• Journal of Pharmaceutical Investigation
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• v.39 no.4
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• pp.263-267
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• 2009

The purpose of this study is to evaluate the efficacy of stipulated cleaning process, and the prohibition of cross-contamination and microbiological contamination, which inadequate cleaning in multi-production could occur, through cleaning validation of multi-purpose facility used to produce five biopharmaceutical products as sterile injection. After production of five biopharmaceutical products such as hGH, rhGCSF, rhEPO, rhFSH and rhIFN using vial filling machine, the cleaning validation such as residual analysis of active ingredients or human serum albumin, measurement of total organic carbon (TOC), residual analysis of detergent and microbiological contamination were carried out. In the case of rhGH and rhGCSF clean validations, drug residues were not detected. Furthermore, in the case of rhEPO, rhFSH and rhIFN clean validations, human serum albumin residues were not detected. At TOC (total organic carbon) analysis, all clean validations gave the TOC of about average 137.93%, not more than 150% of acceptance criteria. At sodium analysis for the checking of residues of cleaning agent, sodium residues were not detected. In sterility test, they showed no microbiological contamination of bacteria and fungi. Thus, this cleaning validation was determined as successful in protection of cross-contamination and induction of safety in multi-purpose facility.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.628-637
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• 2008

The JAXA's TechCLEAN project(2003-present) is summarized, with the interim technical achievements. TechCLEAN is collateral program with the NEDO engine project to accelerate R&D work of small passenger aircraft engine as well as to develop innovative environment technologies applicable to the future improvements. In the project NOx reduction, CO2 reduction and noise reduction are targeted. Component level researches and system demonstrator validation are planned with test facility renovation and demonstration base engines.

• Journal of Korea Multimedia Society
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• v.23 no.11
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• pp.1349-1360
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• 2020

In recent years, Convolutional Neural Networks (CNNs) have been successfully implemented in different tasks of computer vision. Since CNN models are the representatives of supervised learning algorithms, they demand large amount of data in order to train the classifiers. Thus, obtaining data with correct labels is imperative to attain the state-of-the-art performance of the CNN models. However, labelling datasets is quite tedious and expensive process, therefore real-life datasets often exhibit incorrect labels. Although the issue of poorly labelled datasets has been studied before, we have noticed that the methods are very complex and hard to reproduce. Therefore, in this research work, we propose Deep CleanNet - a considerably simple system that achieves competitive results when compared to the existing methods. We use K-means clustering algorithm for selecting data with correct labels and train the new dataset using a deep CNN model. The technique achieves competitive results in both training and validation stages. We conducted experiments using MNIST database of handwritten digits with 50% corrupted labels and achieved up to 10 and 20% increase in training and validation sets accuracy scores, respectively.

• Proceedings of the KIEE Conference
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• 2006.07a
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• pp.420-421
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• 2006

The cost-effective reduction of greenhouse gas(GHG) emission to avert the most severe impacts of climate change remains one of the widely accepted priorities for global action. In order to facilitate cost-effective abatement strategies, the Kyoto Protocol introduced three mechanisms, or flexible instruments, the Emissions Trading(ET), the Joint Implementation(JI) and the Clean Development Mechanism(CDM). The CDM enables Annex I countries to the Kyoto Protocol to partially meet cost-effectively their emission reduction commitments by undertaking GHG mitigation Projects in developing countries, which do not have any GHG abatement obligations and where the emission reductions are cheaper. One of the major barriers hampering the wide spread implementation of CDM is the high transaction costs associated with the initial identification of promising CDM projects. This paper presents development of a pre-validation program of CDM. The developed program may provide a useful aid to potential investors and project developers as a supportive pre-evaluation tool, and may become an effective tool for the promotion of renewable energy and fuel switching projects.

• Transactions of the Korean hydrogen and new energy society
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• v.31 no.5
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• pp.436-443
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• 2020

The performance prediction model of a solid oxide fuel cell stack has been developed using deep neural network technique, one of the machine learning methods. The machine learning has been received much interest in various fields, including energy system mo- deling. Using machine learning technique can save time and cost requried in developing an energy system model being compared to the conventional method, that is a combination of a mathematical modeling and an experimental validation. Results reveal that the mean average percent error, root mean square error, and coefficient of determination (R2) range 1.7515, 0.1342, 0.8597, repectively, in maximum. To improve the predictability of the model, the pre-processing is effective and interpolative machine learning and application is more accurate than the extrapolative cases.

• Journal of Distribution Science
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• v.18 no.1
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• pp.15-26
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• 2020

Purpose: This study is to analyze the image classification using Convolution Neural Network and Transfer Learning for Jeju Island and to suggest related implications. As the biggest tourist destination in Korea, Jeju Island encounters environmental issues frequently caused by marine debris along the seaside. The ever-increasing volume of plastic waste requires multidirectional management and protection. Research design, data and methodology: In this study, the deep learning CNN algorithm was used to train a number of images from Jeju clean and polluted beaches. In the process of validating and testing pre-processed images, we attempted to explore their applicability to coastal tourism applications through probabilities of classifying images and predicting clean shores. Results: We transformed and augmented 194 small image dataset into 3,880 image data. The results of the pre-trained test set were 85%, 70% and 86%, and then its accuracy has increased through the process. We finally obtained a rapid convergence of 97.73% and 100% (20/20) in the actual training and validation sets. Conclusions: The tested algorithms are expected to implement in applications for tourism service distribution aimed at reducing coastal waste or in CCTVs as a detector or indicator for residents and tourists to protect clean beaches on Jeju Island.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.25 no.1
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• pp.7-13
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• 2013

Bio Clean Room(BCR) and pharmaceutical product manufacturing facilities require careful assessment of many factors, including HVAC, controls, room finishes, process equipment, room operations, and utilities. Flow of equipment, personnel, and product must also be considered along with system flexibility, redundancy, and maintenance shutdown strategies. It is important to involve designers, operators, commissioning staff, quality control, maintenance, constructors, validation personnel, and the production representative during the conceptual stage of design. Critical variables for room environment and types of controls vary greatly with the clean space's intended purpose. It is particularly important to determine critical parameters with quality assurance to set limits and safety factors for temperature, humidity, room pressure, and other control requirements. In this paper, oxygen cluster ion equipment was utilized in order to enhance the indoor air quality and to prevent the airborne infection of ward in hospital. Moreover, the performance test of the equipment was also performed in order to develop the optimal sterilization system of BCR using the equipment.

• Journal of Mechanical Science and Technology
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• v.17 no.1
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• pp.129-138
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• 2003

A 1 : 20 laboratory scale test rig of a 200 MW tangentially fired boiler is built up with completely simulated structures such as platen heaters and burners. Iso-thermal turbulent flow in the boiler is mapped by 3-D PDA (Particle Dynamic Analyzer). The 3-D numerical models for the same case are proposed based on the solution of к-$\varepsilon$ model closed RANS (Reynolds time-Averaged Navier-Stokes) equations, which are written in the framework of general coordinates and discretized in the corresponding body-fitted meshes. Not only are the grid lines arranged to fit the inner/outer boundaries. but also to align with the streamlines to the best possibility in order to reduce the NDE (numerical diffusion errors). Extensive comparisons of profiles of mean velocities are carried out between experiment and calculation. Predicted velocities in burner region were quantitatively similar with measured ones, while those in other area have same tendency with experimental counterpart.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.10
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• pp.669-676
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• 2022

In this paper, the carriage load analyses of stores installed on aircraft are conducted to validate the chimera grid method applied to an unstructured CFD solver, UMSAPv. First, the chimera grid method of UMSAPv is verified for the well-known Eglin Wing/Pylon/Finned store problem. Next, an angle of attack sweep of F/A-18C clean configuration is conducted at a subsonic speed. The computed results are compared with the results of the previous study using MSAPv, a structured CFD solver, to show the validity of Umsapv. Finally, the carriage of F/A-18C JDAM is carried out with a chimera grid as well as a single block grid. The computed results are compared with other computational, experimental and the flight tests.

• Asian Journal of Atmospheric Environment
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• v.11 no.4
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• pp.283-299
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• 2017

An analytical method using high-performance liquid chromatography (HPLC) with fluorescence (FL) detection was developed for simultaneously analyzing 10 polycyclic aromatic hydrocarbons (PAHs) and 18 nitro-derivatives of PAHs (NPAHs). The two-dimensional HPLC system consists of an on-line clean-up and reduction for NPAHs in the 1st dimension, and separation of the PAHs and the reduced NPAHs and their FL detection in the 2nd dimension after column-switching. To identify an ideal clean-up column for removing sample matrix that may interfere with detection of the analytes, the characteristics of 8 reversed-phase columns were evaluated. The nitrophenylethyl (NPE)-bonded silica column was selected because of its shorter elution band and larger retention factors of the analytes due to strong dipole-dipole interactions. The amino-substituted PAHs (reduced NPAHs), PAHs and deuterated internal standards were separated on polymeric octadecyl-bonded silica (ODS) columns and by dual-channel detection within 120 min including clean-up and reduction steps. The limits of detection were 0.1-9.2 pg per injection for PAHs and 0.1-140 pg per injection for NPAHs. For validation, the method was applied to analyze crude extracts of fine particulate matter ($PM_{2.5}$) samples and achieved good analytical precision and accuracy. Moreover, the standard reference material (SRM1649b, urban dust) was analyzed by this method and the observed concentrations of PAHs and NPAHs were similar to those in previous reports. Thus, the method developed here-in has the potential to become a standard HPLC-based method, especially for NPAHs.