• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.3
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• pp.465-470
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• 2013

Dempster-Shafer Evidence Theory is available for multi-sensor data fusion. Basic Probability Assignment is essential for multi-sensor data fusion using Dempster-Shafer Theory. In this paper, we proposed a novel method of BPA calculation with signal assessment. We took notice of the signal that reported from the sensor mote at the time slot. We assessed the variation rate of the reported signal from the terminal. The trend of variation implies significant component of the context. We calculated the variation rate of signal for reveal the relation of the variation and the context. We could reach context inference with BPA that calculated with the variation rate of signal.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.5
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• pp.462-469
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• 2023

In this study, we propose a method for evaluating the risk of collision between ships to support determination on the risk of collision in a situation in which ships encounter each other and to prevent collision accidents. Because several uncertainties are involved in the navigation of a ship, must be considered when evaluating the risk of collision. We apply the Dempster-Shafer theory to manage this uncertainty and evaluate the collision risk of each target vessel in real time. The distance at the closest point approach (DCPA), time to the closest point approach (TCPA), distance from another vessel, relative bearing, and velocity ratio are used as evaluation factors for ship collision risk. The basic probability assignments (BPAs) calculated by membership functions for each evaluation factor are fused through the combination rule of the Dempster-Shafer theory. As a result of the experiment using automatic identification system (AIS) data collected in situations where ships actually encounter each other, the suitability of evaluation was verified. By evaluating the risk of collision in real time in encounter situations between ships, collision accidents caused by human errora can be prevented. This is expected to be used for vessel traffic service systems and collision avoidance systems for autonomous ships.

• Journal of Korean Society of Transportation
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• v.29 no.1
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• pp.71-79
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• 2011

Traditionally, a dynamic network model is considered as a tool for solving real-time traffic problems. One of useful and practical ways of using such models is to use it to produce and disseminate forecast travel time information so that the travelers can switch their routes from congested to less-congested or uncongested, which can enhance the performance of the network. This approach seems to be promising when the traffic congestion is severe, especially when sudden incidents happen. A consideration that should be given in implementing this method is that travel time information may affect the future traffic condition itself, creating undesirable side effects such as the over-reaction problem. Furthermore incorrect forecast travel time can make the information unreliable. In this paper, a network-wide travel time prediction model under incidents is developed. The model assumes that all drivers have access to detailed traffic information through personalized in-vehicle devices such as car navigation systems. Drivers are assumed to make their own travel choice based on the travel time information provided. A route-based stochastic variational inequality is formulated, which is used as a basic model for the travel time prediction. A diversion function is introduced to account for the motorists' willingness to divert. An inverse function of the diversion curve is derived to develop a variational inequality formulation for the travel time prediction model. Computational results illustrate the characteristics of the proposed model.