현재 국내외적으로 다양한 강재 댐퍼의 형상에 대한 연구가 이뤄지고 있다. 강재 댐퍼는 제진장치 중 하나로 항복을 통해 지진에너지를 흡수 및 소산하여 본 구조물을 보호한다. 최근 적용되고 있는 면내 전단 변형에 의해 작동하는 댐퍼는 응력이 특정 부위에 집중되어 재료의 대부분이 본래의 역량을 발휘하지 못하는 한계가 있었다. 따라서, 본 연구에서는 이 한계를 극 복하고자 전역 최적화 기법 중 하나인 뻐꾸기 탐색을 적용해 최적 형상을 설계하였다. 탐색된 최적 형상을 토대로 수치해석과 실제 실험을 통한 성능검증을 수행했다. 최적 형상으로 설계된 댐퍼는 안정적인 이력거동을 보이고, 높은 에너지 소산능력을 확 보하는 것으로 나타났다. 본 연구에서는 전역 최적화 기법을 적용한 최적 설계 방법을 제시하고 그 결과를 검증함으로써, 다양한 형태의 공학적 문제 해결에 대한 적용 가능성을 제시하였다.

현재 양송이 품종의 개발은 1980년대에 개발된 방법에 의존하여 진행되고 있다. 유전자가위를 이용한 유전자교정 기술이 다양한 분야에서 각광받고 있고, 이 기술을 버섯 육종에도 적용하기 위하여 진행된 이 연구에서는, CRISPR/Cas9 활용에 필수적인 원형질체 분리 효율을 1.0 × 10 8 /mL까지 안정적으로 끌어올렸고, spermidine을 이용하여PEG 형질전환의 효율 또한 기존 방법에 비해 100배가량 끌어올렸음을 보고한다.

A tilted tall building is actively constructed as landmark structures around world to date. Because lateral displacement responses of a tilted tall building occurs even by its self-weight, reduction of seismic responses is very important to ensure structural safety. In this study, a smart tuned mass damper (STMD) was applied to the example tilted tall building and its seismic response control performance was investigated. The STMD was composed of magnetorheological (MR) damper and it was installed on the top floor of the example building. Control performance of the STMD mainly depends on the control algorithn. Fuzzy logic controller (FLC) was selected as a control algorithm for the STMD. Because composing fuzzy rules and tuning membership functions of FLC are difficult task, evolutionary optimization algorithm (EOA) was used to develop the FLC. After numerical simulations, it has been seen that the STMD controlled by the EOA-optimized FLC can effectively reduce seismic responses fo the tilted tall building.

The multi-layered heat source model is a model that can cover most of existing studies and can be defined with a simple formula. Based on the methodology performed in previous studies, the welding heat source was found through experiments and FEM under the welding power conditions of three cases and the parameters of the welding heat source were analyzed according to the welding power. In this study, parameters of fiber laser welding heat source according to welding power were searched through optimization algorithm and finite element analysis, and the correlation was analyzed. It was confirmed that the concentration of the welding heat source in the 1st layer was high regardless of the welding power, and it was confirmed that the concentration of the welding heat source in the 5th layer (last layer) increased as the welding power increased. This reflects the shape of the weld bead that appears during actual fiber laser welding, and it was confirmed that this study represents the actual phenomenon.

본 연구의 목적은 식품 중 안나토색소를 검출하기 위하여 주성분인 cis-bixin과 cis-norbixin의 동시분석법을 개발 하는 것이다. 최적 시험법 확립을 위해 국내외 기관 중 유럽식품안전청, 일본 후생노동성, 우리나라 식품의약품안전 평가원의 HPLC분석법들을 비교 및 검토하였다. 그리고 직선성, 검출한계, 정량한계 및 분석시간을 고려하여 최적 HPLC 동시분석조건을 선택 후 여러 식품에 적용가능한 최적 전처리법을 개발하였다. 식품의약품안전평가원 HPLC 분석법이 가장 우수한 직선성(R2≥0.999)을 보였으며, cisnorbixin 및 cis-bixin에 대한 검출한계와 정령한계가 각각 0.03과 0.05 μg/mL 그리고 0.097과 0.16 μg/mL로 낮게 나타났다. 모든 보고된 전처리방법은 여러 식품적용에 한계가 있었으며, 어육 및 육가공품, 가공치즈, 음료의 주요 세 식품군 모두에 대해서 높은 회수율을 보이는 최적 전처리법이 새롭게 확립되었다. 이 전처리 방법은 cis-norbixin과 cis-bixin에 대해서 98% 이상의 우수한 동시 회수율을 나 타냈었다. 새로운 전처리방법이 적용된 분석법은 두 성분에 대해 모두 결정계수(R2) 1로서 높은 직선성을 나타냈으며, 정확도(회수율)와 정밀도(%RSD)가 각각 평균 98% 및 0.4-7.9 이었다. 이러한 결과로부터 최적화된 분석시험법은 식품 중 안나토색소의 cis-norbixin과 cis-bixin 두 성분 동시분석에 매우 적합한 것으로 판단되었다.

This study explored the usefulness and implications of the Bayesian hyperparameter optimization in developing species distribution models (SDMs). A variety of machine learning (ML) algorithms, namely, support vector machine (SVM), random forest (RF), boosted regression tree (BRT), XGBoost (XGB), and Multilayer perceptron (MLP) were used for predicting the occurrence of four benthic macroinvertebrate species. The Bayesian optimization method successfully tuned model hyperparameters, with all ML models resulting an area under the curve (AUC) > 0.7. Also, hyperparameter search ranges that generally clustered around the optimal values suggest the efficiency of the Bayesian optimization in finding optimal sets of hyperparameters. Tree based ensemble algorithms (BRT, RF, and XGB) tended to show higher performances than SVM and MLP. Important hyperparameters and optimal values differed by species and ML model, indicating the necessity of hyperparameter tuning for improving individual model performances. The optimization results demonstrate that for all macroinvertebrate species SVM and RF required fewer numbers of trials until obtaining optimal hyperparameter sets, leading to reduced computational cost compared to other ML algorithms. The results of this study suggest that the Bayesian optimization is an efficient method for hyperparameter optimization of machine learning algorithms.

This paper describes an adaptive hybrid evolutionary firefly algorithm for a topology optimization of truss structures. The truss topology optimization problems begins with a ground structure which is composed of all possible nodes and members. The optimization process aims to find the optimum layout of the truss members. The hybrid metaheuristics are then used to minimize the objective functions subjected to static or dynamic constraints. Several numerical examples are examined for the validity of the present method. The performance results are compared with those of other metaheuristic algorithms.

This paper deals with a vehicle routing problem with resource repositioning (VRPRR) which is a variation of well-known vehicle routing problem with pickup and delivery (VRPPD). VRPRR in which static repositioning of public bikes is a representative case, can be defined as a multi-objective optimization problem aiming at minimizing both transportation cost and the amount of unmet demand. To obtain Pareto sets for the problem, famous multi-objective optimization algorithms such as Strength Pareto Evolutionary Algorithm 2 (SPEA2) can be applied. In addition, a linear combination of two objective functions with weights can be exploited to generate Pareto sets. By varying weight values in the combined single objective function, a set of solutions is created. Experiments accomplished with a standard benchmark problem sets show that Variable Neighborhood Search (VNS) applied to solve a number of single objective function outperforms SPEA2. All generated solutions from SPEA2 are completely dominated by a set of VNS solutions. It seems that local optimization technique inherent in VNS makes it possible to generate near optimal solutions for the single objective function. Also, it shows that trade-off between the number of solutions in Pareto set and the computation time should be considered to obtain good solutions effectively in case of linearly combined single objective function.

본 연구에서는 복분자 콤부차의 최적 배합비를 찾고 이화학적 특성과 항산화 활성을 알아보았다. 반응표면분석법을 통해 복분자 콤부차의 최적 배합비는 홍차 농도 10% (w/v), 복분자 농도 8.09% (w/v)로 나타났고 이 배합비로 홍차 발효 주정에 복분자 당침액과 균을 첨가하여 14일간 초산 발효와 젖산 발효 과정을 통해서 산도 4%와 pH 2.77인 콤부차를 만들 수 있었다. 복분자 콤부차의 주요 유기산은 acetic acid, lactic acid, oxalic acid로 나타났으 며 그 중에서 acetic acid가 3,705.34 mg/100 mL로 가장 높았다. 발효 후 총 폴리페놀의 함량은 763.33 μg CE/mL 로 발효 전보다 증가하였으나 콤부차 발효 후 DPPH, ABTS 라디칼 소거활성을 측정한 결과 항산화활성은 각각 46.09%, 89.81%로 발효 전보다 감소하는 것을 확인할 수 있었다. 또한 반응표면분석법을 통해 얻은 최적 배합비의 종속변수 예측값과 최적 배합비 콤부차 실험값의 산도의 평균차는 0.065, 유기산의 평균차는 0.041, ABTS 라디칼 소거활성의 평균차는 0.061이므로 예측값과 실험값은 유사 하다.

A60 급 갑판 관통 관은 선박과 해양플랜트에서 화재사고가 발생할 경우 화염의 확산을 방지하고 인명을 보호하기 위해 수평구조에 설치되는 방화장치이다. 본 연구에서는 다양한 대리모델과 다중 섬유전자 알고리즘을 이용하여 A60 급 갑판 관통 관의 방화설계에 대한 이산변수 근사최적화를 수행하였다. A60 급 갑판 관통 관의 방화설계는 과도 열전달해석을 통해 평가하였다. 근사최적화에서 관통 관의 길이, 지름, 재질, 그리고 단열재의 밀도는 이산설계변수로 적용하였고, 제한조건은 온도, 생산성 및 가격을 고려하였다. 대리모델 기반의 근사최적설계 문제는 제한조건을 만족하면서 A60 급 갑판 관통 관의 중량을 최소화할 수 있는 이산설계변수를 결정하도록 정식화 하였다. 반응표면모델, 크리깅, 그리고 방사기저함수 신경망과 같은 다양한 대리모델이 근사최적화에 사용되었다. 근사최적화의 정확도를 검토하기 위해 최적해의 결과는 실제 계산 결과와 비교하였다. 근사최적화에 사용된 대리모델 중 방사기저함수 신경망 모델이 A60 급 갑판 관통 관의 방화설계에 대해 가장 정확한 최적설계 결과를 나타내었다.

The most important factor in the processing of composite materials is the creation of burrs. In order to minimize the generation of burrs, the preceding researchers changed the shape and processing conditions of the tool. The developed composite material using carbon and aramid fibers has limitations in reducing the formation of burrs. In this study, in order to solve this problem, by applying a jig under the test piece during drilling, it was possible to perform high-quality hole processing with minimal burr generation.

This study wanted to optimize the radiator tank's deformation assembled on the automotive engine block. Among the experimental planning methods, the Taguchi method was used to find optimal molding conditions to minimize plastic covers' deformation. The four main factors used in the Taguchi method were selected as the main factors: resin temperature, pressure time, coolant temperature, and cooling time. The number of cycles for each factor was divided into five stages, and a total of 25 experiments were conducted. The experiment used the Moldflow program, an injection molding analysis program. The maximum deformation obtained under the existing molding conditions was about 1.318mm. Still, the deformation of the mold applied with the optimal molding conditions obtained using the Taguchi method was approximately 1.273mm, which showed that the maximum deformation was reduced by 3.4% compared to the existing molding conditions.

In this study, a welding heat source model was presented and verified during fiber laser welding. The multi-layered heat source model is a model that can cover most of existing studies and can be defined with a simple formula. It consists of a total of 12 parameters, and an optimization algorithm was used to find them. As optimization algorithms, adaptive simulated annealing, multi island genetic algorithm, and Hooke-Jeeves technique were applied for comparative analysis. The parameters were found by comparing the temperature distribution when the STS304L was bead on plate welding and the temperature distribution derived through finite element analysis, and all three models were able to derive a model with similar trends. However, there was a deviation between parameters, which was attributed to the many variables. It is expected that a more clear welding heat source model can be derived in subsequent studies by giving a guide to the relational expression and range between variables and increasing the temperature measurement point, which is the target value.

Welding is the most widely used technology for manufacturing in the automobile, and shipbuilding industries. Fiber laser welding is rapidly introduced into the field to minimize welding distortion and fast welding speed. Although it is advantageous to use finite element analysis to predict welding distortion and find optimized welding conditions, there are various heat source model for fiber laser welding. In this study, a welding heat source was proposed using a multi-layered heat source model that encompasses most of the existing various welding heat source models: conical shape, curved model, exponential model, conical-cylindrical model, and conical-conical model. A case study was performed through finite element analysis using the radius of each layer and the ratio of heat energy of the layer as variables, and the variables were found by comparing them with the actual experimental results. For case study, by applying Adaptive simulated annealing, one of the global optimization algorithms, we were able to find the heat source model more efficiently.