• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1995.03a
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• pp.174-180
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• 1995

• The Korean Journal of Applied Statistics
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• v.27 no.3
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• pp.445-459
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• 2014

As a certain job is repeatedly done by a worker, the outcome comparative to the effort to complete the job gets more remarkable. The outcome may be the time required and fraction defective. This phenomenon is referred to a learning-curve effect. We focus on the parametric modeling of the learning-curve effects on count data using a logistic cumulative distribution function and some probability mass functions such as a Poisson and negative binomial. We conduct various simulation scenarios to clarify the characteristics of the proposed model. We also consider a real application to compare the two discrete-type distribution functions.

• Journal of Ocean Engineering and Technology
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• v.13 no.1 s.31
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• pp.62-69
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• 1999

67Ni-22Cr-10Al-1Y and $ZrO_2-8Y_2O_3$ were coated on the substrate surface of ST304 and Al2024 by the plasma spraying method. The adgesion of the films varies depending on the substrates and the laminating method. In the case of STS304, the cracks were observed at thermal shock temperature difference ${Delta}T$ of $900^{circ}C$ in the non functionally gradient material(NFGM) and at $1100^{circ}C$ in the functionally gradient material(FGM). The film adhesion of the FGM is better than that of the NFGM in ST304. The cumulative AE count of the FGM of STS304 increased continuously at the bending test. But the NFGM of STS304 showed discontinuity of the AE count. The total AE count for the FGM of STS304 decreased as the number of thermal shock increased, and this tendency was evident as the thermal shock temperature difference increased.

• Journal of Radiation Protection and Research
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• v.44 no.2
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• pp.64-71
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• 2019

Background: It is very difficult to distinguish between a radioactive contamination source and background radiation from natural radionuclides in the marine environment by means of online monitoring system. The objective of this study was to investigate a statistical process for triggering abnormal level of count rate data measured from our on-line seawater radioactivity monitoring. Materials and Methods: Count rate data sets in time series were collected from 9 monitoring posts. All of the count rate data were measured every 15 minutes from the region of interest (ROI) for $^{137}Cs$ ($E_{\gamma}=661.6keV$) on the gamma-ray energy spectrum. The Shewhart ($3{\sigma}$), CUSUM, and Bayesian S-R control chart methods were evaluated and the comparative analysis of determination methods for count rate data was carried out in terms of the false positive incidence rate. All statistical algorithms were developed using R Programming by the authors. Results and Discussion: The $3{\sigma}$, CUSUM, and S-R analyses resulted in the average false positive incidence rate of $0.164{\pm}0.047%$, $0.064{\pm}0.0367%$, and $0.030{\pm}0.018%$, respectively. The S-R method has a lower value than that of the $3{\sigma}$ and CUSUM method, because the Bayesian S-R method use the information to evaluate a posterior distribution, even though the CUSUM control chart accumulate information from recent data points. As the result of comparison between net count rate and gross count rate measured in time series all the year at a monitoring post using the $3{\sigma}$ control charts, the two methods resulted in the false positive incidence rate of 0.142% and 0.219%, respectively. Conclusion: Bayesian S-R and CUSUM control charts are better suited for on-line seawater radioactivity monitoring with an count rate data in time series than $3{\sigma}$ control chart. However, it requires a continuous increasing trend to differentiate between a false positive and actual radioactive contamination. For the determination of count rate, the net count method is better than the gross count method because of relatively a small variation in the data points.

• Industrial Engineering and Management Systems
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• v.7 no.2
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• pp.101-112
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• 2008

As charts to monitor the process fraction defectives, P, in the high-yield processes, Mishima et al. (2002) discussed a synthetic chart, the Synthetic CS chart, which integrates the CS (Confirmation Sample)$_{CCC(\text{Cumulative Count of Conforming})-r}$ chart and the CCC-r chart. The Synthetic CS chart is designed to monitor quality characteristics in real-time. Recently, Kotani et al. (2005) presented the EWMA (Exponentially Weighted Moving-Average)$_{CCC-r}$ chart, which considers combining the quality characteristics monitored in the past with one monitored in real-time. In this paper, we present an alternative chart that is more superior to the $EWMA_{CCC-r}$ chart. It is an integration of the $EWMA_{CCC-r}$ chart and the CCC-r chart. In using the proposed chart, the quality characteristic is initially judged as either the in-control state or the out-of-control state, using the lower and upper control limits of the $EWMA_{CCC-r}$ chart. If the process is not judged as the in-control state by the $EWMA_{CCC-r}$ chart, the process is successively judged, using the $EWMA_{CCC-r}$ chart. We compare the ANOS (Average Number of Observations to Signal) of the proposed chart with those of the $EWMA_{CCC-r}$ chart and the Synthetic CS chart. From the numerical experiments, with the small size of inspection items, the proposed chart is the most sensitive to detect especially the small shifts in P among other charts.

• Industrial Engineering and Management Systems
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• v.4 no.2
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• pp.158-164
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• 2005

Kusukawa and Ohta presented the $CS_{CQ-r}$ chart to monitor the process defect $rate{\lambda}$ in high-yield processes that is derived from the count of defects. The $CS_{CQ-r}$ chart is more sensitive to $monitor{\lambda}$ than the CQ (Cumulative Quantity) chart proposed by Chan et al.. As a more superior chart in high-yield processes, we propose a Synthetic chart that is the integration of the CQ_-r chart and the $CS_{CQ-r}$chart. The quality characteristic of both charts is the number of units y required to observe r $({\geq}2)$ defects. It is assumed that this quantity is an Erlang random variable from the property that the quality characteristic of the CQ chart follows the exponential distribution. In use of the proposed Synthetic chart, the process is initially judged as either in-control or out-of-control by using the $CS_{CQ-r}$chart. If the process was not judged as in-control by the $CS_{CQ-r}$chart, the process is successively judged by using the $CQ_{-r}$chart to confirm the judgment of the $CS_{CQ-r}$chart. Through comparisons of ARL (Average Run Length), the proposed Synthetic chart is more superior to monitor the process defect rate in high-yield processes to the stand-alone $CS_{CQ-r}$ chart.

• Asian-Australasian Journal of Animal Sciences
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• v.24 no.7
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• pp.1001-1006
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• 2011

To determine the efficiency of copper (Cu) supplementation, a feeding experiment was carried out with 240 day old broiler chicks (vencobb-100). Birds were divided into four dietary treatments: i) C - no additives, ii) $T_1$-75 mg inclusion of Cu/kg diet, iii) $T_2$-150 mg inclusion of Cu/kg diet, iv) $T_3$-250 mg inclusion of Cu/kg diet. The present study was carried out in the Department of Animal Physiology, West Bengal University of Animal and Fishery Sciences, Kolkata, India for a period of 42 days (6 weeks). Growth performance was measured in terms of live weight gain, cumulative feed intake and feed conversion ratio at the end of $21^{st}$ and $42^{nd}$ day of the experiment and the result was found to be encouraging for commercial enterprises when the chickens were fed at 150 mg Cu/kg ($T_2$) of their diet. Excess dietary copper more than 150 mg/kg reduced the haemoglobin (Hb) concentration in blood and resulted in the accumulation of copper in the liver with decreased blood Hb concentration and packed cell volumes (PCV). Copper supplementation increased the total erythrocyte count (TEC) as copper is involved in erythropoiesis. But, from the result it is indicated that the dietary copper concentration could not alter the total leukocyte count (TLC). In case of different leucocytes count (DLC), there were no significant differences observed among the different treated groups. Statistical analysis showed significant (p<0.01) difference in plasma concentration of copper, zinc, ferrous and cholesterol among the different copper treated groups. The result indicates that supplementation of copper is an effective way of improving the production performance and haematological parameters in broiler chicken.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.04b
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• pp.124-130
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• 1995

연삭작업에 있어서 요구되는 가공능률과 가공정밀도를 얻기 위해서는 적절한 결합도를 갖는 연삭숫돌을 선택하 는 것이 중요한 요건이 된다. 본 연구에서는 결합도를 평가하기 위한 일정한 기준을 설정할 목적으로 AE계측 시스템을 이용하여 대월식결합도시험방법에 따라 연삭숫돌표면의 일부를 파괴시켰을 때 발생되는 AE신호를 검출 하고 연삭숫돌의 파괴량과 AE 신호특성과의 관계 및 AE를 이용한 결합도평가의 가능성을 검토하였다.

• Journal of the Korean Society of Industry Convergence
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• v.22 no.2
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• pp.191-199
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• 2019

In this paper, dissimilar friction welding were produced using 15 mm diameter solid bar in chrome molybedenum steel(SCM440) to stainless steel(STS316L) to investigate their mechanical properties. Consequently, optimal welding conditions were n=2000 rpm, HP=70 MPa, UP=140 MPa, HT=10 sec and UT=10 sec when the metal loss(Mo) is 8.6 mm. In addition, an acoustic emission technique was applied to evaluate the optimal friction welding condition. AE parameters including the cumulative count, amplitude and energy showed a various changes according to the friction condition. A continuous type waveforms and low frequency spectrum was presented in friction time. On the other hand, a burst type waveform and high frequency spectrum was exhibited in pressing time.

• The Transactions of the Korea Information Processing Society
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• v.7 no.12
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• pp.3821-3828
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• 2000

Many software projects collect grouped failure data (failures in some failure interval or in variable time interval) rather than individual failure times or failure count data during the testing or operational phase. This paper presents the neural network (NN) modeling that is dble to predict cumulative failures in the variable future time for grouped failure data. ANN's predictive ability can be affected by what it learns and in its ledming sequence. Eleven training regimes that represents the input-output of NN are considered. The best training regimes dre selected rJdsed on the next' step dvemge reldtive prediction error (AE) and normalized AE (NAE). The suggested NN models are compared with other well-known KN models and statistical software reliability growth models (SHGlvls) in order to evaluate performance, Experimental results show that the NN model with variable time interval information is necessary in order to predict cumulative failures in the variable future time interval.