Wolsong Unit 1, a domestic heavy water reactor nuclear power plant, was permanently shut down in December 2019. Accordingly, Wolsong Unit 1 plans to prepare a Final Decommissioning Plan (FDP), submit it to the government by 2024, receive approval for decommissioning, and begin full-scale decommissioning. One of the important tasks in the decommissioning of Wolsong Unit 1 is to determine the decommissioning strategy. It is necessary to decide on a decommissioning strategy considering various factors and variables, secure the technical background, and justify it. The selection of a decommissioning strategy is best achieved through the use of formal decisionmaking assistance techniques, such as considerations related to influencing factors. It is very important to understand the basic decommissioning strategy alternatives and whether sufficient consideration has been given to situations where only a single unit is permanently shut down in a multi-unit site like Wolsong Unit 1, while the remaining units are in normal operation. As a process for selecting a decommissioning strategy, first, all considerations that could potentially affect decommissioning presented in the KINS Decommissioning Safety Review Guidelines were synthesized, influencing factors to be used in the decision-making process were determined, and the concept was defined. In order to select the most appropriate decommissioning strategy by considering various evaluation attributes of possible decommissioning alternatives (immediate dismantling and delayed dismantling), the Wolsong Unit 1 decommissioning strategy was evaluated by reflecting the AHP decision-making technique.

Domestic commercial low- and intermediate-level radioactive waste storage containers are manufactured using 1.2 mm thick cold-rolled steel sheets, and the outer surface is coated with a thin layer of primer of 10~36 μm. However, the outer surface of the primer of the container may be damaged due to physical friction, such as acceleration, resonance, and vibration during transportation. As a result, exposed steel surfaces undergo accelerated corrosion, reducing the overall durability of the container. The integrity of storage containers is directly related to the safety of workers. Therefore, the development of storage containers with enhanced durability is necessary. This paper provides an analysis of mechanical properties related to the durability of WC (tungsten carbide)-based coating materials for developing low- and intermediate-level radioactive waste storage containers. Three different WC-based coating specimens with varied composition ratios were prepared using HVOF (high-velocity oxy-fuel) technique. These different specimens (namely WC-85, WC-73, and WC-66) were uniformly deposited on cold-rolled steel surfaces ensuring a constant thickness of 250 μm. In this work, the mechanical properties of the three different WCbased coaitng materials evaluated from the viewpoints of microstructure, hardness, adheision force between substrate and coating material, and wear resistance. The cross-sectional SEM-EDS (Scanning Electron Microscope-Energy Dispersive X-ray Spectroscopy) images revealed that elements W (tungsten), C (carbon), Ni (nickel), and Cr (chromium) were uniformly distributed within the each coating layers which was approximately 250 μm thick. The average hardness values of HWC-85 and HWC-73 were found to be 1,091 Hv (Vickers Hardness) and 1,083 Hv, respectively, while the HWC-66 exhibited relatively lower hardness value of 883 Hv. This indicates that a higher WC content results in increased hardness. Adhesion force between and substrates and coating materials exceeded 60 MPa for all specimens, however, there were no significant differences observed based on the tungsten carbide content. Furthermore, a taber-type abrasion tester was used for conducting abrasion resistance tests under specific conditions including an H-18 load weight at 1,000 g with rotational speed set at 60 RPM. The abrasion resistance tests were performed under ambient temperatures (RT: 23±2°C) as well as relative humidity levels (RH: 50±10%). Currently, the ongoing abrasion resistance tests will include some results in this study.

Background: Proprioception in the ankle joint is important for maintaining balance. There is a correlation between joint position sense (JPS), balance and ankle dorsiflexion range of motion (DF-ROM). Objectives: The purpose of this study was to compare the effects of talocrural joint mobilization (TJM) and muscle energy technique (MET) of plantar flexor muscle (PF) on improving joint position sense (JPS) and static balance. Design: Cross-over randomized trial research. Methods: Sixteen participants (male 10, female 6; 20’s of their age) without ankle instability were recruited. In a randomized, three conditions, no intervention, talocalcaneal mobilization, and PF-MET were all applied to 16 participants. For TJM, the Kaltenborn grade 3 joint gliding method, in which the talus moves posteriorly perpendicular to the tacrocrural joint plane, were used. In the PF-MET, MET method were applied gastrocnemius muscle and soleus muscle with 25% of 1 Repeat Maximum of each muscle. The all participants performed PF-MET and TJM for 18 minutes. DF-ROM measured the weightbearing lunge test. JPS measured using the active joint angle reproduction test. Static balance was measured displacement of center of pressure parameter. Results: PF-MET and TJM had significant differences in DF-ROM and AP displacements. PF-MET significantly increased at 5° and 15° of plantar flexion and 5° of dorsiflexion, and COP velocity significantly decreased in JPS compared to TJM. Conclusion: PF-MET and TJM are effective in increasing DF-ROM. However, PF-MET has a more positive effect on improving JPS and static balance than on talocrural joint mobilization.

The metal bush assembling process is a process of inserting and compressing a metal bush that serves to reduce the occurrence of noise and stable compression in the rotating section. In the metal bush assembly process, the head diameter defect and placement defect of the metal bush occur due to metal bush omission, non-pressing, and poor press-fitting. Among these causes of defects, it is intended to prevent defects due to omission of the metal bush by using signals from sensors attached to the facility. In particular, a metal bush omission is predicted through various data mining techniques using left load cell value, right load cell value, current, and voltage as independent variables. In the case of metal bush omission defect, it is difficult to get defect data, resulting in data imbalance. Data imbalance refers to a case where there is a large difference in the number of data belonging to each class, which can be a problem when performing classification prediction. In order to solve the problem caused by data imbalance, oversampling and composite sampling techniques were applied in this study. In addition, simulated annealing was applied for optimization of parameters related to sampling and hyper-parameters of data mining techniques used for bush omission prediction. In this study, the metal bush omission was predicted using the actual data of M manufacturing company, and the classification performance was examined. All applied techniques showed excellent results, and in particular, the proposed methods, the method of mixing Random Forest and SA, and the method of mixing MLP and SA, showed better results.

With the recent advent of Metaverse, the character industry that reflects the characteristics of users' faces is drawing attention. there is a hassle that users have to select face components such as eyes, nose, and mouth one by one. In this paper, we propose a diffusion-based model that automatically generates characters from content human photographs. Our model generates user artistic characters by reflecting content information such as face angle, direction, and shape of a content human photo. In particular, our model automatically analyzes detailed information such as glasses and whiskers from content photo images and reflects them in artistic characters generated. Our network generates the final character through a three-step: diffusion process, UNet, and denoising processes. We use image encoders and CLIP encoders for the connection between style and input data. In the diffusion process, a collection of noise vectors is gradually added to a style vector to enable lossless learning of the detailed styles. All input values except for the style images are vectorized with CLIP encoders and then learned with noise style vectors in the UNet. Subsequently, noise is removed from the vectors through the UNet to obtain the artistic character image. We demonstrate our performance by comparing the results of other models with our results. Our method reflects content information without loss and generates natural high-definition characters.

With the increasing number of aging buildings across Korea, emerging maintenance technologies have surged. One such technology is the non-contact detection of concrete cracks via thermal images. This study aims to develop a technique that can accurately predict the depth of a crack by analyzing the temperature difference between the crack part and the normal part in the thermal image of the concrete. The research obtained temperature data through thermal imaging experiments and constructed a big data set including outdoor variables such as air temperature, illumination, and humidity that can influence temperature differences. Based on the collected data, the team designed an algorithm for learning and predicting the crack depth using machine learning. Initially, standardized crack specimens were used in experiments, and the big data was updated by specimens similar to actual cracks. Finally, a crack depth prediction technology was implemented using five regression analysis algorithms for approximately 24,000 data points. To confirm the practicality of the development technique, crack simulators with various shapes were added to the study.

치유농업의 목적은 건강회복 및 유지, 증진을 위한 사회적 비용의 절감과 농촌의 활력, 소득원의 창출을 통한 농업‧농촌의 지속가능한 성장에 있다. 이러한 치유농업의 가치는 치유농업 대상자뿐만 아니라 농 업인, 지역사회, 보건서비스 제공자 등 다양한 주체가 혜택을 얻는 데 있다. 이 연구는 치유농업의 지속 가능한 생태계 구축방안을 제시하고자 선행연구를 통해 핵심요인을 도출하여 환경생태적, 사회경제 적 편익, 지역사회발전으로 설정하였다. 또한 각각의 요인은 하위요인으로 구분하여 적합도와 중요도 를 파악하였다. 델파이 조사는 치유농업에 실무와 경험이 풍부한 연구자, 교수, 치유농가 등 전문가 15 명을 대상으로 하였는데 이 중 불성실한 답변을 제외하고 최종 12명을 대상으로 2차에 걸친 조사를 하 였다. 분석결과 치유농업의 지속가능한 생태계 구축을 위한 핵심요인을 도출하였다. 또한 측정영역과 하위요인에 대한 중요도와 적합도 분석결과 7개의 상위요인과 33개 하위요인을 선정하였다. 지역사회 의 다양한 주체와의 교류 활성화를 통해 농업, 의료, 사회, 교육 분야에서 농업의 새로운 대안으로써 치 유농업을 운영할 필요가 있다. 이를 통해 치유농업의 지속가능한 생태계 구축을 위한 정책지원과 기초 적인 방향성을 제시하였다는 점에서 의미가 있다.

본 연구는 현재 가설되어 가용 중인 프리스트레스트 구조물에 대해서 긴장 응력을 계측하는 방법에 관한 연구를 위해 외부 자화를 이용한 PSC 텐던의 긴장 응력 계측에 관한 연구를 진행하였다. 이에 유한요소해석을 이용하여 PSC 거더에 외부 자화 시 잔존 긴장 응 력을 검출하기 위해 PSC 거더 내부의 PS 텐던까지 영향을 줄 수 있는 코일 배치 및 크기 등을 고려하여 최적의 센서를 설계하였다. 또 한, 유한요소해석을 이용하여 설계한 센서와 동일한 수치 및 재질 데이터를 이용해 이론적 검증을 진행하였으며 타겟 위치에서 자화 의 세기를 계산하였을 때, 유한요소해석 결과와 동일한 결과를 얻어낼 수 있었다. 이를 통해 설계한 센서의 검증 및 비 접촉 외부 자화 EM 센서를 활용하여 PSC I형 거더 내부 텐던의 자화가 가능함을 확인하였다.

In this study, the physical properties and fracture characteristics according to the tensile load are evaluated on the materials of the polymeric filler and carbon fiber-based composite sleeve technique. The polymeric filler and the composite sleeve technique are applied to areas where the pipe body thickness is reduced due to corrosion in large-diameter water pipes. First, the tensile strength of the polymeric filler was 161.48~240.43 kgf/cm2, and the tensile strength of the polyurea polymeric filler was relatively higher than that of the epoxy. However, the tensile strength of the polymeric filler is relatively very low compared to ductile cast iron pipes(4,300 kgf/cm2<) or steel pipes(4,100 kgf/cm2). Second, the tensile strength of glass fiber, which is mainly used in composite sleeves, is 3,887.0 kgf/cm2, and that of carbon fiber is up to 5,922.5 kgf/cm2. The tensile strengths of glass and carbon fiber are higher than ductile cast iron pipe or steel pipe. Third, when reinforcing the hemispherical simulated corrosion shape of the ductile cast iron pipe and the steel pipe with a polymeric filler, there was an effect of increasing the ultimate tensile load by 1.04 to 1.06 times, but the ultimate load was 37.7 to 53.7% compared to the ductile cast iron or steel specimen without corrosion damage. It was found that the effect on the reinforcement of the corrosion damaged part was insignificant. Fourth, the composite sleeve using carbon fiber showed an ultimate load of 1.10(0.61T, 1,821.0 kgf) and 1.02(0.60T, 2,290.7 kgf) times higher than the ductile cast iron pipe(1,657.83 kgf) and steel pipe(2,236.8 kgf), respectively. When using a composite sleeve such as fiber, the corrosion damage part of large-diameter water pipes can be reinforced with same level as the original pipe, and the supply stability can be secured through accident prevention.

최근 4차산업과 급격한 디지털화, 세계 경제성장률의 하락 등으로 노 동시장은 급격하게 변화하고 있으며 이로 인해 청년 실업률은 매우 높은 상태이다. 또한, 인구구조와 산업구조의 재편에 따라 청년층의 취업여건 은 나아지지 않고, 기업은 신입보다 경험을 가진 인력을 채용하고 있다. 이에 따라 본 연구는 해외 주요국(독일, 덴마크, 영국, 프랑스, 호주)의 청년 일경험 지원정책에 대해 살펴보고 일경험 프로그램을 종합적으로 비교함으로써 우리나라의 청년 일경험 정책에 관한 시사점을 도출하였 다. 분석결과 주요국의 청년 일경험 프로그램은 목적을 달리하여 운영되 고 있고, 대상자의 근로능력 수준에 따라 적합한 프로그램을 연계하고 있다. 또한, 단계별 프로그램과 대상에 따른 프로그램으로 구성되어 있고 운영은 직접 운영과 민간위탁 방식으로 구분되었다. 이러한 비교분석 결 과를 바탕으로 정책적 시사점을 제시하였다.

본 논문에서는 웹툰 캐릭터 영상에 대해서 심층학습에 기반한 3D 안면 재구성 기술을 제안한다. 본 연구에서 제안하는 방법은 기본 사항 모듈과 상세 사항 모듈로 구성된다. 입력 받은 웹툰 캐릭터 영상에 대해서 기본 사항 모듈의 요소인 Albedo 모듈을 적용해서 안면에 들어오는 빛의 양을 계산하여 Albedo 맵을 생성한다. 그 리고 기본 사항 모듈의 다른 구성 요소인 FLAME 모듈에서는 입력 영상에 대한 기본적인 3D 안면 형태를 생 성한다. 이와 동시에 상세사항 모듈을 적용해서 실제 사람과 다르게 이목구비가 변형된 웹툰 캐릭터 영상의 표정이나 얼굴 깊이와 같은 특징을 살리는 세부사항을 추출한다. 계산한 세부사항들을 토대로 세부사항 맵을 생성하여 앞서 FLAME 모듈에서 생성된 3D 안면 형태와 결합하여 세부사항 안면 형태를 생성한다. 그 후 Albedo 모듈에서 생성된 Albedo 맵까지 적용하면 최종적으로 웹툰 캐릭터 영상에 대한 3D 안면 재구성이 완 료된다. 본 연구에서는 웹툰 캐릭터뿐만 아니라 안면이 스타일화된 애니메이 션 캐릭터에 대해서도 결과를 생성하고, 이를 기존 연구와 비교하여 그 우수성을 입증한다.

안면 부 MRI 검사는 주변 조직에 대한 높은 대조도 와 해상력으로 해부학적 구조 파악과 질환 진단에 이용되고 있다. 최근 검사 시간을 줄이는 동시에 영상의 질을 향상하는 딥러닝이 주목받고 있다. 본 연구는 안면 부 MRI 검사에서 딥러닝 의 유용성을 알아보기 위해 34명의 환자를 대상으로 딥러닝 T2 강조 영상과 고식적인 T2 강조 영상의 축상면, 관상면 영상을 각각 획득하여, 무참조 영상 품질평가 기법인 NIQE와 NIMA를 통하여 정량적 평가하였고, 리커트 4점 척도를 통해 정성적 평가하였다. NIQE 결과에서 딥러닝 T2 강조 영상은 고식적인 T2 강조 영상보다 영상 품질이 우수하였고, NIMA 결과에서는 딥러닝 T2 강조 영상의 축상면은 통계적으로 유의한 차이가 없었고, 딥러닝 T2 강조 영상의 관상면에서는 통계 적으로 유의한 차이가 있었다. 정성적 평가지표에서는 입 인두, 후두 인두에서 질적인 이득이 있었다. 연구 결과를 통해 안면 부 영역 중 무의식적인 움직임이 많은 영역에서 딥러닝을 적용함으로써 고식적인 T2 강조 영상보다 높은 영상의 품질 을 제공하고, 상대적으로 움직임이 덜한 구조물에서도 품질을 유지하며 검사 시간을 2분 이상 단축하여 움직임에 의한 인공 물을 감소시킴으로써 응급 환자 및 비협조 환자의 진단에 유용하게 활용될 것으로 사료 된다.

본 연구에서는 형광측정기법을 사용하여 태안반도 인근 8지점의 표층 해수 내 미세플라스틱 정량분포를 조사하였다. 연구결과 미세플라스틱의 검출범위는 0~360.5 particles/l (평균 149.7 ± 46.0 particles/l)로 나타났다. 하지만 해양환경 내 미세플라스틱의 정량분석방법이 표준화되지 않아 타 연구결과와 직접비교 하는 것이 불가능한 상황이다. 본 연구결과 검출된 미세플라스 틱을 크기별로 분류하면 < 50 μm 크기가 우점하지만, St. 3의 경우 > 500 μm이 25.6%를 차 지하여 지역별로 우점하는 미세플라스틱의 크기에 차이가 나타난다. 또한 채집지역, 저질의 종 류, 검출되는 플라스틱 크기에 따른 미세플라스틱의 검출량 비교에도 뚜렷한 상관관계를 가지 는 요인을 확인할 수 없었다. 이는 플라스틱의 재질에 따른 물리 · 화학적 특성, 해양의 동적 조건(해류, 바람, 파도, 조류 등), 지질학적 특성(지형, 경사 등), 연안 생물을 포함한 저질의 구성 및 특성, 상호작용, 인간활동(어업, 개발, 관광 등), 기상조건(홍수, 강우 등)과 같은 다양한 요인 이 미세플라스틱의 거동에 영향을 주기 때문이다. 그러므로 향후 정량분석방법 표준화 및 환 경요인에 대한 분석이 수반된 미세플라스틱 모니터링 기초연구가 필요한 상황이다.

PURPOSES : The purpose of this study is to experimentally analyze the flexural strength characteristics of cement mortar mixtures simultaneously incorporated with graphene oxide (GO) and polyvinyl alcohol (PVA) fibers, and to understand the composite effect of those on enhancing resistance against the initiation and progression of micro-cracks, as well as the control of macro-cracks in flexural behavior. METHODS : Cement mortar(w/c=0.5) specimens for flexural strength test, mixing 6 mm and 12 mm PVA fibers at 1% and 2% volume ratios, were fabricated. Additionally, specimens incorporating GO at a cement weight ratio of 0.05% were prepared for each mixture to analyze the effect of GO. Therefore total eight types of mixture were prepared. The fabricated specimens were subjected to flexural strength tests after curing in waterbath for 7 and 28 days. Concurrently, digital images for analyzing deformation in accordance with loading history were obtained at a rate of 20 Hz using the DIC technique. Through displacement and strain calculation via DIC, the flexural behavior characteristics of the mixtures combined with GO and PVA fibers were precisely analyzed. Furthermore, the composite effect on flexural behavior characteristics when GO and PVA fibers are incorporated was discussed. RESULTS : For the PVA fiber-reinforced cement mortar mixture, the incorporation of 0.05% GO increased the crack initiation load by up to 23%, and the maximum resistive load after cracking by up to 24%. Moreover, introducing GO into the PVA fiber-reinforced mixture increased the flexural strain just before cracking by approximately 30 to 50%, while the maximum resistive load after cracking exhibited similar strain levels with or without GO incorporation. Therefore, under flexural behavior, the integration of GO might delay crack initiation by increasing the strain concurrent with the rise in flexural stress before crack occurrence. It also seems to contribute to reducing crack expansion by synergistically interacting with PVA fibers after crack occurrence. CONCLUSIONS : It was experimentally examined that the flexural strength of PVA fiber reinforced cement mortar is improved by incorporating GO. Moreover, GO enhances resistance of crack occurrance and reduces crack propagation in combination with PVA fibers. This study suggests that simultaneous incorporation of GO and PVA fibers can synergistically improve the performance of cement composites.

Reinforcement learning (RL) is widely applied to various engineering fields. Especially, RL has shown successful performance for control problems, such as vehicles, robotics, and active structural control system. However, little research on application of RL to optimal structural design has conducted to date. In this study, the possibility of application of RL to structural design of reinforced concrete (RC) beam was investigated. The example of RC beam structural design problem introduced in previous study was used for comparative study. Deep q-network (DQN) is a famous RL algorithm presenting good performance in the discrete action space and thus it was used in this study. The action of DQN agent is required to represent design variables of RC beam. However, the number of design variables of RC beam is too many to represent by the action of conventional DQN. To solve this problem, multi-agent DQN was used in this study. For more effective reinforcement learning process, DDQN (Double Q-Learning) that is an advanced version of a conventional DQN was employed. The multi-agent of DDQN was trained for optimal structural design of RC beam to satisfy American Concrete Institute (318) without any hand-labeled dataset. Five agents of DDQN provides actions for beam with, beam depth, main rebar size, number of main rebar, and shear stirrup size, respectively. Five agents of DDQN were trained for 10,000 episodes and the performance of the multi-agent of DDQN was evaluated with 100 test design cases. This study shows that the multi-agent DDQN algorithm can provide successfully structural design results of RC beam.

Radioactive Oxide is formed on the surface of the coolant pipe of the nuclear power plant. In order to remove the oxide film that is formed on the surfaces of the coolant pipe, chemical and physical decontamination technologies are used. The disadvantage of traditional technologies is that they produce secondary radioactive wastes. Therefore, in this study, the short-pulsed laser eco-friendly technology was used in order to reduce the production of secondary radioactive wastes. It was also used to minimize the damage that was caused to the base material and to remove the contaminated oxide film. The study was carried out using a Stainless steel 304 specimen that was coated with nickel-ferrite particles. Additionally, a transport robot was 3D modeled and manufactured in order to efficiently remove the oxide film from the coolant pipe of the nuclear power plant. The transport robot has a fixed laser head to move inside the horizontal and vertical pipes. The rotating laser head removes the contaminated oxide film on the inner surface of the coolant pipe. In the future, as a condition of the 1064nm short-pulsed laser ablation technique determined by basic analysis, we plan to analyze whether the transport robot is applicable to the radiation contamination site of the nuclear power plant.

The purpose of this study was to examine whether galvanic corrosion of copper occurs by inserting a third barrier layer with a higher corrosion potential than copper between copper and cast iron when the copper layer is locally perforated by pitting or partial corrosion. A triple layer composed of copper, inserted metal, and carbon steel was manufactured by cold spray coating of inserting metal powders such as Ag, Ni, and Ti on carbon steel plate followed by Cu coating on it. First, the corrosion properties were evaluated electrochemically for each metal coating. As a result of Tafel plot anaylsis in KURT groundwater condition, the corrosion potential of Fe (-567 mV) was much lower than that of Cu (-91 mV), and the corrosion potential of Ni (-150 mV) was also lower than that of Cu. Therefore, Ni was likely to corrode before Cu. However, the corrosion current of Ni was lower than that of the Cu. In the galvanic specimen where the copper and inserting metal were exposed together, Cu-Fe was much lower corrosion potential of -446 mV, and the corrosion potential of Cu-Ti, Cu-Ni, and Cu-Ag were slightly higher than that of Cu. Therefore, it seemed that Ag, Ni, and Ti all might promote galvanic corrosion of surrounding copper when the copper layer was perforated to the inserted metal layer. If the metal insertion presented in this study operates properly, the disposal container does not need to worry about the partial corrosion or non-uniform corrosion of external copper layer.