• Journal of Korean Society for Quality Management
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• v.44 no.1
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• pp.167-180
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• 2016

Purpose: The problem for the traditional control chart is that it is unable to monitor the very small fraction of nonconforming and the underlying distribution is the normal distribution. $Z_p$ control chart is useful where it controls the vert small fraction on nonconforming. In this study, we will design the $Z_p$ control chart in order to use under non-normal process. Methods: $Z_p$ is calculated not by failure rate based on attribute data but using variable data. Control limit for non-normal $Z_p$ control chart is designed based on ${\alpha}$-risk calculated by cumulative distribution function of Burr distribution. ${\beta}$-risk, which is for performance evaluation, obtains in the Burr distribution's cumulative distribution function and control limit. Results: The control limit for non-normal $Z_p$ control chart is designed based on Burr distribution. The sensitivity can be checked through ARL table and OC curve. Conclusion: Non-normal $Z_p$ control chart is able to control not only the very small fraction of nonconforming, but it is also useful when $Z_p$ distribution is non-normal distribution.

• Journal of the Korea Safety Management & Science
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• v.14 no.3
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• pp.167-174
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• 2012

In the manufacturing process the most widely used $\bar{X}$ chart has been applied to control the process mean. Also, Accelerated Life Test(ALT) is commonly used for efficient assurance of product life in development phases, which can be applied in production reliability acceptance test. When life data has lognormal distribution, through censored ALT design so that censored ALT data has asymptotic normal distribution, $ALT\bar{X}$ control chart integrating $\bar{X}$ chart and ALT procedure could be applied to control the mean of process in the manufacturing process. In the situation that process variation is controlled, $Z_p$ control chart is an effective method for the very small fraction nonconforming of quality characteristic. A simultaneous control scheme with $ALT\bar{X}$ control chart and $Z_p$ control chart is designed for the very small fraction nonconforming of product lifetime.

• Structural Engineering and Mechanics
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• v.81 no.5
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• pp.647-664
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• 2022

The powerful data mapping capability of computational deep learning methods has been recently explored in academic works to develop strategies for structural health monitoring through appropriate characterization of dynamic responses. In many cases, these studies concern laboratory prototypes and finite element models to validate the proposed methodologies. Therefore, the present work aims to investigate the capability of a deep learning algorithm called Sparse Autoencoder (SAE) specifically focused on detecting structural alterations in real-case studies. The idea is to characterize the dynamic responses via SAE models and, subsequently, to detect the onset of abnormal behavior through the Shewhart T control chart, calculated with SAE extracted features. The anomaly detection approach is exemplified using data from the Z24 bridge, a classical benchmark, and data from the continuous monitoring of the San Vittore bell-tower, Italy. In both cases, the influence of temperature is also evaluated. The proposed approach achieved good performance, detecting structural changes even under temperature variations.

• Korean Journal of Psychosomatic Medicine
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• v.23 no.2
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• pp.129-135
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• 2015

Objectives : In children and adolescents, the prevalence of overweight has increased in the last 20 years. little research is available on the prevalence of obesity in children with autism spectrum disorders(ASD). The purpose of our study was to determine the prevalence of overweight among a clinical population of children diagnosed with autism spectrum disorders(ASD). Methods : Retrospective chart review of children ages 3-18 years seen between 2012 and 2015 at a Konyang University hospital psychiatric clinic. Diagnostic, medical, and demographic information was extracted from the charts. Body mass index(BMI) was calculated from measures of height and weight recorded in the child's chart. The Center for Disease Control's BMI growth reference was used to determine an age and gender-specific BMI z-score for the children. Results : In our study, children with ASD compared to non ASD group had significantly higher BMI percentiles (p=0.032). The prevalence of overweight($BMI{\geq}85th$ to 95th percentiles) and obesity($BMI{\geq}95th$ percentiles) was 35% and 19% respectively in children with ASD and without ASD. Conclusions : Despite noted limitations, our data suggest that overweight and obesity in children with ASD are more prevalent than without ASD. Longitudinal and further study is needed to examine the factors associated with obesity in this population.