• Maritime Security
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• v.6 no.1
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• pp.83-107
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• 2023

To Republic of Korea in the Indo-Pacific region, the stability and prosperity of the area are directly coupled with national survival, interest. Currently, on account of strategic competition between U.S.A, and China in this area as well as supranational·non-conventional threats, the security environment is unstable, required joint/allied responses. So, lots of states inside(U.S.A., Japan, ASEAN, etc.), also, out of this Indo-Pacific area like EU-NATO, open their own strategy related in Indo-Pacific issues to be involved. R.O.K. government took its own line with publishing self strategy following this international trend, it can be shown korean government's strong intend to join in the pending security issues in this region with 'strategic clarity'. Now, R.O.K. Navy has to back up governments, policy. First, R.O.K. Navy should strengthen the relationship inside and take part in the multilateral cooperation of security. Second, to convey this deterrence message to potential threats, Navy is required to show of forces and have combined exercises. Also, as naval forces of advanced countries, Korea, Navy should be taken action in non-traditional threats, and help developing states improve their sea power. To fulfill this tasks in the wide ocean area, Navy need five inherent characteristics(mobility, flexibility, sustainability, capability of presence and projection). In the end, this paper propose the development plan following operational environment (SWOT) analysis as well.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.127-142
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• 2016

Deep learning model is a kind of neural networks that allows multiple hidden layers. There are various deep learning architectures such as convolutional neural networks, deep belief networks and recurrent neural networks. Those have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. Among those architectures, convolutional neural networks and recurrent neural networks are classified as the supervised learning model. And in recent years, those supervised learning models have gained more popularity than unsupervised learning models such as deep belief networks, because supervised learning models have shown fashionable applications in such fields mentioned above. Deep learning models can be trained with backpropagation algorithm. Backpropagation is an abbreviation for "backward propagation of errors" and a common method of training artificial neural networks used in conjunction with an optimization method such as gradient descent. The method calculates the gradient of an error function with respect to all the weights in the network. The gradient is fed to the optimization method which in turn uses it to update the weights, in an attempt to minimize the error function. Convolutional neural networks use a special architecture which is particularly well-adapted to classify images. Using this architecture makes convolutional networks fast to train. This, in turn, helps us train deep, muti-layer networks, which are very good at classifying images. These days, deep convolutional networks are used in most neural networks for image recognition. Convolutional neural networks use three basic ideas: local receptive fields, shared weights, and pooling. By local receptive fields, we mean that each neuron in the first(or any) hidden layer will be connected to a small region of the input(or previous layer's) neurons. Shared weights mean that we're going to use the same weights and bias for each of the local receptive field. This means that all the neurons in the hidden layer detect exactly the same feature, just at different locations in the input image. In addition to the convolutional layers just described, convolutional neural networks also contain pooling layers. Pooling layers are usually used immediately after convolutional layers. What the pooling layers do is to simplify the information in the output from the convolutional layer. Recent convolutional network architectures have 10 to 20 hidden layers and billions of connections between units. Training deep learning networks has taken weeks several years ago, but thanks to progress in GPU and algorithm enhancement, training time has reduced to several hours. Neural networks with time-varying behavior are known as recurrent neural networks or RNNs. A recurrent neural network is a class of artificial neural network where connections between units form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior. Unlike feedforward neural networks, RNNs can use their internal memory to process arbitrary sequences of inputs. Early RNN models turned out to be very difficult to train, harder even than deep feedforward networks. The reason is the unstable gradient problem such as vanishing gradient and exploding gradient. The gradient can get smaller and smaller as it is propagated back through layers. This makes learning in early layers extremely slow. The problem actually gets worse in RNNs, since gradients aren't just propagated backward through layers, they're propagated backward through time. If the network runs for a long time, that can make the gradient extremely unstable and hard to learn from. It has been possible to incorporate an idea known as long short-term memory units (LSTMs) into RNNs. LSTMs make it much easier to get good results when training RNNs, and many recent papers make use of LSTMs or related ideas.

• Korean Chemical Engineering Research
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• v.52 no.5
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• pp.632-637
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• 2014

A microfluidic method for the in situ production of monodispersed alginate hydrogels using biocompatible polymer gelation by crosslinker mass transfer is described. Gelation of the hydrogel was achieved in situ by the dispersed calcium ion in the microfluidic device. The capillary number (Ca) and the flow rate of the disperse phase which are important operating parameters mainly influenced the formation of three distinctive flow regions, such as dripping, jetting, and unstable dripping. Under the formation of dripping region, monodispersed alginate hydrogels having a narrow size distribution (C.V=2.71%) were produced in the microfluidic device and the size of the hydrogels, ranging from 30 to $60{\mu}m$, could be easily controlled by varying the flow rate, viscosity, and interfacial tension. This simple microfluidic method for the production of monodisperse alginate hydrogels shows strong potential for use in delivery systems of foods, cosmetics, inks, and drugs, and spherical alginate hydrogels which have biocompatibility will be applied to cell transplantation.

• Corrosion Science and Technology
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• v.8 no.2
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• pp.62-67
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• 2009

Ti and Ti-alloys have good biocompatibility, appropriate mechanical properties and excellent corrosion resistance. However, the widely used Ti-6Al-4V is found to release toxic ions (Al and V) into the body, leading to undesirable long-term effects. Ti-6Al-4V has much higher elastic modulus (100 GPa) than cortical bone (20 GPa). Therefore, titanium alloys with low elastic modulus have been developed as biomaterials to minimize stress shielding. The electrochemical behavior of surface-modified and MC3T3-E1 cell-cultured Ti-30(Nb,Ta) alloys with low elastic modulus have been investigated using various electrochemical methods. Surfaces of test samples were treated as follows: $0.3{\mu}m$ polished; $25{\mu}m$, $50{\mu}m$ and $125{\mu}m$ sandblasted. Specimen surfaces were cultured with MC3T3-E1 cells for 2 days. Average surface roughness ($R_a$) and morphology of specimens were determined using a surface profilometer, OM, and FE-SEM. Corrosion behavior was investigated using a potentiostat(EG&G PARSTAT 2273), and electrochemical impedance spectroscopy was performed (10 mHz to 100 kHz) in 0.9% NaCl solution at $36.5{\pm}1^{\circ}C$. The microstructures of the Ti-30(Ta,Nb) alloys had a needle-like appearance. The $R_a$ of polished Ti-30Ta and Ti-30Nb alloys was lower than that of the sandblasted Ti alloy. Cultured cells displayed round shapes. For polished alloy samples, cells were well-cultured on all surfaces compared to sandblasted alloy samples. In sandblasted and cell-cultured Ti-30(Nb,Ta) alloy, the pitting potential decreased and passive current density increased as $R_a$ increased. Anodic polarization curves of cell-cultured Ti alloys showed unstable behavior in the passive region compared to non-cell-cultured alloys. From impedance tests of sandblasted and cell-cultured alloys, the polarization resistance decreased as $R_a$ increased, whereas, $R_a$ for cell-cultured Ti alloys increased compared to non-cell-cultured Ti alloys.

• Atmosphere
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• v.23 no.2
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• pp.187-204
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• 2013

This study analyzed the synoptic distribution and vertical structure about four cases of precipitation occurrences using NCEP/NCAR reanalysis data and upper level data of winter intensive observation to be performed by National Institute of Meteorological Research at Bukgangneung, Incheon, Boseong during 63days from 4 JAN to 6 MAR in 2012, and Observing System Experiment (OSE) using 3DVAR-WRF system was conducted to examine the precipitation predictability of upper level data at western and southern coastal regions. The synoptic characteristics of selected precipitation occurrences were investigated as causes for 1) rainfall events with effect of moisture convergence owing to low pressure passing through south sea on 19 JAN, 2) snowfall events due to moisture inflowing from yellow sea with propagation of Siberian high pressure after low pressure passage over middle northern region on 31 JAN, 3) rainfall event with effect of weak pressure trough in west low and east high pressure system on 25 FEB, 4) rainfall event due to moisture inflow according to low pressures over Bohai bay and south eastern sea on 5 MAR. However, it is identified that vertical structure of atmosphere had different characteristics with heavy rainfall system in summer. Firstly, depth of convection was narrow due to absence of moisture convergence and strong ascending air current in middle layer. Secondly, warm air advection by veering wind with height only existed in low layer. Thirdly, unstable layer was limited in the narrow depth due to low surface temperature although it formed, and also values of instability indices were not high. Fourthly, total water vapor amounts containing into atmosphere was small due to low temperature distribution so that precipitable water vapor could be little amounts. As result of OSE conducting with upper level data of Incheon and Boseong station, 12 hours accumulated precipitation distributions of control experiment and experiments with additional upper level data were similar with ones of observation data at 610 stations. Although Equitable Threat Scores (ETS) were different according to cases and thresholds, it was verified positive influence of upper level data for precipitation predictability as resulting with high improvement rates of 33.3% in experiment with upper level data of Incheon (INC_EXP), 85.7% in experiment with upper level data of Boseong (BOS_EXP), and 142.9% in experiment with upper level data of both Incheon and Boseong (INC_BOS_EXP) about accumulated precipitation more than 5 mm / 12 hours on 31 January 2012.

• Cartoon and Animation Studies
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• s.33
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• pp.203-221
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• 2013

While visual industry of the 21st century is becoming higher value-added industry, animation which is a representative genre of visual industry is also developed to the industry focused by government. Korea and China also pursue the development of animation as government executed animation industry promotion policy after the 2000s. However, its results are still under the expectation. Korea hopes advance of Chinese market because of the limitation of market and China is also one of the markets we want to grow together due to the vitalization of Korean visual market. Therefore, if examining characteristics of Korean and Chinese animation industry which is still influenced by government policy and analyzing characteristics of government policies of two countries so as to seek measures to exchange each other with mutual confidence, Korea showed manufacture supporting policies focusing on creation and design, but China focuses on support policies grasping markets of animation and establishing the space for production. Now, it has been about 10 years and Korean market of animation is unstable and it's difficult to release new manpower because of the insufficient space of creation. But, China secured animation market and established creation space in every region of the whole country, but there are not desirable outcomes because of the limitation of materials and characters and insufficient factors of designing and creation. If trying collaboration or joint production and sharing the market by utilizing characteristics of Korean and Chinese animation, both of two countries will obtain better outcomes.

• Current Research on Agriculture and Life Sciences
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• v.24
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• pp.1-8
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• 2006

When tea trees were introduced to Korea peninsular from China? Historically, Mr. Taeryum, an envoy of Shilla dynasty brought tea seeds from China during Tang dynasty and the seeds were planted at Jiri Mt. by the order of King Heungduk at AD828. During Koryo Dynasty(918 1392), Buddhism spread rapidly all over the country and the tea culture reached its highest stage of prosperity. At the Chosun Dynasty, however, the ceremonial drinking of tea vanished almost completely due to the flourishing Confucian tradition, a kind of substitution of Buddhism. But a few people have supported the traditional tea culture by themselves. Since the independence of Korea soon after the World War II at 1945, Korean War have been exploded at 1950. After economic evolution have been succeeded at 1980, the cultivation area of tea trees has been increased about 2,000ha and the cultural tradition of tea drinking has become popular again at a tea consume quantities amounted to 100g per capita at 2004. The northern limited area of tea plant is lined on the southern part of Korea peninsular. It is very small region compared to China about one million ha and to Japan over 60 thousand ha. It is problem not only the area of tea fields but also the methodology of tea cultivation, for examples without clonal cultivars and mechanical systems. WTO treatments was discussing with Korea, China and Japan government at 2005. Green tea custom is very high at 514% in Korea. If three countries will be agreed the imported tax will be cut off, the Korean tea farmers will be confused because of unstable situation of tea markets. All most of tea farmers should be made the tea fields by seeding not clonal propagation. Because of clonal cultivars have not developed in Korea, there have not been the research institutes for tea plants and manufactures before 1992. Now there are three research institute of tea in Korea; Tea Experiment Station at Bosung of Jeonnam Agricultural Research & Extension Services, Mokpo Experimental Station of National Institute of Crop Science, and Green Tea Cluster Institute of Hadong. Mokpo and Hadong Research Station were established at 2004 and at 2005 but Bosung Station was established at 1992. Seven clonal tea cultivers were selected at Bosung Station; Bohyang, Myngsun, Chanlok, Sunhyang, Mihyang, Jinhyang and Ohsun until 2004. Mokpo Experimental Institute was started the tea provenance testing about 4 provenances: Kangwon-do, Jeonlabuk-do, Jeonlanam-do, and Kyungsangnam-do. Korean new tea cultivers should be selected because Koran wild tea population have been high genetic variation. If tea breeding research will be successful to select new clonal cultivers, the tea farmers of Korea will be stable after WTO treatment with each country.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.15 no.1
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• pp.26-34
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• 1982

The East China Sea and the Yellow Sea are abundant in nutritions because of river inflows and are important as the nursery and spawning grounds of demersal and pelagic fishes. The remarkable thermal front between the Yellow Sea Bottom Cold Water and the Tsushima Warm Current is formed in this region, and the fluctuation of this front may affect the variation of the yellow croaker fishing ground. To investigate the mechanism of the yellow croaker fishing ground, the distribution ana seasonal change of the fishing ground are examined by using catch of stow net fishery (Fisheries Research and Development Agency, 1970-1979) and the water temperature data (Japan Hydrographic Association, 1978). The main fishing ground of yellow croaker was nine sea areas (rectangle of 30' latitude by 30' longitude) located at 40-150 nautical miles west and southwest of Jeju Island, the area of which occupies no more than $11\%$ of all fishing grounds, and it appeared that about $70\%$ of total catch of ten years was concentrated in this area. The main fishing periods were from March to May and September to October. The coefficients of variation of the catch for the main fishing ground were from 0.8 to 2.1 and the condition of all fishing grounds was generally unstable. The mean CPUE was 27kg/haul at the main fishing ground, while it was the largest on boundary area of the Yellow Sea Bottom Cold Water. It was found that the seasonal movement of fishing ground is related to the expansion and reduction of the Yellow Sea Bottom Cold Water ($10^{\circ}C$).

• Korean Journal of Fisheries and Aquatic Sciences
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• v.23 no.1
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• pp.12-24
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• 1990

Two trypsin-like enzymes, designated trypsin A and 3, purified from the intestine of menhaden by $(NH_4)_2SO_4$ fractionation, Benzamidine-Sepharose 6B affinity chromatography, DEAE-Sephacel ion exchange chromatography and Sephadex G-75 gel filtration chromatography. The two trypsins were subjected to compare the enzymatic properties of the trypsin-like enzymes from the other dark fleshed fishes. Both trypsins catalysed the hydrolysis of N$\alpha$-benzoyl-DL-arginine-p-nitroanilide and they were remarkably inhibited by several well known trypsin-inhibitors, tosyllysyl chloromethyl ketone, soybean trypsin inhibitor, be-nzamidine, leupeptin and antipain, etc. Therefore, it was ascertained that the two enzymes are serine-type trypsins. The molecular weights of these enzymes were about 25,000 and 26,200, respectively, ;Is determined by SDS-PAG electrophoresis and by Sephadex G-100 gel filtration, and the molecular weights of these two enzymes are somewhat fewer than those from the other dark fleshed fishes. Both enzymes had less basic amino acids such as arginine and Iysine, whereas they had slightly high contents of neutral amino acids, glycine, alanine and tryptophane. The enzymes showed a pH optimum of $8\~11$ at $60^{\circ}C$ against the $N\alpha$-benzoyl-DL-argi-nine-p-nitroanilide substrate and they were quite unstable above $40^{\circ}C$ and under the atidic pH region. The Km constant of the two enzymes against the $N\alpha$-benzoyl-DL-arginine-p-nitroanilide was $1.4\times10^{-4}M$ for trypsin A and $4.3\times10^{-5}M$ for trypsin B, respectively.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.2
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• pp.137-145
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• 2008

An experimental study was performed in order to investigate the influence of Reynolds number on the drag coefficient variations of an oscillating airfoil. A NACA 0012 airfoil was sinusoidally pitched at the quarter chord point with an oscillating amplitude of ${\pm}6^{\circ}$. The free-stream velocities were 1.98, 2.83 and 4.03 m/s and the corresponding chord Reynolds numbers were $2.3{\times}10^4$, $3.3{\times}10^4$ and $4.8{\times}10^4$, respectively. The drag coefficient was calculated from the ensemble average velocity measured by an X-type hot-wire probe(X-type, 55R51) in the near-wakes region. In the case of Re=$2.3{\times}10^4$, variation of drag coefficient shows a negative damping (counter-clockwise variation), which implies an unstable state which could be excited by aerodynamic force, whereas the drag coefficient represents the positive damping (clockwise variation) as the Reynolds number increases from Re=$3.3{\times}10^4$ to $4.8{\times}10^4$. Hence, the drag coefficient variations show significant differences between Re=$2.3{\times}10^4$ and $4.8{\times}10^4$이다.