• 한국데이터정보과학회:학술대회논문집
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• 2004.04a
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• pp.225-236
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• 2004

Every web server comprises a repository of all actions and events that occur on the server. Server logs can be used to quantify user traffic. Intelligent analysis of this data provides a statistical baseline that can be used to determine server load, failed requests and other events that throw light on site usage patterns. This information provides valuable leads on marketing and site management activities. In this paper, we propose a method of design for log analysis system using RTMA(realtime monitoring and analysis) technique.

• Journal of the Korean Data and Information Science Society
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• v.18 no.3
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• pp.773-782
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• 2007

The statistical process control charts are very extensively used for monitoring of process mean, deviation, defect rate or reject rate. In this paper we consider a control chart to monitor the process reject rate in the high yield process, which is based on the observed cumulative probability of the number of items inspected until r defective items are observed. We first propose selection of the optimal value of r in the CPC-r charts, and also consider the usefulness of the chart in high yield process such as semiconductor or TFT-LCD manufacturing process.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.445-456
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• 2003

Spatial data are usually obtained at selected locations even though they are potentially available at all locations in a continuous region. Moreover the monitoring locations are clustered in some regions, sparse in other regions. One important goal of spatial data analysis is to predict unknown response values at any location throughout a region of interest. Thus, an appropriate space model should be set up and their estimates and predictions must be accompanied by measures of uncertainty. In this study we see that a space model proposed allows a best interpolation to annual rainfall data in South Korea.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2001.10a
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• pp.97-115
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• 2001

cDNA microarray technology allows the monitoring of expression levels for thousands of genes simultaneously. Many statistical analysis tools become widely applicable to the analysis of cDNA microarray data. In this talk, we consider a two-way ANOVA model to differentiate genes that have high variability and ones that do not. Using this model, we detect genes that have different gene expression profiles among experimental groups. The two-way ANOVA model is illustrated using cDNA microarrays of 3,800 genes obtained in an experiment to search for changes in gene expression profiles during neuronal differentiation of cortical stem cells.

• Communications for Statistical Applications and Methods
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• v.4 no.3
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• pp.715-723
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• 1997

Multivariate control statistics to simultaneously monitor both means and variances for several quality variables under multivariate normal process are proposed. Performances of the proposed multivariate charts are evaluated in terms of average run length(ARL). Multivariate Shewhart chart is also proposed to compare the performances of multivariate exponentially weighted moving average(EWMA) charts. A numerical comparison shows that multivariate EWMA charts are more efficient than multivariate Shewhart chart for small and moderate shifts and multivariate EWMA scheme based on accumulate-combine approach is more efficient than corresponding multivariate EWMA chart based on combine-accumulate approach.

• Proceedings of the Korean Statistical Society Conference
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• 2005.05a
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• pp.191-196
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• 2005

Knowing the time of the process change could lead to quicker identification of the responsible special cause and less process down time, and it could help to reduce the probability of incorrectly identifying the special cause. In this paper, we propose a MLE of the process change point when control charts with the fixed sampling rate (FSR) scheme or the variable sampling rate (VSR) scheme monitor a process to detect changes in the process mean and/or variance of a normal quality variable.

• Communications for Statistical Applications and Methods
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• v.12 no.2
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• pp.265-273
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• 2005

In this paper, we proposed multivariate EWMA control charts for both combine-accumulate and accumulate-combine approaches to monitor dispersion matrix of multiple quality variables. Numerical performance of the proposed charts are evaluated in terms of average run length(ARL). The performances show that small smoothing constants with accumulate-combine approach is preferred for detecting small shifts of the production process.

• Environmental Engineering Research
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• v.11 no.2
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• pp.63-76
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• 2006

Multivariate analysis and batch monitoring on a pilot-scale sequencing batch reactor (SBR) are described for integrated wastewater treatment management system, where a batchwise multiway independent component analysis method (MICA) are used to extract meaningful hidden information from non-Gaussian wastewater treatment data. Three-way batch data of SBR are unfolded batch-wisely, and then a non-Gaussian multivariate monitoring method is used to capture the non-Gaussian characteristics of normal batches in biological wastewater treatment plant. It is successfully applied to an 80L SBR for biological wastewater treatment, which is characterized by a variety of error sources with non-Gaussian characteristics. The batchwise multivariate monitoring results of a pilot-scale SBR for integrated wastewater treatment management system showed more powerful monitoring performance on a WWTP application than the conventional method since it can extract non-Gaussian source signals which are independent and cross-correlation of variables.

• Proceedings of the Korea Contents Association Conference
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• 2015.05a
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• pp.15-16
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• 2015

Measuring sensor, equipment, ICT facilities and their software have relatively short life time comparing to constructional structure so that we should exchange or fix them continuously in the process of maintenance and management. In this paper, we propose a novel design of integrated maintenance, management, and measuring monitoring system applying the concept of mobile cloud. For the sake of disaster prevention for constructional structures such as bridge, tunnel, and other traditional buildings in the village of local heritage, we analyze status of these structures in the long term or short term period as well as disaster situations. Collecting data based on mobile cloud and analyzing future expectations based on probabilistic and statistical techniques, we implement our integrated monitoring system for constructional structures to solve these existing problems. Final results of this design and implementation are basically applied to the monitoring system for more than 10,000 structures spread over national land in Korea. In addition, we can specifically apply the monitoring system presented here to a bridge of timber structure in Asan Oeam Village and a traditional house in Andong Hahoe Village to watch them from possible disasters. Total procedure of system design and implementation as well as development of the platform LinkSaaS and application services of monitoring functions implemented on the platform. We prove a good performance of our system by fulfilling TTA authentication test, web accommodation test, and operation test using real measuring data.

• Earthquakes and Structures
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• v.11 no.6
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• pp.1143-1164
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• 2016

Time Delay of Arrival (TDOA) estimation methods based on correlation function analysis play an important role in the micro-seismic event monitoring. It makes full use of the similarity in the recorded signals that are from the same source. However, those methods are subjected to the noise effect, particularly when the global similarity of the signals is low. This paper proposes a new approach for micro-seismic monitoring based on cross wavelet transform. The cross wavelet transform is utilized to analyse the measured signals under micro-seismic events, and the cross wavelet power spectrum is used to measure the similarity of two signals in a multi-scale dimension and subsequently identify TDOA. The offset time instant associated with the maximum cross wavelet transform spectrum power is identified as TDOA, and then the location of micro-seismic event can be identified. Individual and statistical identification tests are performed with measurement data from an in-field mine. Experimental studies demonstrate that the proposed approach significantly improves the robustness and accuracy of micro-seismic source locating in mines compared to several existing methods, such as the cross-correlation, multi-correlation, STA/LTA and Kurtosis methods.