This paper proposes a noble multi-robot path planning algorithm using Deep q learning combined with CNN (Convolution Neural Network) algorithm. In conventional path planning algorithms, robots need to search a comparatively wide area for navigation and move in a predesigned formation under a given environment. Each robot in the multi-robot system is inherently required to navigate independently with collaborating with other robots for efficient performance. In addition, the robot collaboration scheme is highly depends on the conditions of each robot, such as its position and velocity. However, the conventional method does not actively cope with variable situations since each robot has difficulty to recognize the moving robot around it as an obstacle or a cooperative robot. To compensate for these shortcomings, we apply Deep q learning to strengthen the learning algorithm combined with CNN algorithm, which is needed to analyze the situation efficiently. CNN analyzes the exact situation using image information on its environment and the robot navigates based on the situation analyzed through Deep q learning. The simulation results using the proposed algorithm shows the flexible and efficient movement of the robots comparing with conventional methods under various environments.

A 1MV AMS was installed in KIGAM (Korea Institute of Geoscience and Mineral Resources). After 4 months of installation, the AMS started normal operation from January 2008. This multi-element AMS was developed by HVEE to measure 14C, 10Be, and 26Al. The results of an acceptance test demonstrate that this machine is capable of routine 14C age dating and of measurements of other radioisotopes in terms of accuracy and precision as well as the background level. After installation, an investigation aimed at determining the stable operating conditions was conducted, and background levels were determined to be as low as 10–15 for 14C and 10–14 for 10Be and 26Al.

ABSTRACT. Many previous studies on the sample preparation of various kinds of radiocarbon dating samples by accelerator mass spectrometry (AMS) have been examined at KIGAM (Korea Institute of Geoscience and Mineral Resources) and our own procedures have been established. Furthermore, an automated reduction system has been developed. The volume of the reduction region was minimized to improve the reduction yield, and air-actuated pneumatic valves and solenoid arrays were used for computer control of the system. Operation of all the valves and vacuum pumps and signals from the temperature sensors and pressure gauges were interfaced to a personal computer with an A/D board. A computer program was also developed to perform automatic operation of the reduction system. This system consistently shows a higher reduction yield than 90%. The reduction time of the system is currently 140 min.

Tenreiro, C (Tenreiro, Claudio). Univ Talca, Talca, Chile.The Korea Rare Isotope Accelerator, currently referred to as KoRIA, is briefly presented. The KoRIA facility is aimed to enable cutting-edge sciences in a wide range of fields. It consists of a 70 kW isotope separator on-line (ISOL) facility driven by a 70 MeV, 1 mA proton cyclotron and a 400 kW in-flight fragmentation (IFF) facility. The ISOL facility uses a superconducting (SC) linac for post-acceleration of rare isotopes up to about 18 MeV/u, while the SC linac of IFF facility is capable of accelerating uranium beams up to 200 MeV/u, 8 p mu A and proton beams up to 600 MeV, 660 mu A. Overall features of the KoRIA facility are presented with a focus on the accelerator design