• Journal of Korean Society for Atmospheric Environment
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• v.12 no.2
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• pp.167-177
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• 1996

To investigate the source-receptor relationships aerosol model has been used to simulate the distribution behavior of the yellow sand. Data for meteorological fields were obtained by Meso-scale Analysis and Prediction Model System/Seoul National University (MAPMS/SNU) for five days (10-14 April 1988). To obtain the distributions of concentration of yellow sand,the aerosol model has been modified to allow quantifications of relative concentration distributions of yellow sand. Source regions of yellow sand were delineated by soil maps of China and emission rate as a function of wind stress(Westphal et al., 1987). Using 3-dimensional wind fields the backward trajectories from 3 receptor grids at the layer of .sigma. =0.95, 0.9, 0.85, 0.8 were calculated. In order to facilitate quantitative assessment of source-receptor relationships, it was assumed that the perturbations in along-trajectory and cross-trajectory proceed linearly with time, in accord with Gaussian distribution characteristics. On the basis of this assumption, the probability fields were calculated from every grid point with source strength 1. Using these probability fields and emission retes, the potential contributions of upstream sources along the trajectories were estimated. The results of this study indicate that the application of trajectory modeling is useful in investigating the quantitative relationship between source and receptor regions.

• Journal of the Korean Society of Safety
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• v.29 no.4
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• pp.1-8
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• 2014

In this study, the numerical simulation using AUTODYN-3D program was investigated angle trajectory prediction for inclined impacts of projectiles. The penetration and perforation of polycarbonate plate by 7.62 mm projectile was investigated numerically. The characteristic structure of the projectile's trajectory in the polycabonate plates was studied. Two combined failure criteria were used in the target plate, and the target plate was modeled with the properties of polycarbonate for simulating the ricochet phenomenon. The effect of the angle of inclination on the trajectory and kinetic energy of the projectile were studied. The dynamic deformation behaviors tests of polycabonate were compared with numerical simulation results which can be used as predictive purpose. From the simulation, the ricochet phenomenon was occurred for angles of inclination of $0^{\circ}{\leq}{\theta}{\leq}20^{\circ}$. The projectile perforated the plate for ${\theta}{\leq}30^{\circ}$, thus defining a failure envelope for numerical configuration. The numerical analyses are used to study the effect of the projectile impact velocity on the depth of penetration (DOP). It can be observed that the residual velocities were almost linear relative to penetration velocities. It means that polycarbonate has high resistance at higher velocities.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.9
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• pp.1584-1590
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• 2008

The combined query which consists of the region and trajectory query finds trajectories of moving objects which locate in a certain region. The trajectory query is very informant factor to determine query performance because it processes a point query continuously to find predecessors. This results in bad performance due to revisiting nodes in an index. This paper suggests an efficient method for the combined query based on the 3-dimensional R-tree which has good performance of the region query. The basic idea is that we define the least common search line which enables to search single path and a filtering method based on prediction without revisiting nodes.

• 한국전산유체공학회:학술대회논문집
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• 2006.10a
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• pp.136-141
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• 2006

A prediction of store separation trajectory using grid survey method has been conducted. For the grid survey method, store's aerodynamic flowfield data such as freestream and grid data is needed to solve 6 degree of freedom(6-DOF) equations of motion. In the presented study, aerodynamic flowfield data was generated by Euler solver instead of CTS wind tunnel test. The predicted trajectories shows good agreement with CTS test results.

• 한국전산유체공학회:학술대회논문집
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• 2007.04a
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• pp.134-137
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• 2007

Aero-Heating phenomenon is one of the severe problems occurring in high speed missile flight. in the high speed flight, not only stagnation point but also aft body parts encounter high temperature related structural problems. But the phenomenon is not easy to predict accurately because unsteady calculation according to a flight trajectory is needed, and takes much time. In this Paper, a fast and precise scheme is introduced, which calculates heat flow and temperature by simple pressure field prediction on a missile.

• Proceedings of KOSOMES biannual meeting
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• 2017.11a
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• pp.2-2
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• 2017

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.2
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• pp.168-175
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• 1999

In this paper, a modified RBF(Radial Basis Function) network structure is suggested for the prediction of a time-series with non-linear, non-stationary characteristics. Coventional RBF network predicting time series by using past outputs sense the trajectory of the time series and react when there exists strong relation between input and hidden activation function's RBF center. But this response is highly sensitive to level and trend of time serieses. In order to overcome such dependencies, hidden activation functions are modified to react to the increments of input variable and multiplied by increment(or dectement) for prediction. When the suggested structure is applied to prediction of Macyey-Glass chaotic time series, Lorenz equation, and Rossler equation, improved performances are obtained.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.4
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• pp.503-508
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• 2010

Recently, the advance of mobile devices has made various services possible beyond simple communication. One of services is the predicting the future path of users and providing the most suitable location based service based on the prediction results. Almost of these prediction methods are based on previous path data. Thus, calculating similarities between current location information and the previous trajectories for path prediction is an important operation. The collected trajectory data have a huge amount of location information generally. These information needs the high computational cost for calculating similarities. For reducing computational cost, the meaningful location based trajectory model approaches are proposed. However, most of the previous researches are considering only the physical information such as stay time and the distance for extracting the meaningful locations. Thus, they will probably ignore the characteristics of users for meaningful location extraction. In this paper, we suggest a meaningful location extracting and trajectory simplification approach considering the stay time, distance, and additionally interaction information of user. The method collects the location information using GPS device and interaction information between the user and the others. Using these data, the proposed method defines the proximity of the people who are related with the user. The system extracts the meaningful locations based on the calculated proximities, stay time and distance. Using the selected meaningful locations the trajectories are simplified. For verifying the usability of the proposed method, we collect the behavioral data of smart phone users. Using these data, we measure the suitability of meaningful location extraction method, and the accuracy of prediction approach based on simplified trajectories. Following these result, we confirmed the usability of proposed method.

• International Journal of Control, Automation, and Systems
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• v.3 no.4
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• pp.509-523
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• 2005

We are developing a novel framework, PRIDE (PRediction In Dynamic Environments), to perform moving object prediction (MOP) for autonomous ground vehicles. The underlying concept is based upon a multi-resolutional, hierarchical approach which incorporates multiple prediction algorithms into a single, unifying framework. The lower levels of the framework utilize estimation-theoretic short-term predictions while the upper levels utilize a probabilistic prediction approach based on situation recognition with an underlying cost model. The estimation-theoretic short-term prediction is via an extended Kalman filter-based algorithm using sensor data to predict the future location of moving objects with an associated confidence measure. The proposed estimation-theoretic approach does not incorporate a priori knowledge such as road networks and traffic signage and assumes uninfluenced constant trajectory and is thus suited for short-term prediction in both on-road and off-road driving. In this article, we analyze the complementary role played by vehicle kinematic models in such short-term prediction of moving objects. In particular, the importance of vehicle process models and their effect on predicting the positions and orientations of moving objects for autonomous ground vehicle navigation are examined. We present results using field data obtained from different autonomous ground vehicles operating in outdoor environments.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.8
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• pp.321-328
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• 2014

In this paper, we present a MapReduce-based mobility pattern mining system which can predict efficiently the next place of mobile users. It learns the mobility pattern model of each user, represented by Hidden Markov Models(HMM), from a large-scale trajectory dataset, and then predicts the next place for the user to visit by applying the learned models to the current trajectory. Our system consists of two parts: the back-end part, in which the mobility pattern models are learned for individual users, and the front-end part, where the next place for a certain user to visit is predicted based on the mobility pattern models. While the back-end part comprises of three distinct MapReduce modules for POI extraction, trajectory transformation, and mobility pattern model learning, the front-end part has two different modules for candidate route generation and next place prediction. Map and reduce functions of each module in our system were designed to utilize the underlying Hadoop infrastructure enough to maximize the parallel processing. We performed experiments to evaluate the performance of the proposed system by using a large-scale open benchmark dataset, GeoLife, and then could make sure of high performance of our system as results of the experiments.