• Fashion & Textile Research Journal
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• v.16 no.2
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• pp.266-274
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• 2014

Parametric bodies reproduce the actual shape of human body parts and should be convenient for general users to change size to judge the visual fit of clothes on-line. In this study, three parametric bodies(i.e. I, C, D ) were compared to verify the accuracy of the provided body dimensions and reproducibility to a target model. To compare reproducibility, the 20s female standard virtual model developed for an apparel industry by Korean agency for technology and standards is used. The investigation of existing parameters showed that the numbers and kinds of parameters provided by each program were different with some errors in notation; in addition, some of virtual body dimensions went beyond the maximum allowable error. The result of changing each parametric body to the 20s female standard body showed that D, C, I in order produced better reproducibility for body dimensions. There were different levels of protrusion and concavity in the virtual cross sections and virtual longitudinal sections despite the small differences in body dimensions and cross sectional areas; in addition, some parametric body was not bilateral symmetry. The results of this study can be used as basic information in the standardization of a virtual model used in a virtual garment program.

• Korean Journal of Social Welfare
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• v.63 no.2
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• pp.81-107
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• 2011

The purpose of the study is to examine the effect of error management culture and psychological capital on job satisfaction and organizational commitment from social workers. This study was focused on the relationship between error management culture, psychological capital and job satisfaction and organizational commitment, controlled organizational fairness. The data was collected from social workers in Seoul, Kyunggi areas using stratified sampling method. A total of 564 social workers and 89 human service organizations were finally used for multilevel analysis. The survey had conducted for 18 days, from October 27 to November 13 in 2009 by mail. The major finding of this study are as follows. First, interaction effect between error management culture and psychological capital was significant job satisfaction and organizational commitment in positive direction. Second, main effect of error management culture in human service organizations was positively significant job satisfaction and organizational commitment on social workers. So it was important to enhance the level of error management culture in order to increase the job satisfaction and organizational commitment. Third, main effect of psychological capital on social workers was positively significant job satisfaction and organizational commitment. According to the results of this study, it was suggested the theoretical and practical implications for increasing and strengthen the error management culture and psychological capital.

• Journal of Internet Computing and Services
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• v.8 no.6
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• pp.155-167
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• 2007

There are four types of human beings according to the Sasang Typology, Oriental medical doctors frequently prescribe healthcare information and treatment depending on one's type, The feature ratios (Table 1) on the human face are the most important criterions to decide which type a patient is. In this paper, we proposed a system to extract these feature ratios from the people's side face, There are two challenges in acquiring the feature ratio: one that selecting representative features; the other, that detecting region of interest from human profile facial image effectively and calculating the feature ratio accurately. In our system, an adaptive color model is used to separate human side face from background, and the method based on geometrical model is designed for region of interest detection. Then we present the error analysis caused by image variation in terms of image size and head pose, To verify the efficiency of the system proposed in this paper, several experiments are conducted using about 173 korean's left side facial photographs. Experiment results shows that the accuracy of our system is increased 17,99% after we combine the front face features with the side face features, instead of using the front face features only.

• Journal of the Korea Society for Simulation
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• v.27 no.1
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• pp.119-126
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• 2018

This study intended to exploratively testify human error causing factors for railway traffic controller, using AMOS structural equation model. Through literature survey, fatigue and stress as exogenous variable, errors in information process such as cognitive, memory, storage, and execution error as endogenous variable, and accident and incident(near-miss) as dependent variable were set up. Results based on AMOS using 201 railway traffic controllers' questionnaire showed that a clear causality loop like as 'stress ${\rightarrow}$ memory error ${\rightarrow}$ storage error ${\rightarrow}$ incident(near-miss) ${\rightarrow}$ accident' is formed. This result suggests that for the purpose of mitigation of traffic controller's accident, it is so necessary to reduce memory and execution error in the information processing process based on the effective management of stress, as the precedent of them.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.449-451
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• 2006

With the increasing danger of personal information being exposed, a technique to protect personal information by identifying a non-user in case it is exposed. A study to construct a neural network recognizer for developing a economical and effective user protecting system. For this, time variables regarding user typing patterns from a pattern extraction device. With the variations in the standard deviation for the collected time variables, non-user patterns were generated. The recognition performance increased with the increase in the standard deviation and a higher recognition was achieved at 2.5. Also, five types of training data were generated and the recognition performance was examined as a function of the number of non-user patterns. With the increase in non-suer patterns, the recognition error quantified in the root mean square error (RMSE) was reduced. The smallest RMSE was obtained at the type 5 and 90 non-user patterns. In overall, the type 3 model yielded the highest recognition accuracy Particularly, a perfect recognition of 100% was achieved at 45 non-user patterns.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1996.04a
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• pp.1-8
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• 1996

Many studies showed that small imperfections can also have a considerable influence on the behaviour of structures. Especially, in Single-Layer Latticed Domes, initial imperfection occurred by human error and construction error is very important to the buckling load. The definition of imperfection is that a node of structure shifts from perfect condition. For example, in the case of truss structures, imperfections are represented by shifting the location of nodal points relative to the position in which they would be for a perfect structure. This paper uses Arc-length Method in nonlinear iteration analysis, choosing star dome, in which many studies have been accomplished, as a model. The results of analysis show that initial imperfection can reduce the buckling load of structures.

• Journal of Environmental Health Sciences
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• v.48 no.3
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• pp.151-158
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• 2022

Background: Chemical emissions in the environment have rapidly increased with the accelerated industrialization taking place in recent decades. Residents of industrial complexes are concerned about the health risks posed by chemical exposure. Objectives: This study was performed to suggest modeling methods that take into account multimedia and multi-pathways in human exposure and risk assessment. Methods: The concentration of benzene emitted at industrial complexes in Daesan, South Korea and the exposure of local residents was estimated using the Caltox model. The amount of human exposure based on inhalation rate was stochastically predicted for various activity stages such as resting, normal walking, and fast walking. Results: The coefficient of determination (R²) for the CalTOX model efficiency was 0.9676 and the root-mean-square error (RMSE) was 0.0035, indicating good agreement between predictions and measurements. However, the efficiency index (EI) appeared to be a negative value at -1094.4997. This can be explained as the atmospheric concentration being calculated only from the emissions from industrial facilities in the study area. In the human exposure assessment, the higher the inhalation rate percentile value, the higher the inhalation rate and lifetime average daily dose (LADD) at each activity step. Conclusions: Prediction using the Caltox model might be appropriate for comparing with actual measurements. The LADD of females was higher ratio with an increase in inhalation rate than those of males. This finding would imply that females may be more susceptible to benzene as their inhalation rate increases.

• Korean Journal of Construction Engineering and Management
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• v.25 no.2
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• pp.69-80
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• 2024

Currently rebar spacing inspection is carried out by human inspectors who heavily rely on their individual experience, lacking a guarantee of objectivity and accuracy in the inspection process. In addition, if incorrectly placed rebars are identified, the inspector need to correct them. Recently, laser scanning and AR technologies have been widely used because of their merits of measurement accuracy and visualization. This study proposes a technology for rebar spacing inspection and fixing by combining laser scanning and AR technology. First, scan data acquisition of rebar layers is performed and the raw scan data is processed. Second, AR-based visualization and fixing are performed by comparing the design model with the model generated from the scan data. To verify the developed technique, performance comparison test is conducted by comparing with existing drawing-based method in terms of inspection time, error detection rate, cognitive load, and situational awareness ability. It is found from the result of the experiment that the AR-based rebar inspection and fixing technology is faster than the drawing-based method, but there was no significant difference between the two groups in error identification rate, cognitive load, and situational awareness ability. Based on the experimental results, the proposed AR-based rebar spacing inspection and fixing technology is expected to be highly useful throughout the construction industry.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.8
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• pp.393-400
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• 2016

Recently, there have been various researches on efficient and automatic analysis on urban environment methods that utilize the computer vision and machine learning technology. Among many new analyses, urban safety analysis has received a major attention. In order to predict more accurately on safety score and reflect the human visual perception, it is necessary to consider the generic and local information that are most important to human perception. In this paper, we use Double-column Convolutional Neural network consisting of generic and local columns for the prediction of urban safety. The input of generic and local column used re-sized and random cropped images from original images, respectively. In addition, a new learning method is proposed to solve the problem of over-fitting in a particular column in the learning process. For the performance comparison of our Double-column Convolutional Neural Network, we compare two Support Vector Regression and three Convolutional Neural Network models using Root Mean Square Error and correlation analysis. Our experimental results demonstrate that our Double-column Convolutional Neural Network model show the best performance with Root Mean Square Error of 0.7432 and Pearson/Spearman correlation coefficient of 0.853/0.840.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.10
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• pp.697-704
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• 2013

The identification of haplotypes, which encode SNPs in a single chromosome, makes it possible to perform haplotype-based association tests with diseases. Minimum Error Correction model, one of models to computationally assemble a pair of haplotypes for a given organism from Single Nucleotide Polymorphism fragments, has been known to be NP-hard even for gapless cases. In the previous work, an improved branch and bound algorithm was suggested and showed that it is more efficient than naive branch and bound algorithm by performing experiments for Apis mellifera (honeybee) data set. In this paper, to show the extensibility of the algorithm to other organisms we apply the improved branch and bound algorithm to the human data set and confirm the efficiency of the algorithm.