• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.919-929
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• 2013

We compared two basic methods, combine-accumulate method and accumulate-combine method, using the past quality information in multivariate quality control procedure for monitoring mean vector of multivariate normal process. When small or moderate shifts have occurred, accumulate-combine method yields smaller average run length (ARL) and average time to signal (ATS) than combine-accumulate method. On the other hand, we have found from our numerical results that combine-accumulate method has better performances in terms of switching behavior than accumulate-combine method. In industry, a quality engineer could select one of the two method under the comprehensive consideration about the required time to signal, switching behavior, and other physical factors in the production process.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.18-18
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• 1996

This paper reviews some of the most prominent and promising areas of chemical process control both in relations to batch and continuous processes. These areas include the modeling, optimization, control and monitoring of chemical processes and entire plants. Most of these areas explicitly utilize a model of the process. For this purpose the types of models used are examined in some detail. These types of models are categorized in knowledge-driven and datadriven classes. In the areas of modeling and optimization, attention is paid to batch reactors using the Tendency Modeling approach. These Tendency models consist of data- and knowledge-driven components and are often called Gray or Hybrid models. In the case of continuous processes, emphasis is placed in the closed-loop identification of a state space model and their use in Model Predictive Control nonlinear processes, such as the Fluidized Catalytic Cracking process. The effective monitoring of multivariate process is examined through the use of statistical charts obtained by the use of Principal Component Analysis (PMC). Static and dynamic charts account for the cross and auto-correlation of the substantial number of variables measured on-line. Centralized and de-centralized chart also aim in isolating the source of process disturbances so that they can be eliminated. Even though significant progress has been made during the last decade, the challenges for the next ten years are substantial. Present progress is strongly influenced by the economical benefits industry is deriving from the use of these advanced techniques. Future progress will be further catalyzed from the harmonious collaboration of University and Industrial researchers.

• Journal of the Society of Naval Architects of Korea
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• v.55 no.6
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• pp.505-513
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• 2018

Main engine failures in ship operations can lead to a major damage in terms of the vessel itself and the financial cost. In this respect, monitoring of a vessel's main engine condition is crucial in ensuring the vessel's performance and reducing the maintenance cost. The collection of a huge amount of vessel operational data in the maritime industry has never been easier with the advent of advanced data collection technologies. Real-time monitoring of the condition of a vessel's main engine has a potential to create significant value in maritime industry. This study presents a case study on the establishment of upper control limit to detect vessel's main engine failures using multivariate control chart. The case study uses sample data of an ocean-going vessel operated by a major marine services company in Korea, collected in the period of 2016.05-2016.07. This study first reviews various main engine-related variables that are considered to affect the condition of the main engine, and then attempts to detect abnormalities and their patterns via multivariate control charts. This study is expected to help to enhance the vessel's availability and provide a basis for a condition-based maintenance that can support proactive management of vessel's main engine in the future.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.451-459
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• 2017

There are many researches showing that when a process change has occurred, variable sampling intervals (VSI) control chart is better than the fixed sampling interval (FSI) control chart in terms of reducing the required time to signal. When the process engineers use VSI control procedure, frequent switching between different sampling intervals can be a complicating factor. However, average number of samples to signal (ANSS), which is the amount of required samples to signal, and average time to signal (ATS) do not provide any control statistics about switching performances of VSI charts. In this study, we evaluate numerical switching performances of multivariate VSI EWMA chart including average number of switches to signal (ANSW) and average switching rate (ASWR). In addition, numerical study has been carried out to examine how to improve the performance of considered chart with accumulate-combine approach under several different smoothing constant and sample size. In conclusion, process engineers, who want to manage the correlation coefficients of related quality variables, are recommended to make sample size as large and smoothing constant as small as possible under permission of process conditions.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.10
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• pp.6109-6114
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• 2013

Aim: To determine whether beta-blockers (BBs) improve the overall survival (OS) of patients with metastatic non-small cell lung cancer (NSCLC). Materials and Methods: The medical charts of 107 patients with metastatic NSCLC were retrospectively assessed. Thirty-five patients (BB group) using BBs during chemotherapy (CT) were compared with 72 controls [control=(C) group] who did not use BBs following the diagnosis of NSCLC. The histological tumor subtype, performance status (ECOG), age, gender, smoking status, comorbidities, other medications and chemotherapeutics that were received in any line of treatment were recorded. We compared the overall survival (OS) of the patients in the BB and C groups. Results: The mean age of the patients was 61 years (range 42-81 years) and all patients were administered CT. The BB group was more likely to have HT and IHD and was more likely to use RAS blockers (p<0.01 for all) compared with the C group, as expected. The mean follow-up time was 17.8 months (range 1-102 months) for the entire group. The most commonly prescribed BB agent was metoprolol (80% of cases). At the time of the analysis, 74 (69%) of all patients had died. In the univariate analysis the median overall survival (OS) was 19.25 (${\pm}2.87$) months (95%CI: 13.62-24.88) in the BB group and 13.20 (${\pm}2.37$) months (95%CI: 8.55-17.85) in the C group (p=0.017). However, the benefit of BBs on survival disappeared in the multivariate analysis. Conclusions: The use of BBs during CT may be associated with an improved OS for patients with metastatic NSCLC.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1353-1366
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• 2015

These days Shewhart control chart for evaluating stability of the process is widely used in various field. But it must follow strict assumption of distribution. In real-life problems, this assumption is often violated when many quality characteristics follow non-normal distribution. Moreover, it is more serious in multivariate quality characteristics. To overcome this problem, many researchers have studied the non-parametric control charts. Recently, SVDD (Support Vector Data Description) control chart based on RBF (Radial Basis Function) Kernel, which is called K-chart, determines description of data region on in-control process and is used in various field. But it is important to select kernel parameter or etc. in order to apply the K-chart and they must be predetermined. For this, many researchers use grid search for optimizing parameters. But it has some problems such as selecting search range, calculating cost and time, etc. In this paper, we research the efficiency of selecting parameter regions as data structure vary via simulation study and propose a new method for determining parameters so that it can be easily used and discuss a robust choice of parameters for various data structures. In addition, we apply it on the real example and evaluate its performance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.11
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• pp.5094-5101
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• 2011

In order to investigate recurrence rate and risk factors for recurrence of gastric cancer after curative resection, we examined blood biochemical profile and clinicopathological features of 386 gastric cancer patients by using medical charts and data of hospital cancer registry from September 2010 to May 2011. Chi-square test and logistic regression analyses were performed to identify risk factors. The recurrence rate of all gastric cancer patients was 2.6%; The recurrence rate by gender was 3.45% in male and 0.89% in female, and that was 4 times higher in male than in female. On univariate logistic regression analyses, lymph node metastases(OR=8.793), Helicobactor pylori infecton(OR=6.495), abnormal total cholesterol(OR=14.333) were related to recurrence. On multivariate logistic regression analyses, lymph node metastases and H. pylori infection were very important risk factors for recurrence of gastric cancer. In conclusion, lymph node metastases, H. pylori infection, and total cholesterol control were very important to prevent recurrence of gastric cancer, and it needs to monitor blood biochemical (C-reactive protein, carcinoembryonic antigen, etc) for the early detection of gastric cancer recurrence.