A procedure for minimizing the environmental burden and maximizing the efficiency of storage sites used for the final disposal of spent fuel has been proposed. In this procedure, fission products (highly mobile and producing heat) are collected, and uranium and TRU-RE (transuranium-rare earth) oxide are independently stored. The possibility and applicability of radiation measurement for monitoring the nuclear materials effectively throughout the process has been simulated and evaluated. For the simulation, the properties of the chemical processes were analyzed, the major radiation emitters were determined, and the production of nuclear materials by chemical reactions were evaluated. In each process, the content of nuclear material was changed by up to 20% to represent abnormal conditions. The results showed that the plutonium peak was matched with the change in the TRU content and the measured signal was changed linearly with respect to the content change of the plutonium. From the neutron measurement, a linear response of the TRU content variation was obtained. In addition, a logic diagram was developed for the nuclear monitoring. The integration of radiation detections is recommended for monitoring the process effectively and efficiently.

In this study, we proposed a simulator for the development of a digital multi-process welding machine and a welding process monitoring system. The simulator, which mimics the data generation process of the welding machine, is composed of process control circuit, peripheral device circuit, and wireless communication circuit. Utilizing this simulator, we aimed to develop a welding process monitoring system that can monitor the welding situations of four multi-process welding machines and three processes each, with data transmission through wireless communication. Through the operation of the proposed simulator, sequential digital processing of multi-process welding data and wireless communication were achieved. The welding process monitoring system enabled real-time monitoring and accumulation of the process data. The selection of upper and lower limits for process variables was carried out using a deep neural network based on allowable changes in bead shape, enabling the management of welding quality by applying a process control technique based on the trend of received data.

Governments around the world are enacting laws mandating explainable traceability when using AI(Artificial Intelligence) to solve real-world problems. HAI(Human-Centric Artificial Intelligence) is an approach that induces human decision-making through Human-AI collaboration. This research presents a case study that implements the Human-AI collaboration to achieve explainable traceability in governmental data analysis. The Human-AI collaboration explored in this study performs AI inferences for generating labels, followed by AI interpretation to make results more explainable and traceable. The study utilized an example dataset from the Ministry of Oceans and Fisheries to reproduce the Human-AI collaboration process used in actual policy-making, in which the Ministry of Science and ICT utilized R&D PIE(R&D Platform for Investment and Evaluation) to build a government investment portfolio.

As the use of nuclear energy has been expanded, issues in a spent nuclear fuel management are raised. Several methods have been proposed and developed to manage spent fuels safely and efficiently. One method is to reduce environmental burden in disposal of spent fuels by decreasing volume of high-level waste. A nuclides management process (NMP) is one example. Through this novel process, it is able to separate highly mobile nuclides (ex. iodine, krypton), high thermal emission nuclides (ex. strontium, barium), and optionally, uranium from spent fuels. Since the NMP is a back-end fuel cycle technology, a reliable safeguards system should be employed in the facility. As international atomic energy agency (IAEA) recommends safeguards-by-design (SBD), it is desirable to investigate an appropriate safeguards approach at a step of technology development. Process monitoring (PM) is a complemental safeguards technology for traditional safeguards technologies which based on mass balance. PM traces nuclear materials indirectly but consecutively by using process parameters such as temperature, pressure, and flow of fluid. These parameters are obtainable by installing appropriate sensors. In a respect of SBD, PM is a promising approach to achieve the safeguards goal, the timely detection of diversion of a nuclear material. However, it is necessary to classify useful process parameters from all available signals which provided from PM in order to properly utilize PM. In this study, we investigated application methods of the PM approach to NMP. NMP consists of several unit processes in series. Firstly, we inspected a principle and a feature of each unit process. Based on the results, we evaluated applicability of the PM approach to each unit process according to effectiveness in enhancing safeguardability. Several unit processes were expected that their safeguards are able to be enhanced by using certain process parameters from PM.

In the case of a die-casting process, defects that are difficult to confirm by visual inspection, such as shrinkage bubbles, may occur due to an error in maintaining a vacuum state. Since these casting defects are discovered during post-processing operations such as heat treatment or finishing work, they cannot be taken in advance at the casting time, which can cause a large number of defects. In this study, we propose an approach that can predict the occurrence of casting defects by defect type using machine learning technology based on casting parameter data collected from equipment in the die casting process in real time. Die-casting parameter data can basically be collected through the casting equipment controller. In order to perform classification analysis for predicting defects by defect type, labeling of casting parameters must be performed. In this study, first, the defective data set is separated by performing the primary clustering based on the total defect rate obtained during the post-processing. Second, the secondary cluster analysis is performed using the defect rate by type for the separated defect data set, and the labeling task is performed by defect type using the cluster analysis result. Finally, a classification learning model is created by collecting the entire labeled data set, and a real-time monitoring system for defect prediction using LabView and Python was implemented. When a defect is predicted, notification is performed so that the operator can cope with it, such as displaying on the monitoring screen and alarm notification.

For highly contaminated elements such as reactor pressure vessels or reactor internals, it is a viable option to cool-down and dismantle these elements in submerged (e.g. underwater) state. Several tools and processes such as saw cutting, water jet cutting or plasma cutting are currently used for underwater cutting, with each of them having their own advantages and disadvantages. The main disadvantage of these existing methods, especially saw and water jet cutting, is the generation of secondary waste that then needs to be filtered out of the water. In addition, in the case of water jet cutting, a considerable amount of abrasive material is added, which must also be stored. To overcome this drawback, the feasibility of using laser cutting under water to minimize secondary waste production has been actively studied recently. One of the challenges with the underwater laser cutting is to visually monitor the cutting process. Flowing air bubbles generated by the cutting gas and the flashing light emitted from the laser and melting material prohibit visual monitoring of the cutting process. This study introduces a method to enhance the video from a monitoring camera. Air bubbles can be detected by computing optical flows and the video quality can be enhanced by selective removal of the detected bubbles. In addition, suppressing the frame image update from flashing light area can also effectively enhance the video quality. This paper describes the simple yet effective video quality enhancement method and reports preliminary results.

This study suggests a model of production information system that can reduce manufacturing lead time and uniformize quality by using DNC S/W as a part of constructing production information management system in the industrial field of the existing marine engine block manufacturing companies. Under the effect of development of this system, the NC machine interface device can be installed in the control computer to obtain the quality information of the workpiece in real time so that the time to inspect the process quality and verify the product defect information can be reduced by more than 70%. In addition, the reliability of quality information has been improved and the external credibility has been improved. It took 30 minutes for operator to obtain, analyze and manage the quality information when the existing USB memory is used, but the communication between the NC controller computer and the NC controller in real time was completed to analyze the workpiece within 10 seconds.

A number of plating companies have been exposed to the risk of fire due to unexpected temperature increasing of water in a plating bath. Since the companies are not able to forecast the unexpected temperature increasing of water and most of raw materials in the plating process have low ignition temperature, it is easy to be exposed to the risk of fire. Thus, the companies have to notice the changes immediately to prevent the risk of fire from plating process. Due to this reason, an agile and systematic temperature monitoring system is required for the plating companies. Unfortunately, in case of small size companies, it is hard to purchase a systematic solution and be offered consulting from one of the risk management consulting companies due to an expensive cost. In addition, most of the companies have insufficient research and development (R&D) experts to autonomously develop the risk management solution. In this article, we developed a real time remote temperature monitoring system which is easy to operate with a lower cost. The system is constructed by using Raspberry Pi single board computer and Android application to release an economic issue for the small sized plating manufacturing companies. The derived system is able to monitor the temperature continuously with tracking the temperature in the batch in a short time and transmit a push-alarm to a target-device located in a remoted area when the temperature exceeds a certain hazardous-temperature level. Therefore, the target small plating company achieves a risk management system with a small cost.

산업의 빠른 발전 속도에 따라 연구 개발도 함께 발전해야 한다. 따라서 현재 제조공정에 대한 품질 특성치의 분석방법으로 공정 모수의 작은 변화도 쉽게 탐지를 할 수 있는 EWMA 관리도와 Shewhart 관리도보다 공정 변화에 민감하게 탐지 가능한 CUSUM 관리도에 관한 연구가 많이 이루어지고 있다. 하지만 식스시그마 공정관리에 맞춘 평균, 불량률, 미세 분산을 동시에 감지할 수 있는 동시 관리 체계 연구는 많이 미흡하다. 본 연구에서는 기존의 CUSUM, EWMA 관리도 기법보다 빠른 이상 감지를 위해서 평균, 불량률, 분산 3가지가 동시에 관리되어질 수 있도록 Zp-s 관리도를 소개한다. Zp-s 관리도는 ARL을 통해 기존 관리도보다 민감함을 확인할 수 있다.

관리도를 사용하여 공정평균, 공정산포 등 여러 가지 공정모수를 관리할 수 있다. 그러나 공정모수의 미세 변동을 효과적으로 관리할 수 있는 기법체계는 아직 미완이다. 식스시그마 공정관리 등 정밀공정관리를 위해서는 미세 공정평균과 공정산포관리가 전제되어야한다. 특히 높은 수준의 공정능력을 유지하기위해서는 공정산포관리가 선결과제이다. 본 본문에서는 공정평균과 공정불량률, 공정산포의 미세변동을 효과적으로 관리할 수 있는 기술체계의 연구동향을 분석하고 미세공정산포관리를 위한 대안을 제시하고자 한다.

When monitoring an instrumental process, one often collects a host of data such as characteristic signals sent by a sensor in short time intervals. Characteristic data of short time intervals tend to be autocorrelated. In the instrumental processes often the practice of adjusting the setting value simply based on the previous one, so-called ‘adjacent point operation’, becomes more critical, since in the short run the deviations are harder to detect and in the long run they have amplified consequences. Stochastic modelling using ARIMA or AR models are not readily usable here. Due to the difficulty of dealing with autocorrelated data conventional practice is resorting to choosing the time interval where autocorrelation is weak enough then to using I-MR control chart to judge the process stability. In the autocorrelated instrumental processes it appears that using the Shewhart chart and the time interval data where autocorrelation is relatively not existent turns out to be a rather convenient and very useful practice to determine the process stability. However in the autocorrelated instrumental processes we intend to show that one would presumably do better using the EWMA control chart rather than just using the Shewhart chart along with some arbitrarily intervalled data, since the former is more sensitive to shifts given appropriate weights.

The process of berthing/deberthing operations for entering/leaving vessels in Busan northern harbor was analyzed and evaluated by using an integrated VTS(vessel traffic service) system installed in the ship training center of Pukyong National University, Busan, Korea. The integrated VTS system used in this study was consisted of ARPA radar, ECDIS(electronic chart display and information system), backup(recording) system, CCTV(closed-circuit television) camera system, gyro-compass, differential GPS receiver, anemometer, AIS(automatic identification system), VHF(very high frequency) communication system, etc. The network of these systems was designed to communicate with each other automatically and to exchange the critical information about the course, speed, position and intended routes of other traffic vessels in the navigational channel and Busan northern harbor. To evaluate quantitatively the overall dynamic situation such as maneuvering motions for target vessel and its tugboats while in transit to and from the berth structure inside a harbor, all traffic information in Busan northern harbor was automatically acquired, displayed, evaluated and recorded. The results obtained in this study suggest that the real-time tracking information of traffic vessels acquired by using an integrated VTS system can be used as a useful reference data in evaluating and analyzing exactly the dynamic situation such as the collision between ship and berth structure, in the process of berthing/deberthing operations for entering/leaving vessels in the confined waters and harbor.

This paper describes on the real-time monitoring of net setting and hauling process for fishing operations of Danish seine vessels in the southern waters of Korea as an application of a PC based ECDIS system. Tracking of fishing process was performed for the large scale Danish seine vessel of G/T 90 and 350 PS class using the fishing gear which the length of net, ground rope, head rope and sweep line including warp in both sides were 86m, 104m, 118m and 3,200m, respectively. Tracking information for net setting and hauling process was continuously recorded for 23 fishing operations performed on November and December, 2003. All measurement data, such as trawl position, heading, towing course and past track which was individually time stamped during data acquisition, was processed in real time on the ECDIS and displayed simultaneously on the ENC chart. The results indicated that after the separation of a marker buoy from Danish seiner, the averaged running speed of vessel and the averaged setting period while shooting the seine on the course of diamond shape to surround the fish school in the 23 fishing operations were 8.3 knots and 13.1 minutes, respectively. And with the maker buoy taken on board, the averaged running speed of vessel and the averaged towing period while closing the seine on the straight route was 1.0 knots and 47.0 minutes, respectively. After the closing stage of hand rope, the hand rope was towed by the averaged speed of 2.2 knots during the 13.0 minutes. The average area for route of diamond shape swept by sweep lines of the seine in 23 fishing grounds was 709,951.6m2. Further investigation is also planed to provide more quantitative tracking information and to achieve more effective surveillance and control of Danish seine vessels in EEZ fishing grounds.

자연식생 재현을 파악하기 위하여 리싸이클링에코녹화공법을 이용한 장흥다목적댐 배면부에 모니터링 시험구를 선정하였다. 모니터링 시험구는 2004년 5월에 설치하였고, 2004년 5월부터 2005년 10월까지 4차례 걸쳐 식물상, 식물군집구조, 자연이입종, 고사율을 모니터링 하였다. 우드칩 멀칭 후 출현 종 수 감소와 함께 출현식물의 도시화지수가 감소하여 자연성은 증진되었으며, 덩굴식물의 세력은 확장되었다. 낙엽활엽수군락에서 자연이 입종 수가 가장 많았고, 식재수목의 고사율도 높았다.

이 연구의 목적은 서울대학교 백운산연습림 낙엽활엽수림에서 벌채 후 주연부 식생 변화를 모니터링 하는 것이다. 벌채 후 8년,10년이 경과한 시점 인 4차 조사(2001년)와 5차 조사(2003년) 결과에서 주연부식생의 변화는 다음과 같다. 시간이 경과함에 따라산림주연부에서 경쟁력 이 우수한수종은 비목나무, 병꽃나무,조록싸리, 생강나무,두릅나무 등이 었고,산림주연부와 인접한조사구에서 경쟁력이 우수한수종은 비목나무, 병꽃나무,고추나무 등이 었다. 벌채지 산림내부에서 경쟁력 이 우수한수종은 비목나무,생강나무 등으로 나타났다. 산림주연부에서 높은 경쟁력을 갖는 수종은 방위,광량, 고도, 기존 특정종의 성장에 따라 차이가 있었으나 양사면에서 높은 경쟁력을 갖는 수종은 비목나무와 병꽃나무이 었다. 조릿대는산림 벌채 후 초기 에 왕성한 생장력을 보였으나 시간이 경과함에 따라수관층이 형성되면서 감소세를 보였으며, 피복율은 남서향 사면보다 북동향 사면에서 우세하게 출현하였다. 조사기간별 유사도지수는 벌채시 산림주연부에서 산림내부로 거 리가 멀어질수록 낮아지는 경향을 나타냈다.

낙엽활엽수림 개벌 후 벌채지에서 주연부 식생구조 발달과정을 규명하기 위하여 서울대학교 농업생명과학대학 부속 남부연습림 내 백운산지 역 제26임반 벌채적지를 대상지로 선정하였다. 1994년에 두 개의 모니터링 조사구를 설치하였고, 1994년, 1997년, 1999년에 식생조사를 실시하였다. 벌채 후 6년간 주연부식생 변화는 다음과 같다. 시간이 경과함에 따라 산림주연부에서 경쟁력이 우수한 수종은 병꽃나무, 비목나무, 국수나무, 산초나무 등이었고, 산림주연부와 인접한 조사구에서 경쟁력이 우수한 수종은 고추나무, 비목나무, 덜꿩나무 등이었으며, 벌채지 산림내부에서 경쟁력이 우수한 수종은 병꽃나무, 비목나무, 생강나무등으로 나타났다. 두개의 모니터링 조사구에서 주연부 천이단 계상 우세종은 방위와 국지저 위치, 기존 우점수종에 따라 차이를 나타내었다. 벌채 후 경과년도에 따라 각 벌채지 산림 주연부에서 벌채지 산림내부로 거리가 멀어질수록 유사도지수도 낮아지는 경향을 나타내었고, 종다양도지수, 종수, 개체수 및 수관피도는 벌채지 산림주연부에서 벌채지 산림내부로 갈수록 감소하였다.