• Journal of Korean Society for Quality Management
• /
• v.29 no.2
• /
• pp.37-45
• /
• 2001

A survival variable is a non-negative random variable X with distribution function F(t) satisfying F(0) : 0 and a survival function F(t): 1-F(t). This variable is said to be New Better than Used of specified age t$_{0}$ if F(x+ t$_{0}$)$\leq$F(x).F(t$_{0}$) for all x$\geq$0 and a fixed t$_{0}$. We propose the test for H$_{0}$ : F(x+t$_{0}$)=F(x).F(t$_{0}$) for all x$\geq$0 against H$_1$: F(x+t$_{0}$) $\leq$ F(x).F(t$_{0}$) for all x$\geq$0 when the specified age to is unknown but can be estimated from the data when t$_{0}$=ζ$_{p}$, the pth percentile of F. This test statistic, which is based on the normalized spacings between the ordered observations, is readily applied in the case of small sample. Also, our test is more simple than Ahmad's test (1998). Finally, the performance of our test is presented.our test is presented.

• Proceedings of the KSME Conference
• /
• 2003.11a
• /
• pp.1384-1387
• /
• 2003

Pressurization test is usually required in aircraft full-scale static test. There are several test conditions including pressurization of cockpit, fuselage fuel tank, air inlet duct for T-50 full-scale static test. In this paper, the test conditions, equipment, piping analysis for the pressurization test are introduced. Tank simulation test is performed to verify the validity of piping analysis and to find good tuning parameters for the pressurization channel in the servo controller. Several test setup for pressurization of T-50 test is introduced. Test article volume is filled by form, $60%{\sim}80%$ volume is reduced for the T-50 full-scale static test. Pressurization system is connected to servo controller which also controls hydraulic actuator. Load and pressure control is synchronized by using the same servo controller during T-50 test. Typical control result for pressurization test condition is shown. Pressurization tests of T-50 full-scale static test was completed successfully.

• KIPS Transactions on Software and Data Engineering
• /
• v.11 no.2
• /
• pp.67-80
• /
• 2022

Recently, many laundry home appliances have been released with Internet of Things (IoT) functions, but there are few quality evaluation tests for IoT functions. In particular, since IoT test items are not prepared for laundry home appliances applied with IoT functions, it is difficult to find the cause even if defects occur, and test institutions are limited in selecting test items related to IoT and conducting proper performance tests. In this paper, we design a test item framework that separates IoT test items into commonality and variability to identify product defects and causes for laundry home appliances with IoT features among products in the field of home appliances. Through the proposed research, manufacturers and test institutions can test the proper performance of laundry products with IoT functions, which can improve the completeness of the products and ensure reliability.

• Journal of the Korean Statistical Society
• /
• v.26 no.4
• /
• pp.467-476
• /
• 1997

The two-sample t-test is not expected to be optimal when the two samples are not drawn from normal populations. According to Box and Cox (1964), the transformation is estimated to enhance the normality of the tranformed data. We investigate the asymptotic relative efficiency of the ordinary t-test versus t-test applied transformation introduced by Yeo and Johnson (1997) under Pitman local alternatives. The theoretical and simulation studies show that two-sample t-test using transformed date gives higher power than ordinary t-test for location-shift models.

• KIPS Transactions on Software and Data Engineering
• /
• v.10 no.6
• /
• pp.211-222
• /
• 2021

Recently, frozen and refrigerated appliances on the market are being released with additional IoT functions, but there are few tests on IoT functions. In particular, the existing test system does not have IoT test items for IoT-based frozen and refrigerated appliances, making it difficult for companies to find the cause even if defects occur, and test institutions are also restricted from selecting IoT-related test items and conducting correct performance tests. In this paper, we design a test item framework that can identify product defects and identify causes in the performance test process of frozen and refrigerated products with IoT functions among products in the home appliance field, and propose test methods and management measures using them. Through the proposed research, manufacturers and testing institutions can test the correct performance of IoT-based frozen and refrigerated products, thereby enhancing the completeness and securing reliability of the products.

• Korean Journal of Forensic Psychology
• /
• v.11 no.1
• /
• pp.21-36
• /
• 2020

In P300-based concealed information test (P300 CIT), it evaluates whether the P300 amplitude for the probe is significantly greater than that of the irrelevant to determine if the suspect is telling a lie. An independent sample t-test or a bootstrap method can be used as a statistical test to make that decision. Rosenfeld et al. (2004) used the bootstrap method, claiming that "t tests on single sweeps are too insensitive to use to compare mean probe and irrelevant P300s within individuals" and their method has been accepted to date. The purpose of the study is to evaluate whether the power of t-test is lower than that of the bootstrap method in the P300 CIT. The Monte Carlo study was conducted by using EEG collected from 39 participants. The results showed that the type I error rates of the t-test and the percentile bootstrap method were similar and the power of the percentile bootstrap method was slightly higher than that of the t-test. The type I error rates of the t-test and the percentile bootstrap method were slightly lower than the significance level and the powers of the two tests were also slightly lower than that of the theoretical t-test. On the other hand, the type I error rate and power of the standard error Bootstrap method were the same as those of the theoretical t-test and its power was .012 ~ .081 higher than that of t-test depending on experimental conditions.

• Journal of the Society of Disaster Information
• /
• v.17 no.2
• /
• pp.236-244
• /
• 2021

Purpose: The purpose of this study is to develop IoT multi-fire detectors combined smoke/carbon monoxide/heat and wireless IoT communication and to confirm the detect performance by smoke generator fire test and cotton wicks fire test. Method: The IoT multi-fire detector combined smoke and CO and combined smoke and heat were experimented the detect performance by smoke generator test and fire test of cotton wicks. And the case of fire alarm was checked. Result: The IoT multi-fire detector combined smoke and CO rung the alarm at the fire test of cotton wicks, did not ring the alarm at the smoke generator test. In comparison, the IoT multi-fire detector combined smoke and heat did not ring the alarm both at the smoke generator test and the fire test of cotton wicks. Conclusion: The IoT multi-fire detector combined smoke and CO detected the only smoke including the carbon monoxide and the IoT multi-fire detector combined smoke and heat did not ring the alarm for lack of heat. As a result, when the developed IoT multi-fire detector was detected the signal more than the set point, the fire alarm was sounded through cotton wicks fire test and smoke generator.

• Journal of Integrative Natural Science
• /
• v.12 no.3
• /
• pp.91-99
• /
• 2019

Many researchers in various study fields use the two sample t-test to confirm their treatment effects. The two sample t-test is generally used for small samples, and assumes that two independent random samples are selected from normal populations, and the population variances are unknown. Researchers often conduct F-test, the test of equality of variances, before testing the treatment effects, and the test statistic or confidence interval for the two sample t-test has two formats according to whether the variances are equal or not. Researchers using the two sample t-test often want to know how large sample sizes they need to get reliable test results. This research gives some guidelines for sample sizes to them through simulation works. The simulation had run for normal populations with the different ratios of two variances for different sample sizes (${\leq}30$). The simulation results are as follows. First, if one has no idea equality of variances but he/she can assume the difference is moderate, it is safe to use sample size at least 20 in terms of the nominal level of significance. Second, the power of F-test for the equality of variances is very low when the sample sizes are small (<30) even though the ratio of two variances is equal to 2. Third, the sample sizes at least 10 for the two sample t-test are recommendable in terms of the nominal level of significance and the error limit.

• Journal of Aerospace System Engineering
• /
• v.1 no.3
• /
• pp.13-20
• /
• 2007

The objective of this study is to investigate endurance test of localization part for T-50 aircraft engine. Major localization parts of the engine (F404-STW-102) was performed using Accelerated Simulated Mission Engine Test. The purpose of this test is to evaluate of quality demonstration capability, to verify design of engine localization parts, and to improvement safety and operation rate of T-50 advanced trainer by finding out operational problems in production phase and fixing it.

• Proceedings of the Korean Reliability Society Conference
• /
• 2000.04a
• /
• pp.185-191
• /
• 2000

A survival variable is a nonnegative random variable X with distribution function F and a survival function (equation omitted)=1－F. This variable is said to be New Better than Used of specified age $t_{0}$ if (equation omitted) for all $\chi$$\geq$0 and a fixed to. We propose the test for $H_{0}$ : (equation omitted) for all $\chi$$\geq$0 against $H_1$：(equation omitted) for all $\chi$$\geq$0 when the specified age $t_{0}$ is unknown but can be estimated from the data when $t_{0}$=${\mu}$, the mean of F, and also when $t_{0}$=$\xi_p$, the pth percentile of F. This test statistic, which is based on a linear function of the order statistics from the sample, is readily applied in the case of small sample. Also, this test statistic is more simple than the test statistic of Ahmad's test statistic (1998). Finally, the performance of this test is presented.