• Korean Journal of Clinical Pharmacy
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• v.9 no.1
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• pp.49-54
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• 1999

This study was carried out to compare the bioavailability of Hanmi clarithromycin (250 mg/tablet) with that of $Klaricid^{(R)}$ The bioavailability was examined on 20 volunteers who received a single dose (500 mg) of each drug in the fasting state in a randomized balanced 2-way crossover design. After dosing, blood samples were collected for a period of 12 hours. Plasma samples were analyzed for clarithromycin and roxithromycin(internal standard) by HPLC/Coulometric BCD. The pharmaco-kinetic parameters ($AUC_{0-l2hr}$, Cmax, Tmax, $AUC_{inf}$, Ka, Kel, $t_{1/2}$, Vd/F and Cl/F) were calculated from the plasma clarithromycin concentration-time data of each volunteer. The computer program 'WinNonlin' was used for compartmental analysis. One compartment model with first-order input, from order output with lag time, weighting factor $l/y^2$ was chosen as the appropriate pharmacokinetic model. The major pharmacokinetic parameters ($AUC_{0-l2hr},\;AUC_{inf}$, Cmax and Tmax) of Hanmi clarithromycin were $10.7\pm0.5\;{\mu}g{\cdot}hr{\cdot}ml^{-1},\;12.7\pm0.7\;{\mu}g{\cdot}hr{\cdot}ml^{-1},\;1.7\pm0.1\;{\mu}g/ml\;and\;2.0\pm0.2\;hr$, respectively, and those of $Klaricid^{(R)}\;were\;9.8\pm0.5\;{\mu}g{\cdot}hr{\cdot}ml^{-1},\;11.7\pm0.6\;{\mu}g{\cdot}hr{\cdot}ml^{-1},\;1.6\pm0.1\;{\mu}g/ml\;and\;2.1\pm0.1\;hr$, respectively. The differences in mean values of $AUC_{0-l2hr},\;AUC_{inf}$ and Cmax between two products were $9.88\%,\;8.94%\;and\;6.59\%$, respectively. The least significant differences at $\alpha=0.05$ for $AUC_{0-l2hr},\;AUC_{inf}$ and Cmax were $16.08\%,\;17.81\%\;and\;18.94\%$, respectively. Though the plasma clarithromycin concentrations of Hanmi clarithromycin were higher than those of $Klaricid^{(R)}$ at all observed times, the bioavailability of Hanmi clarithromycin appeared to be bioequivalent with that of $Klaricid^{(R)}$. The Ka, Kel, $t_{1/2}$, Vd/F and Cl/F of the Hanmi clarithromycin were $2.69\pm0.53\;hr^{-1},\;0.18\pm0.01 hr^{-1},\;3.9\;hr,\;248.8\pm11.4\;L\;and\;43.7\pm2.6\;L/hr$, respectively, and those of $Klaricid^{(R)} were 2.19\pm0.51\;hr^{-1},\;0.18\pm0.02\;hr^{-1},\;3.7\;hr,\;266.7\pm22.4\;L\;and\;45.3\pm2.8L/hr$, respectively. There were no statistically significant differences between two drugs in all pharmacokinetic parameters.

• Journal of Korean Society of Transportation
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• v.23 no.2
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• pp.161-172
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• 2005

This study is to develop Bus Transit Route Analysis and Evaluation Model that can product the quantitative performance measures for Bus Transit Route Network Design. So far, in Korea, there are no so many models that evaluate a variety of other performance measures or service quality that are of concern to both the transit users and operator because of lower-level bus database system and transit route network analysis algorithm's limit. The BTRAEM in this research differ from the previous approach in that the BTRAEM employs a multiple path transit trip assignment model that explicitly considers the transfer and different travel time after boarding. And we develop input-output data structure and quantitative performance measure for the BTRAEM. In the numerical experimental applying BTRAEM to Mandl transit network, We got the meaningful results on performance measure of bus transit route network. In the future, we expect BTRAEM to give a good solution in real transit network.

• Korean Journal of Remote Sensing
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• v.17 no.3
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• pp.231-242
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• 2001

In 2-pass differential SAR interferometry(DInSAR), the topographic phase signature can be removed by using a digital elevation model(DEM) to isolate the contribution of deformation from interferometric phase. This method has an advantage of no unwrapping process, but applicability is limited by precision of the DEM used. The residual phase in 2-pass differential interferogram accounts for error of DEM used in the processing provided that no actual deformation exits. The objective of this paper is a preliminary study to improve DEM precision using low precision DEM and 2-pass DInSAR technique, and we applied the 2-pass DInSAR technique to Asan area. ERS-1/2 tandem complex images and DTED level 0 DEM were used for DInSAR, and the precision of resulting DEM was estimated by a 1:25,000 digital map. The input DEM can be improved by simply adding the DInSAR output to the original low precision DEM. The absolute altitude error of the improved DEM is 9.7m, which is about the half to that of the original DTED level 0 data. And absolute altitude error of the improved DEM is better than that from InSAR technique, 15.8m. This approach has an advantage over the InSAR technique in efficiently reducing layover effects over steep slope region. This study demonstrates that 2-pass DInSAR can also be used to improve DEM precision.

• Journal of Korea Port Economic Association
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• v.25 no.3
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• pp.229-250
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• 2009

This paper aims to implement an empirical research about the efficiency of America and Northeast Asia pots, and to suggest an effective strategy which can operate these ports more well. This study tries to apply the Data Envelopment Analysis(DEA) model to America and Northeast Asia ports. DEA is a methodology of comparing the relative efficiency of each decision making unit(DMU) by comparing it with other DMUs having similar input and output structure, and is specially very useful when a form of production function of each DMU such as a port is not known. DEA provides the extent of inefficiency of DMUs, which is practically useful information (like the efficiency score and reference sets) required to improve efficiency. This paper analyzed the relative efficiency of 35 ports in America and Northeast Asia for 3 years from 2005 to 2007 through DEA-CCR, DEA-BCC model and scale efficiency. Accordingly, this paper evaluates the efficiency of America and Northeast Asia ports, grasps the position at the present time, and suggests an advanced direction in future.

• Journal of Korea Port Economic Association
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• v.37 no.1
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• pp.19-30
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• 2021

The 21st Century Maritime Silk Road (MSR) is an important part of Belt and Road Initiative(BRI). As an economic and trade corridor for dozens of countries in Asia, Europe and Africa, and the port as an important link node, the efficiency of port operation directly affects the implementation of BRI's strategy. On the basis of combining BRI and related evaluation methods of port efficiency, this paper uses DEA-BCC model to select port production berth number and production berth length as input index container throughput and cargo throughput as output index to analyze the port efficiency of 14 ports in China. The results show that: (1) The overall efficiency level of the ports along the MSR is relatively low. Most of the ports have not reached the DEA efficiency and there are different degrees of problems in scale investment and technological improvement. However, this situation is accompanied by the implementation of China's maritime cooperation strategy and becoming better year by year. (2) The low operating efficiency of ports along China's MSR is mainly due to the lack of coordination between scale efficiency and technical efficiency, which is caused by insufficient scale investment in the port itself, weak economic linkage between the hinterland and the port, (3) Whether a port has a strong comprehensive strength does not entirely depend on the cargo throughput or scale but also includes the port's operating efficiency.

• Clean Technology
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• v.24 no.4
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• pp.269-274
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• 2018

In this study, Life Cycle Assessment (LCA) of wet tissue manufacturing process was performed. The wet tissue manufacturing process consists of preparation of wetting agent (chemical liquid), impregnation of nonwoven fabric into wetting agent and primary and secondary packaging. Data and information were collected on the input and output of the actual process from a certain company and the database of the Korea Ministry of Environment and some foreign countries (when Korean unavailable) were employed to connect the upper and the lower process flow. Based on the above and the potential environmental impacts of the wet tissue manufacturing process were calculated. As a result of the characterization, Ozone Layer Depletion (OD) is 3.46.E-06 kg $CFC_{11}$, Acidification (AD) is 5.11.E-01 kg $SO_2$, Abiotic Resource Depletion (ARD) is $3.52.E+00\;1yr^{-1}$, Global Warming (GW) is 1.04.E+02 kg $CO_2$, Eutrophication (EUT) is 2.31.E-02 kg ${PO_4}^{3-}$, Photochemical Oxide Creation (POC) was 2.22.E-02 kg $C_2H_4$, Human Toxicity (HT) was 1.55.E+00 kg 1,4 DCB and Terrestrial Ecotoxicity (ET) was 5.82.E-04 kg 1,4 DCB. In order to reduce the environmental impact of the manufacturing process, it is necessary to improve the overall process as other general cases and change the raw materials including packaging materials with less environmental impact. Conclusively, the energy consumed in the manufacturing process has emerged as a major issue, and this needs to be reconsidered other options such as alternative energy. Therefore, it is recommended that a process system should be redesigned to improve energy efficiency and to change to an energy source with lower environmental impact. Due to the nature of LCA, the final results of this study can be varied to some extent depending on the type of LCI DB employed and may not represent of all wet tissue manufacturing processes in the current industry.

• Journal of Climate Change Research
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• v.5 no.1
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• pp.21-36
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• 2014

Forest ecosystem provides variety goods and services for human being. Unlike goods, forest ecosystem services could not be easily priced by market mechanism. This uncertainty has been caused to conflict in decision-making related forest ecosystem services. Quantification of forest ecosystem services is required to understand the importance of ecosystem services and their contribution to decision-making. As a growing concern of climate change, it is necessary to quantify and calculate carbon storage and sequestration in forest. In this study, for quantifying carbon storage and sequestration, we compared scale, output, input data availability of the models and analyzed the applicability of the models to Korea. The results of this study show that most models are applicable for quantifying carbon storage and sequestration. However, relatively few models are applicable for other regulating services (air quality regulation, flood mitigation, erosion control, water quality, etc.) of forest. This study would be helpful for quantifying regulating services of forest ecosystem research.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.161-170
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• 2019

Multi-product mass production small and medium enterprise factories have a wide variety of products and a large number of products, wasting manpower and expenses for inventory management. In addition, there is no way to check the status of inventory in real time, and it is suffering economic damage due to excess inventory and shortage of stock. There are many ways to build a real-time data collection environment, but most of them are difficult to afford for small and medium-sized companies. Therefore, smart factories of small and medium enterprises are faced with difficult reality and it is hard to find appropriate countermeasures. In this paper, we implemented the contents of extension of existing inventory management method through character extraction on label with barcode and QR code, which are widely adopted as current product management technology, and evaluated the effect. Technically, through preprocessing using OpenCV for automatic recognition and classification of stock labels and barcodes, which is a method for managing input and output of existing products through computer image processing, and OCR (Optical Character Recognition) function of Google vision API. And it is designed to recognize the barcode through Zbar. We propose a method to manage inventory by real-time image recognition through Raspberry Pi without using expensive equipment.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.8
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• pp.783-790
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• 2019

Cognitive science attempts to explain human intelligence on the basis of success of artificial neural network, which is called connectionism. The neural network, e.g., deep learning, seemingly promises connectionism to go beyond what it is. But those(Fodor & Pylyshyn, Fodor, & McLaughlin) who advocate classical computationalism, or symbolism claim that connectionism must fail since it cannot represent the relation between human thoughts and human language. The neural network lacks systematicity, so any output of neural network is at best association or accidental combination of data plugged in input units. In this paper, I first introduce structure of artificial neural network and what connectionism amounts to. Second, I shed light on the problem of systematicity the classical computationalists pose for the connectionists. Third, I briefly introduce how those who advocate connectionism respond to the criticism while noticing Smolensky's theory of vector product. Finally, I examine the debate of computationalism and connectionism on systematicity, and show how the problem of systematicity contributes to the development of connectionism and computationalism both.

• Journal of the Korean Society of Radiology
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• v.14 no.7
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• pp.991-1001
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• 2020

Supervised deep learning technologies for improving the image quality of computed tomography (CT) need a lot of training data. When input images have different characteristics with training images, the technologies cause structural distortion in output images. In this study, an imaging model based on the deep reinforcement learning (DRL) was developed for overcoming the drawbacks of the supervised deep learning technologies and reducing noise in CT images. The DRL model was consisted of shared, value and policy networks, and the networks included convolutional layers, rectified linear unit (ReLU), dilation factors and gate rotation unit (GRU) in order to extract noise features from CT images and improve the performance of the DRL model. Also, the quality of the CT images obtained by using the DRL model was compared to that obtained by using the supervised deep learning model. The results showed that the image accuracy for the DRL model was higher than that for the supervised deep learning model, and the image noise for the DRL model was smaller than that for the supervised deep learning model. Also, the DRL model reduced the noise of the CT images, which had different characteristics with training images. Therefore, the DRL model is able to reduce image noise as well as maintain the structural information of CT images.