Nuclear power plants in Korea stores approximately 3,800 drums of paraffin solidification products. Due to the lack of homogeneity, these solidification products are not allowed to be disposed of. There is therefore a need for the separation of paraffin from the solidification products. This work developed an equipment for a selective separation of paraffin from the solidification product using the vacuum evaporation and condensational recovery method in a closed system. The equipment mainly consists of a vacuum evaporator and a condensational deposition recovery chamber. Nonisothermal vacuum TGAs, kinetic analyses and kinetic predictions were conducted to set appropriate operation conditions. Its basic operability under the established conditions was first confirmed using pure paraffin solid. Simulated paraffin solidification product fixing dried boric acid waste including nonradioactive Co and Cs were then fabricated and tested for the capability of selective separation of paraffin from the simulated waste. Paraffin was selectively separated without entertainment of Co and Cs. It was confirmed that the developed equipment could separate and recover paraffin in the form of nonradioactive waste.

Recently, simultaneous localization and mapping (SLAM) approaches employing Rao-Blackwellized particle filter (RBPF) have shown good results. However, no research is conducted to analyze the result representation of SLAM using RBPF (RBPF-SLAM) when particle diversity is preserved. After finishing the particle filtering, the results such as a map and a path are stored in the separate particles. Thus, we propose several result representations and provide the analysis of the representations. For the analysis, estimation errors and their variances, and consistency of RBPF-SLAM are dealt in this study. According to the simulation results, combining data of each particle provides the better result with high probability than using just data of a particle such as the highest weighted particle representation.

The Mobile robots are increasingly being used to perform tasks in unknown environments. The potential of robots to undertake such tasks lies in their ability to intelligently and efficiently search in an environment. An algorithm has been developed for robots which explore the environment to measure the physical properties (dust in this paper). While the robot is moving, it measures the amount of dust and registers the value in the corresponding grid cell. The robot moves from local maximum to local minimum, then to another local maximum, and repeats. To reach the local maximum or minimum, simple gradient following is used. Robust estimation of the gradient using perturbation/correlation, which is very effective when analytical solution is not available, is described. By introducing the probability of each grid cell, and considering the probability distribution, the robot doesn’t have to visit all the grid cells in the environment still providing fast and efficient sensing. The extended algorithm to coordinate multiple robots is presented with simulation results.