This paper addresses the maintenance optimization problem in multi-component systems in which parts are connected in series, carrying out several missions interspersed with scheduled finite breaks. Due to limited time or resources, maintenance actions can be only carried out on a limited set of components. The decision maker then has to decide which components to maintain to ensure a pre-specified performance level during next mission. Most of the existing models in the literature usually assume only one system and enough spare parts. However, there are situations in which maintenance is required for multiple systems of the same type. To overcome this restrictive assumption, this study optimizes the maintenance problem considering the lack of repair parts and cannibalism for many identical systems. This study presents two optimization models with different objectives to solve the problem and analyzes the results so that the decision maker can decide. The results of this study are expected to be used for the maintenance of multiple systems of the same type, such as swarm drones.

Companies are making design changes by improving product quality and function to succeed while meeting customer requirements continuously. Design changes are changing the product BOM's amount, item, specification, and shape while causing a change in the product's structure. At this time, the problem of inventory exhaustion of parts before design change is a big topic. If the inventory exhaustion fails, the pieces before the design change become unused and are discarded, resulting in a decrease in asset value, and the quality cost of the design change affects the company's profits. Therefore, it is necessary to decide to minimize quality costs while minimizing waste inventory costs at the time of application of design changes. According to the analysis, priorities should be prioritized according to urgency because the quantity of items before the design change affects the applied lead time.

Transportation in urban area has been getting hard to fulfill the demand on time. There are various uncertainties and obstacles related with road conditions, traffic congestions, and accidents to interrupt the on-time deliveries. With this situation, the last mile logistics has been a keen issue for researchers and practitioners to find the best strategy of the problem. A way to resolve the problem is to use parcel lockers. Parcel locker is a storage that customers can pick up their products. Transportation vehicles deliver the products to parcel lockers instead of all customer sites. Using the parcel lockers, the total delivery costs can be reduced. However, the inconvenience of customer has to increase. Thus, we have to optimal solution to balance between the total delivery costs and customers' inconvenience. This paper formulates a mathematical model to find the optimal solution for the vehicle routing problem and the location problem of parcel lockers. Experimental results provide the viability to find optimal strategy for the routing problem as well as the location problem.

현대 사회가 복잡해지고 기술이 진보하면서 하나의 제품을 설계할 때 단일 특성이 아닌 다 특성을 갖는 제품을 설계하는 것이 요구되고 있다. 다 특성 설계 최적화 문제에서는 설계 변수의 최적값이 특성별로 다르게 되는 현상이 발생하고 이에 따라 하나의 특성에 대한 결과값이 좋아지면 다른 특성에 대한 결과값이 나빠지는 경우에 대해서 trade-off가 필요하다. 따라서 다 특성을 갖는 제품 설계 시 각각의 특성에 대한 성능을 최대화할 수 있는 효율적인 설계 방법의 필요성이 중요한 문제로 떠오르고 있다. 다 특성을 갖는 제품을 설계하는 연구는 오래전부터 중요한 문제로 인식되어 왔는데, 단일 특성에 대해 성능을 최대화하며 강건한 설계를 할 수 있는 다구치 기법과 다기준/다특성 의사 결정 방법인 TOPSIS(Technique for Order of Preference by Similarity to Ideal Solution), GRA(Grey relational analysis), PCA(Principle Component Analysis), Fuzzy Logic System을 통합한 연구들이 진행되어 왔다. 하지만 기존 연구들은 각각의 설계 대안들에 대해 다 특성 결과값에 대한 점수를 통합하여 종합 점수를 비교해 최적의 설계안을 도출할 뿐 설계자의 호감도에 대한 정보는 설계에 반영하지 못한다. 따라서 본 연구에서는 다구치 기법을 기반으로 각각의 특성들에 대한 설계자의 서로 다른 호감도를 함수로써 표현하여 강건성과 함께 설계자의 호감도를 동시에 설계에 반영할 수 있는 방법을 제안한다.

Additive manufacturing is a new approach to design and production. This applies in particular to processes such as repair and rework of selected components. Additive manufacturing can produce almost any shape, and from an MRO part perspective, additive manufacturing offers tremendous advantages. The special feature of additive manufacturing is that it is particularly economical for small-volume production as the number of units is irrelevant compared to the existing manufacturing process. The purpose of this study was started from the MRO point of view, and it Identify changes and respond to the Blisk It is a study on the effect of changing the conditions on the path of the toolpath and the CAM during additive manufacturing using CAM after finding suitable conditions. metal powder.The metal powder withstands various corrosive environments and age hardening occurs very slowly. Inconel 718, which can be used in various applications such as nuclear facility-related parts, aerospace, oil facilities, turbines, and valves, was used. This is SUS 316L with good high temperature strength. The variable of the laser used in the study is the laser power, and the variables on the CAM are Operation, Stepover, Pattern, etc. In the relation between laser power and feed, when feed is specified as 500mm/min, laser power of 700W was most suitable. As for the conditions on NX CAM, ADDITIVE PROFILE Stepover was 0.8mm for Operation, and Infills and Finish for Pattern. When stacking, each layer should be overlapped with the result. Therefore, the stepover should be smaller than the laser spot size and reprocessing should be done in terms of repair, so infills and finish were applied to work larger than the actual product shape.

Despite the long history of mushroom use, studies examining the genetic function of mushrooms and the development of new varieties via bio-molecular methods are significantly lacking compared to those examining other organisms. However, owing to recent developments, attempts have been made to use a novel gene-editing technique involving CRISPR/Cas9 technology and genetic scissors in mushroom studies. In particular, research is actively being conducted to utilize ribonucleoprotein particles (RNPs) that can be genetically edited with high efficiency without foreign gene insertion for ease of selection. However, RNPs are too large for Cas9 protein to pass through the cell membrane of the protoplasmic reticulum. Furthermore, guide RNA is unstable and can be easily decomposed, which remarkably affects gene editing efficiency. In this study, nanoparticles were used to mitigate the shortcomings of RNP-based gene editing techniques and to obtain transformants stably. We used Lentinula edodes (shiitake mushroom) Sanjo705-13 monokaryon strain, which has been successfully used in previous genome editing experiments. To identify a suitable osmotic buffer for the isolation of protoplast, 0.6 M and 1.2 M sucrose, mannitol, sorbitol, and KCl were treated, respectively. In addition, with various nanoparticle-forming materials, experiments were conducted to confirm genome editing efficiency via the formation of nanoparticles with calcium phosphate (CaP), which can be bound to Cas9 protein without any additional amino acid modification. RNPs/NP complex was successfully formed and protected nuclease activity with nucleotide sequence specificity.

ROK Navy intends to secure the Korean-type aircraft carrier in order to effectively prepare for various future security threats. In general, the Korean national competency is considered to be at the level of having an aircraft carrier, but it is unclear what scale aircraft carrier would be appropriate. In this study, the efficiency was evaluated through the relative comparison between national competency(national power, economic power) and the scale of aircraft carriers, and the optimal scale of the Korean-type aircraft carrier that could be acquired was presented. A DEA(Data Envelopment Analysis) model was applied to aircraft carriers(19 aircraft carriers in 11 countries) currently in operation and scheduled to be possessed in the world. As input variables, CINC(Composite Index of National Capability) and GDP(Gross Domestic Product), which are the most widely used as indicators of national and economic power, and as output variables, the full-load displacement, length, and width of aircraft carriers were selected. ARIMA(short-term within 5 years) and simple regression(long-term over 5 years) were used to estimate the future national competency of each country at the time of aircraft carriers acquisition. The relative efficiency score of the Korean-type aircraft carrier currently being evaluated is 1.062, and it was evaluated as small-scale aircraft carrier compared to the national competency. Based on Korean national competency, the optimal scale of the Korean-type aircraft carrier calculated by aggregating benchmark groups, is 58,308.1 tons of full-load displacement, 279.4m in length, and 68.3m in width.

블록 매칭 및 3D 필터링(BM3D) 알고리즘은 단일 필터의 문제점을 보완하기 위하여 non-local means 기반으로 만들 어진 융합형 노이즈 제거 알고리즘이다. 하지만, 그 수식 인자의 조절에 관한 연구는 이루어지지 않고 있어 본 연구에서는 자기공명영상에서 발생하는 Rician 노이즈를 제거하기 위해 BM3D 알고리즘의 평활화 정도를 결정하는 노이즈 전력 스펙 트럼 밀도(noise power spectrum density,  )에 대한 최적화를 진행하고자 하였다. MRiLab 시뮬레이션 프로그램을 이 용하여 뇌 조직을 모사할 수 있는 뇌척수액(cerebrospinal fluid, CSF)/회색질(gray matter, GM)/백질(white matter, WM) 팬텀의 T1 강조영상을 획득하였고, 노이즈 레벨이 0.1, 0.15, 0.2, 0.25, 그리고 0.3인 Rician 노이즈를 각각 부가 한 후, BM3D 알고리즘의  값을 0.01부터 0.99까지 0.01씩 증가시키며 각각의 노이즈가 부가된 영상에 적용하였다. 정량 적 평가를 통해 최적화 값을 선정하기 위하여 CSF, GM, WM, 그리고 배경 영역에 관심 영역을 설정한 후 조직별 신호 대 잡음비(signal to noise ratio, SNR), 총 변동계수(coefficient of variation, COV), 그리고 평균 제곱근 오차(root mean square error, RMSE)를 측정하였다. 결과적으로, 조직별로 계산된 SNR, COV, 그리고 RMSE를 종합적으로 평가 했을 때 모든 조직에서 노이즈 레벨 0.1부터 0.3까지 증가함에 따라  값 또한 함께 증가하는 경향이 나타났으며 일정  값 이상에서는 노이즈뿐만 아니라 영상신호까지 함께 제거되어 개선 폭이 감소하는 것으로 관찰되었으며, 노이즈 레벨에 따라 각각 0.09, 0.13, 0.17, 0.21, 그리고 0.25의  값이 설정된 BM3D 알고리즘이 적용되었을 때 가장 합리적인 영상 특성을 보이는 것으로 나타났다. 결론적으로, 효과적인 노이즈 제거를 위해서 고정된 값이 아닌 노이즈 레벨에 따른 적합한 값을 적용해야 함을 증명할 수 있었다.

This paper proposes a solution to the out-of band oscillation signal and in-band low transmitter power output that occurrs during the low-temperature operation test for the new mine detector GPR signal transmission and reception module. Tests were performed by applying the optimal values of capacitors and inductors through circuit analysis simulation under the limited space, as a result, it was confirmed that the gain and return loss were improved at all-band thereby preventing oscillation signal and low transmitter power output.

In this paper, to improve the optical quality of aspherical plastic lenses for mobile use, the optimal molding conditions that can minimize the phase difference are derived using injection molding simulation, design of experiments, and machine learning. First, factors affecting the phase difference were derived using the design of the experiment method, and a data set was created using the derived factors, followed by the machine learning process. After predicting the model trained using the generated training data as test data and verifying it with the performance evaluation index, the model with the best predictive performance was the random forest model. Therefore, to derive the optimal molding conditions, random forests were used to predict 10,000 random pieces of data. As a result of applying the derived optimal molding conditions to the injection molding simulation, the phase difference of the lens could be reduced by 8.2%.

The sub-frame is located on the lower body of a monocoque type vehicle and serves as an engine and suspension, and is an important object part that receives a lot of load. The existing press-type sub-frame has a large number of parts for assembling, which causes an increase in cost. Changing the machining form of this part from the existing press-type machining method to the hydro-forming machining method has the advantage of reducing the cost and weight at the same time due to the reduction of the process. Therefore, in this study, the purpose of this study is to change the design so that the sub-frame of the existing press type can be changed to the hydro-forming process method. To this end, we intend to present a design method by analyzing the effect on the rigidity of the sub-frame using the existing machining method through shape optimization analysis.

본 논문은 딥러닝 알고리즘을 이용하여 딸기 영상 데이터의 병충해 존재 여부를 자동으로 검출할 수 있는 서비스 모델을 제안한다. 또한 병징에 특화된 분할 이미지 데이터 세트를 제 안하여 딥러닝 모델의 병충해 검출 성능을 향상한다. 딥러닝모델은 CNN 기반 YOLO를 선정하여 기존의 R-CNN 기반 모델의 느린 학습속도와 추론속도를 개선하였다. 병충해 검 출 모델을 학습하기 위해 일반적인 데이터 세트와 제안하는 분할 이미지 데이터 세트를 구축하였다. 딥러닝 모델이 일반 적인 학습 데이터 세트를 학습했을 때 병충해 검출률은 81.35%이며 병충해 검출 신뢰도는 73.35%이다. 반면 딥러닝 모델이 분할 이미지 학습 데이터 세트를 학습했을 때 병충해 검출률은 91.93%이며 병충해 검출 신뢰도는 83.41%이다. 따 라서 분할 이미지 데이터를 학습한 딥러닝 모델의 성능이 우 수하다는 것을 증명할 수 있었다.