwhile, the optimal training model was selected by comparing MSE (Mean Squared Error) and coefficient of determination ($R^2$) and was used to find the correct elastic moduli of layers and the coefficient of lateral pressure. In future, it is expected that the suggested method of this study can be applied to determine the optimum tunnel support pattern under given ground conditions.

• Journal of the Korean Institute of Gas
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• v.26 no.6
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• pp.30-36
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• 2022

In commercial transactions of energy sources using hydrogen charging stations, high-accuracy flow meters are needed to prevent accidents such as overcharging due to inaccurate measurements and to ensure transparency in hydrogen commercial transactions through accurate measurements. This research developed a Corioli-type flowmeter prototype and conducted a risk assessment to prevent accidents during a process change comparison experiment for existing charging stations to verify the measurement performance. A process change section was defined for the installation of measurement facilities for empirical experiments and HAZOP was conducted. In addition, JSA was also conducted to secure the safety of experimenters, such as preventing valve mis-opening during empirical experiments. Measures were established to improve the risk factors derived through HAZOP, and work procedures were established to minimize human errors and ensure the safety of workers through JSA. The design change and system manufacturing for the installation of the metering system were completed by reflecting the risk assessment results, and safety could be confirmed through the performance comparison test of the developed meter prototype. The developed prototype flow meter showed a total of 30 flow measurements under the operating conditions of 70 MPa, and the average error was -1.58% to 3.96%. Such a metering error was analyzed to have the same performance as a flow meter installed and operated for commercial use.

• Journal of Korean Society of Rural Planning
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• v.13 no.1 s.34
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• pp.73-84
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• 2007

The first theme of this study is to preserve and manage rural multi-functionality resource Information. This study is to suggest the method that can irradiate rural multi-functionality resource Information efficiently and constructively. GIS uses PDA and Tablet PC as an investigation tool and verifies the outcome of the development in the investigation system. This study enhanced the mobility function of PDA by installing recording system and camera to the PDA. Also, Using GPS has been ensured scientific precision and realism to the investigation. Direct input on spot can save time, cost and minimize human error by simplifying the investigation process. Database is composed of characters like scale, form, location, distance, resident's opinion and image of 37 resources. The survey system was applied in 170 villages and got a total of 12,270 resources data. Management system should be easy to input and output the surveyed information and to get reports in any kind of form ( i.e. final result can be produced as a map). By utilizing of the Rural Resource information system, the study made a simulation to compare the target areas before and after. Also, digitalized investigation system, minimized re-input and reprocessing of data and enabled to simplify and standardize the process than memorandum investigation. Data collected through digital system could offer people useful information by Web-GIS. It was need to specify practical way in decision-making and a way to measure the value of resources to align with the regional plan. Also, need to keep on developing statistical data and application program that can connect us to present the best solution to support regional planning. Therefore, quality of data is very important. Finally, it is very important to develop various programs to analyze space md rural resource by monitoring rural environment.

• Journal of Korean Society of Transportation
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• v.27 no.6
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• pp.79-88
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• 2009

The objective of this study is to develop the new vehicle classification algorithm and minimize classification errors. The existing vehicle classification algorithm collects data from loop and piezo sensors according to the specification("Vehicle classification guide for traffic volume survey" 2006) given by the Ministry of Land, Transport and Maritime Affairs. The new vehicle classification system collects the vehicle length, distance between axles, axle type, wheel-base and tire type to minimize classification error. The main difference of new system is the "Wandering" sensor which is capable of measuring the wheel-base and tire type(single or dual). The wandering sensor obtains the wheel-base and tire type by detecting both left and right tire imprint. Verification tests were completed with the total traffic volume of 762,420 vehicles in a month for the new vehicle classification algorithm. Among them, 47 vehicles(0.006%) were not classified within 12 vehicle types. This results proves very high level of classification accuracy for the new system. Using the new vehicle classification algorithm will improve the accuracy and it can be broadly applicable to the road planning, design, and management. It can also upgrade the level of traffic research for the road and transportation infrastructure.

• Journal of radiological science and technology
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• v.39 no.2
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• pp.143-148
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• 2016

The purpose of this study was examined the measurement error factor on AMARES of jMRUI method for magnetic resonance spectroscopy (MRS) quantitative analysis by skilled and unskilled observer method and identified the reason of independent observers. The Point-resolved spectroscopy sequence was used to acquired magnetic resonance spectroscopy data of 10 weeks male Sprague-Dawley rat liver. The methylene protons ($(-CH_{2-})n$) of 1.3 ppm and water proton ($H_2O$) of 4.7 ppm ratio was calculated by LCModel software for using the reference data. The seven unskilled observers were calculated total lipid (methylene/water) using the jMRUI AMARES technique twice every 1 week, and we conducted interclass correlation coefficient (ICC) statistical analysis by SPSS software. The inter-observer reliability (ICC) of Cronbach's alpha value was less than 0.1. The average value of seven observer's total lipid ($0.096{\pm}0.038$) was 50% higher than LCModel reference value. The jMRUI AMARES analysis method is need to minimize the presence of the residual metabolite by identified metabolite MRS profile in order to obtain the same results as the LCModel.

• Korean Journal of Remote Sensing
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• v.27 no.4
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• pp.457-466
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• 2011

The objectives of this study are to produce level 3 type LULC map and analysis of phenological features of North Korea, ISODATA clustering of the 88scenes of MVC of MODIS NDVI in 2008 and 8scenes in 2009 was carried out. Analysis of phenological phases based mapping method was conducted, In level 2 type map, the confusion matrix was summarized and Kappa coefficient was calculated. Total of 27 typical habitat types that represent the dominant species or vegetation density that cover land surface of North Korea in 2008 were made. The total of 27 classes includes the 17 forest biotopes, 7 different croplands, 2 built up types and one water body. Dormancy phase of winter (${\sigma}^2$ = 0.348) and green up phase in spring (${\sigma}^2$ = 0.347) displays phenological dynamics when much vegetation growth changes take place. Overall accuracy is (851/955) 85.85% and Kappa coefficient is 0.84. Phenological phase based mapping method was possible to minimize classification error when analyzing the inaccessible land of North Korea.

• Journal of Construction Engineering and Project Management
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• v.5 no.1
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• pp.11-19
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• 2015

This paper presents optimized artificial neural networks (ANNs) claims prediction and decision awareness framework that guides owner organizations in their pre-bid construction project decisions to minimize claims. The framework is composed of two genetic optimization ANNs models: a Claims Impact Prediction Model (CIPM), and a Decision Awareness Model (DAM). The CIPM is composed of three separate ANNs that predict the cost and time impacts of the possible claims that may arise in a project. The models also predict the expected types of relationship between the owner and the contractor based on their behavioral and technical decisions during the bidding phase of the project. The framework is implemented using actual data from international projects in the Middle East and Egypt (projects owned by either public or private local organizations who hired international prime contractors to deliver the projects). Literature review, interviews with pertinent experts in the Middle East, and lessons learned from several international construction projects in Egypt determined the input decision variables of the CIPM. The ANNs training, which has been implemented in a spreadsheet environment, was optimized using genetic algorithm (GA). Different weights were assigned as variables to the different layers of each ANN and the total square error was used as the objective function to be minimized. Data was collected from thirty-two international construction projects in order to train and test the ANNs of the CIPM, which predicted cost overruns, schedule delays, and relationships between contracting parties. A genetic optimization backward analysis technique was then applied to develop the Decision Awareness Model (DAM). The DAM combined the three artificial neural networks of the CIPM to assist project owners in setting optimum values for their behavioral and technical decision variables. It implements an intelligent user-friendly input interface which helps project owners in visualizing the impact of their decisions on the project's total cost, original duration, and expected owner-contractor relationship. The framework presents a unique and transparent hybrid genetic algorithm-ANNs training and testing method. It has been implemented in a spreadsheet environment using MS Excel$^{(R)}$ and EVOLVERTM V.5.5. It provides projects' owners of a decision-support tool that raises their awareness regarding their pre-bid decisions for a construction project.