• Korean Journal of Remote Sensing
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• v.17 no.2
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• pp.183-188
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• 2001

The NOAA AVHRR remotely sensed SST data, collected by the National Fisheries Research and Development Institute (NFRDI), are analyzed in order to understand the spatial and temporal distributions of SST in the sea near korea. Our study is based on 10-day SST images during last 7 years (1991-1997). For a time series analysis of multiple SST images, all of images must be consistent exactly at the same position by adjusting the scales and positions of each SST image. We devised an algorithm which automatically detects cloud pixels from multiple SST images. The cloud detection algorithm is based on a physical constraint that SST anomalies in the ocean do not exceed certain limits (we used $\pm$3$^{\circ}C$ as a criterion of SST anomalies). The remotely sensed SST data are tuned by comparing remotely sensed data with observed SST at coastal stations. Seasonal variations of SST are studied by harmonic fit of SST normals at each pixel and the SST anomalies are studied by statistical method. It was found that the SST anomalies are rather persistent for one or two months. Utilizing the persistency of SST anomalies, we devised an algorithm for a prediction of future SST. In the Markov lprocess model of SST anomalies, autoregression coefficients of SST anomalies during a time elapse of 10 days are between 0.5 and 0.7. The developed algorithm with automatic cloud pixel detection and rediction of future SST is expected to be incorporated to the operational real time service of SST around Korea.

• Korean Journal of Remote Sensing
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• v.15 no.3
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• pp.195-208
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• 1999

In the classification of satellite images, the representative of training of classes is very important factor that affects the classification accuracy. Hence, in order to improve the classification accuracy, it is required to optimize pre-classification stage which determines classification parameters rather than to develop classifiers alone. In this study, the normality of training are calculated at the preclassification stage using SPOT XS and LANDSAT TM. A correlation coefficient of multivariate Q-Q plot with 5% significance level and a variance of initial training are considered as an object function of genetic algorithm in the training normalization process. As a result of normalization of training using the genetic algorithm, it was proved that, for the study area, the mean and variance of each class shifted to the population, and the result showed the possibility of prediction of the distribution of each class.

• Journal of Korean Society of Archives and Records Management
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• v.19 no.4
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• pp.35-61
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• 2019

The recent demographic cliff caused the schooling population to steadily decline and the number of college admissions to sharply drop, which led to the prediction of the Ministry of Education wherein 38 universities in Korea will be closed within the next 3 years. As such, the legal, administrative, historical, and informational values of university records shed light on the importance of the follow-up process and management of closed university records. Although closed university records need to be systematically managed according to legal procedures similar to other public records, there is no practical and clear legal standard for the management of such records at present. Moreover, management standards considering the characteristics of (closed) university records and individual universities' situations and specificity have been insufficient. This study, therefore, examines the ambiguous standards for closed university records management and analyzed relevant problems. Furthermore, an integrated management system is suggested as a way to improve the records management of closed universities.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2019.10a
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• pp.87-87
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• 2019

The consumption of herbal medicine and related products (herbal products) have increased in South Korea. At the same time the quality, safety, and efficacy of herbal products is being raised. Currently, the herbal products are standardized and controlled according to the requirements of the Korean Pharmacopoeia, the National Institute of Health and the Ministry of Public Health and Social Affairs. The validation of herbal products and their medicinal component is important, since many of these herbal products are composed of two or more medicinal plants. However, there are no tools to support the validation process. Interest in deep learning has exploded over the past decade, for herbal medicine using algorithms to achieve herb recognition, symptom related target prediction, and drug repositioning have been reported. In this study, individual images of four herbs (Panax ginseng C.A. Meyer, Atractylodes macrocephala Koidz, Poria cocos Wolf, Glycyrrhiza uralensis Fischer), actually sold in the market, were achieved. Certain image preprocessing steps such as noise reduction and resize were formatted. After the features are optimized, we applied GoogLeNet_Inception v4 model for herb image recognition. Experimental results show that our method achieved test accuracy of 95%. However, there are two limitations in the current study. Firstly, due to the relatively small data collection (100 images), the training loss is much lower than validation loss which possess overfitting problem. Secondly, herbal products are mostly in a mixture, the applied method cannot be reliable to detect a single herb from a mixture. Thus, further large data collection and improved object detection is needed for better classification.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.188-188
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• 2017

Rainfall is an important input to hydrological models. The accuracy of hydrological studies for water resources and floods management depend primarily on the estimation of rainfall. Thailand is among the countries that have regularly affected by floods. Flood forecasting and warning are necessary to prevent or mitigate loss and damage. Merging near real time satellite-based precipitation estimation with relatively high spatial and temporal resolutions to ground gauged precipitation data could contribute to reducing uncertainty and increasing efficiency for flood forecasting application. This study tested the applicability of satellite-based rainfall for water resources management and flood forecasting. The objectives of the study are to assess uncertainty associated with satellite-based rainfall estimation, to perform bias correction for satellite-based rainfall products, and to evaluate the performance of the bias-corrected rainfall data for the prediction of flood events. This study was conducted using a case study of Thai catchments including the Chao Phraya, northeastern (Chi and Mun catchments), and the eastern catchments for the period of 2006-2015. Data used in the study included daily rainfall from ground gauges, telegauges, and near real time satellite-based rainfall products from TRMM, GSMaP and PERSIANN CCS. Uncertainty in satellite-based precipitation estimation was assessed using a set of indicators describing the capability to detect rainfall event and efficiency to capture rainfall pattern and amount. The results suggested that TRMM, GSMaP and PERSIANN CCS are potentially able to improve flood forecast especially after the process of bias correction. Recommendations for further study include extending the scope of the study from regional to national level, testing the model at finer spatial and temporal resolutions and assessing other bias correction methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.397-400
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• 2009

This paper describes ASIC design of Multimedia application SoC platform based RISC processor with BTB(Branch Target Buffer). For performance enhancement of platform, we use a simple branch prediction scheme, BTB structure, that stores a target address for branch instruction to remove pipeline harzard. Also, the platform includes a number of peripheral such as VGA controller, AC97 controller, UART controller, SRAM interface and Debug interface. The platform is designed and verified on a Xilinx VERTEX-4 FPGA using a number of test programs for functional tests and timing constraints. Finally, the platform is implemented into a single ASIC chip which can be operated at 100MHz clock frequency using the Chartered 0.18um process. As a result of performance estimation, the proposed platform shows about 5~9% performance improvement in comparison with the previous SoC Platform.

• Applied Chemistry for Engineering
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• v.30 no.5
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• pp.615-619
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• 2019

Statistical design of experiments was performed to optimize MOF-235 synthesis process. Concentrations of terephthalic acid (TPA), iron (III) chloride hexahydrate, N,N-dimethylformamide (DMF) and ethanol were important factors to develop the crystal structure of MOF-235. MOF-235 was synthesized with various concentrations of the listed chemicals above and the crystallinity was measured by XRD. The effect of the composition on the synthesis of MOF-235 was evaluated using a statistical analysis. For the variance analysis using F-test, the concentration of ethanol showed the greatest effect on the crystallinity and TPA the least influential. A regression model for predicting the crystallinity of MOF-235 was derived and the prediction results for two synthetic variables were presented using contour plots. Finally, the crystallinity was predicted by a mixture method with $FeCl_3$, ethanol and DMF.

• Journal of the Society of Naval Architects of Korea
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• v.54 no.4
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• pp.329-334
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• 2017

As the motion response of heave for floating bodies on the water surface is relatively large near the natural frequency, it is necessary to predict its value accurately from the stage of initial design. Bodies accelerating in fluid experience force acted upon by the fluid, and this force is quantified by using the concept of added mass. For predicting the natural frequency of heave we need to know the added mass, which is given as a function of frequency, and hence the natural frequency can be obtained through only by iteration process, as was pointed out by Lee (2008). His method was applied to circular cylinders, and two dimensional cylinders of Lewis form by making use of the Ursell-Tasai method in the previous works, Lee and Lee (2013), Kim and Lee (2013), and Song and Lee (2015). In this work, a similar algorithm employing the concept of strip method is adopted for predicting the heave natural frequency of KCS(KRISO Container Ship), and the obtained computational result was compared with other existing experimental data, and the agreement seems reasonable. Furthermore, through the error analysis, it is shown that why the frequency corresponding to the local minimum of the added mass and the natural frequency are very close. And it seems probable that we can predict the heave natural frequency if we know only the local minimum of added mass and the corresponding frequency under a condition, which holds for ship-like bodies in general.

• Journal of the Society of Naval Architects of Korea
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• v.57 no.1
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• pp.23-34
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• 2020

This paper covers the development of prediction techniques for ice load on ice-breakers operating in continuous ice-breaking under level ice conditions using particle-based continuum mechanics. Ice is assumed to be a linear elastic material until the fracture occurs. The maximum normal stress theory is used for the criterion of fracture. The location of the crack can be expressed using a local scalar function consisting of the gradient of the first principal stress and the corresponding eigen-vector. This expression is used to determine the relative position of particle pair to the new crack. The Hertz contact model is introduced to consider the collisions between ice fragments and the collisions between hull and ice fragments. In order to verify the developed technique, the simulation results for the three-point bending problems of ice-specimen and the continuous ice-breaking problem around a wedge-type model ship with bow angle of 20° are compared with the experimental results carrying out at Korea Research Institute of Ships and Ocean Engineering (KRISO).

• Elastomers and Composites
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• v.39 no.3
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• pp.193-208
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• 2004

Morphology of injection molded polymer blend was investigated by experimental and theoretical approach. In experiments, the effects of injection speed and injection temperature on the morphology of injection molded MPPO/Nylon 6 blend were investigated. The morphology distribution across the part thickness was clearly observed in injection molded blend. We could observe several distinct regions across the thickness of molded part: skin layer, subskin layer and core region. The skin layer where the dispersed phase is fine and highly deformed to the flow direction is observed to be located near the part surface. The subskin layer located at inner region of the skin layer also observed. In the subskin layer, the dispersed phase is coarser than that of skin layer and deforms to the flow direction. Based on the experimental results, the calculation scheme to predict the morphology of injection molded polymer blend was suggested. The morphology of injection molded polymer blend could be predicted in corporation with the result of flow analysis obtained from commercial software for injection molding process and the theory of drop behavior under the flow. The suggested calculation scheme could predict the effect of injection conditions on the morphology of injection molded parts.