• 제어로봇시스템학회논문지
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• 제9권10호
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• pp.832-835
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• 2003

The objective of this study is to investigate the possibility of chemical process synthesis purely based on mathematical programming when given an objective, feed conditions, product specifications, and model equations for available process units. A method based on a state space approach is proposed, and applied to an example problem with a reactor, a heat exchanger, and a separator. The results indicate that a computer can automatically synthesize an optimal process without any heuristics or expertise in process design provided that global optimization techniques are improved to be suitable for large problems.

• Journal of Information Processing Systems
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• 제17권2호
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• pp.242-252
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• 2021

Following the intuition that the local information in time instances is hardly incorporated into the posterior sequence in long short-term memory (LSTM), this paper proposes an attention augmented mechanism for fault diagnosis of the complex chemical process data. Unlike conventional fault diagnosis and classification methods, an attention mechanism layer architecture is introduced to detect and focus on local temporal information. The augmented deep network results preserve each local instance's importance and contribution and allow the interpretable feature representation and classification simultaneously. The comprehensive comparative analyses demonstrate that the developed model has a high-quality fault classification rate of 95.49%, on average. The results are comparable to those obtained using various other techniques for the Tennessee Eastman benchmark process.

• 한국산업경영시스템학회:학술대회논문집
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• 한국산업경영시스템학회 2002년도 춘계학술대회
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• pp.359-364
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• 2002

In case of pn control chart often used in mass production system of plant industry and so on, we could evaluate it's performance by the approximation to normal distribution. It has many differences according to sample sizes and defective fraction, and have disadvantage that needs much samples to use the normal distribution approximation. Existent control charts can not detect the cause of process something wrong because it is taking the sampling intervals of fixed length about all times from the process. Therefore, to overcome this shortcoming we use VSI(variable sampling intervals) techniques in this paper. This technique takes a long sampling interval to have the next sampling point if the sample point is in stable state, and if the sample point is near control lines, it takes short sampling interval because the probability to escape control limit is high. To analyze performance of pn control charts that have existent fixed sampling intervals(FSI) and that use VSI technique, we compare ATS of two charts, and analyze the performance of each control chart by the sample sizes, process fraction defective and control limits that Ryan and Schwertman had proposed.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 추계 학술대회 학술발표 논문집
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• pp.95-98
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• 2003

In this paper, an adaptive fuzzy neural control of unknown nonlinear systems based on the rapid learning algorithm is proposed for optimal parameterization. We combine the advantages of fuzzy control and neural network techniques to develop an adaptive fuzzy control system for updating nonlinear parameters of controller. The Fuzzy Neural Network(FNN), which is constructed by an equivalent four-layer connectionist network, is able to learn to control a process by updating the membership functions. The free parameters of the AFN controller are adjusted on-line according to the control law and adaptive law for the purpose of controlling the plant track a given trajectory and it's initial values are off-line preprocessing, In order to improve the convergence of the learning process, we propose a rapid learning algorithm which combines the error back-propagation algorithm with Aitken's $\delta$$\^$2/ algorithm. The heart of this approach ls to reduce the computational burden during the FNN learning process and to improve convergence speed. The simulation results for nonlinear plant demonstrate the control effectiveness of the proposed system for optimal parameterization.

• Journal of Advanced Marine Engineering and Technology
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• 제28권8호
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• pp.1313-1323
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• 2004

In accordance with adoption of new convention for the control of ship's ballast water at the diplomatic conference held in London Feb, 2004, every country has to regulate the ballast water and deposit matters. When this Resolution comes into effect in 2009, all vessels engaged in international voyage must have ballast water control program, ballast water records, equipments which are suitable to the standard of exchange and performance for the ballast water. This study estimates objectively their performances, merits and demerits of the ballast water treatment technique and exchanging techniques for safe operation of ships. It is desirable to design an equipment to control the ballast water using the brush-type vacuum suction nonstop reverse cleaning system to overcome the clogging phenomenon and the direct disc filtering to maximize filtering area for the optimum process considering biological availabilities. It will be expected to protect against marine pollution and to maintain clean sea if it is secured to develop new ballast water treatment techniques. And it will also be expected to cope with the Resolution and each regulation of the developed countries from the ballast water.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1081-1081
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• 2001

The constant need for quality improvement and production rationalization in the chemical and related industries has led to the increasing replacement of conservative control procedures by more specific and environmentally compatible analytical techniques. In this respect, vibrational spectroscopy has developed over the last yews - in combination with new instrumental accessories and statistical evaluation procedures - to one of the most important analytical tools for industrial chemical quality control and process monitoring in a wide field of applications. In the present communication this potential is demonstrated in order to further support the implementation of mid-infrared (MIR), near-infrared (NIR) and Raman spectroscopy Primarily as industrial on-line tools. To this end the data of selected feasibility studies will be discussed in terms of the individual strengths of the different techniques for the respective application.

• 한국정밀공학회지
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• 제15권8호
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• pp.145-155
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• 1998

An $H{\infty}$ controller with a roll eccentricity filter is proposed to alleviate the effect of entry thickness variation and roll eccentricity occured in rolling stands themselves of tandem cold mills. A robust controller to the disturbances is designed by H$_{\infty}$ control techniques, which can reflect the input direction of disturbances and the knowledge of disturbance spectrum in the frequency domain. First, fundamental problems in tandem cold mills such as process transport delay inherent in the exit thickness measurement and the feedforward loading of roll eccentricity signals on the exit thickness be overcome by the roll eccentricity filtering and the compensation for the error of gaugemeter thickness estimator. And non-satndard $H{\infty}$ control problem caused by the selection of weighting function having poles on the $J{\omega}$-axis is discussed. The resultant controller composed by an $H{\infty}$ controller and an estimator for the roll eccentricity is evaluated through computer simulations. The effectiveness of the proposed control method is compared to that of the conventional LQ controller method and a feedforward controller for the roll eccentricity, which has been already studied.

• 영어어문교육
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• 제13권1호
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• pp.181-210
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• 2007

This study is to suggest systematic and effective reading comprehension techniques or strategies to be used in EFL reading classes. According to the definition of reading and reading process, six essential elements of reading comprehension are categorized: 1) reading speed; 2) skimming and scanning; 3) logical organization; 4) pleasure reading; 5) vocabulary; 6) cultural background and world knowledge. To present a more effective teaching and learning approach to EFL reading comprehension than ever, an experiment was performed. The hypothesis of the experimental study was that there would be a difference in students' reading speed as well as reading comprehension and vocabulary between an experimental group and a control group depending upon the teaching approaches (experimental vs. traditional). The result of the study indicates that the experimental teaching approach which intensifies speed reading and pleasure reading techniques as well as 4 other essential techniques of reading comprehension is more effective than the traditional one in teaching and learning reading comprehension.

• 반도체디스플레이기술학회지
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• 제22권4호
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• pp.53-58
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• 2023

The storage capacity of 3D-NAND flash memory has been enhanced by the multi-layer dielectrics. The deposition process has become more challenging due to the tight process margin and the demand for accurate process control. To reduce product costs and ensure successful processes, process diagnosis techniques incorporating artificial intelligence (AI) have been adopted in semiconductor manufacturing. Recently there is a growing interest in process diagnosis, and numerous studies have been conducted in this field. For higher model accuracy, various process and sensor data are required, such as optical emission spectroscopy (OES), quadrupole mass spectrometer (QMS), and equipment control state. Among them, OES is usually used for plasma diagnostic. However, OES data can be distorted by viewport contamination, leading to misunderstandings in plasma diagnosis. This issue is particularly emphasized in multi-dielectric deposition processes, such as oxide and nitride (ON) stack. Thus, it is crucial to understand the potential misunderstandings related to OES data distortion due to viewport contamination. This paper explores the potential for misunderstanding OES data due to data distortion in the ON stack process. It suggests the possibility of excessively evaluating process drift through comparisons with a QMS. This understanding can be utilized to develop diagnostic models and identify the effects of viewport contamination in ON stack processes.

• 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.