• Journal of the Korean Chemical Society
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• v.57 no.1
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• pp.40-51
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• 2013

In order to develop a quantitation method for Perfluorooctanesulfonic acid(PFOS) contained in plastics that are mainly used in electric and electronic equipment, this study consisted of conducting method validations with LDPE samples using soxhlet solvent extraction and LC/MS. As a result, the limits of detection and quantitation (LOD, LOQ) were $2.58{\mu}g/L$ and $7.82{\mu}g/L$, respectively. Additionally, the recovery was 96-102%. For the correlation coefficient of LC/MS, the $r^2$ value was 0.9992 in the concentration range of $7.82-100{\mu}g/L$, which confirmed its linearity. Furthermore, for the standardization of the analysis method for PFOS in electric and electronic equipment to correspond to EU environmental regulations, we conducted a proficiency test with a number of domestic and international testing laboratories. Three of the ten testing laboratories that participated in the proficiency test submitted outliers. Accordingly, we examined the cause of the outliers using the $^{19}F$ NMR, finding that the main cause was an error in the processing of the results for isomers in PFOS that existed in standard solutions and samples.

• Journal of Fashion Business
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• v.1 no.4
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• pp.49-54
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• 1997

Methods of the flare skirt pattern making are presented variously among foreign and domestic test books. Three of them are : First the method is using the basic skirt pattern, secondly the method is quartering rectangle of skirt length $\times\frac{W}4$, finally the method is substituting waist size for formula. But, these books don't include the calculating method of the hem circumference. This calculating method express the shape of flare skirt and the using length of trimming -race and frill-. This study aims at proposing the calculating formula of the hem circumference and the standardization of pattern making technical skill. The result were as follows. 1. The calculating formula of hem circumference had regular ratio in $180^{\circ}$, $270^{\circ}$ and $360^{\circ}C$ flare. That was (HEM) = $(\frac{(5\;{\times}\;(W\;+\;1)}{4\;{\times}\;A}+(SK.L))\;{\times}\;A\;{\times}\;0.785$. A was 4 in $180^{\circ}$, 6 in $270^{\circ}$, 8 in $360^{\circ}$. 2. The error of hem circumference from 46 to 86 centimeter of waist size was between 0.11875 and -0.63125 centimeter in $180^{\circ}$, $270^{\circ}$ and $360^{\circ}$ flare skirt. This formula was less in the error.

• Design & Manufacturing
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• v.13 no.3
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• pp.53-58
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• 2019

This paper presents Artificial Neural Network(ANN) method to predict maximum injection pressure of injection molding machine and weights of injection molding products. 5 hidden layers with 10 neurons is used in the ANN. The ANN was conducted with 5 Input parameters and 2 response data. The input parameters, i.e., melt temperature, mold temperature, fill time, packing pressure, and packing time were selected. The combination of the orthogonal array L27 data set and 23 randomly generated data set were applied in order to train and test for ANN. According to the experimental result, error of the ANN for weights was $0.49{\pm}0.23%$. In case of maximum injection pressure, error of the ANN was $1.40{\pm}1.19%$. This value showed that ANN can be successfully predict the injection pressure and the weights of injection molding products.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.603-622
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• 2023

Human error (HE) is an important concern in safety-critical systems such as nuclear power plants (NPPs). HE has played a role in many accidents and outage incidents in NPPs. Despite the increased automation in NPPs, HE remains unavoidable. Hence, the need for HE detection is as important as HE prevention efforts. In NPPs, HE is rather rare. Hence, anomaly detection, a widely used machine learning technique for detecting rare anomalous instances, can be repurposed to detect potential HE. In this study, we develop an unsupervised anomaly detection technique based on generative adversarial networks (GANs) to detect anomalies in manually collected surveillance data in NPPs. More specifically, our GAN is trained to detect mismatches between automatically recorded sensor data and manually collected surveillance data, and hence, identify anomalous instances that can be attributed to HE. We test our GAN on both a real-world dataset and an external dataset obtained from a testbed, and we benchmark our results against state-of-the-art unsupervised anomaly detection algorithms, including one-class support vector machine and isolation forest. Our results show that the proposed GAN provides improved anomaly detection performance. Our study is promising for the future development of artificial intelligence based HE detection systems.

• The Journal of Korean Academy of Sensory Integration
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• v.2 no.1
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• pp.11-19
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• 2004

Objective : This study is to provide the norms of normal children when comparing the performance ability of preschool children while using the kinesthesia test of Sensory Integration and Praxis Tests(SIPT). Methods : Participants consisted of 90 normal children ranging in age from four to six years. The kinesthesia test of SIPT was utilized to investigate the performance ability. Results : 1. Regarding the kinesthesia ability according to age, the average value of kinesthesia performance error decreased as age getting older and that value showed the statistically significant differences between four and five, six age(p<0.05). 2. The kinesthesia performance ability according to gender, the accuracy of both hands and the dominant hand did not show the statistically significant differences. 3. Regarding the kinesthesia performance ability of test items, 1R item and 6R item(26.2cm), 5R item and 2L item(20.2cm) passing through the midline of body and having the large movement in distance and angle showed the difficulty to perform in all the children between 4 and 6 age. Conclusion : By providing the norms of the kinesthesia performance ability in normal children of the above results to the occupational therapists treating children, the helpful data to the hand skill development of children, exercise plan and implementation, and the performance therapy of ADL through the proper evaluation and training of kinesthesia is considered for the occupational therapists to be provided.

• Journal of Korean Society of Rural Planning
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• v.30 no.3
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• pp.75-84
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• 2024

This study developed the procedure and method for the accuracy assessment of unmanned boat survey data, based on the reservoir water depth data of Misan Reservoir, measured by the manned and unmanned boats in 2009 by Korea Rural Community Corporation. In the first step, this study devised the method to extract the contour map of NGIS data in AutoCAD to generate easily the reservoir boundary map used to set the survey range of reservoir water depth and to test the survey accuracy. The surveyed data coordinate systems of the manned and the unmanned boat were also unified by using ArcGIS for the standards of accuracy assessment. In the accuracy assessment, the spatial correlation coefficient of the grid maps of the two measurement results was 0.95, showing high pattern similarity, although the average error was high at 78cm. To analyze in more detail assessment, this study generated randomly the 3,250m transverse profile route (PR), and then extracted grid values of water depth on the PR. In the results of analysis to the extracted depth data on PR, the error average difference of the unmanned boat measurements was 73.18cm and the standard deviation of the error was 55cm compared to the manned boat. This study set these values as the standard for the correction value by average shift and noise removal of the unmanned boat measurement data. By correcting the unmanned boat measurements with these values, this study has high accuracy results, the reservoir water depth and surface area curve with R² = 0.97 and the water depth and storage volume curve with R² = 0.999.

• Magazine of the Korean Society of Agricultural Engineers
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• v.22 no.3
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• pp.75-87
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• 1980

Most hydro]ogic phenomena are the complex and organic products of multiple causations like climatic and hydro-geological factors. A certain significant correlation on the run-off in river basin would be expected and foreseen in advance, and the effect of each these causual and associated factors (independant variables; present-month rainfall, previous-month run-off, evapotranspiration and relative humidity etc.) upon present-month run-off(dependent variable) may be determined by multiple regression analysis. Functions between independant and dependant variables should be treated repeatedly until satisfactory and optimal combination of independant variables can be obtained. Reliability of the estimated function should be tested according to the result of statistical criterion such as analysis of variance, coefficient of determination and significance-test of regression coefficients before first estimated multiple regression model in historical sequence is determined. But some error between observed and estimated run-off is still there. The error arises because the model used is an inadequate description of the system and because the data constituting the record represent only a sample from a population of monthly discharge observation, so that estimates of model parameter will be subject to sampling errors. Since this error which is a deviation from multiple regression plane cannot be explained by first estimated multiple regression equation, it can be considered as a random error governed by law of chance in nature. This unexplained variance by multiple regression equation can be solved by stochastic approach, that is, random error can be stochastically simulated by multiplying random normal variate to standard error of estimate. Finally hybrid model on estimation of monthly run-off in nonhistorical sequence can be determined by combining the determistic component of multiple regression equation and the stochastic component of random errors. Monthly run-off in Naju station in Yong-San river basin is estimated by multiple regression model and hybrid model. And some comparisons between observed and estimated run-off and between multiple regression model and already-existing estimation methods such as Gajiyama formula, tank model and Thomas-Fiering model are done. The results are as follows. (1) The optimal function to estimate monthly run-off in historical sequence is multiple linear regression equation in overall-month unit, that is; Qn=0.788Pn+0.130Qn-1-0.273En-0.1 About 85% of total variance of monthly runoff can be explained by multiple linear regression equation and its coefficient of determination (R2) is 0.843. This means we can estimate monthly runoff in historical sequence highly significantly with short data of observation by above mentioned equation. (2) The optimal function to estimate monthly runoff in nonhistorical sequence is hybrid model combined with multiple linear regression equation in overall-month unit and stochastic component, that is; Qn=0. 788Pn+0. l30Qn-1-0. 273En-0. 10+Sy.t The rest 15% of unexplained variance of monthly runoff can be explained by addition of stochastic process and a bit more reliable results of statistical characteristics of monthly runoff in non-historical sequence are derived. This estimated monthly runoff in non-historical sequence shows up the extraordinary value (maximum, minimum value) which is not appeared in the observed runoff as a random component. (3) "Frequency best fit coefficient" (R2f) of multiple linear regression equation is 0.847 which is the same value as Gaijyama's one. This implies that multiple linear regression equation and Gajiyama formula are theoretically rather reasonable functions.

• Industrial Engineering and Management Systems
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• v.6 no.2
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• pp.106-118
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• 2007

This paper present a new optimization model to generate aggregate production planning by considering electric cost. The new Time Of Switching (TOS) electric type is introduced by switching over Time Of Day (TOD) and Time Of Use (TOU) electric types to minimize the electric cost. The fuzzy demand and Dynamic inventory tracking with multiple plant capacity are modeled to cover the uncertain demand of customer. The constraint for minimum hour limitation of plant running per one start up event is introduced to minimize plants idle time. Furthermore; the Optimal Weight Moving Average Factor for customer demand forecasting is introduced by monthly factors to reduce forecasting error. Application is illustrated for multiple cement mill plants. The mathematical model was formulated in spreadsheet format. Then the spreadsheet-solver technique was used as a tool to solve the model. A simulation running on part of the system in a test for six months shows the optimal solution could save 60% of the actual cost.

• Journal of the Korean Mathematical Society
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• v.46 no.5
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• pp.1007-1025
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• 2009

First-order least-squares method of a distributed optimal control problem for the incompressible Stokes equations is considered. An optimality system for the optimal solution are reformulated to the equivalent first-order system by introducing the vorticity and then the least-squares functional corresponding to the system is defined in terms of the sum of the squared $H^{-1}$ and $L^2$ norms of the residual equations of the system. Finite element approximations are studied and optimal error estimates are obtained. Resulting linear system of the optimality system is symmetric and positive definite. The V-cycle multigrid method is applied to the system to test computational efficiency.

• Journal of the Korea Institute of Building Construction
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• v.17 no.4
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• pp.305-312
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• 2017

To evaluate the reliability of fly ash quality supplied to ready-mixed concrete plant using mass cylinder and hydrometer, in this research, the correlationship between the fly ash properties provided from certification and density measurement with suspension was evaluated. As a result, the reliability of the certification, except fineness and loss on ignition, all properties had a discord. Additionally, in the case of density, fineness, and L.O.I, the relation with the density measured using hydrometer showed high correlation, especially fineness was strongly related with the density measured using hydrometer. Furthermore, according to the comparative analysis with previous research, the fly ash used in this research was similar measurement with raw powder without any refining process, it is considered that the constant error of blaine test or using raw ash sample as a fly ash. In current standard regarding fly ash, the fineness range of class 2 can be changed from $3,000-4,500cm^2/g$ to $3,500-4,500cm^2/g$ for improved quality of fly ash in fineness aspect.