• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.229-233
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• 1990

A new on-line optimization technique of a distillation column is presented. The equation decoupling method and the simultaneous solution method are combined to simulate the distillation process. The storage requirement is small enough to run on a PC. A improved successive quadratic programming is used to find the optimum operating conditions. The optimizer is intensively tested by using the dynamic simulator, SPEEDUP. After this, the technique is applied to a binary distillation column that treats methanol and water.

• 한국정보전자통신기술학회논문지
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• 제4권1호
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• pp.61-67
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• 2011

YAG:Ce 형광제 제조에 있어서 공침(co-precipitation) 후 n-butanol 공비증류(azeotropic distillation)시 HMDS(hexadimethyldisilazane)를 첨가하였을 때, 형광체가 광학특성에 미치는 영향에 관하여 연구하였다. 물과 유기용제에 의한 공비증류 시 유기용제의 분자량이 크면 표면의 수소결합이 유기용제로 치환됨으로서 표면장력을 감소시켜 분체의 원형화(conglobation)와 응집(agglomerate)을 감소시키므로 유기용제로 n-butanol보다 분자량이 큰 HMDS을 첨가하였다. N-butanol 만을 사용한 형광체가 HMDS를 첨가한 것 보다 응집(agglomerate)되는 현상이 감소하였으며 우수한 광학적 특성을 나타내었다.

• 방사성폐기물학회지
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• 제9권3호
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• pp.141-148
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• 2011

방사성폐기물이 처분장에 인도 될 때 주요 방사성 핵종의 방사능 농도를 규명 하도록 방사성폐기물 인도 규정에 명시되어 있다. 저준위 방사성폐기물 시료의 경우 측정된 방사능 농도가 최소검출 방사능 농도(MDA: Minimum dectable activity) 이하의 값을 나타낼 경우가 많으며, MDA는 시료의 양, 바탕 값, 계측시간 및 계측효율 등에 따라서 달라지므로 MDA를 낮추기 위하여 가능한 많은 양의 시료를 취할 필요가 있다. 모의 잡고체 시료에 첨가된 요오드의 회수율을 결정하기 위한 방법으로서 모의 시료 중 비 방사성 요오드를 비휘발성 산으로 침출시킨 후 침출액을 직접 증발시키는 방법과 음이온 교환수지를 이용하I-를 흡착하여 분리하는 칼럼용리방법으로 측정하여 회수율을 비교한 결과, 증류법과 칼럼용리방법의 회수율은 각각 $86.5{\pm}0.9%$, $87.3{\pm}2.7%$로 나타났다. 증류법에 의한 모의 방사성 시료 중 $^{129}I$ 요오드의 회수율 및 MDA는 $84.6{\pm}1.6%$, $1.2{\times}10^{-4}Bq/g$로 각각 나타났으며, 분리공정을 단순화하고 많은 양의 시료를 취함으로써, 칼럼용리 방법의 단점을 보완하고 10배 이상 MDA를 낮출 수 있는 결과를 얻을 수 있었다.

• Nuclear Engineering and Technology
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• 제50권7호
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• pp.1184-1189
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• 2018

Pyroprocessing uses various molten salts during electrochemical unit processes. Reaction products after the electrochemical processes must contain a significant amount of residual salts to be separated. Vacuum distillation is a common method to separate the residual salts; however, its high operation temperature may cause side reactions. In this study, a simple rotation technique using centrifugal force was suggested to separate the residual salts from the reaction products at relatively low temperature compared to the distillation technique. When a reaction product container with porous wall rotates inside a vessel heated above the melting point of the residual salt, the residual salt in the liquid phase is separated through centrifugal force. It was shown that the $LiNO_3-Al_2O_3$ mixture can be separated by this technique to leave solid $Al_2O_3$ inside the container, with a separation efficiency of 99.4%.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.1766-1769
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• 1997

Fast and accurate distillation design requires a model that significantly reduces the problem size withour loss of accruacy is especially suitable for rela-time applicatoins. the reduced order model is obtained by use of Principal Component Anlysis(PCA). Then the extended Kalman filter and the Recursie Predictiuon Error(RPE) mehtod are appliced to identify the model parameters and the feed compostion form the measuremenets of the coumn. as a consequence it is found that the model reduction thechique can account for the dynamics of the rigorous distillation model and not only the model parameters, bu also the feed compostion can be identified efficiently. this technique is applied to industrial operation data verify the performance of reduced order model.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1986년도 한국자동제어학술회의논문집; 한국과학기술대학, 충남; 17-18 Oct. 1986
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• pp.238-243
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• 1986

Distillation columns are widely used in almost every chemical plant. The use of multivariable control for such units is attractive because of the strong interactions exhibited between outputs and inputs and the desire to control simultaneously both top and bottom products. In this research design of a robust multivariable controller for distillation column was considered; output feedback controller with proportional and integral modes was designed using pole assignment. The transfer function matrix was obtained by fitting the step response realtions between single input double output pairs of variables. This matrix was then converted to linear time invariant state space model by multivariable realization technique. With the proposed multivariable proportional and integral controller applied to the process, the result of the digital computer simulation showed a good performance of asymtotic tracking. The limited experimental performance of this multivariable control was compared with the result from simulation. It was found that the proposed controller performed satisfactorily for the distillation column which separated binary mixture of methanol and water.

• 문화기술의 융합
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• 제8권6호
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• pp.631-636
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• 2022

막걸리라고도 불리우는 전통 병행복발효주 탁주의 중요한 규격인 알코올분은 풍미와 연관이 깊은 요인이다. 알코올분 분석은 증류과정과 주정계 측정으로 구성된 부칭법이 주로 사용되고 있다. 본 연구에서는 GC법을 개선하여 신뢰성을 검증한 후 시중 유통 탁주 14종의 알코올분을 부칭법과 개선된 GC법으로 분석하였다. GC법의 정밀성 및정확성은 만족스러운 수준이었으며, 정량한계 및 정성한계는 0.5도 및 0.1도로 확인되었다. 표시도수 ±1을 초과한 비살균 탁주 3종 중 표시도수 6.0도를 초과하는 9.9도 (GC법) 및 10.1도 (부칭법)로 측정된 1종을 제외한 나머지는 -1.1도를 초과하였으나, 분석법의 에러범위에 해당되는 수준이었다. 14종 탁주의 알코올분 평균 정밀도 (%RSD)는 부칭법 36.2%에 비하여 GC법이 12.8%로 우수하였다. 본 연구에서 개선된 GC법은 넓은 범위의 탁주 중 알코올분 규격 관리 및 개선에 효율적 사용이 가능할 것으로 평가되었다.

• 한국멀티미디어학회논문지
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• 제24권12호
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• pp.1598-1605
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• 2021

Recently, convolutional neural networks (CNNs) have been widely used with excellent performance in various computer vision fields, including super-resolution (SR). However, CNN is computationally intensive and requires a lot of memory, making it difficult to apply to limited hardware resources such as mobile or Internet of Things devices. To solve these limitations, network lightening studies have been actively conducted to reduce the depth or size of pre-trained deep CNN models while maintaining their performance as much as possible. This paper aims to lighten the SR CNN model, SRGAN, using the knowledge distillation among network lightening technologies; thus, it proposes four techniques with different methods of transferring the knowledge of the teacher network to the student network and presents experiments to compare and analyze the performance of each technique. In our experimental results, it was confirmed through quantitative and qualitative evaluation indicators that student networks with knowledge transfer performed better than those without knowledge transfer, and among the four knowledge transfer techniques, the technique of conducting adversarial learning after transferring knowledge from the teacher generator to the student generator showed the best performance.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.234-239
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• 1990

A neural controller for process control was proposed that combines a simple feedback controller with a neural network. This control was applied to distillation control. The feedback error learning technique was used for on-line learning. Important characteristics on neural controller were analyzed. The proposed neural controller can cope well with strong interactions, significant time delays, sudden changes in process dynamics without any prior knowledge of the process. It was shown that the neural controller has good features such as fault tolerance, interpolation effect and random learning capability

• 대한임베디드공학회논문지
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• 제17권4호
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• pp.209-215
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• 2022

This paper proposes a novel approach for training an unsupervised depth estimation algorithm. The objective of unsupervised depth estimation is to estimate pixel-wise distances from camera without external supervision. While most previous works focus on model architectures, loss functions, and masking methods for considering dynamic objects, this paper focuses on the training framework to effectively use depth cue. The main loss function of unsupervised depth estimation algorithms is known as the photometric error. In this paper, we claim that direct depth cue is more effective than the photometric error. To obtain the direct depth cue, we adopt the technique of knowledge distillation which is a teacher-student learning framework. We train a teacher network based on a previous unsupervised method, and its depth predictions are utilized as pseudo labels. The pseudo labels are employed to train a student network. In experiments, our proposed algorithm shows a comparable performance with the state-of-the-art algorithm, and we demonstrate that our teacher-student framework is effective in the problem of unsupervised depth estimation.