The inorganic scintillator used in gamma spectroscopy must have good efficiency in converting the kinetic energy of charged particles into light as well as high light output and high light detection efficiency. Accordingly, various studies have been conducted to enhance the net-efficiency. One way to improve the light yield has been studied by coating scintillators with various nanoparticles, so that the scintillation light can undergo resonance on surface between scintillators and nanoparticles resulting in higher light yield. In this study, an inorganic scintillator coated with CsPbBr3 perovskite nanocrystals using dip coating technique was proposed to improve scintillation light yield. The experiment was carried out by measuring scintillation light output, as the result of interaction between inorganic scintillator coated with CsPbBr3 perovskite nanocrystals and gamma-ray emitted from Cs-137 gamma source. The experimental results show that the channel corresponding to 662 keV full energy peak in the Cs-137 spectrum shifted to the right by 14.37%. Further study will be conducted to investigate the detailed relationships between the scintillation light yield and the characteristics of coated perovskite nanoparticles, such as diameter of nanoparticles, coated area ratio and width of coated region.

As drones expand beyond military purposes to the private sector, the level of use of drones in various fields is increasing. However, the world was shocked by the attempt to attack with a drone equipped with a C4 bomb in the US and the attempt to assassinate a head of state using a drone in Venezuela. Drone threats to domestic nuclear power plants are also increasing due to the expansion of drone use, terrorist threats, and North Korea’s invasion of drones. Overseas, various drone threats to nuclear power plants have occurred. In October 2014, French electricity company Electricite de France confirmed that it had observed unauthorized drones over seven nuclear power plants across France. A drone threat occurred at the Savannah River Site (SRS), a U.S. Department of Energy facility that processes and stores nuclear materials. In 2016, eight drones were observed by security personnel. In 2016, a drone flew over the cooling tower of the Liebstadt nuclear power plant in Switzerland, and publicly shared the filmed video on YouTube. In July 2018, Greenpeace activists intentionally crashed a drone into the outer wall of the spent fuel building in Boughey, France. In January 2019, they used drones to drop smoke bombs and release videos at Orangeo’s nuclear facility containing irradiated fuel. In January 22, Sweden saw drones flying over three nuclear power plants. Drone was also seen at the Forsmark nuclear power plant on Friday and at two other Swedish nuclear power plants in Oskarshamn and Ringhals on Monday. Anti-drone technology to counter the threat of drone terrorism is also developing. Anti-drone technology detects, tracks, and identifies illegal drones to neutralize them. Various technologies such as radar, EO/IR cameras, Lidar, sensor, and RF scanners are being developed for drone detection. Depending on the detection technology, it has advantages such as detection distance and remote control drone detection. However, there are also disadvantages, such as obstacles, weather condition, and the inability to detect drones that do not transmit signals. Methods such as jammer, capture, and destruction have been developed for incapacitation technology. While it has advantages such as stability and portability, it has disadvantages such as limited use and damage to the surroundings. Accordingly, it is necessary to draw realistic measures to defend against the threat of nuclear power plants by paying constant attention to the various detection, identification, and neutralization anti-drone systems that continue to evolve.

In South Korea, the exporters of items related to nuclear power generation are diversified. Consequently, there is a risk of illegitimate export by companies failing to recognize the export control system because the awareness about this system for the strategic items among the subcontractors of nuclear power facilities is limited. To prevent illegitimate export of the strategic items, it is necessary to conduct outreach activities regarding the export control system for the related companies. Additionally, the exporters and export license examiners should consider whether an export target is on the Denial List, who may divert the strategic items to weapons of mass destruction. Therefore, the Korea Institute of Nuclear Nonproliferation and Control developed two systems for controlling illegitimate export of the Trigger List items. The first system, Nuclear Industry Information Collection and Analysis System, can gather information about the key nuclear industries in Korea and analyze the dealing of strategic items. The second system, Denied Persons Information Gathering System, can regularly gather information about the denied persons and provide the updated data to the exporters and regulatory examiners. These two systems can be used for outreach activities and export license examination to prevent illegitimate export of the strategic items.

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.

유방조직 확장기(BTE)는 MR 금기로 분류되고 있으나 이전 연구들에서 제한적인 조건에서 MRI 검사가 가능함을 발표하였다. 본 연구는 미국재료시험 협회에서 권고하는 변위력과 토크뿐 만 아니라 뒤틀림을 측정하여 MR 안전성을 평가하였다. 3.0 T MRI 장치와 350 ㎖, 650 ㎖ BTE를 사용하였다. 변위력은 자기장이 가장 큰 지점에서 측정 기구에 BTE를 매달고 발생된 최대 변위각을 3회씩 측정하였다. 토크는 원형 용기에 생리식염수를 단계적으로 채운 BTE를 놓고 자기장에 의해 발생된 회전력을 45˚ 간격에서 정성적으로 측정하였다. 뒤틀림은 생리식염수를 최대로 채운 뒤 자성 밸브의 뒤틀림 각도를 포켓 경사계를 이용하여 두부, 어깨, 유방, 간, 골반이 중심일 때 유방 위치에서 각각 측정하였다. 측정 결과 변위각 은 350 ㎖의 경우 0 ㏄, 150 ㏄에서 각각 125˚, 54.3˚, 650 ㎖은 0 ㏄에서만 123.3˚로 MR 금기 기준을 초과했다. 토크는 350 ㎖의 경우 모두 +4로 측정되었고, 650 ㎖의 경우 450 ㏄ 생리식염수 미만에서는 모두 +4, 550 ㏄ 이상에서는 +1∼+4로 나타났으며, 일부를 조건을 제외하고 모두 MR 금기 조건을 초과했다. 뒤틀림은 350 ㎖의 경우 5˚∼80˚ 를 나타냈으며. 650 ㎖는 15˚∼90˚를 나타내어 환자에게 통증 유발 가능성을 확인했다. BTE의 변위각, 토크, 뒤틀림을 측정하여 MR 금기로 평가하였으며, 어떠한 조건하에서도 MR 환경에서 환자의 안전을 보장할 수 없다.

현재 대부분의 게임은 가상공간에서만 이루어지기 때문에 사용자의 실질적인 움직임이 없이 단순한 조작으로 수행되며, 이로 인해 사용자의 신체 및 정신에 다양한 부작용이 발생한다. 대 체 현실 게임은 기존 온라인 게임과 달리 가상 공간과 현실 세계를 연계하여 기존 게임의 부작 용을 완화할 수 있는 새로운 게임 장르이며, 게임 참여자간의 사회적 친밀감의 증가로 단순한 게임을 넘어서 새로운 사회적 유대를 형성하는 수단이 될 것으로 기대되고 있다. 본 논문에서 는 모바일 기기와 NFC 태그를 활용한 대체 현실 게임의 실제 구현을 보인다. 이를 위해 대체 현실 게임을 제작하기 위한 새로운 편집기와 이를 플레이하기 위한 모바일 기기 기반의 게임 실행기를 구현하였으며, 편집기를 통해 제작된 게임은 모바일 게임 실행기를 통하여 실행된다.

많은 장르의 게임들에서 타 플레이어의 캐릭터와 다르게 특성화된 플레이어 고유의 캐릭터는 게임의 주된 흥미 요소 중 하나이다. 현재 보통의 경우, 이러한 요소를 제공해주기 위한 시스템 은 대부분 유저들이 직접 특성 항목에 캐릭터 성장에 따라 주어지는 포인트를 할당하여 캐릭터 에 고유한 특성을 부여하는 형태로 이루어져 있다. 이러한 시스템에서 개개의 유저 스스로에게 합리적인 캐릭터 특성화를 제공하기에 많은 어려움과 제약이 따른다. 이 연구에서는 유저의 플 레이 타입에 입각한 성장 시스템을 제안한다. 플레이어의 행동이나 타입에 의해 결정되는 성장 형태는 페트리네트에 의해 생성되며, 실험 결과와 분석을 통해 제안된 방식이 캐릭터 성장 시 스템에 적합함을 보인다.