• Child Health Nursing Research
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• v.25 no.2
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• pp.85-94
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• 2019

Purpose: This study presents methodological research that aimed to verify the content validity, construct validity, reliability, and criterion-related validity of the Child Abuse Potential Inventory (CAPI), originally developed by Milner and then translated into Korean by Ahn. Methods: Data used in this study were collected from 209 mothers of infants, toddlers, and children of preschool age in D metropolitan city. The collected data were analyzed using SPSS version 24. Results: The Korean version of the Child Abuse Potential Inventory (K-CAPI) was developed by condensing 44 of the original 77 CAPI items. Four factors of K-CAPI were extracted using principal component analysis. These 4 factors-distress; problems with child, self, family, and others; unhappiness; rigidity-accounted for 54.01% of variance. The Cronbach's ${\alpha}$ was .96, the Guttman split-half coefficient was .88, and test-retest reliability was r=.86 (p<.001). Conclusion: The results of this study established the reliability and validity of the K-CAPI and found it to be an appropriate tool to evaluate mothers' potential to abuse their children.

• Transactions of Materials Processing
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• v.24 no.6
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• pp.418-425
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• 2015

Based on eco-friendly advantages and the enhanced development in the chemical and physical characteristics, liquid silicone rubber (LSR) is widely used in producing precision parts in the automotive, medical, electronics, aeronautical and many other industries. In the current work, a thermoplastic-thermoset multi component injection molding (MCM) was developed for a waterproof automotive connector housing using PBT and LSR resins. Measurements of the rheological characteristics of PBT and LSR were made to improve the reliability of the numerical analysis for the multi component injection process. With the measured viscosity, pvT and curing data, numerical analysis of the multi cycle injection molding was conducted using simulation software (Sigma V5.0).

• Journal of Industrial Technology
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• v.28 no.B
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• pp.215-226
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• 2008

MIL-HDBK-217 has widely been used for electronics reliability predictions. Recently, the $217Plus^{TM}$ has been developed by DoD RIAC and may replace MIL-HDBK-217. A overview of the $217Plus^{TM}$ has been performed in this paper. We first reviewed the overall concepts and reliability prediction procedures. We then explained the component models and the system level model with process grading concepts. Bayesian approach incorporating field data into the predicted failure rate is another feature of this methodology.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.1
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• pp.1-6
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• 2016

Domain analysis is an important component for reliability of development project. Domain analysis error have an effect in the whole system. As a result, the system reliability will be deteriorated. Therefore, we need a methodology to analyze domain characteristic for a reliable analysis in the domain analysis phase. In this paper, we propose a methodology for domain categorization and estimation of complexity for reliability improvement of domain analysis.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.6
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• pp.1-10
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• 2023

The nuclear power plant(NPP)'s Instrumentation and Control(I&C) system periodically conducts integrity checks for the maintenance of self-diagnostic function during normal operation. Additionally, it performs functionality and performance checks during planned preventive maintenance periods. However, there is a need for technological development to diagnose failures and prevent accidents in advance. In this paper, we studied methods for estimating the reliability function by utilizing environmental data and self-diagnostic data of the I&C equipment. To obtain failure data, we assumed probability distributions for component features of the I&C equipment and generated virtual failure data. Using this failure data, we estimated the reliability function using representative artificial intelligence(AI) models used in survival analysis(DeepSurve, DeepHit). And we also estimated the reliability function through the Cox regression model of the traditional semi-parametric method. We confirmed the feasibility through the residual lifetime calculations based on environmental and diagnostic data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.453-456
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• 2022

The role of the power grid is to ensure stable power supply. It is necessary to take various measures to prepare for unstable situations without notice. After identifying the relationship between features through exploratory data analysis using weather data, a machine learning based energy production prediction model is modeled. In this study, the prediction reliability was increased by extracting the features that affect energy production prediction using principal component analysis and then applying it to the machine learning model. By using the proposed model to predict the production energy for a specific period and compare it with the actual production value at that time, the performance of the energy production prediction applying the principal component analysis was confirmed.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.297-302
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• 2007

Vibrating test of vehicle component can be possible in lab-based simulators instead of field testing owing to the development of technology in control algorithm as well as computational process. Currently, Multi-Axial Simulation Table(MAST) is recommended as a vibrating equipment, which excites a target component for 3-directional translation and rotation motion simultaneously and hence, vibrational condition can be fully approximated to that of real road test. But, the vibration-free performance of target component is not guaranteed with MAST system, which is only simulator subjective to the operator. Rather, the reliability of multi-axial vibration test is dependent on the quality of input profile which should cover the required severity of vibrating condition on target component. In this paper, multi-axial vibration testing methodology of vehicle component is presented here, from data acquisition of vehicle accelerations to the obtaining the input profile of MAST using severe data at proving ground. To compare the severity of vibration condition, between real road test and proving ground one, energy principle of equivalent damage is proposed to calculate energy matrices of acceleration data and then, it is determined the optimal combination of special events on proving ground which is equivalent to real road test at the aspects of vibration fatigue using sequential searching optimal algorithm. To explain the vibration methodology clearly, seat and door component of vehicle are selected as a example.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.31 no.1 s.256
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• pp.26-35
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• 2007

The vehicle of urban transit is a complex system that consists of various electric, electronic, and mechanical equipments, and the maintenance cost of this complex and large-scale system generally occupies sixty percent of the LCC (Life Cycle Cost). For reasonable establishing of maintenance strategies, safety security and cost limitation must be considered at the same time. The concept of system reliability has been introduced and optimized as the key of reasonable maintenance strategies. For optimization, three preceding studies were accomplished; standardizing a maintenance classification, constructing RBD (Reliability Block Diagram) of VVVF (Variable Voltage Variable Frequency) urban transit, and developing a web based reliability evaluation system. Historical maintenance data in terms of reliability index can be derived from the web based reliability evaluation system. In this paper, we propose applying inverse problem analysis method and hybrid neuro-genetic algorithm to system reliability optimization for using historical maintenance data in database of web based system. Feed-forward multi-layer neural networks trained by back propagation are used to find out the relationship between several component reliability (input) and system reliability (output) of structural system. The inverse problem can be formulated by using neural network. One of the neural network training algorithms, the back propagation algorithm, can attain stable and quick convergence during training process. Genetic algorithm is used to find the minimum square error.

• Journal of the Korean Operations Research and Management Science Society
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• v.22 no.1
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• pp.141-157
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• 1997

The structure and dimensionality of the User Information Satisfaction (UIS) construct is an important theoretical issue that received considerable attentions. The acceptance of UIS as a standardized instrument requires confirmation that it explains and measures the user information satisfaction construct and its component. Based on a simple of 670 respondents who participated in dealing with the Public Management Information System (PMIS), this research used a confirmatory factor analysis to test the alternavtive models of underlying factor structure and assessed the reliability and validity of these factors and items in the PMIS. The result provided a support for a revised UIS model with four first-order factors and one PMIS The result provided a support for a revised UIS model with four first-order factors and one second-order (higher-order) factor in PMIS. To cross-validata these results, the author reexamined two prior data sets. The results showed that the revised model provides better model-data fit in all three data sets.

• Journal of the Korean Data and Information Science Society
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• v.10 no.1
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• pp.119-133
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• 1999

A Markov Chain Monte Carlo method is developed to compute the software reliability model. We consider computation problem for determining of posterior distibution in Bayseian inference. Metropolis algorithms along with Gibbs sampling are proposed to preform the Bayesian inference of the Mixed model with record value statistics. For model determiniation, we explored the prequential conditional predictive ordinate criterion that selects the best model with the largest posterior likelihood among models using all possible subsets of the component intensity functions. To relax the monotonic intensity function assumptions. A numerical example with simulated data set is given.