Earticle

독널은 4∼6세기 영산강 유역의 고유한 장례문화를 보여준다. 그러나 중⋅대형 독널은 크기와 중량, 구조적 취약성으로 인해 전시⋅수장⋅이동 시 관리가 어렵다. 이에 본 연구는 3차원 스캔 데이터를 활용하여 중⋅대형 독널에 적합한 예방보존형 수장 구조를 설계 및 축소 모형 기반 구조 적용 및 수장효율 향상 가능성을 검토하였다. 안전 보관 상자는 유물 형상을 분석하여 외부 프레임, 내부 지지 구조체, 코어 지지대로 구성된 분리형 삼중 구조로 설계하였다. 내부 지지 구조체는 유물 외형에 대응하는 곡률 적합형 면 접촉 구조로, 국부 응력 집중을 완화하여 하중 분산 효과를 유도하였다. 또한 외부프레임은 적층⋅이동이 가능하도록 설계하였다. 4단 적층을 가정한 이론적 공간점유면적은 기존 방식 대비 약 34.9% 감소하였으며, 이는 적층을 가정한 공간 활용 가능성을 보여준다. 본 연구는 중량급 문화유산의 예방보존을 고려한 디지털 기반 맞춤형 수장 구조 설계의 적용 가능성을 제시하고자 한다.

This study proposes a preventive conservation-oriented storage system for medium and large-sized jar coffins using three-dimensional scanning data and examines its preliminary applicability using a reduced-scale model. A separable three-layer structure comprising an outer frame, an inner support structure, and core supports was developed based on precise geometric analysis. The inner support structure adopts a curvature-conforming surface-contact design intended to reduce localized stress and promote load distribution. The outer frame was designed to allow stacking and modular storage. Assuming four-tier stacking, the theoretical floor area occupied per artifact was calculated to be approximately 34.9% lower than that of the existing storage method using transport pallets. This result indicates the potential for improved spatial utilization under the assumed stacking conditions. Material comparison during reduced-scale model fabrication suggested that high-density foam was a suitable candidate material for implementing the proposed support structure. However, the mechanical properties and long-term stability of the material were not quantitatively evaluated in this study. The proposed design should therefore be regarded as a conceptual preventive conservation-oriented storage structure, and its structural safety and long-term performance require further verification through full-scale mechanical testing and structural analysis.

NP-난제로 다항시간으로 최적 해를 찾는 알고리즘이 제안되지 않고 있는 일반화된 배정 문제에 대해 기존에는 전적으로 메타휴리스틱 기법들에 치중하여 연구가 진행되었다. 반면에, 본 논문에서는 해를 찾아가는 규칙을 가진 휴리 스틱 탐욕 알고리즘을 제안한다. 첫 번째로, m대의 기계(용기)에 n개의 작업(물품)을 담을 수 있도록 l=n/m개가 되도 록 각 기계의 용량  에 대해 가중치 Wij≤bi/l 데이터로 축소시킨다. 축소된 데이터들을 대상으로 각 작업의 최대 이득 작업을 해당 기계에 배정하였다. 두 번째로, 각 기계에 배정된 가중치 합이 기계 용량을 초과하지 않도록 배정을 조정하 였다. 마지막으로 이득을 최대화시키기 위해 k-opt 교환 최적화를 수행하였다. 제안된 알고리즘을 50개 벤치마킹 데이 터들에 적용한 결과 약 1/3 데이터에 대해서는 알려진 최적 해를 찾을 수 있었으며, 나머지 2/3 데이터에 대해서는 메타휴리스틱 기법들과 견줄만한 결과를 보였다. 따라서 제안된 알고리즘은 GAP에 대해 다항시간으로 해를 찾아가는 규칙이 존재할 가능성을 보여 NP-난제에서 P-문제로 될 수 있음을 실험을 통해 증명하였다.

The researchers entirely focused on meta-heuristic method for generalized assignment problem(GAP) that is known as NP-hard problem because of the optimal solution within polynomial time algorithm is unknown yet. On the other hand, this paper proposes a heuristic greedy algorithm with rules for finding solutions. Firstly, this paper reduces the weight matrix of original data to Wij≤bi/l in order to n jobs(items) pack m machines(bins) with l=n/m. The maximum profit of each job was assigned to the machine for the reduced data. Secondly, the allocation was adjusted so that the sum of the weights assigned to each machine did not exceed the machine capacity. Finally, the k-opt swap optimization was performed to maximize the profit. The proposed algorithm is applied to 50 benchmarking data, and the best known solution for about 1/3 data is to solve the problem. The remaining 2/3 data showed comparable results to metaheuristic techniques. Therefore, the proposed algorithm shows the possibility that rules for finding solutions in polynomial time exist for GAP. Experiments demonstrate that it can be a P-problem from an NP-hard.

As a valid numerical method to obtain a high-resolution result of a flow field, computational fluid dynamics (CFD) have been widely used to study coolant flow and heat transfer characteristics in fuel rod bundles. However, the time-consuming, iterative calculation of Navier-Stokes equations makes CFD unsuitable for the scenarios that require efficient simulation such as sensitivity analysis and uncertainty quantification. To solve this problem, a reduced-order model (ROM) based on proper orthogonal decomposition (POD) and machine learning (ML) is proposed to simulate the flow field efficiently. Firstly, a validated CFD model to output the flow field data set of the rod bundle is established. Secondly, based on the POD method, the modes and corresponding coefficients of the flow field were extracted. Then, an deep feed-forward neural network, due to its efficiency in approximating arbitrary functions and its ability to handle high-dimensional and strong nonlinear problems, is selected to build a model that maps the non-linear relationship between the mode coefficients and the boundary conditions. A trained surrogate model for modes coefficients prediction is obtained after a certain number of training iterations. Finally, the flow field is reconstructed by combining the product of the POD basis and coefficients. Based on the test dataset, an evaluation of the ROM is carried out. The evaluation results show that the proposed POD-ROM accurately describe the flow status of the fluid field in rod bundles with high resolution in only a few milliseconds.

Wavelet transforms provide basis functions for time-frequency analysis and have properties that are particularly useful for the compression of analogue point on wave transient and disturbance power system signals. This paper evaluates the compression properties of the discrete wavelet transform using actual power system data. The results presented in the paper indicate that reduction ratios up to 10:1 with acceptable distortion are achievable. The paper discusses the application of the reduction method for expedient fault analysis and protection assessment.

Permutation test has been in common use to get a statistical significance of an association measure when genomic association study is conducted. Its process involves repeated randomizing of the relationships between phenotype and genotype parts of the data as well as the calculation of the testing measure to form a null distribution point from each of the repetition. To compensate for the multiple comparison effect, which can result in overly optimistic p-values, the null distribution should be constructed by the maximum values among all the SNP combinations from each iteration. With increasing number of SNPs, especially when studying higher order gene-gene interactions, the amount of computation may soon become formidable. Possibility of dealing with reduced number of SNPs while producing the comparable result has been investigated in this paper. We found that, down to about 5% of the data considered, the estimated null distribution, verified by p-values were comparable to the one with original set of data.

Filtered-Back-Projection technique is used in X-ray CT image reconstruction. This requires X-ray transmission data from all directions. As the transverse cross-section of the body is approximately 50 cm, transmitted X-rays in this direction are strongly attenuated. If X-ray transmission data in this direction is avoided, exposure to the patients seems to be reduced one 20th of usual value. Some alternative method has to be found for clinically sufficient image quality. New methods are under development and tentative results are reported that utilizes the principle of superposition.

군집분석은 유사한 특성들을 지닌 관측값들을 같은 군집으로, 다른 특성들을 지닌 관측값들은 서로 다른 군집으로 분류하는 분석 기법이다. 많은 변수를 포함한 고차원 자료에서는 일반적인 군집분석 대신 차원축소를 통하여 군집분석을 실시하는 방법들이 제안되어 왔다. 주성분 분석을 통해 차원을 축소한 후 축소된 차원에서 군집분석을 실시하는 직렬분석 방법보다 차원축소와 군집분석을 결합하여 동시에 실시하는 방법들이 더 우수한 성능을 보인다는 것이 알려져 있다. 한편, 대부분의 자료는 결측값을 포함하고 있는데 결측값이 포함된 자료에 대하여 군집분석을 실시하는 경우 불완전하게 관측된 자료들은 어느 군집으로도 분류되지 않는 문제가 발생한다. 따라서 군집분석을 실시하기 전에 먼저 결측값 대체를 실시하는 것이 일반적이다. 본 연구에서는 고차원 결측자료에 대하여 차원축소를 통한 k-평균 군집분석을 실시할 때 결측값 대체를 결합하여 실시하는 방법을 제안한다. 이 방법은 군집 정보를 이용한 결측값 대체를 통해 정확한 차원축소를 통한 군집분석이 가능하게 하는 장점을 지닌다. 제안된 방법은 모의실험을 통해 성능을 평가하였고 결측값을 대체한 후 대체된 자료에 대하여 차원축소를 통한 군집분석을 실시하는 직렬식 분석방법과 비교하였다. 제안된 방법은 적절한 차원축소를 통한 k-평균 군집분석을 실시한다면 직렬식 분석보다 오분류율이 낮게 나타났다.

Cluster analysis classify similar observations into the same cluster and different observations into different clusters. When data include many variables, reduced dimension clustering methods have been suggested instead of the standard clustering methods. The joint analysis of dimension reduction and clustering is known to perform better than tandem analysis that sequentially conducts dimension reduction and clustering. On the other hand, most data include missing values. When cluster analysis is conducted with incomplete data, incomplete observations can not be classified into any group. To avoid this problem, it is common to impute missing values before conducting cluster analysis. In this study, we suggest a method for combining dimension reduction k-means clustering and missing data imputation. The suggested method has an advantage to accurate classify observations through imputation using cluster information. A simulation is conducted to evaluate performance of the suggested method and compare the result with the one based on tandem analysis. The suggested method using an appropriate dimension reduction k-means clustering showed lower misclassification rates than tandem analysis.