• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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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.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.4 no.1
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• pp.61-67
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• 2011

Influence of hexamethyldisilazane(HMDS) in post processing technique by using n-butanol azeotropic distillation on the luminescence properties of YAG:Ce nanophosphor were studied. The organic solvent(n-butanol) azeotropic distillation which prevent powders from conglobation since lager molecules decrease the surface tension and more complete replace the residual water in the precipitate. HMDS that had larger molecules than that of n-butanol was added in azeotropic distillation. The phosphor synthesized from n-butanol azeotropic distillation exhibited lower agglomerate and better photoluminescence properties than that from HMDS added heterogeneous azeotropic distillation.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.9 no.3
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• pp.141-148
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• 2011

It is clarified in the radioactive waste transfer regulation that the concentration of radioactive waste for the major radio nuclide has to be examined when radioactive waste is guided to the radioactive waste stores. In case of the low level radioactive waste sample, the analytical results of radioactive waste concentration frequently show a value lower than minimum detectable activity (MDA). Since the MDA value basically depends on the amount of a sample, background value, measurement time, counting efficiency, and etc, it would be necessary to increase a sample amount with a intention of minimizing MDA. In order to measure a concentration of $^{129}I$ in low and medium level radioactive waste, $^{129}I$ was collected by using a distillation technique after leaching the simulated radioactive waste sample with a non-volatile acid. The recovery of $^{129}I$ measured was compared with that measured with column elution technique which is a conventional method using an anion-exchange resin. The recovery of inactive iodide by using the distillation method and column elution were found as $86.5{\pm}0.9%$ and $87.3{\pm}2.7%$, respectively. The recovery and MDA value calculated for distillation technique when 100 g of extracted solution of $^{129}I$ was taken, were found to be $84.6{\pm}1.6%$ and $1.2{\times}10^{-4}Bq/g$, respectively. Consequently, the proposed technique with simplified process lowered the MDA value more than 10 times compared to the column elution technique that has a disadvantage of limited sampling amount.

• Nuclear Engineering and Technology
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• v.50 no.7
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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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• 1997.10a
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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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• 1986.10a
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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.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.631-636
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• 2022

Alcohol content, which is an important standard for Takju, a traditional multiple parallel fermentation liquor called makgeolli, is a factor that can affect the flavor. For alcohol content analysis, the distillation/hydrometry technique is mainly used. In this study, we analyzed the alcohol content of 14 commercially available Takju by the distillation/hydrometry technique and the improved GC method, respectively, after verifying the reliability of improved GC method. The precision and accuracy of the GC method were satisfactory, and LOQ and LOD were evaluated as 0.5% and 0.1% of ethanol contents, respectively. Among the three Takju exceeding the labelled alcohol content ±1, one Takju was quantitated as alcohol content 9.9% (by GC method) and 10.1% (distillation/hydrometry technique) exceeding labelled 6.0%. It was within the analytical error range of alcohol content for other two Takju, where the alcohol contents were exceeded -1.1%. The average precision (%RSD) of 14 Takju analyzed by the distillation/hydrometry technique (36.2%) and the GC method (12.8%), confirming that the GC method was better than the other. The improved GC method was evaluated to be effective in managing and improving the alcohol content standard of Takju with the wide range of alcohol content.

• Journal of Korea Multimedia Society
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• v.24 no.12
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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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• 1990.10a
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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

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.4
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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.