• 한국빅데이터학회지
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• 제1권2호
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• pp.1-8
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• 2016

사용자로그는 많은 숨겨진 정보를 포함하고 있지만 데이터 정형화가 이루어지지 않았고, 데이터 크기도 너무 방대하여 처리하기 까다로워서 아직 밝혀져야 할 부분들을 많이 내포하고 있다. 특히 행동마다의 모든 시간정보를 포함하고 있어서 이를 응용하여 많은 부분을 밝혀낼 수 있다. 하지만 로그데이터 자체를 바로 분석으로 사용할 수는 없다. 유저 행동 모델 분석을 위해서는 별도의 프레임워크를 통한 변환과정들이 필요하다. 이 때문에 유저 행동모델 분석 프레임워크를 먼저 파악을 하고 데이터에 접근해야 한다. 이 논문에서는, 우리는 유저 행동모델을 효과적으로 분석하기 위한 프레임워크 모델을 제안한다. 본 모델은 대규모 데이터를 빨리 처리하기 위한 분산환경에서의 MapReduce 프로세스와 유저별 행동분석을 위한 데이터 구조 설계에 대한 부분을 포함한다. 또한 실제 온라인 서비스 로그의 구조를 바탕으로 어떤 방식으로 MapReduce를 처리하고 어떤 방식으로 유저행동모델을 분석을 위해 데이터 구조를 어떤식으로 변형할지 설명하고, 이를 통해 어떤 방식의 모델 분석으로 이어질지에 대해 상세히 설명한다. 이를 통해 대규모 로그 처리방법과 분석모델 설계에 대한 기초를 다질 수 있을 것이다.

• 한국경영과학회지
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• 제26권1호
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• pp.109-116
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• 2001

The detection of corporate failures is a subject that has been particularly amenable to cross-sectional financial ratio analysis. In most of firms, however, the financial data are available over past years. Because of this, a model utilizing these longitudinal data could provide useful information on the prediction of bankruptcy. To correctly reflect the longitudinal and firm-specific data, the generalized linear model with assuming the first order AR(autoregressive) process is proposed. The method is motivated by the clinical research that several characteristics are measured repeatedly from individual over the time. The model is compared with several other predictive models to evaluate the performance. By using the financial data from manufacturing corporations in the Korea Stock Exchange (KSE) list, we will discuss some experiences learned from the procedure of sampling scheme, variable transformation, imputation, variable selection, and model evaluation. Finally, implications of the model with repeated measurement and future direction of research will be discussed.

• 한국주조공학회지
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• 제42권1호
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• pp.35-41
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• 2022

In the present paper, the data management of casting processes has been discussed. In order to construct a smart factory in the foundry industry, understanding of the whole casting processes has to be in the first place. Casting process data can be obtained at the kiosk operated by casting engineers and data acquired by sensors in the foundry facility. However, preprocessing of the casting process data must be carried out in order to analyze the casting process by the data. Techniques and some examples for data preprocessing in the foundry was introduced.

• 대한산업공학회지
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• 제27권3호
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• pp.289-296
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• 2001

This paper proposes multivariate control chart for autocorrelated data which are common in chemical and process industries and lead to increase in the number of false alarms when conventional control charts are applied. The effect of autocorrelated data is modeled as a vector autoregressive process, and canonical analysis is used to reduce the dimensionality of the data set and find the canonical variables that explain as much of the data variation as possible. Charting statistics are constructed based on the residual vectors from the canonical variables which are uncorrelated over time, and therefore the control charts for these statistics can attenuate the autocorrelation in the process data. The charting procedures are illustrated with a numerical example and Monte Carlo simulation is conducted to investigate the performances of the proposed control charts.

• 산업공학
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• 제12권1호
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• pp.79-93
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• 1999

As most of information systems developed are data-centric rather than process-centric, it is difficult for users to understand and manage the system from the viewpoint of work processes. To resolve the problem of the data-centric design, we propose a new method to design WFMSs(Workflow Management Systems), which are focused on processes and modified from current information engineering methods. In this research, the drawing approval and engineering change approval process of a K manufacturing company has been analyzed as a sample process. This method takes two steps, i.e., process analysis and system design. In the prosess analysis, data and processes are analyzed, and functions and tasks are derived from the processes. In the system design, a data model for the operation of WFMS is designed, and based on this data model, build-time and run-time functions of WFMS are designed.

• 산업공학
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• 제24권1호
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• pp.8-14
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• 2011

Principal Component Analysis (PCA) reduces the dimensionality of the process by creating a new set of variables, Principal components (PCs), which attempt to reflect the true underlying process dimension. However, for highly nonlinear processes, this form of monitoring may not be efficient since the process dimensionality can't be represented by a small number of PCs. Examples include the process of semiconductors, pharmaceuticals and chemicals. Nonlinear correlated process variables can be reduced to a set of nonlinear principal components, through the application of Kernel Principal Component Analysis (KPCA). Support Vector Data Description (SVDD) which has roots in a supervised learning theory is a training algorithm based on structural risk minimization. Its control limit does not depend on the distribution, but adapts to the real data. So, in this paper proposes a non-linear process monitoring technique based on supervised learning methods and KPCA. Through simulated examples, it has been shown that the proposed monitoring chart is more effective than $T^2$ chart for nonlinear processes.

• 한국신뢰성학회지:신뢰성응용연구
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• 제18권1호
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• pp.20-32
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• 2018

Purpose: The purpose of this study is to set up an anomaly detection criteria for sensor data coming from a motorcycle. Methods: Five sensor values for accelerator pedal, engine rpm, transmission rpm, gear and speed are obtained every 0.02 second from a motorcycle. Exploratory data analysis is used to find any pattern in the data. Traditional process control methods such as X control chart and time series models are fitted to find any anomaly behavior in the data. Finally unsupervised learning algorithm such as k-means clustering is used to find any anomaly spot in the sensor data. Results: According to exploratory data analysis, the distribution of accelerator pedal sensor values is very much skewed to the left. The motorcycle seemed to have been driven in a city at speed less than 45 kilometers per hour. Traditional process control charts such as X control chart fail due to severe autocorrelation in each sensor data. However, ARIMA model found three abnormal points where they are beyond 2 sigma limits in the control chart. We applied a copula based Markov chain to perform statistical process control for correlated observations. Copula based Markov model found anomaly behavior in the similar places as ARIMA model. In an unsupervised learning algorithm, large sensor values get subdivided into two, three, and four disjoint regions. So extreme sensor values are the ones that need to be tracked down for any sign of anomaly behavior in the sensor values. Conclusion: Exploratory data analysis is useful to find any pattern in the sensor data. Process control chart using ARIMA and Joe's copula based Markov model also give warnings near similar places in the data. Unsupervised learning algorithm shows us that the extreme sensor values are the ones that need to be tracked down for any sign of anomaly behavior.

• 한국펄프종이공학회:학술대회논문집
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• 한국펄프종이공학회 2006년도 PAN PACIFIC CONFERENCE vol.2
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• pp.191-195
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• 2006

Multivariate data analysis tools were used to improve the understanding of the wet end chemistry and white water system of the Papermill at NorskeCanada Crofton Division. Specifically, the analysis was aimed at identifying what variables were contributing to increased retention aid use and wet end instability. Several models were developed using data sets with up to 88 process variables and over 3000 observations. It was found that increased retention aid use was driven primarily by PCC and TMP usage as well as the addition of Alaskan White Spruce to the TMP furnish.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제2권11호
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• pp.461-470
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• 2013

본 논문은 정밀한 메모리 워크로드 분석을 위해 리눅스 기반의 커널 수준에서 프로세스의 메모리 관리 구조체에 직접 접근하는 방법을 이용하여 고속으로 커널 데이터를 수집하는 기법을 제안한다. 기존의 분석기들은 데이터 수집 속도가 느리고 제공되는 데이터의 제한으로 인하여 확장성이 부족하다. 제안 기법은 메모리 관리 구조체 내의 프로세스 메모리정보, 페이지 테이블, 페이지 구조체를 직접 수집하는 방법을 이용하여 기존의 기법 보다 빠르게 커널 데이터를 수집하며, 사용자가 원하는 데이터를 선택하여 수집할 수 있다. 제안 기법을 통해 실제 실행 중인 프로세스의 메모리 관리 데이터를 수집하고 메모리 워크로드에 대한 분석을 수행하였다.

• 한국멀티미디어학회논문지
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• 제22권3호
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• pp.366-373
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• 2019

The development of monitoring and analysis systems to increase productivity while saving energy is needed as a method to reduce huge amount of energy consumed in the process of producing large forged products. In this paper, we propose a system to monitor and analyze energy usage in real-time collected from gas-meter, wattmeter, and thermometer based on IoT installed in forging factories. The system consists of a data collection server for collecting and processing data from IoT- based platform and existing SCADA equipment and ERP/MES system in forging factories, and an application server for providing services to users. To develop the system, the overall system structure is logically diagrammed, and the databases configuration and implementation modules to efficiently store and manage data are presented. In the future, the system will be utilized to reduce energy consumption by analyzing energy usage pattern and optimizing process works with real-time energy usage and production process data for each facility.