• Journal of KIISE:Software and Applications
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• v.30 no.7_8
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• pp.649-658
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• 2003

We propose a hierarchical architecture of ART2 Network for performance improvement and fast pattern classification model using fitness selection. This hierarchical network creates coarse clusters as first ART2 network layer by unsupervised learning, then creates fine clusters of the each first layer as second network layer by supervised learning. First, it compares input pattern with each clusters of first layer and select candidate clusters by fitness measure. We design a optimized fitness function for pruning clusters by measuring relative distance ratio between a input pattern and clusters. This makes it possible to improve speed and accuracy. Next, it compares input pattern with each clusters connected with selected clusters and finds winner cluster. Finally it classifies the pattern by a label of the winner cluster. Results of our experiments show that the proposed method is more accurate and fast than other approaches.

• Journal of Ocean Engineering and Technology
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• v.19 no.6 s.67
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• pp.78-85
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• 2005

Initial alignment and localization are important topics in inertial navigation systems, since misalignment and initial position error wholly propagate into the navigation systems and deteriorate the performance of the systems. This paper presents the error convergence characteristics of the hybrid navigation system for underwater vehicles initial position, which is based on an inertial measurement unit (IMU) accompanying a range sensor. This paper demonstrates the improvement on the navigational performance oj the hybrid system with the range information, especially focused on the convergence of the estimation of underwater vehicles initial position error. Simulations are performed with experimental data obtained from a rotating ann test with a fish model. The convergence speed and condition of the initial error removal for random initial position errors are examined with Monte Carlo simulation. In addition, numerical simulation is conducted with an AUV model in lawn-mowing survey mode to illustrate the error convergence of the hybrid navigation System for initial position error.

• Toxicological Research
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• v.28 no.3
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• pp.159-164
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• 2012

Allergic skin inflammation such as atopic dermatitis (AD) is characterized by edema and infiltration with various inflammatory cells such as mast cells, basophils, eosinophils and T cells. Thymic stromal lymphopoietin (TSLP) is produced mainly by epidermal keratinocytes, as well as dermal fibroblasts and mast cells in the skin lesions of AD. Omega-3 polyunsaturated fatty acids in fish oil can reduce inflammation in allergic patients. Fermentation has a tremendous capacity to transform chemical structures. The antiinflammatory effects of fish oil have been described in many diseases, but the beneficial effects by which fermented olive flounder oil (FOF) modulates the allergic response is poorly understood. In this study, we produced FOF and tested its ability to suppress the various allergic inflammatory responses. The ability of FOF to modulate the immune system was investigated using a mouse model of AD. The FOF-treated group showed significantly decreased immunoglobulin E (IgE) and histamine in serum. Also, the increased TSLP expression was significantly inhibited in the FOF group; the FOF-treated group was not appreciably different from the hydrocort cream treatment group. In addition, FOF treatment resulted in a smaller spleen size with reduced the thickness and length compared to the induction group. Splenocytes from mice treated with FOF produced significantly less IFN-${\gamma}$, IL-4, T-box transcription factor (T-bet) and GATA binding protein 3 (GATA3) expression compared with the induction group. These results suggest that FOF may be effective in treating the allergic symptoms of AD. 5.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.21 no.1
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• pp.103-118
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• 2018

Ecosystems in subalpine regions are recognized as areas vulnerable to climatic changes because rainfall and the possibility of flora migration are very low due to the characteristics of topography in the regions. In this context, habitat niche was formulated for representative species of arbors in subalpine regions in order to understand the effects of climatic changes on alpine arbor ecosystems. The current potential habitats were modeled as future change areas according to the climatic change scenarios. Based on the growth conditions and environmental characteristics of the habitats, the study was conducted to identify direct and indirect causes affecting the habitat reduction of Abies nephrolepis. Diverse model algorithms for explanation of the relationship between the emergence of biological species and habitat environments were reviewed to construct the environmental data suitable for the six models(GLM, GAM, RF, MaxEnt, ANN, and SVM). Weights determined through TSS were applied to the six models for ensemble in an attempt to minimize the uncertainty of the models. Based on the current climate determined by averaging the climates over the past 30years(1981~2010) and the HadGEM-RA model was applied to fabricate bioclimatic variables for scenarios RCP 4.5 and 8.5 on the near and far future. The results of models of the alpine region tree species studied were put together and evaluated and the results indicated that a total of eight national parks such as Mt. Seorak, Odaesan, and Hallasan would be mainly affected by climatic changes. Changes in the Baekdudaegan reserves were analyzed and in the results, A. nephrolepis was predicted to be affected the most in the RCP8.5. The results of analysis as such are expected to be finally utilizable in the survey of biological species in the Korean peninsula, restoration and conservation strategies considering climatic changes as the analysis identified the degrees of impacts of climatic changes on subalpine region trees in Korean peninsula with very high conservation values.

• Journal of Korean Society of Environmental Engineers
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• v.37 no.1
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• pp.1-6
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• 2015

The purpose of this study is to suggest the effective operation method by developing prediction model for the gas production rate, an indicator of the effectiveness of anaerobic digestion tank, using data mining. At the result, gas production estimate model is developed by using ANN within 10% error. It is expected to help operation of anaerobic digestion by suggesting selected parameter. Meanwhile case based reasoning is applied to develop dewatering cake management technology. Case based reasoning uses the most similar examples of past when a new problem occurs, therefore in this study, management measures are developed that proposes dewatering cake minimization with the minimum change by applying the case based reasoning to sludge disposal process.

• Journal of the Society of Naval Architects of Korea
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• v.33 no.1
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• pp.65-76
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• 1996

A screw propeller is usually accepted as a propulsor of many kinds of ships. However, for high speed vessels, screw propeller has large cavitation area on the blades so propeller efficiency is decreased and erosion can be happened. To avoid this problem, supercavitating propeller and waterjet are generally used for high speed vessels. In this paper, we introduced the self-propulsion test procedure which has been developed for high speed vessels in Hyundai Maritime Research Institute. The model ship used in experiment represents catamaran about 5.3 m in length. To minimize the experimental errors, two impellers were driven by a single motor. Thrust was calculated by converting the measured pressure to flow rates at the nozzle exit. The test procedure is composed of resistance test, self propulsion test and analysis. In order to measure the pressure, pressure tabs were installed around the nozzle exit and connected to the pressure sensor by vinyl tube.

• YAKHAK HOEJI
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• v.28 no.4
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• pp.207-215
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• 1984

Effects of Ssang Wha Tang (SWT), a blended Chinease traditional medicine, on the pharmacokinetics of sulfobromophthalein (BSP) in the rats of hepatic failure induced by carbon tetrachloride were examined. The disposition of plasma BSP in carbon tetrachloride-treated rats (Group I) and in carbon tetrachloride+SWT-treated rats (Group II) followed a three-compartment model, while those in control group followed two-compartment model. GOT, GPT level and some pharmacokinetic paramiters like plasma clearance but except distribution volume (Vdss) recovered in Group II compared to Group I. Therefore, SWT seemed to have an apparent restoring effect of hepatic function damaged by carbon tetrachloride treatment. From the fact that Vdss of BSP in Group II was considered as an one of the probable mechanisms. More intensive increase in BSP-free fraction ($f_p$) in Group II than that in Group I might also explain the increases of BSP clearance and Vdss in Group II compared to Group I. Assuming no changes in hepatic plasma flow(Q) in each group, hepatic intrinsic clearance($CL^h_{int}$) decreased in Group I did not recovered not at all in Group II. Therefore SWT seemed not to have any restoring effect of true hepaticfunction to biotransform and excrete BSP, and the apparent restoring effect of SWT might be due only to the replacement of BSP-plasma protein binding. Whether $f_p$ is actually higer in Group II than in Group I, and Q is constant in each group are being examined in our laboratory. The changes of Q, which might lead to another conculusions, also should be taken into consideration to clarify the apparent hepatorestoring effect of SWT.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.7
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• pp.2963-2968
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• 2011

Plastic injection products have been widely used in various electronic appliances and high-tech commodities. However, plastic injection product manufacturers have to spare no efforts to shorten new product development period to introduce new products into the market ahead of other competitors, gaining competitiveness and satisfying customers. The manufacturers cannot only get big target market share rapidly but also the advantage of leading the product price in order to survive in highly competitive market. This paper proposes the cost estimation approach of feature-based plastic injection products by using hybrid artificial neural network and genetic algorithm. The proposed method is to dramatically simplify and shorten the complex conventional cost estimation procedures and the requested computation parameters of plastic injection products. The case study demonstrates the efficiency and effectiveness of the proposed model in solving the cost estimation problem of plastic injection products at the development stage.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.81-88
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• 2021

Convertible bonds are financial products that contain the nature of both bonds and shares, which are generally issued by companies with lower credit ratings to increase liquidity. Conversion bonds rely on qualitative judgment in the past, although decision-making on whether and when to exercise the right to convert is the most important issue. Therefore, this paper proposes to apply artificial neural network techniques to scientifically determine the exercise of conversion rights. We distinguish between a total of 1,800 learning data published in the past and 200 predictive experimental data and build an artificial neural network learning model. As a result, the parity performance in most groups was excellent, achieving an average excess of about 10% or more. In particular, groups 3-6 recorded an average excess of about 20% and group 6 recorded an average excess of about 37%. This paper is meaningful in that it focused on solving decision problems by converging and applying machine learning techniques, a representative technology of the fourth industry, to the financial sector.

• Journal of Ocean Engineering and Technology
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• v.33 no.5
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• pp.427-434
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• 2019

Artificial intelligence (AI)-aided research currently enjoys active use in a wide array of fields thanks to the rapid development of computing capability and the use of Big Data. Until now, forecasting methods were primarily based on physics models and statistical studies. Today, AI is utilized in disaster prevention forecasts by studying the relationships between physical factors and their characteristics. Current studies also involve combining AI and physics models to supplement the strengths and weaknesses of each aspect. However, prior to these studies, an optimization algorithm for the AI model should be developed and its applicability should be studied. This study aimed to improve the forecast performance by constructing a model for neural network optimization. An artificial neural network (ANN) followed the ever-changing path of a typhoon to produce similar typhoon predictions, while the optimization achieved by the neural network algorithm was examined by evaluating the activation function, hidden layer composition, and dropouts. A learning and test dataset was constructed from the available digital data of one typhoon that affected Korea throughout the record period (1951-2018). As a result of neural network optimization, assessments showed a higher degree of forecast accuracy.