In this study, we evaluate artificial neural network (ANN) models that estimate the positions of gamma-ray sources from plastic scintillating fiber (PSF)-based radiation detection systems using different filtering ratios. The PSF-based radiation detection system consists of a single-stranded PSF, two photomultiplier tubes (PMTs) that transform the scintillation signals into electric signals, amplifiers, and a data acquisition system (DAQ). The source used to evaluate the system is Cs-137, with a photopeak of 662 keV and a dose rate of about 5 μSv/h. We construct ANN models with the same structure but different training data. For the training data, we selected a measurement time of 1 minute to secure a sufficient number of data points. Conversely, we chose a measurement time of 10 seconds for extracting time-difference data from the primary data, followed by filtering. During the filtering process, we identified the peak heights of the gaussian-fitted curves obtained from the histogram of the time-difference data, and extracted the data located above the height which is equal to the peak height multiplied by a predetermined percentage. We used percentage values of 0, 20, 40, and 60 for the filtering. The results indicate that the filtering has an effect on the position estimation error, which we define as the absolute value of the difference between the estimated source position and the actual source position. The estimation of the ANN model trained with raw data for the training data shows a total average error of 1.391 m, while the ANN model trained with 20%-filtered data for the training data shows a total average error of 0.263 m. Similarly, the 40%-filtered data result shows a total average error of 0.119 m, and the 60%-filtered data result shows a total average error of 0.0452 m. From the perspective of the total average error, it is clear that the more data are filtered, the more accurate the result is. Further study will be conducted to optimize the filtering ratio for the system and measuring time by evaluating stabilization time for position estimation of the source.

Multi-floor navigation of a mobile robot requires a technology that allows the robot to safely get on and off the elevator. Therefore, in this study, we propose a method of recognizing the elevator from the current position of the robot and estimating the location of the elevator locally so that the robot can safely get on the elevator regardless of the accumulated position error during autonomous navigation. The proposed method uses a deep learning-based image classifier to identify the elevator from the image information obtained from the RGB-D sensor and extract the boundary points between the elevator and the surrounding wall from the point cloud. This enables the robot to estimate the reliable position in real time and boarding direction for general elevators. Various experiments exhibit the effectiveness and accuracy of the proposed method.

In this study, the characteristics of a horizontal sundial from the Joseon Dynasty were investigated. Korea’s Treasure No. 840 (T840) is a Western-style horizontal sundial where hour-lines and solar-term-lines are engraved. The inscription of this sundial indicates that the latitude (altitude of the north celestial pole) is 37° 39´, but the gnomon is lost. In the present study, the latitude of the sundial and the length of the gnomon were estimated based only on the hour-lines and solar-termlines of the horizontal sundial. When statistically calculated from the convergent point obtained by extending the hourlines, the latitude of this sundial was 37° 15´ ± 26´, which showed a 24´ difference from the record of the inscription. When it was also assumed that a convergent point is changeable, the estimation of the sundial’s latitude was found to be sensitive to the variation of this point. This study found that T840 used a vertical gnomon, that is, perpendicular to the horizontal plane, rather than an inclined triangular gnomon, and a horn-shaped mark like a vertical gnomon is cut on its surface. The length of the gnomon engraved on the artifact was 43.1 mm, and in the present study was statistically calculated as 43.7 ± 0.7 mm. In addition, the position of the gnomon according to the original inscription and our calculation showed an error of 0.3 mm.

This paper concerns the applications of the Kalman filter to navigation and the develment of computer programs of the navigational calculations. Methods to apply the Kalman filter to celestial fix, fix by cross bearing and cocked hat are proposed, and numerical simulations under various noise conditiions are conducted. The accuracy of the optimal positions obtained by the Kalman filter is compared with that of the fixed positiions by radial error method. In the case of celestial fix, an algorithm to estimate the optimal positions by using the linear Kalman filter is presented. The optimal positions by the Kalman filter are compared with the running fixes and with the most probable positions obtained from a single line of position. It is confirmed that the resutls of the proposed method are more accurate than the others. In practical piloting, bearings are generally measured intermittently and the measurement process is nonlinear. It is, therefore, difficult for us to apply the Kalman filter to fix by cross bearing. In order to be used in such an unfavorable case, the extended Kalman filter is revised and the aplicability of the revised extended Kalman filter is checked by numerical simulation under various noise conditions. In a cocked hat, an inside or outside fix is dependent only upon azimuth spread, if the error of each line of position is assumed to be equal both in magnitude and sign. A new technique of selecting a ship's position between an inside fix and an outside fix in a cocked hat by using fix determinant derived from the equation of three lines of position is also presented. The relations among the optimal position by Kalman filter, incentre (or excentre) and random error centtre of the cocked hat are discussed theoretically and the accuracy of the optimal position is compared with that of the others by numerical simulation.