• The Journal of the Acoustical Society of Korea
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• v.22 no.6
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• pp.512-518
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• 2003

In this paper, resonance durability analysis is performed for the fatigue life assessment considering vibration effect of a vehicle system. In the resonance durability analysis, the frequency response and the dynamic load on frequency domain are used. Multi-body dynamic analysis, finite element analysis, and fatigue life prediction method are applied for the virtual durability assessment. To obtain the frequency response and the dynamic load history, the computer simulations running over typical pothole and Belgian road are carried out by utilizing vehicle dynamic model. The durability estimations on the rear suspension system of the passenger car are performed by using the resonance durability analysis technique and compared with the quasi-static durability analysis. The study shows that the fatigue life considering resonant frequency of vehicle system can be effectively estimated in early design stage.

• Aerospace Engineering and Technology
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• v.5 no.2
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• pp.149-158
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• 2006

A review has been made on what kind of method can be applied to predict the dynamic derivatives of the separated PLF(Payload Fairing) halves of a launch vehicle in consideration of technology and budget. An optimal approach is selected considering the geometric characteristics of the PLF halves, the aerodynamic conditions and the required accuracy. The time history of aerodynamic force/moment coefficients are obtained for the forced harmonic motions by solving the unsteady Euler equations derived with respect to the inertial reference frame. and the dynamic derivatives are deduced by integration of the aerodynamic coefficients for one period. In this research, the dynamic derivatives are presented for 0.6$\leq$ M $\leq$2.0, $-180^{\circ}$ $\leq$$\alpha$ $\leq$$180^{\circ}$ and $-90 ^{\circ}$$\leq$$\beta$$\leq$$90 ^{\circ}$.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.14-19
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• 2021

This paper presents LiDAR static obstacle map based vehicle dynamic state estimation algorithm for urban autonomous driving. In an autonomous driving, state estimation of host vehicle is important for accurate prediction of ego motion and perceived object. Therefore, in a situation in which noise exists in the control input of the vehicle, state estimation using sensor such as LiDAR and vision is required. However, it is difficult to obtain a measurement for the vehicle state because the recognition sensor of autonomous vehicle perceives including a dynamic object. The proposed algorithm consists of two parts. First, a Bayesian rule-based static obstacle map is constructed using continuous LiDAR point cloud input. Second, vehicle odometry during the time interval is calculated by matching the static obstacle map using Normal Distribution Transformation (NDT) method. And the velocity and yaw rate of vehicle are estimated based on the Extended Kalman Filter (EKF) using vehicle odometry as measurement. The proposed algorithm is implemented in the Linux Robot Operating System (ROS) environment, and is verified with data obtained from actual driving on urban roads. The test results show a more robust and accurate dynamic state estimation result when there is a bias in the chassis IMU sensor.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.

• Journal of Korean Association for Spatial Structures
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• v.20 no.2
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• pp.51-58
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• 2020

Recently, deep learning that is the most popular and effective class of machine learning algorithms is widely applied to various industrial areas. A number of research on various topics about structural engineering was performed by using artificial neural networks, such as structural design optimization, vibration control and system identification etc. When nonlinear semi-active structural control devices are applied to building structure, a lot of computational effort is required to predict dynamic structural responses of finite element method (FEM) model for development of control algorithm. To solve this problem, an artificial neural network model was developed in this study. Among various deep learning algorithms, a recurrent neural network (RNN) was used to make the time history response prediction model. An RNN can retain state from one iteration to the next by using its own output as input for the next step. An eleven-story building structure with semi-active tuned mass damper (TMD) was used as an example structure. The semi-active TMD was composed of magnetorheological damper. Five historical earthquakes and five artificial ground motions were used as ground excitations for training of an RNN model. Another artificial ground motion that was not used for training was used for verification of the developed RNN model. Parametric studies on various hyper-parameters including number of hidden layers, sequence length, number of LSTM cells, etc. After appropriate training iteration of the RNN model with proper hyper-parameters, the RNN model for prediction of seismic responses of the building structure with semi-active TMD was developed. The developed RNN model can effectively provide very accurate seismic responses compared to the FEM model.

• Journal of the Korea Society for Simulation
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• v.17 no.3
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• pp.19-26
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• 2008

In this paper, we propose a dynamic prediction algorithm to predict the bond price using actual data set of treasure note (T-Note). The proposed algorithm is based on term structure model of the interest rates, which takes place in various financial modelling, such as the standard Gaussian Wiener process. To obtain cumulative distribution functions (CDFs) of actual data for the interest rate measurement used, we use the natural cubic spline (NCS) method, which is generally used as numerical methods for interpolation. Then we also use the random number generation scheme (RNGS) to calculate the pricing of bond through the obtained CDF. In empirical computer simulations, we show that the lower values of precision in the proposed prediction algorithm corresponds to sharper estimates. It is very reasonable on prediction.

• Journal of Digital Convergence
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• v.19 no.1
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• pp.67-75
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• 2021

Despite the increase in sales of imported vehicles in Korea, research on the sales forecast of parts logistics centers is very limited. This study aims to perform a sales prediction on bestselling goods in the automobile part logistics center. System dynamics was adopted as a methodology for the prediction method, which considered causal relationship of variables that affected the dynamic characteristics and feedback loops. The analysis results showed that the consumable sales amount of oil increased over time. As a result of conducting the MAPE, the model was assessed to be a reasonable predictive model of 31.3%. In addition, the sales of battery products increased from every October in both of actual and predicted data followed by the peak sales in December and then decrease from next February. This study has academic implications that it secured actual data of specific imported automobile part logistics center, which has not done before in previous studies and quantitatively analyzed the prediction of the quantity of released goods of future sales through system dynamics.

• International Journal of Aeronautical and Space Sciences
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• v.16 no.2
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• pp.264-277
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• 2015

This article investigates various transcription techniques for the Legendre pseudospectral (PS) method to compare the pros and cons of each approach. Eight combinations from four different types of collocation points and two discretization methods for dynamic constraints, which differentiate Legendre PS transcription techniques, are implemented to solve a carefully selected test set of nonlinear optimal control problems (NOCPs). The convergence property and prediction accuracy are compared to provide a useful guideline for selecting the best combination. The tested NOCPs consist of the minimum time, minimum energy, and problems with state and control constraints. Therefore, the results drawn from this comparative study apply to the solution of similar types of NOCPs and can mitigate much debate about the best combinations. Additionally, important findings from this study can be used to improve the numerical efficiency of the Legendre PS methods. Three PS applications to the aerospace engineering problems are demonstrated to prove this point.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.4 s.247
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• pp.379-386
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• 2006

The hyper-elastic material has been used gradually and its range was extended all over the industry. The performance prediction of hyper-elastic material was required not only experimental methods but also numerical methods. In this study, we presented the process how to use numerical method for hyper-elastic material and applied it to seat-ring of butterfly valve. The finite element analysis was executed to evaluate the mechanical characteristics of hyper-elastic material. And the optimum model considered conditions and features. According to that model, the load conditions were obtained by using CFD analysis.

• Wind and Structures
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• v.5 no.2_3_4
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• pp.151-164
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• 2002

Predictions of the pedestrian level wind speeds for the downtown area of Auckland that have been obtained by wind tunnel and computational fluid dynamic (CFD) modelling are presented. The wind tunnel method involves the observation of erosion patterns as the wind speed is progressively increased. The computational solutions are mean flow calculations, which were obtained by using the finite volume code PHOENICS and the $k-{\varepsilon}$ turbulence model. The results for a variety of wind directions are compared, and it is observed that while the patterns are similar there are noticeable differences. A possible explanation for these differences arises because the tunnel prediction technique is sensitivity to gust wind speeds while the CFD method predicts mean wind speeds. It is shown that in many cases the computational model indicates high mean wind speeds near the corner of a building while the erosion patterns are consistent with eddies being shed from the edge of the building and swept downstream.