• Proceedings of the Society of Korea Industrial and System Engineering Conference
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• 2002.05a
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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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.15 no.26
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• pp.119-124
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• 1992

본 논문은 장치산업에서 적용되는 다변량 EWMA 공정제어와 통계적 공정감시 통합시스템을 제안한다. 본 논문에서 제안한 통합시스템은 자동공정제어(APC)의 예측, 조정기능과 통계적 정정감시(SPM)의 이상점 발견 및 제거등의 각각의 장점을 이용하였다. 기존의 다변량 EWMA연구는 데이타간의 독립성을 가정하였으나 본 논문은 데이타간의 종속적인 형태인 IMA(1,1)모델을 대상으로 하였다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.40-45
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• 2018

Intelligent monitoring and diagnosis of production processes based on multivariate statistical methods has been one of important tasks for safety and quality issues. This is due to the fact that faults and unexpected events may have serious impacts on the operation of processes. This study proposes a diagnostic scheme based on effective representation of process measurement data and is evaluated using simulation process data. The effects of utilizing a preprocessing step and nonlinear statistical methods are also tested using fifteen faults of the simulation process. Results show that the proposed scheme produced more reliable results and outperformed other tested schemes with none of the filtering step and nonlinear methods. The proposed scheme is expected to be robust to process noises and easy to develop due to the lack of required rigorous mathematical process models or expert knowledge.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.6
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• pp.550-557
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• 2005

The multivariate statistical analysis methods, using both multiple linear regression(MLR) and partial least square(PLS), have been applied to predict the reactor properties and the product quality of a direct esterification reactor for polyethylene terephthalate(PET) synthesis. On the basis of the set of data including the flow rate of water vapor, the flow rate of EG vapor, the concentration of acid end groups of a product and other operating conditions such as temperature, pressure, reaction times and feed monomer mole ratio, two multi-variable analysis methods have been applied. Their regression and prediction abilities also have been compared. The prediction results are critically compared with the actual plant data and the other mathematical model based results in reliability. This paper shows that PLS method approach can be used for the reasonably accurate prediction of a product quality of a direct esterification reactor in PET synthesis process.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.3
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• pp.533-544
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• 2021

Using Shor algorithm, factoring and discrete logarithm problem can be solved effectively. The public key cryptography, such as RSA and ECC, based on factoring and discrete logarithm problem can be broken in polynomial time using Shor algorithm. NIST has been conducting a PQC(Post Quantum Cryptography) standardization process to select quantum-resistant public key cryptography. The multivariate quadratic based signature scheme, which is one of the PQC candidates, is suitable for IoT devices with limited resources due to its short signature and fast sign and verify process. We analyzes classic attacks and quantum attacks for Rainbow which is the only multivatiate quadratic based signature scheme to be finalized up to the round 3. Also we compute the attack complexity for the round 3 Rainbow parameters, and analyzes the security level of Rainbow, one of the PQC standardization candidates.

• Journal of the Chungcheong Mathematical Society
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• v.27 no.2
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• pp.237-242
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• 2014

In this paper we provide a valuation method for multi-asset derivatives with single jump regime-switching volatilities. We suppse that volatilities of assets are affected by an n-dimensional independent Markov regime-switching process.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.391-391
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• 2000

Manufacturing process generally exhibits major or minor variations due to deviation of raw materials, equipment degradation, controller malfunction, and so on. Extensive research based on multivariate statistical process control has been done to monitor the unstable states and indicate a corrective action. A prototype of intelligent operation support system ("ISYS-MSPC") has been developed as a tool that supports the enhanced operation to guarantee the high productivity and a uniform high quality product. This system has been applied to the industrial batch and continuous processes and its performance has been validated .

• 한국전산유체공학회:학술대회논문집
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• 1999.05a
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• pp.1-10
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• 1999

This paper briefly introduces the work done up to 1998 during the past twenty years for numerical modeling of ocean process focussing on the neighbouring seas of Korean Peninsula. Modeling of global ocean dynamics has also been performed as a pathway to understand the regional ocean dynamics. The ocean simulation produces a vast amount of multidimensional multivariate dataset therefore adoption of scientific and technical visualization techniques were essential to properly understand the physics involved.

• Communications for Statistical Applications and Methods
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• v.11 no.3
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• pp.465-474
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• 2004

Data visualization is the automation process and the discovery process to data sets in an effort to discover underlying information from the data. It provides rich visual depictions of the data. It has distinct advantages over traditional data analysis techniques such as exploring the structure of large scale data set both in the sense of number of observations and the number of variables by allowing great interaction with the data and end-user. We discuss the principles of data visualization and evaluate the characteristics of various tools of visualization according to these principles.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.2
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• pp.187-194
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• 2014

In this paper, we analyze the outage probability of macro diversity combining in cellular networks in consideration of aggregate interference from other mobile stations (MSs). Different from existing works analyzing the outage probability of macro diversity combining, we focus on a diversity gain attained by selecting a base station (BS) subject to relatively low aggregate interference. In our model, MSs are randomly located according to a Poisson point process. The outage probability is analyzed by approximating the multivariate distribution of aggregate interferences on multiple BSs by a multivariate lognormal distribution.