• Communications for Statistical Applications and Methods
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• 제25권5호
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• pp.501-512
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• 2018

We propose Sequential Patient Recruitment Monitoring (SPRM), a new monitoring procedure for patient recruitment in a clinical trial. Based on the sequential probability ratio test using improved stopping boundaries by Woodroofe, the method allows for continuous monitoring of the rate of enrollment. It gives an early warning when the recruitment is unlikely to achieve the target enrollment. The packet data approach combined with the Central Limit Theorem makes the method robust to the distribution of the recruitment entry pattern. A straightforward application of the counting process framework can be used to estimate the probability to achieve the target enrollment under the assumption that the current trend continues. The required extension of the recruitment period can also be derived for a given confidence level. SPRM is a new, continuous patient recruitment monitoring tool that provides an opportunity for corrective action in a timely manner. It is suitable for the modern, centralized data management environment and requires minimal effort to maintain. We illustrate this method using real data from two well-known, multicenter, phase III clinical trials.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2006년도 춘계 학술발표회 논문집
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• pp.1046-1053
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• 2006

In the large scale recent reclaiming works performed within the wide spatial boundary, evaluation of long-term consolidation settlement and residual settlement of the whole construction area is sometimes made with the results of the limited ground investigation and measurement. Then the reliability of evaluation has limitations due to the spatial uncertainty. Additionally, in case of large scale deep excavation works such as urban subway construction, there are a lot of hazardous elements to threaten the safety of underground pipes or adjacent structures. Therefore it is necessary to introduce a damage prediction system of adjacent structures and others. For the more accurate analysis of monitoring information in the wide spatial boundary works and large scale urban deep excavations, it is necessary to perform statistical and spatial analysis considering the geographical spatial effect of ground and monitoring information in stead of using diagrammatization method based on a time-series data expression that is traditionally used. And also it is necessary that enormous ground information and measurement data, digital maps are accumulated in a database, and they are controlled in a integrating system. On the abovementioned point of view, we developed Geomonitor 2.0, an Internet based real time monitoring program with a new concept by adding GIS and geo-statistical analysis method to the existing real time integrated measurement system that is already developed and under useful use. The new program enables the spatial analysis and database of monitoring data and ground information, and helps the construction- related persons make a quick and accurate decision for the economical and safe construction.

• 한국산업경영시스템학회:학술대회논문집
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• 한국산업경영시스템학회 2002년도 춘계학술대회
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• pp.387-396
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• 2002

Batch processes are a significant class of processes in the process industry and play an important role in the production of high quality speciality materials. Examples include the production of semiconductors, chemicals, pharmaceuticals, and biochemicals. With on-line sensors connected to most batch processes, massive amounts of data are being collected routinely during the batch on easily measured process variables such as temperatures, pressures, and flowrates. In this paper, multivariate SPC charts for on-line monitoring of the progress of new batches are developed which utilize the information in the on-line measurements in real-time. We propose the formation of statistical model which describes the normal operation of a batch at each time interval during the batch operation. An on-line monitoring scheme based on the proposed method can handle both cross-correlation among process variables at any one time and auto-correlation over time. And the control limits for the monitoring charts are established from sound statistical framework unlike previous researches which use the external reference distribution. The proposed charts perform real-time, on-line monitoring to ensure that the batch is progressing in a manner that will lead to a high-quality product or to detect and indicate faults that can be corrected prior to completion of the batch. This approach is capable of tracking the progress of new batch runs, identifying the time periods in which the fault occurred and detecting underlying cause.

• Smart Structures and Systems
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• 제17권6호
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• pp.1031-1053
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• 2016

The health conditions of in-service civil infrastructures can be evaluated by employing structural health monitoring technology. A reliable health evaluation result depends heavily on the quality of the data collected from the structural monitoring sensor network. Hence, the problem of sensor fault diagnosis has gained considerable attention in recent years. In this paper, an innovative sensor fault diagnosis method that focuses on fault detection and isolation stages has been proposed. The dynamic or auto-regressive characteristic is firstly utilized to build a multivariable statistical model that measures the correlations of the currently collected structural responses and the future possible ones in combination with the canonical correlation analysis. Two different fault detection statistics are then defined based on the above multivariable statistical model for deciding whether a fault or failure occurred in the sensor network. After that, two corresponding fault isolation indices are deduced through the contribution analysis methodology to identify the faulty sensor. Case studies, using a benchmark structure developed for bridge health monitoring, are considered in the research and demonstrate the superiority of the new proposed sensor fault diagnosis method over the traditional principal component analysis-based and the dynamic principal component analysis-based methods.

• 한국지하수토양환경학회지:지하수토양환경
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• 제26권6호
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• pp.186-195
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• 2021

In Korea, groundwater quality is monitored through National Groundwater Quality Monitoring Network (NGQMN) administered by Ministry of Environment. For a given contaminant, compliance to groundwater quality standards is assessed on a annual basis by monitoring the number of incidents that concentration exceeds the regulatory limit. However, this approach provides only a fractional information about groundwater quality degradation, and more crucial information such as location and severity of the contamination cannot be obtained. For better groundwater quality management on a watershed, a more spatially informative and intuitive method is required. This study presents two statistical methods to convert point-wise monitoring data into information on groundwater quality status of a watershed by using a proposed grading scale. The proposed grading system is based on readily available reference standards that classify the water quality into 4 grades. The methods were evaluated with NO₃^-, Cl^-, and total coliform data in Geum River basin. The analyses revealed that groundwater in most watersheds of Geum River basin is good for domestic or/and drinking with no treatment. But, there was notable quality degradation in Bunam seawall and So-oak downstream standard watersheds contaminated by NO₃^- and Cl^-, respectively.

• 응용통계연구
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• 제35권3호
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• pp.335-346
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• 2022

통계적 공정관리에서 프로파일 관리도란 다수의 품질 특성치 간 함수관계의 변화를 탐지하는 것을 말한다. 두 변수 간 선형의 관계가 있는 경우, 선형 프로파일을 가정하고 절편과 기울기가 일정한지 모니터링한다. 이때 선형 프로파일에 관한 대부분의 기존 연구에서는 모든 프로파일에서 설명변수의 관측치가 동일하다고 가정한다. 그러나 프로파일마다 설명변수의 값이 랜덤하게 관측되는 경우도 존재한다. 본 논문에서는 단순 선형 프로파일 모니터링에서 설명변수가 프로파일마다 랜덤하게 관측된다는 가정하에 기존의 방법을 확장 적용하고자 한다. 모의실험을 통해 제안한 방법의 탐지 성능을 확인하고 네트워크 침입 탐지 알고리즘 성능을 비교하기 위한 NSL-KDD 데이터를 이용하여 제안된 침입 탐지 결과를 비교해 보았다.

• 반도체디스플레이기술학회지
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• 제22권1호
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• pp.1-5
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• 2023

Most of the production process is performed in a cleanroom in the case of facilities that produce semiconductor chips or display panels. Therefore, environmental management of cleanrooms is very important for product yield and quality control. Among them, airborne particles are a representative management item enough to be the standard for the actual cleanroom rating, and it is a part of the Fab or Facility monitoring system, and the sequential particle monitoring system is mainly used. However, this method has a problem in that measurement efficiency decreases as the length of the sampling tube increases. In addition, a statistically significant test of deterioration in efficiency has rarely been performed. Therefore, in this study, the statistically significant test between the number of particles measured by InSitu and the number of particles measured for each sampling tube ends(Remote). Through this, the efficiency degradation problem of the sequential particle monitoring system was confirmed by a statistical method.

• 대한안전경영과학회지
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• 제6권4호
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• pp.195-213
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• 2004

This paper deals with an efficient and effective method on measuring, monitoring and evaluating safety and environmental performances of a process using SPC control charts. We propose 7 safety control charts as a tool to monitor hazard dendritics, and we propose 15 environment control charts to monitor pollution emissions. We also propose useful guidelines that SPC(Statistical Process Control) control charts can be used for safely and environmental monitoring.

• Smart Structures and Systems
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• 제14권3호
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• pp.377-395
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• 2014

Vibration-based fault detection and condition monitoring of rotating machinery, using statistical process control (SPC) combined with statistical pattern recognition methodology, has been widely investigated by many researchers. In particular, the discrete wavelet transform (DWT) is considered as a powerful tool for feature extraction in detecting fault on rotating machinery. Although DWT significantly reduces the dimensionality of the data, the number of retained wavelet features can still be significantly large. Then, the use of standard multivariate SPC techniques is not advised, because the sample covariance matrix is likely to be singular, so that the common multivariate statistics cannot be calculated. Even though many feature-based SPC methods have been introduced to tackle this deficiency, most methods require a parametric distributional assumption that restricts their feasibility to specific problems of process control, and thus limit their application. This study proposes a nonparametric multivariate control chart method, based on multiscale wavelet scalogram (MWS) features, that overcomes the limitation posed by the parametric assumption in existing SPC methods. The presented approach takes advantage of multi-resolution analysis using DWT, and obtains MWS features with significantly low dimensionality. We calculate Hotelling's $T^2$-type monitoring statistic using MWS, which has enough damage-discrimination ability. A bootstrap approach is used to determine the upper control limit of the monitoring statistic, without any distributional assumption. Numerical simulations demonstrate the performance of the proposed control charting method, under various damage-level scenarios for a bearing system.

• 한국지하수토양환경학회지:지하수토양환경
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• 제17권1호
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• pp.22-32
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• 2012

This study suggests a method to grade groundwater quality quantitatively using statistical approaches for evaluating the quality of groundwater in wells included in the Groundwater Quality Monitoring Network (GQMN). The proposed analysis method is applied to GQMN data from 2001 to 2008 for nitrate nitrogen, chloride, trichloroethylene, potential of hydrogen (pH), and electrical conductivity. The analysis results are obtained as groundwater quality grades of the groundwater representing each of the monitoring stations. The degree of groundwater contamination is analysed for water quality parameters, district, and usage. The results show that the degree of groundwater contamination is relatively high by nitrate nitrogen, bacteria and electrical conductivity and at Seoul, Incheon, Gwangju, Gyeonggido and Jeollado. The degree of contamination by nitrate nitrogen and trichloroethylene is especially high when the groundwater is used for agricultural and industrial water, respectively. It is evaluated that potable groudnwater in GQMN is significantly vulnerable to nitrate nitrogen and bacteria contamination.