• Journal of Industrial Technology
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• v.17
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• pp.277-289
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• 1997

The numerical model for prediction of longshore current with set-up/down effect on a plane beach is developed using the longshore component of the depth-integrated momentum balance equation. To predict the longshore current, the wave height model should first be formulated because the longshore current depends on the wave height directly. Two wave model, regular wave model and random wave model, are developed based on the energy flux balance equation. Also, the numerical model estimating the set-up inside the shoreline is developed using both the on-offshore momentum equation and the moving boundary technique. The numerical models are verified by the analytical solution, and compared with laboratory data. It is found from the comparison that developed models may be predicted accurately the longshore current with set-up/down effect on a plane beach.

• Journal of Information Technology Services
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• v.10 no.3
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• pp.95-112
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• 2011

This paper proposes a new registry model for systematically referencing standards in e-Business field. We have many systems that provide standards and additional information. However, there are several problems such as inefficient standard information, dependency on a standard type, high standard information acquisition cost, no relations between standard information, and so on. In this paper, a new registry model and its prototype implementation are described. The proposed model is defined based on ISO/IEC 11179-Metadata registries, which is one of the international standards for interoperability between data. The proposed model provides an integrated- systematic standard information support, and also considers technology stack and business processes for e-Business systems. This paper develops a prototype for the proposed model and implementation result. Finally, to show the contribution of our proposal, this paper shows the comparative evaluation between previous systems and our proposal with various comparative items.

• Journal of Ocean Engineering and Technology
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• v.4 no.1
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• pp.49-54
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• 1990

This study describes the application of a two dimensional depth integrated numerical model. The explict scheme of finite difference method had been applied to the model of circulation. The nonlinear terms showed a slight difference for the variations of water elevation when calculated grid was small. They were also found to be minor when calculated grid size was increased. For verification of the numerical model, numerical results were compared with predicted values and field data. In the model, the effect of nonlinear advective terms proved not to be significant.

• Magazine of the Korean Society of Agricultural Engineers
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• v.41 no.2
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• pp.37-43
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• 1999

This study was conducted to develop the simple and convenient irrigation control model which can maintain the appropriate rates of irrigation and drainage of nutrient solution according to the enviornmental conditions and growth stages in soilless culture of cucumber. In order to obtain fundamental data for development of the model, investigation of the actual state of soilless culture practices was carried out. Most irrigatioin systems of soillness culture were controlled by the time colock. Evapotranspiration of cucumber in soilness culture was investigated and correlations with environmental conditions were analyzed , and its estimating model was developed. In order to develop the irrigation system which can control the amount of nutrient solution applied according to seasons, weather conditions, and growth stages, a irrigation clock control was developed. Applicability of the model was tested by simulation. Drainage rates of nutrient solution controlled by conventional time clock, integrated solar radiation, and the developed model were 61% , 20%, and 32% , respectively in cucumber perlite culture.

• Asia pacific journal of information systems
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• v.11 no.4
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• pp.99-111
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• 2001

The prediction of stock price index is a very difficult problem because of the complexity of stock market data. It has been studied by a number of researchers since they strongly affect other economic and financial parameters. The movement of stock price index has a series of change points due to the strategies of institutional investors. This study presents a two-stage forecasting model of stock price index using change-point detection and artificial neural networks. The basic concept of this proposed model is to obtain intervals divided by change points, to identify them as change-point groups, and to use them in stock price index forecasting. First, the proposed model tries to detect successive change points in stock price index. Then, the model forecasts the change-point group with the backpropagation neural network(BPN). Finally, the model forecasts the output with BPN. This study then examines the predictability of the integrated neural network model for stock price index forecasting using change-point detection.

• Journal of Drive and Control
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• v.20 no.4
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• pp.54-63
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• 2023

High and intermittent loading cycles induce fatigue damage to transmission components, resulting in premature gearbox failure. To identify gearbox defects, numerous vibration-based diagnostics techniques, using several artificial intelligence (AI) algorithms, have recently been presented. In this paper, an optimized deep belief network (DBN) model for gearbox problem diagnosis was designed based on time-frequency visual pattern identification. To optimize the hyperparameters of the model, a particle swarm optimization (PSO) approach was integrated into the DBN. The proposed model was tested on two gearbox datasets: a wind turbine gearbox and an experimental gearbox. The optimized DBN model demonstrated strong and robust performance in classification accuracy. In addition, the accuracy of the generated datasets was compared using traditional ML and DL algorithms. Furthermore, the proposed model was evaluated on different partitions of the dataset. The results showed that, even with a small amount of sample data, the optimized DBN model achieved high accuracy in diagnosis.

• Journal of applied mathematics & informatics
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• v.5 no.2
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• pp.373-382
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• 1998

Most of the works in Time Series Analysis are based on the Auto Regressive Integrated Moving Average (ARIMA) models presented by Box and Jeckins(1976). If the data exhibits no ap-parent deviation from stationarity and if it has rapidly decreasing autocorrelation function then a suitable ARIMA(p,q) model is fit to the given data. Selection of the orders of p and q is one of the crucial steps in Time Series Analysis. Most of the methods to determine p and q are based on the autocorrelation function and partial autocor-relation function as suggested by Box and Jenkins (1976). many new techniques have emerged in the literature and it is found that most of them are over very little use in determining the orders of p and q when both of them are non-zero. The Durbin-Levinson algorithm and Innovation algorithm (Brockwell and Davis 1987) are used as recur-sive methods for computing best linear predictors in an ARMA(p,q)model. These algorithms are modified to yield an effective method for ARMA model identification so that the values of order p and q can be determined from them. The new method is developed and its validity and usefulness is illustrated by many theoretical examples. This method can also be applied to an real world data.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1634-1637
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• 2003

Any given object on the motor-driven turntable is pictured from 8 to 72 different views with a digital camera. 3D shape reconstruction is performed with the integrated software called by Scanware from these multiple digital photographs. There are several steps such as configuration, calibration, capturing, segmentation, shape creation, texturing and merging process during the shape reconstruction process. 3D geometry data can be exported to cad data such as Autocad input file. Also 3D image model is generated from 3D geometry and texture data, and is used to advertise the model in the internet environment. Consumers can see the object realistically from wanted views by rotating or zooming in the internet browsers with Scanbull spx plug-in. The spx format allows a compact saving of 3D objects to handle or download. There are many types of scan equipments such as laser scanners and photogrammetric scanners. Line or point scan methods by laser can generate precise 3D geometry but cannot obtain color textures in general. Reversely, 3D image modeling with photogrammetry can generate not only geometries but also textures from associated polygons. We got various 3D image models and introduced the process of getting 3D image model of an internet-connected watchdog robot.

• Journal of Aerospace System Engineering
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• v.14 no.2
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• pp.50-56
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• 2020

In this study, we investigated whether long short-term memory (LSTM) can be used in the future to predict F10.7 index data; the F10.7 index is a space environment factor affecting atomic oxygen erosion. Based on this, we compared the prediction performances of LSTM, the Autoregressive integrated moving average (ARIMA) model (which is a traditional statistical prediction model), and the similar pattern searching method used for long-term prediction. The LSTM model yielded superior results compared to the other techniques in the prediction period starting from the max/min points, but presented inferior results in the prediction period including the inflection points. It was found that efficient learning was not achieved, owing to the lack of currently available learning data in the prediction period including the maximum points. To overcome this, we proposed a method to increase the size of the learning samples using the sunspot data and to upgrade the LSTM model.

• Journal of the Society of Naval Architects of Korea
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• v.61 no.2
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• pp.98-105
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• 2024

Due to the low accuracy of measured data obtained from low-cost GNSS and IMU devices, it was hard to secure the required accuracy of the measured position and heading angle for autonomous navigation which was conducted by a model-scale marine mobility. In this paper, a localization technique using the Extended Kalman Filter (EKF) is proposed for coping with the issue. First of all, a position and heading angle estimator is developed using EKF with the assumption of a point mass model. Second, the measured data from GNSS and IMU, including position, heading angle, and velocity are used for the estimator. In addition, the heading angle is additionally obtained by comparing the LiDAR point cloud with map information for a temporal water tank. The newly acquired heading angle is integrated into the estimator as an additional measurement to correct the inaccuracy in the heading angle measured from the IMU. The effectiveness of the proposed approach is investigated using data acquired from preliminary tests of the model-scale autonomous marine mobility.