The stochastic point-source model has been widely used in generating artificial ground motions, which can be used to develop a ground motion prediction equation and to evaluate the seismic risk of structures. This model mainly consists of three different functions representing source, path, and site effects. The path effect is used to emulate decay in ground motion in accordance with distance from the source. In the stochastic point-source model, the path attenuation effect is taken into account by using the geometrical attenuation effect and the inelastic attenuation effect. The aim of this study is to develop accurate equations of ground motion attenuation in the Korean peninsula. In this study, attenuation was estimated and validated by using a stochastic point source model and observed ground motion recordings for the Korean peninsula.

PURPOSES: This investigational survey is to observe a proper spatial aggregation method for path travel time estimation using the hi-pass DSRC system. METHODS: The links which connect the nodes of section detectors location are used for path travel time estimation traditionally. It makes some problem such as increasing accumulation errors and processing times. In this background, the new links composition methods for spatial aggregation are considered by using some types of nodes as IC, JC, RSE combination. Path travel times estimated by new aggregation methods are compared with PBM travel times by MAE, MAPE and statistical hypothesis tests. RESULTS : The results of minimum sample size and missing rate for 5 minutes aggregation interval are satisfied except for JC link path travel time in Seoul TG~Kuemho JC. Thus, it was additionally observed for minimum sample size satisfaction. In 15, 30 minutes and 1 hour aggregation intervals, all conditions are satisfied by the minimum sample size criteria. For accuracy test and statistical hypothesis test, it has been proved that RSE, Conzone, IC, JC links have equivalent errors and statistical characteristics. CONCLUSIONS : There are some errors between the PBM and the LBM methods that come from dropping vehicles by rest areas. Consequently, this survey result means each of links compositions are available for the estimation of path travel time when PBM vehicles are missed.

This paper proposes an integrated positioning system to localize a moving object in the shadow-area that exists in the water tank. The new water tank for underwater robots is constructed to evaluate the navigation performance of underwater vehicles. Several sensors are integrated in the water tank to provide the position information of the underwater vehicles. However there are some areas where the vehicle localization becomes very poor since the very limited sensors such as sonar and depth sensors are effective in underwater environment. Also there are many disturbances at sonar data. To reduce these disturbances, an extended Kalman filter has been adopted in this research. To localize the underwater vehicles under the hostile situations, a SVR (Support Vector Regression) has been systematically applied for estimating the position stochastically. To demonstrate the performance of the proposed algorithm (an extended Kalman filter + SVR analysis), a new UI (User Interface) has been developed.