• Journal of the Korean Society of Marine Environment & Safety
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• v.30 no.4
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• pp.365-372
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• 2024

The South Korean marine industry is emerging as a significant market, driven by the growing popularity of various water leisure activities, including sailing. This trend suggests a rising demand for sailing yachts. Consequently, since 2022, the design and development of a 28ft sailing yacht have been ongoing, supported by the government and the Ministry of Oceans and Fisheries, to promote yachting culture in South Korea. The Velocity Prediction Program (VPP) analysis was conducted using WinDesign during the preliminary design stage to evaluate performance and determine design parameters. The hydrodynamic model used for this vessel is based on regression methods developed from years of experience in naval architecture and yacht research at the Wolfson Unit, providing reliable estimates for most modern yachts. However, owing to the lack of specific hydrodynamic data from towing tank tests or CFD numerical analysis, verification of the hydrodynamic model has faced some challenges. Additionally, an incomplete weight estimate resulted in variable VCG values, potentially affecting stability and overall performance. The optimal boat speed for this vessel was determined at true wind speeds (TWS) of 4, 8, 12, 16, and 20 knots, using both the jib (up to 120° TWA) and the spinnaker (from 80° TWA). The optimized speed of the yacht was found to be comparable to that of international similar-class yachts.

• Journal of the Korean earth science society
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• v.30 no.3
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• pp.294-304
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• 2009

A statistical prediction model for the typhoon intensity and track in the Northwestern Pacific area was developed based on the artificial neural network scheme. Specifically, this model is focused on the 5-day prediction after tropical cyclone genesis, and used the CLIPPER parameters (genesis location, intensity, and date), dynamic parameters (vertical wind shear between 200 and 850hPa, upper-level divergence, and lower-level relative vorticity), and thermal parameters (upper-level equivalent potential temperature, ENSO, 200-hPa air temperature, mid-level relative humidity). Based on the characteristics of predictors, a total of seven artificial neural network models were developed. The best one was the case that combined the CLIPPER parameters and thermal parameters. This case showed higher predictability during the summer season than the winter season, and the forecast error also depended on the location: The intensity error rate increases when the genesis location moves to Southeastern area and the track error increases when it moves to Northwestern area. Comparing the predictability with the multiple linear regression model, the artificial neural network model showed better performance.

• Journal of the korean Society of Automotive Engineers
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• v.18 no.6
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• pp.23-32
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• 1996

차량에서 엔진은 가장 큰 질량 집중체(concentrated mass)이다. 만약 엔진이 적절하게 구속되지 않거나 절연되어 있지 않으면, 차체에 진동을 일으키는 원인이 된다. 엔진은 다양한 진동 교란을 받는데 엔진 마운트는 이러한 모든 것들을 고립시키는 역할을 해야 하며, 엔진은 정적인 장착 하중에 대한 지지와 전후, 좌우 및 수직 방향의 운동에 대해 적절한 강성을 가져야 한다. 또한 정숙성을 향상시키기 위해서는 엔진 마운트의 재료인 고무의 강성계수를 낮추는 것이 필요한데 이는 일반적으로 내구성의 저하를 가져온다. 따라서 개발과정에서 강성계수를 낮추는 변경을 하면 부품의 내구성을 보정함에 따르는 재평가 또한 필요하게 된다. 엔진 마운트에 쓰이는 고무부품의 해석은 엔진 마운트 시스템에 대한 진동 해석 및 내구수명의 예측과 병행해야 하며, 진동해석으로부터 얻은 하중 지지 능력 등의 모든 요구 특성을 만족하기 위해서는 고무 재료의 특성에 대한 지식, 엔진 마운트의 장착 위치에 대한 결정 능력과 함께 주어진 조건에 대한 형상의 최적 설계 능력 등이 요구된다. 본 연구에서는 기본적인 형상을 파라미터화하여 엔진 마운트의 형상을 최적화 하는 절차를 제안하였다. 현재 승용차에 널리 사용되고 있는 부시형(bush type) 엔진마운트를 적용 모델로 선택하였으며, 엔진 마운트의 기본적인 형상을 몇개의 파라미터를 사용하여 정의하고 설계 사양으로 주어지는 강성값과 각 파라미터들의 조합으로 구성되는 형상이 갖는 강성값의 차이가 최소가 되도록 파라미터 값들을 최적화하였다. 최적화된 파라미터 값들로 구성되는 형상을 내구 성능, 성형성등을 고려하여 최종 형상으로 결정한다. 내구성능의 예측은 금속부품의 내구수명 예측에 널리 이용되고 있는 방법이 방진 고무부품의 경우에도 적용 가능한지를 검토하고, 방진 고무부품에도 일반적으로 적용될수 있는 내구수명 예측방안의 개발 가능성을 타진해 보았다. 본 연구의 목표는 시제품을 제작하기 이전에 설계된 부품에 대한 스프링 상수 및 내구특성을 체계적으로 규명하여 제품 시험의 횟수를 줄이고, 보다 정밀한 제품을 제작할 수 있도록 하기 위한 것이다.

• The Korean Journal of Applied Statistics
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• v.33 no.2
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• pp.147-160
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• 2020

In this paper, we study an algorithm that automatically determines the orders of past observations and conditional mean values that play an important role in count time series models. Based on the orders of the ARIMA model, the algorithm constitutes the order candidates group for time series generalized linear models and selects the final model based on information criterion among the combinations of the order candidates group. To evaluate the proposed algorithm, we perform small simulations and empirical analysis according to underlying models and time series as well as compare forecasting performances with the ARIMA model. The results of the comparison confirm that the time series generalized linear model offers better performance than the ARIMA model for the count time series analysis. In addition, the empirical analysis shows better performance in mid and long term forecasting than the ARIMA model.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.7
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• pp.139-148
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• 2012

An ensemble of classifiers is to employ a set of individually trained classifiers and combine their predictions. It has been found that in most cases the ensembles produce more accurate predictions than the base classifiers. Combining outputs from multiple classifiers, known as ensemble learning, is one of the standard and most important techniques for improving classification accuracy in machine learning. An ensemble of classifiers is efficient only if the individual classifiers make decisions as diverse as possible. Bagging is the most popular method of ensemble learning to generate a diverse set of classifiers. Diversity in bagging is obtained by using different training sets. The different training data subsets are randomly drawn with replacement from the entire training dataset. The random subspace method is an ensemble construction technique using different attribute subsets. In the random subspace, the training dataset is also modified as in bagging. However, this modification is performed in the feature space. Bagging and random subspace are quite well known and popular ensemble algorithms. However, few studies have dealt with the integration of bagging and random subspace using SVM Classifiers, though there is a great potential for useful applications in this area. The focus of this paper is to propose methods for improving SVM performance using hybrid ensemble strategy for bankruptcy prediction. This paper applies the proposed ensemble model to the bankruptcy prediction problem using a real data set from Korean companies.

• Journal of the Earthquake Engineering Society of Korea
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• v.14 no.2
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• pp.75-84
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• 2010

An in-cabinet response spectrum should be generated to perform the seismic qualification of devices and instruments mounted inside safety-related electrical equipment installed in nuclear power plants. The response spectrum is available by obtaining accurate seismic responses at the device mounting location of the cabinet. The dynamic behavior of most of electrical equipment may not be easily analyzed due to their complex mass and stiffness distributions. Considering these facts, this study proposes a procedure to estimate the seismic responses of a structure by a combination of a test and subsequent analysis. This technique firstly constructs the modal equations of the structure by using the experiment modal parameters obtained from the impact test. Then the seismic responses of the structure may be calculated by a mode superposition method. A simple steel frame structure was fabricated as a specimen for the validation of the proposed method. The seismic responses of the specimen were estimated by using the proposed technique and compared with the measurements obtained from the shaking table tests. The study results show that it is possible to accurately estimate the seismic response of the structure by using the experimental modal parameters obtained from the impact test.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.17 no.3
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• pp.251-259
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• 2004

The prediction accuracy of prestress plays an important role in the quality of maintenance and the decision on rehabilitation of infrastructure such as prestressed concrete bridges. In this paper, the Bayesian statistical method that uses in-situ measurement data for reducing the uncertainties or updating long-term prediction of prestress is presented. For Bayesian analysis, prior probability distribution is developed to represent the uncertainties of creep and shrinkage of concrete and likelihood function is derived and used with data acquired in site. Posterior probability distribution is then obtained by combining prior distribution and likelihood function. The numerical results of this study indicate that more accurate long-term prediction of prestress forces due to creep and shrink age is possible.

• Journal of the Korean Geotechnical Society
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• v.34 no.6
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• pp.37-47
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• 2018

In this study, 34 laboratory load test data were collected, and analyzed to propose the equations for predicting ultimate bearing capacity of sand compaction pile (SCP) and gravel compaction pile (GCP) reinforced clay. The collected data were compared with the ultimate bearing capacity estimated by existing theoretical equations, and the prediction accuracy of the existing theoretical equations was identified. Also, multiple regression analysis was performed to predict the ultimate bearing capacity, and the most efficient number and type of input variables were selected through error evaluation by leave-one-out cross validation. Finally, the multiple regression equations for estimating the ultimate bearing capacity of laboratory load test for SCP and GCP were proposed, and their performance was evaluated.

• Journal of Digital Convergence
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• v.13 no.5
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• pp.237-244
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• 2015

Export-led policies, FTA signed and economics of scale through a variety of market-oriented policies, such as regulations to improve market grew constantly. Accordingly, the correct decision making accurately analyze the economics market for decision, a problem has been an important issue in predicting. For accurate analysis and decision-making of the most common indicators of the stock market by proposing a number of indicators of economic transformation techniques were applied to the convergence model combining estimation and forecasts problem confirmed its effectiveness. Experimental result, gave the model estimation method to apply a transform to show the valid combinations proposed model state estimation result was confirmed in a very similar exercise aspect of the physical problem and the KOSPI index prediction.

• Journal of KIISE
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• v.43 no.7
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• pp.781-785
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• 2016

Learning from human behaviors in the real world is essential for human-aware intelligent systems such as smart assistants and autonomous robots. Most of research focuses on correlations between sensory patterns and a label for each activity. However, human activity is a combination of several event contexts and is a narrative story in and of itself. We propose a novel approach of human activity prediction based on event cognition. Egocentric multi-sensor data are collected from an individual's daily life by using a wearable device and smartphone. Event contexts about location, scene and activities are then recognized, and finally the users" daily activities are predicted from a decision rule based on the event contexts. The proposed method has been evaluated on a wearable sensor data collected from the real world over 2 weeks by 2 people. Experimental results showed improved recognition accuracies when using the proposed method comparing to results directly using sensory features.