Earticle

본 연구에서는 화협옹주 회곽함에서 출토된 화장품 중 갈색 고체 잔여물이 나타난 화장 품을 중점으로 연구를 진행하였다. 이 화장품은 1752년 부장된 회곽함 안의 청화백자합에서 출토되었다. 국립고궁박물관에서는 유물분석과 문헌조사를 진행하였다. 이를 바탕으로 출토화 장품의 원재료와 제조법을 추정하여 크림 형태의 화장품재현을 시도하였다. 이후 출토품과 유 사한 열화 양상을 확인하기 위하여 고온, 자외선, 이산화탄소 조건에서 열화를 시행하였다. 이에 따른 변화와 화장품으로써의 효과를 확인하고자 과학적 분석과 기능성 평가를 진행하였다. 유 지류가 높은 비율로 혼합된 재현품에서는 고온열화가 진행됨에 따라 색차값이 약 32 차이가 나타났고, O-H 결합과 C=C 결합에서 화학적 변화가 발생하여 유지류 산화가 발생한 것을 확인 할 수 있었다. 또한, 자외선 열화를 통해서는 pH 값이 노출 시간에 따라 산성화되는 것을 확인할 수 있었다. 재현품의 기능성 평가로 진행한 차폐율은 현대 화장품의 차폐 기준치보다 낮게 나타났다.

This study focused on analyzing solid brown cosmetic residues that were excavated from the tomb of the Hwahyeop princess. The cosmetics had been buried in 1752 in Cheonghwa Baekja (small porcelain jars) within a Huigwakham (a stone box sealed with lime). A literature search and chemical analyses of the relics were conducted at the National Palace Museum of Korea. Based on the results of these analyses, raw materials and manufacturing methods for reproduction of the cosmetic were selected and then c ream-form cosmetics were p repared. Ag ing tests u sing heat, u ltraviolet (UV) radiation, and CO2 were carried out to emulate the deterioration of the excavated cosmetics. Further chemical analyses and functionality tests were performed to evaluate changes from the tests and the effect as a cosmetic. As heat aging progressed, among the reproduced cosmetics, the one which has a high proposition of oil showed a color difference value of 32; the oxidation of oils involved chemical changes in O-H and C=C groups. As the cosmetic cream deteriorated under UV aging, the pH value became acidic. The result of the functional coverage test of the reproduced cosmetic cream was lower than the standard coverage ratio of modern cosmetics.

We have tried the experimental research of lost-wax casting to reconstruct Gilt-Bronze Pensive Bodhisattva; preliminary and reconstruction experiment based on ancient texts. Main object to reconstruct is Korean National Treasure No.83, Gilt-Bronze Pensive Bodhisattva (Maitreya), then we measure alloy ratio and casting method based on the scientific analysis. Other impurities were removed from the base metal components(copper : tin : lead) and their ratio was set to 95.5 : 6.5 : 3 where the ratios for tin and lead were increased by 2.5% each. The piece-mold casting method was used, and piece-mold casting experiments were carried out twice in this study but supplementary research on piece-mold casting was necessary. The microstructure was confirmed to be typical cast microstructure and the component analysis result was similar to that of the prior study. Analysis of the chemical composition is confirmed to copper, tin, lead, and zinc, and the chemical composition of the matrix was 87.8%Cu-7.5%Sn-2.7%Pb-2.1%Zn, and similar to previous experimental research. Also resulted in the detection of small impurity in Zn. Analysis of the mould revealed that the mould was fabricated by adding quartz and organic matter for structural stability, fire resistance, and air permeability. We expect that our research will contribute to provide base data for advanced researches in future.

고문헌 조사를 토대로 밀랍주조법을 이용한 사전실험, 재현실험을 통해 청동반가사유상 의 복원 연구를 실시하였다. 복원대상은 국보 제83호 금동미륵보살반가사유상으로, 이에 대한 과학적 분석결과를 바탕으로 합금비, 주조방법 등을 설정하였다. 사전실험에서 합금비는 구리: 주석:납=95.5:4.0:0.5로 설정하였으나, 본실험에서는 기화량을 감안하여 납과 주석을 각각 2.5% 씩 증량하여 장입하였다. 밀랍주조법을 적용하였으며, 본실험에서는 밀랍 경도 실험을 실시하여 송진 30%를 투입하였을 때 적정 강도를 가짐을 알 수 있었다. 미세조직은 일반적인 주조조직(α-Cu, δ)이 확인되었고, 성분분석 결과 선행연구와 유사한 양상을 나타내었다. 거푸집 분석결과, 구조 적 안정성과 내화성을 위해 석영 비짐을 넣었으며, 다수의 유기물이 확인되었다. 본 연구가 향후 청동반가사유상의 전통주조법과 복원기술 연구에 대한 기초 자료로 활용될 것을 기대한다.

We attempted an experimental study of lost-wax casting to reconstruct the Gilt-bronze pensive Bodhisattva; The main object we aimed to reconstruct was the Korean national treasure No.83, Gilt-bronze pensive Bodhisattva(Maitreya); thus, we scientifically analyzed and measured the alloy ratio and casting method. Initially, the alloy ratio we used during the preliminary experiment was Cu:Sn:Pb = 95.5:4.0:0.5; we then increased tin and lead by 2.5% to consider vaporization during the main experiment. After applying lost-wax casting, we realized that 30% rosin contributed to proper hardness after the wax hardness experiment. The microstructure revealed normal casting character(α-Cu, δ), and the results of the chemical analysis are identical to those of previous studies. The analysis of the mold suggests the presence of quartz powder for structural stability and fire-resistance along with other organic materials whose contribution is still unknown. We expect that our research will serve to provide basic data for advanced studies in the future.

도자기 보존담당자에 있어서 도자기의 전반적인 지식에 대한 이해는 그것들이 만들어진 방법이나 물리적인 특성이 손상과 밀접한 관련을 가지고 있으므로 필수적이라고 할 수 있다. 또한 도자기의 사용 중에 발생한 파손에대처하는 여러 가지 수리복원 행위들 또한 반드시 규명되어야만 한다. 이렇게 파악된 지식은 도자기의 손상 원인을 파때문이다. 이러한 수리복원 재료에 대한 규명의 일환으로 이번 연구에서는 도자기 구연에 부착된 금속 테두리의 제작목적과 제작시기, 사용된 재질의 성분 조사를 실시하였다. 연구대상 도자기 중 과학적인 분석이 가능한 금속 테두리에대해서는 SEM-EDS 및 이동형 X-선 형광분석기를 사용하여 성분분석을 시행하였고, 반면에 분석이 어려운 도자기를 부착하였음을 확인할 수 있었다.

Understanding the whole knowledge of ceramics is essential for a person in charge of conservation ofceramics since their methods of manufacturing or materialistic features have very close relationship with their dam-aging process. Also, various executions of repair and restoration dealing with any damage in use have to be lookedelement when choosing a proper conservation treatment method and utilizes as indispensable data for permanentpreservation. As part of scrutinizing materials of repair and restoration, this study examines the purpose, date, and theconstituents of the materials used in metal bound rims adhered to the brim of ceramics. Among the subjected ceram-ics, the metal bound rims possible to scientific analysis were gone through element analyses with Portable -XRFtion of exhibition catalogues. The results of this study confirm that metal bound rims consisted of diverse materialswere adhered to the brim of ceramics.

Purpose The purpose of this paper is to examine differences between two factor analytical methods and their relevance for symptom cluster research: common factor analysis (CFA) versus principal component analysis (PCA). Methods Literature was critically reviewed to elucidate the differences between CFA and PCA. A secondary analysis (N= 84) was utilized to show the actual result differences from the two methods. Results CFA analyzes only the reliable common variance of data, while PCA analyzes all the variance of data. An underlying hypothetical process or construct is involved in CFA but not in PCA. PCA tends to increase factor loadings especially in a study with a small number of variables and/or low estimated communality. Thus, PCA is not appropriate for examining the structure of data. Conclusion If the study purpose is to explain correlations among variables and to examine the structure of the data (this is usual for most cases in symptom cluster research), CFA provides a more accurate result. If the purpose of a study is to summarize data with a smaller number of variables, PCA is the choice. PCA can also be used as an initial step in CFA because it provides information regarding the maximum number and nature of factors. In using factor analysis for symptom cluster research, several issues need to be considered, including subjectivity of solution, sample size, symptom selection, and level of measure. [Asian Nursing Research 2008;2(1):17?24]

The packaged terminal air conditioner, the typical cooling system for the residential buildings, consumes a large amount of electricity in a short period time during peak hours. In order to reduce the peak load and conserve the electricity, the thermally activated building system can be used as a secondary system to handle the partial cooling load. However, the thermally activated building system may cause condensation and under-cooling. Thus, design of both systems should be performed with careful investigation in characteristics of both systems to amplify the advantages. Since the thermally activated building system has the time-delay effect which may cause under-cooling, the system is designed to handle the base load of the building. Hence, simple simulation with EnergyPlus was performed to observe the characteristics of cooling load in residential buildings. Once the possible range of the load handling ratio of the thermally activated building system was decided, characteristics of system was analyzed in terms of hardware component and operation parameters. The hardware components were analyzed in plant and system aspects and the operation parameter was evaluated in the thermal comfort aspect. As the load handling ratio increased, the thermal comfort increased due to the lower radiant mean temperatures. Within the range of thermal comfort, the several adjustments were made in setpoint temperature and electricity consumptions of difference cases were observed to decide which components and parameters were important for designing the systems.

The water quality of Lake Shihwa had been rapidly deteriorating since 1994 due to wastewater input from the watersheds, limited water circulation and the lack of a wastewater treatment policy. In 2000, the government decided to open the tidal embankment and make a comprehensive management plan to improve the water quality, especially inflowing stream water around Shihwa and Banwol industrial complex. However, the water quality and microbial community have not as yet been fully evaluated. The purpose of this study is to investigate the influent water quality around the industrial area based on chemical and biological analysis, and collected surface water sample from the Siheung Stream, up-stream to down-stream through the industrial complex, Samples were collected in July 2009. The results show that the downstream site near the industrial complex had higher concentrations of heavy metals (Cu, Mn, Fe, Mg, and Zn) and organic matter than upstream sites. A combination of DGGE (Denaturing Gradient Gel Electrophoresis) gels, lists of K-WQI (Korean Water Quality Index), cluster analysis, MDS (Multi-Dimensional Scaling) and PCA (Principal Component Analysis) has demonstrated clear clustering between Siheung stream 3 and 4 and with a high similarity and detected metal reducing bacteria (Shewanella spp.) and biodegrading bacteria (Acinetobacter spp.). These results suggest that use of both chemical and microbiological marker would be useful to fully evaluate the water quality.

This paper presents a blind watermark detection scheme for an additive watermark embedding model. The proposed estimation-correlation-based watermark detector first estimates the embedded watermark by exploiting non-Gaussian of the real-world audio signal and the mutual independence between the host-signal and the embedded watermark and then a correlation-based detector is used to determine the presence or the absence of the watermark. For watermark estimation, blind source separation (BSS) based on independent component analysis (ICA) is used. Low watermark-to-signal ratio (WSR) is one of the limitations of blind detection with the additive embedding model. The proposed detector uses two-stage processing to improve the WSR at the blind detector; the first stage removes the audio spectrum from the watermarked audio signal using linear predictive (LP) filtering and the second stage uses the resulting residue from the LP filtering stage to estimate the embedded watermark using BSS based on ICA. Simulation results show that the proposed detector performs significantly better than existing estimation-correlationbased detection schemes.

The EBP (Error Back-Propagation) Algorithm was initially proposed for training MLP's (Multi-Layer Perceptrons) and is now widely used for training deep neural networks. This supervised learning algorithm minimizes the error function between the actual output values of MLP's and their desired values. However, ICA (Independent Component Analysis) is an unsupervised learning algorithm that aims to maximize the independence among the outputs of neural networks. ICA has been shown to realize visual features in the V1 layer of the human brain by learning from natural scenes and cochlear features of the human ear by learning from auditory signals. In this paper, we propose merging the supervised EBP algorithm with the unsupervised ICA algorithm to enhance the performance of neural networks by training independent features in the initial learning stage. This approach mirrors the feature-learning process observed in mammals during the early stages of life. Furthermore, the proposed approach is verified through simulations on isolated-word recognition tasks, achieving improved classification performance with faster learning convergence. In detail, when the number of hidden nodes is 100, EBP with ICA reaches a misclassification ratio of 2.78% on the test data at 160 epochs, while EBP achieves 3.28% at 300 epochs.

Robust PCA is a modification of PCA, which works well on corrupted observations. Existing robust PCA algorithms are typically based on batch optimization, and have to load all the samples into memory. Therefore, those algorithms have large computational complexity as the size of data increases, and have difficulty with real time processing. In this paper, we propose a projection based Robust Principal Component Analysis (RPCA) in order to use RPCA as an online algorithm for real time processing. The proposed online algorithm in this paper reduces computational complexity significantly, although the proposed algorithm has negligible performance degradation compared to conventional schemes. The proposed technique can be applied to various applications, which need real time processing of RPCA.

The purpose of this study was to investigate the fatty acid profiles in 18 soybean cultivars grown in Korea. A total of eleven fatty acids were identified in the sample set, which was comprised of myristic (C14:0), palmitic (C16:0), palmitoleic (C16:1, ${\omega}7$), stearic (C18:0), oleic (C18:1, ${\omega}9$), linoleic (C18:2, ${\omega}6$), linolenic (C18:3, ${\omega}3$), arachidic (C20:0), gondoic (C20:1, ${\omega}9$), behenic (C22:0), and lignoceric (C24:0) acids by gas-liquid chromatography with flame ionization detector (GC-FID). Based on their color, yellow-, black-, brown-, and green-colored cultivars were denoted. Correlation coefficients (r) between the nine major fatty acids identified (two trace fatty acids, myristic and palmitoleic, were not included in the study) were generated and revealed an inverse association between oleic and linoleic acids (r=-0.94, p<0.05), while stearic acid was positively correlated to arachidic acid (r=0.72, p<0.05). Principal component analysis (PCA) of the fatty acid data yielded four significant principal components (PCs; i.e., eigenvalues>1), which together account for 81.49% of the total variance in the data set; with PC1 contributing 28.16% of the total. Eigen analysis of the correlation matrix loadings of the four significant PCs revealed that PC1 was mainly contributed to by oleic, linoleic, and gondoic acids, PC2 by stearic, linolenic and arachidic acids, PC3 by behenic and lignoceric acids, and PC4 by palmitic acid. The score plots generated between PC1-PC2 and PC3-PC4 segregated soybean cultivars based on fatty acid composition.

Alzheimer's disease (AD) is an insidious and degenerative neurological disease. It is a new topic for AD patients to use magnetic resonance imaging (MRI) and computer technology and is gradually explored at present. Preprocessing and correlation analysis on MRI data are firstly made in this paper. Then kernel principal component analysis (KPCA) is used to extract features of brain gray matter images. Finally supervised classification schemes such as AdaBoost algorithm and support vector machine algorithm are used to classify the above features. Experimental results by means of AD program Alzheimer's Disease Neuroimaging Initiative (ADNI) database which contains brain structural MRI (sMRI) of 116 AD patients, 116 patients with mild cognitive impairment, and 117 normal controls show that the proposed method can effectively assist the diagnosis and analysis of AD. Compared with principal component analysis (PCA) method, all classification results on KPCA are improved by 2%-6% among which the best result can reach 84%. It indicates that KPCA algorithm for feature extraction is more abundant and complete than PCA.

Purpose: The purpose of the present current study was to examine control of upper limb multi-joint movements with differential coordination stability. To achieve the goals of the study, torque analyses were utilized to answer questions about how torque components were differed among various elbow-wrist coordination patterns. Methods: Eight self-reported right-handed college students (3 males and 5 females, mean age=20.6 yr) were volunteered. The task required participants to rhythmically coordinate the flexion-extension motions of their elbow and wrist with coordination relationship of $0^{\circ}$, $90^{\circ}$, and $180^{\circ}$relative phases between the two joints. Mean relative phase and phase stability (standard deviation of relative phase) were computed to for analysisze of overall coordination performance. To determine the figure out characteristics of torque components in elbow and wrist joints, impulse values of muscle torque (MT) and interactive torque (IT) and MT as a percentage of cycle duration (MT-PCD) were analyzed. Results: Torque results showed that the proximal elbow joint generated motions with mainly muscle efforts regardless of coordination patterns, while the distal wrist joint adjusted the coordination patterns by changing amount of MT. Impulse analyses showed that the least stable $90^{\circ}$ pattern was performed by utilizing a similar coordination strategy of the most stable $0^{\circ}$ pattern. Conclusion: The present current study suggests that the roles of distal and proximal joints differ in order to achieve various multi-joint coordination movements. This study provides information for use in gives an idea to development of rehabilitation or training programs for to persons with an impaired upper limb motor ability.

A crucial first step in the planning of any scientific experiment is to evaluate an appropriate sample size to permit sufficient statistical power to detect the desired effect. In this study, we investigated the optimal sample size of quantitative trait locus (QTL) linkage analysis for simple random sibship samples in pedigreed chicken populations, under the variance component framework implemented in the genetic power calculator program. Using the program, we could compute the statistical power required to achieve given sample sizes in variance component linkage analysis in random sibship data. For simplicity, an additive model was taken into account. Power calculations were performed to relate sample size to heritability attributable to a QTL. Under the various assumptions, comparative power curves indicated that the power to detect QTL with the variance component method is highly affected by a function of the effect size of QTL. Hence, more power can be achievable for QTL with a larger effect. In addition, a marked improvement in power could be obtained by increasing the sibship size. Thus, the use of chickens is advantageous regarding the sampling unit issue, since desirable sibship size can be easily obtained compared with other domestic species.

Image captioning is a task to generate a new caption using the training data of the image and caption. Since existing deep learning is a black-box model, it is crucial to analyze the influence on each module for understanding the model. In this paper, we analyze the impact of the five modules and do a comparative analysis according to three losses and two optimizations using two datasets. From extensive experiments, the best component of each module has been identified as an improved method.

In this study, contribution evaluation method applying Independent Component Analysis (ICA) was proposed. The necessity of applying ICA to the contribution evaluation was investigated through numerical simulation. The simulation modeled a scenario where the vibration/noise sources were physically overlapped in a small space, and their frequency characteristics were similar. For comparison between the conventional contribution evaluation method and the proposed method, the contribution evaluation was performed using the ordinary and partial contribution evaluation methods. Through this analysis, it was confirmed that the proposed method can identify contributions by restoring the signal when the frequency characteristics of the vibration/noise sources were similar, and their positions overlapped. These results confirm that the contribution evaluation method based on independent component analysis is effective in appropriately analyzing vibration/noise sources when their frequency characteristics are similar, and their positions overlap.

The objectives of this study were thus to identify most significant factors that determine milk component yield (MCY) using a meta-analysis and, if possible, to develop equations to predict MCY using variables that can be easily measured in the field. A literature database was constructed based on the research articles published in the Journal of Dairy Science from Oct., 2007 till May, 2010. The database consisted of a total of 442 observed means for MCY from 118 studies. The candidate factors that determine MCY were those which can be routinely measured in the field (e.g. DMI, BW, dietary forage content, chemical composition of diets). Using a simple linear regression, the best equations for predicting milk fat yield(MFY) and milk protein yield (MPY) were MFY = 0.351 (±0.068) + 0.038 (±0.003) DMI (R2 = 0.27), and MPY = 0.552 (±0.071) + 0.031 (±0.002) DMI - 0.004 (±0.001) FpDM (%, forage as a percentage of dietary DM) (R2 = 0.38), respectively. The best equation for predicting milk fat content (%) explained only 12% of variations in milk fat content, and none of a single variable can explain more than 5% of variations in milk protein content. We concluded that among the tested variables, DMI was the only significant factor that affects MFY and both DMI and FpDM significantly affect MPY. However, predictability of linear equations was relatively low. Further studies are needed to identify other variables that can predict milk component yield more accurately.

The technology of the SWG (Smart Water Grid) as one of most important national projects results in significant assignment that is closely associated with systematic management and effective operation. The individual component technics are required to establish directory and classification for the purpose of effectively managing their information related to research and development (R&D). The national science technology (S&T) standard classification tree which results in the representative example has been established with an intention to manage R&D information, human resource, and budget. It has been also revised every five years and then used in the various fields related to the evaluation, administration, and prediction of the national R&D projects. In addition, the standard classification system for R&D projects has been widely used in the UNESCO (United Nations Educational, Scientific and Cultural Organization) and EU (European Union) since the Frascati Manual was established in the Organization for Economic Cooperation and Development (OECD). Therefore, it is necessary for SWG techniques to develop the standard S&T classification tree for research management and evaluation. For this, it is essential to draw the core techniques for the SWG, which are incorporated with IT (Information Technology), NT (Nano Technology), and BT (Biology Technology).

Background/Objectives: This study was designed to compare the Varimax analysis of Principal Component Analysis(PCA) with the Exploratory Factor Analysis(EFA) method using Maximum Likelihood Estimation(MLE) and oblique rotation(Direct Oblimin) for the multicultural experience of university students. multicultural experience variables of college students were analyzed as two factors through Maximum Likelihood Estimation(MLE). Methods/Statistical analysis Q1, Q2, Q3 are multicultural direct experiences, and Q4, Q5, Q6, Q7 are multicultural indirect experiences. Findings: As a result of this study, it is suggested to use Maximum Likelihood Estimation(MLE) and oblique rotation(Direct Oblimin). As a prerequisite for applying the Maximum Likelihood Estimation(MLE), it is thought that Improvements/Applications it is necessary to increase the accuracy of the response rate by using the face-toface interview method when conducting a survey.

The main purpose of this study was to suggest a solution for inflamma-tion caused by urban daily life based on supercritical extraction of Cha-maecyparis obtusa Endl. (C. obtusa). The leaves, cone, wood, and bark of C. obtusa were separately extracted with supercritical extraction. Al-pha-pinene, anti-inflammatory active compound of C. obtusa, was ana-lyzed by electrospray ionization-mass spectrometry, gas chromatogra-phy, and gas chromatography mass spectrometry from each part of ex-tracts. As a result of the reduction rate of nitric oxide in RAW 264.7 cells, the wood extract exhibited the highest reduction rate was about 45% among extracts from four part of C. obtusa. The wood extract from C. obtusa can be developed as a good anti-inflammatory natural raw ma-terial for natural product industry.