Gamma spectrometry is one of the main analysis methods used to obtain information about unknown radioactive materials. In gamma-ray energy spectrometry, even for the same gamma-ray spectrum, the analysis results may be slightly different depending on the skill of the analyst. Therefore, it is important to increase the proficiency of the analyst in order to derive accurate analysis results. This paper describes the development of the virtual spectrum simulator program for gamma spectrometry training. This simulator program consists of an instructor module and trainee module program based on an integrated server, in which the instructor transmits a virtual spectrum of arbitrarily specified measurement conditions to the students, allowing each student to submit analysis results. It can reproduce a virtual gamma-ray energy spectrum based on virtual reality and augmented reality technique and includes analysis function for the spectrum, allowing users to experience realistic measurement and analysis online. The virtual gamma-ray energy spectrum DB program manages a database including theoretical data obtained by Monte Carlo simulation and actual measured data, which are the basis for creating a virtual spectrum. The currently developed database contains data on HPGe laboratory measurement as well as in-situ measurements (ground surface, decommissioned facility wall, radiowaste drum) of portable HPGe detectors, LaBr3(Ce) detector and NaI detector. The analysis function can be applied not only to the virtual spectrum, but also to the input measured spectrum. The parameters of the peak analysis algorithm are customizable so that even low-resolution spectra can be properly analyzed. The validity of the database and analysis algorithm was verified by comparing with the results derived by the existing analysis programs. In the future, the application of various in-situ gamma spectrometers will be implemented to improve the profiling of the depth distribution of deposited nuclides through dose rate assessment, and the applicability of the completed simulator in actual in-situ gamma spectrometry will be verified.

During the decommissioning of nuclear facilities, 3D digital model that precisely describes the work environment can expedite the accomplishment of the work. Thus, the workers’ exposure to radiation is minimized and the safety risk to the workers is reduced, while precluding inadvertent effects on the environment. However, it is common that the 3D model does not exist for legacy nuclear facilities as most of the initial design drawings are 2D drawings and even some of the 2D drawings are missing. Even in the case that all of the 2D drawings are intact, these initial design drawings need to be updated using asbuilt data because facilities get modified through years of operation. In those cases, 3D scanning can be a good option to quickly and accurately generate a structure’s actual 3D geometric information. 3D scanning is a technique used to capture the shape of an object in the form of point cloud. Point cloud is a collection of large number of points on the external surfaces of objects measured by 3D scanners. The conversion of point cloud to 3D digital model is a labor-intensive process as a human worker needs to recognize objects in the point cloud and convert the objects into 3D model, even though some of the conversion process can be automated by using commercial software packages. With the aim of full automation of scan-to-3D-model process, deep learning techniques that take point cloud as input and generate corresponding 3D model have been studies recently. This paper introduces an efficient scan simulation method. The simulator generates synthetic point cloud data used to train deep learning models for classifying reactor parts in robotic nuclear decommissioning system. The simulator is built by implementing a ray-casting mechanism using a python library called ‘Pycaster’. In order to improve the speed of simulation, multiprocessing is applied. This paper describes the ray casting simulation mechanism and compares the in-house scan simulator with an open source sensor simulation package called Blensor.

Material balance evaluation is an important measure to determine whether or not nuclear material is diverted. A prototype code to evaluate material balance has been developed for uranium fuel fabrication facility. However, it is difficult to analyze the code’s functionality and performance because the utilization of real facility data related to material balance evaluation is very limited. It is also restricted to deliberately implement various abnormal situations based on real facility data, such as nuclear diversion condition. In this study, process flow simulator of uranium fuel fabrication facility has been developed to produce various process data required for material balance evaluation. The process flow simulator was developed on the basis of the Simulink-SimEvents framework of the MathWorks. This framework is suitable for batch-based process modeling like uranium fuel fabrication facility. It dynamically simulates the movement of nuclear material according to the time function and provides process data such as nuclear material amount at inputs, outputs, and inventories required for Material Unaccounted For (MUF) and MUF uncertainty calculation. The process flow simulator code provides these data to the material balance evaluation code. And then the material balance evaluation code calculates MUF and MUF uncertainty to evaluate whether or not nuclear material is diverted. The process flow simulator code can simulate the movement of nuclear material for any abnormal situation which is difficult to implement with real process data. This code is expected to contribute to checking and improving the functionality and performance of the prototype code of material balance evaluation by simulating process data for various operation scenarios.

This study introduces the Three-Memory-Model (Cherry, 2019) in education into Maritime Simulator- based training in Sri Lanka and conducts empirical research. In simulator-based education what is disseminated as knowledge during the Briefing, Scenario and Debriefing phases must be transferred from short-term, across working memory to long-term-memory. Working memory gained during the scenario phase, could be encoded into long-term-memory through rehearsal probes. But the number of probes which could be tolerated by the participants of simulator-based training has not undergone empirical investigation. Thus, selecting the Open Sea scenario phase as its setting the research questions aim to identify tolerance limits in the participants for the number of freezes and the number of probes introduced during each freeze. The methodology selects a population of seafarers (n = 60). Through stratified random sampling this population was subdivided based on experience at sea as Group A (n = 30): Mean of 2 years and Group B (n = 30): Mean of 13.6 years of sea experience. The duration of the open sea scenario phase is 35 minutes with freezes at 10-minute intervals. The number of probes were given a range of 7 to. Data analysis utilized SPSS. The highest percentage mean value was obtained for three freezes for the Open Sea scenario phase while two freezes had the next highest percentage mean value. The mean value of the tolerance limits for questions during one freeze is approximately 9 and 6 probes for Group A and B respectively. Citing prior research on working memory, visuo-spatial vs. verbal working memory, reaction time and age this study raises a counter argument against the findings: the self-declared tolerance limits of the number of questions the participants feel comfortable to answer during each freeze. The findings of this research are valuable to maritime Simulator-based instructional designers outside and within Sri Lanka.

자율주행 시물레이터는 자율 주행을 시험하고 검증하는 일에 있어 현실에 비해 높은 비용 절감의 효과를 가 지고 오지만 높은 컴퓨터 연산량에 의해 많은 하드웨어 기기를 요구하게 된다. 게임을 이용하여 자율 주행에 필요한 학습 데이터를 획득하는 경우도 있다. 게임은 저비용 시뮬레이터로 활용되고 있지만 게임 외적인 특정 상황을 모의하기에도, 필요한 데이터 획득에도 제한적이다. 또 다른 방법으로 게임 엔진을 통한 가상 환경 모 의 연구가 수행되고 있다. 하지만 게임 엔진에서는 사용자가 직접 필요한 모델링을 해줘야 하기 때문에 개발 비용이 크게 작용된다. 특히, 3D LIDAR는 360도로 Ray를 쏴서 정밀 거리를 최소 10Hz 이내의 실시간 획득이 필요하다. 실시간으로 3D LIDAR 데이터를 획득하는 것은 GPU(Graphics Processing Unit) 사용량이 많은 작업 이기 때문에, 저비용 시뮬레이터를 위해서는 저비용 3D LIDAR 모의가 필요하다. 본 논문에서는 낮은 컴퓨터 연산을 사용하는 C++ 기반 3D LIDAR 모의 프레임 워크를 제안한다. 제안된 3D LIDAR는 다수의 언덕으로 이 루어진 비포장 Map에서 성능을 검증 하였으며, 성능 검증을 의해 본 논문에서 생성된 3D LIDAR로 간단한 LPP(Local Path Planning) 생성 방법도 소개한다. 제안된 3D LIDAR 프레임 워크는 저비용 실시간 모의가 필요 한 자율 주행 분야에 적극 활용되길 바란다.

4차 산업혁명 시대의 흐름에 맞춰 농업에서도 ICT 기술을 활용한 스마트팜의 개발 및 보급을 통해 경쟁력을 높이기 위한 노력이 진행되고 있다. 과거 농부의 경험에 의해 축적된 지식을 이용하던 농업에서 각종 센서를 이용하여 다양한 재배 환경을 분석하고 이를 이용하여 최적의 재배 환경을 제어하는 지능형 시스템으로 변 하고 있으며, 네트워크를 통하여 시간과 공간의 제약이 없이 작물 재배가 가능한 환경이 만들어지고 있다. 본 논문에서는 기존에 구축된 클라우드 기반 스마트팜과 연동하여 팜 시뮬레이터를 구현하는 방법을 제안한 다. 클라우드에 누적된 환경 데이터와 제어 데이터를 이용하여 환경 변수에 대한 예측 모델을 학습하고 실제 운영중인 스마트팜의 실시간 환경 데이터를 이용하면 보다 현실감 있는 시뮬레이션이 가능하게 되어 사용자 의 몰입을 유도할 수 있다. 단순 시뮬레이션에서 벗어나 학습 모드를 통해 실제 농부의 스마트팜 운영 데이 터를 학습할 수 있도록 하고, 운영 모드에서는 실제 스마트팜의 운영 결과와 비교를 통하여 경쟁을 통한 성 취감을 얻을 수 있도록 하였다. 이러한 경험이 누적되면 작물재배에 관심이 있는 사용자들에게 실제 스마트 팜을 통한 작물 재배의 경험을 제공할 수 있는 사업 모델로의 확장도 가능할 것이다. 추후 메타버스 (metaverse) 상에 스마트팜을 연동하는 연구를 통하여 가상 공간에서 보다 사실적으로 스마트팜을 운영하는 사용자 경험을 제공해 줄 수 있도록 확장할 수 있을 것이다.

In recent years, importance of blockchain systems has been grown after success of bitcoin. Distributed consensus algorithm is used to achieve an agreement, which means the same information is recorded in all nodes participating in blockchain network. Various algorithms were suggested to resolve blockchain trilemma, which refers conflict between decentralization, scalability, security. An algorithm based on Byzantine Agreement among Decentralized Agents (BADA) were designed for the same manner, and it used limited committee that enables an efficient consensus among considerable number of nodes. In addition, election of committee based on Proof-of-Nonce guarantees decentralization and security. In spite of such prominence, application of BADA in actual blockchain system requires further researches about performance and essential features affecting on the performance. However, performance assessment committed in real systems takes a long time and costs a great deal of budget. Based on this motivation, we designed and implemented a simulator for measuring performance of BADA. Specifically, we defined a simulation framework including three components named simulator Command Line Interface, transaction generator, BADA nodes. Furthermore, we carried out response surface analysis for revealing latent relationship between performance measure and design parameters. By using obtained response surface models, we could find an optimal configuration of design parameters for achieving a given desirable performance level.