• Journal of the Korean Society of Safety
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• v.24 no.6
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• pp.7-12
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• 2009

This study focuses on the classification of fire equipments for certification based on the risk evaluation. In general, known statistics on fire equipment-related accidents needs to be used for risk evaluation. When statistics is not available, however, expected frequency and severity of accident for individual equipment can be taken into account in evaluating the related risks. Based on the level of inherent risks, each equipment is then classified into three categories for certification. For equipments that risk evaluation is not possible, characteristics of those products such as reliability are considered for classification. Once classified, each equipment is assigned an appropriate certification module.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.113-119
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• 2022

In software fault prediction, a multi classification model that predicts the fault severity category of a module can be much more useful than a binary classification model that simply predicts the presence or absence of faults. A small number of severity-based fault prediction models have been proposed, but no classifier using deep learning techniques has been proposed. In this paper, we construct MLP models with 3 or 5 hidden layers, and they have a structure with a fixed or variable number of hidden layer nodes. As a result of the model evaluation experiment, MLP-based deep learning models shows significantly better performance in both Accuracy and AUC than MLPs, which showed the best performance among models that did not use deep learning. In particular, the model structure with 3 hidden layers, 32 batch size, and 64 nodes shows the best performance.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.823-830
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• 2022

The risk of project execution increases due to the enlargement and complexity of Engineering, Procurement, and Construction (EPC) plant projects. In the fourth industrial revolution era, there is an increasing need to utilize a large amount of data generated during project execution. The design is a key element for the success of the EPC plant project. Although the design cost is about 5% of the total EPC project cost, it is a critical process that affects the entire subsequent process, such as construction, installation, and operation & maintenance (O&M). This study aims to develop a system using machine-learning (ML) techniques to predict risks and support decision-making based on big data generated in an EPC project's design and construction stages. As a result, three main modules were developed: (M1) the design cost estimation module, (M2) the design error check module, and (M3) the change order forecasting module. M1 estimated design cost based on project data such as contract amount, construction period, total design cost, and man-hour (M/H). M2 and M3 are applications for predicting the severity of schedule delay and cost over-run due to design errors and change orders through unstructured text data extracted from engineering documents. A validation test was performed through a case study to verify the model applied to each module. It is expected to improve the risk response capability of EPC contractors in the design and construction stage through this study.

• International Journal of Automotive Technology
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• v.7 no.6
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• pp.757-762
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• 2006

Reliability of automotive parts has been one of the most interesting fields in the automotive industry. Especially, a small DC motor was issued because of the increasing adoption for passengers' safety and convenience. For several years, small DC motors have been studied and some problems of a life test method were found out. The field condition was not considered enough in the old life test method. It also needed a lot of test time. For precise life estimation and accelerated life test, new life test procedure was developed based on measured field condition. The vibration condition on vehicle and latent force on fan motor shaft were measured and correlated with each other. We converted the acceleration data into the load data and calculated the equivalent load from integrated value. We found the relationship which can be used for accelerated life test without changing the severity by using different loading factors.

• Phonetics and Speech Sciences
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• v.10 no.4
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• pp.173-177
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• 2018

Purpose: The primary goal of this study was to discover whether the articulatory diadochokinesis (sequential motionrate, SMR) collected using the Motor Speech Disorder Assessment (MSDA) software module can diagnose dysarthria and determine its severity. Methods: Two subject groups, one with spastic dysarthria (n=26) and a control group of speakers (n=30) without neurological disease, were set up. From both groups, the SMR was collected by MSDA at a time, and then analyzed using descriptive statistics. Results: For the parameters of syllable rate, jitter, and the mean syllable length (MSL) at the front and back, the control group displayed better results than the dysarthria patients. Conclusions: At the level of articulatory diadochokinesis, the results showed that the use of MSDA software in clinical practice was generally suitable for quickly recording the parameters of syllable rate, jitter, and mean syllable length.

• Anxiety and mood
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• v.13 no.2
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• pp.123-131
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• 2017

Objective : The PTSD Checklist (PCL) is a self-report screen for posttraumatic stress disorder (PTSD) that can be scored for both diagnostic assessment and symptom severity measurement. The most recent revision of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) contains a number of changes to the definition of PTSD, and the aim of this study was to assess the psychometric properties of the Korean version of the PCL for the DSM-5 (PCL-5-K). Methods : The participants were 204 Korean veterans of the Vietnam War who completed the PCL-5-K, the Mini Mental Status Examination (MMSE), PTSD module of Structured Clinical Interview for DSM-5, Research Version (SCID5-RV PTSD module), Korean version of Impact of Event Scale-Revised (IES-R-K) and Combat Exposure Scale (CES-K). Results : The PCL-5-K demonstrated good internal consistency (${\alpha}=0.972$) and test-retest reliability (r=0.96); the suggested cut-off score for PTSD diagnosis was ${\geq}37$ with 0.88 sensitivity and 0.96 specificity. The PCL-5-K scale correlated highly with the IES-R-K and CES-K. Factor analysis identified only one factor. Conclusion : Among elderly Korean veterans of the Vietnam War, the PCL-5-K demonstrated similar psychometric qualities to those of both the original PCL and subsequent versions. It is expected that the PCL-5-K will be a useful PTSD screening tool.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.10a
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• pp.338-345
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• 2013

Induction motors play an increasing importance in industrial manufacturing. Therefore, the state monitoring systems also have been considering as the key in dealing with their negative effect by absorbing faulty symptoms in motors. There are numerous proposed systems in literature, in which, several kinds of signals are utilized as the input. To solve the multiple faults problem of induction motors, like the proposed system, the vibration signals is good candidate. In this study, a new signal processing scheme was utilized, which transforms the time domain vibration signal into the spatial domain as an image. Then the spatial features of converted image then have been extracted by applying the dominant neighbourhood structure (DNS) algorithm. In addition, these feature vectors were evaluated to obtain the fruitful dimensions, which support to discriminate between states of motors. Because of reliability, the conventional one-against-all (OAA) multi-class support vector machines (MCSVM) have been utilized in the proposed system as classifier module. Even though examined in severity levels of signal-to-noise ratio (SNR), up to 15dB, the proposed system still reliable in term of two criteria: true positive (TF) and false positive (FP). Furthermore, it also offers better performance than five state-of-the-art systems.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.1
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• pp.49-56
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• 2024

An automated dynamic structural analysis module stands as a crucial element within a structural integrated mitigation system. This module must deliver prompt real-time responses to enable timely actions, such as evacuation or warnings, in response to the severity posed by the structural system. The finite element method, a widely adopted approximate structural analysis approach globally, owes its popularity in part to its user-friendly nature. However, the computational efficiency and accuracy of results depend on the user-provided finite element mesh, with the number of elements and their quality playing pivotal roles. This paper introduces a computationally efficient adaptive mesh generation scheme that optimally combines the h-method of node movement and the r-method of element division for mesh refinement. Adaptive mesh generation schemes automatically create finite element meshes, and in this case, representative strain values for a given mesh are employed for error estimates. When applied to dynamic problems analyzed in the time domain, meshes need to be modified at each time step, considering a few hundred or thousand steps. The algorithm's specifics are demonstrated through a standard cantilever beam example subjected to a concentrated load at the free end. Additionally, a portal frame example showcases the generation of various robust meshes. These examples illustrate the adaptive algorithm's capability to produce robust meshes, ensuring reasonable accuracy and efficient computing time. Moreover, the study highlights the potential for the scheme's effective application in complex structural dynamic problems, such as those subjected to seismic or erratic wind loads. It also emphasizes its suitability for general nonlinear analysis problems, establishing the versatility and reliability of the proposed adaptive mesh generation scheme.

• Korean Journal of Community Nutrition
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• v.16 no.6
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• pp.771-781
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• 2011

The objective of this study was to assess the reliability and validity of food security measures, which was developed based on the US household food security survey module (US HFSSM) with content validity in the Korean population. The reliability and validity were assessed by internal consistency, construct validity and criterion-related validity. The study included 446 households. Among those, 46.2% were households with children. The proportion of food insecure households was 33.3%. Among those, 35.4% and 64.6% households were food insecure with hunger and without hunger, respectively. The Cronbach's alpha coefficients were 0.84 and the infit value by the Rasch model analysis ranged from 0.68 to 1.43. The scale item response curves by food insecurity severity explained well the nature and characteristics of food security, indicating the highest proportion of "yes" for the items on diet quality, followed by those with diet quantity. The result of criterion-related validity showed that food insecurity status was significantly related in a dose-response manner with the household income level, food expenditure, subjective health state, subjects' educational level. Household food security status was also related to dietary diversity regarding protein foods, fruits and fruit juice, and milk and dairy product. These findings suggest that the food security instrument is reliable and valid and would be used to assess food security status in the Korean population.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.24 no.2
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• pp.145-153
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• 2021

Purpose: Children treated for Hirschsprung disease (HD) are adversely affected by fecal incontinence and soiling. This can be detrimental to their physical, psychosocial quality of life (QoL) and impacts the normal functioning of their family. QoL studies in HD are predominantly from developed countries. We measured general quality of life, impact on family and functional bowel status using validated questionnaires in HD children in a developing country. Methods: Patients with HD, treated in a tertiary paediatric institution in India between 2010 and 2017, were identified. Patients and/or their proxy completed the Pediatric Quality of Life and Family Impact Module questionnaires. Functional outcomes were assessed using Rintala's score. Results: A 86 children and their parents participated in the study. Majority had rectosigmoid disease (67.4%) and underwent Soave's endoanal pull through (74.4%). A 21% of patients had low Rintala score indicating poor functional bowel outcomes. Only 11% of children had poor QoL scores. Family functioning outcomes were also severely affected in the same subgroup of patients. There was statistically significant correlation between Rintala score and QoL scores (p-value<0.001). Disease severity, type of surgery, and duration of follow-up did not have a statistically significant impact on the QoL. Conclusion: QoL in children with HD was comparable to the general population. Bowel dysfunction affects a notable number of children and was the most significant determinant of poor QoL.