경기만 바깥수역에 조석 전선 형성이 식물플랑크톤 생태에 미치는 영향을 알기 위 하여 국립 수산 진흥원의 정기 정선 관측 자료 분석과 3차에 걸친 현장조사가 서해 중 동부 연안 수역에서 이루어 졌다. 수온와 용존산소의 자료를 분석한 결과 조석 전선이 경기만 바깥수역인 태안 반도 서편에 춘·하계에 걸쳐 형성되는 것이 발견되었다. 조 석 전선의 형성은 식물플랑크톤의 분포와 일차 생산력에 영향을 주는 것으로 나아났 다. 전선 형성 수역의 식물플랑크톤 현존량, 색소량, 일차 생산력이 연안 혼합 수역이 나 바깥 성층 형성 수역의 현존량 색소량, 일차 생산력보다 높게 나타났다. 전선 형성 수역이 이러한 높은 일차 생산력은 좋은 광조건과 풍부한 영양염 공급에 기인된 것으 로 보인다. 전선수역을 경계로 연안 수역은 비교적 높은 색소량과 낮은 일차 생산력을 보이고 성층 형성 수역은 낮은 색소량과 비교적 높은 일차 생산력을 보였다. 성층 형 성 수역의 비교적 높은 일차 생산력은 표층의 미세플랑크톤에 의한 높은 동화율과 수 온 약층 아래에 많이 나타나는 부유성 저서 규조류가 좋은 관조건으로 높은 생산력을 보였기 때문이다. 대체로 본 조사수역은 춘하계의 전선형성에 따라 연안 혼합 수괴, 전선 형성 수괴, 성층 형성수괴로 구분되며 이에 따라 Cholorophyll 분포, 일차 생산 력 분포, 식물플랑크톤의 정량 분포도 이에 영향을 받고 있음을 보여주었다. 【In order to study the formation and structure of tidal fronts and their influence on the distribution and productivity of phytoplankton in the outer of Kyonggi Bay, analyses on the water temperature data from 1977 to 1986 and 3 surveys from 1981 to 1986 were carried out in the mid0eastern coast of the Yellow Sea. Temperature gradients and dissolved oxygen gradients were implied that the tidal fronts are formed at the outer of the Kyonggi Bay along the western side of Tae-An peninsula from spring to summer. the formations of tidal fronts in this study area influence the distribution of phytoplankton and primary productivity. The standing stocks, chlorophyll concentrations and primary productivity of phytoplankton in the frontal area are higher than those of the outer stratified waters and the inner coastal mixed waters. These high production in the frontal area are resulted from good light condition and rich nutrient within the water columns. With a boundary of frontal area, there are relatively high chlorophyll concentrations and primary productivity in the coastal mixed waters while there are low chlorophyll concentrations and relatively high primary productivity in the stratified waters. These relatively high primary productivity in the outer area are resulted from the high potential production by nanoplankton in the surface layer and the high production of tychopelagic diatoms under the thermocline with the deep transparency.】

황해 중.동부 연안 수역의 조석전선이 식물 플랑크톤 생산력과 분포에 미치 는 영향

In this paper, the predictive maintenance(PdM) technology was applied to the semiconductor manufacturing equipment and the DBSCAN algorithm parameters were optimized. The data obtained from the semiconductor equipment was obtained using acceleration sensors and was used to determine the conditions of the equipment. To simulate the malfunction condition of the equipment, the virtual error data was artificially generated. The DBSCAN clustering algorithm is applied to both acceleration sensor data and virtual error data to trace the moving of the center point of each cluster. By tracing the center points, the appropriate time for equipment maintenance can be determined. Also, the parameters of the DBSCAN algorithm were optimized using one-way factorial design to improve to determine the exact time of maintenance.

Clustering Parameter Optimization of Predictive Maintenance Algorithm for Semiconductor Equipment Using One-way Factorial Design

제품 관여도 및 브랜드 사전 인지 여부에 따른 광고효과 분석

Destructive manual experiments are primarily used for quality assessment of electron beam welded components, which consume the resources and time significantly. Vision-based and other NDT-based quality monitoring in EBW is rarely reported, and consequently, more investigation is required. An inexpensive quality evaluation technique for the welded joints has been proposed using the weld bead surface attributes. These attributes have a good correlation with quality indicators like aspect ratio and mechanical properties of the joint. Here, electron beam welding of CuCrZr alloy was conducted to get varying weld profiles, and the surface weld attributes were measured. The model correlating top and bottom bead widths with the process variables was established using support vector regression. The predicted unlabelled weld attributes were employed as inputs for the unsupervised clustering algorithms, namely fuzzy C-mean clustering and density-based clustering. The clustering algorithms provided different clusters of the joints, namely poor, fair and good. FCM was found to be more precise for the clustering of welding data. Poor category of the joints was represented by that with partial penetration and lower aspect ratio, and the corresponding process variables’ combination could be avoided in the next iteration through the feedback. The fair category represented the joints with a high aspect ratio and keyhole-based profiles with moderate strength and ductility. The good category signified the weld joints having the maximum joint strength and ductility, and an acceptable aspect ratio with the conduction mode of welding. Keyhole-based weld profile could be obtained using high beam power (5.5 kW to 6.3 kW) with high welding speed (1000 mm/min and above). A combination of 0.1 mm oscillation amplitude and 900 Hz oscillation frequency was found to be the most favourable one, in this study. 

Prediction of electron beam weld quality from weld bead surface using clustering and support vector regression

In travel, exchanging and obtaining information become easier and more accessible, which led e-WOM (Electronic Word-of-Mouth) to be the most influential communication tool in modern society. Regarding the phenomenon, travel agencies also consider this trend as a significant fact. This research was conducted on 330 university students in Seoul and suburban areas for studying difference among WOM Acceptance, WOM Activity and Brand Attitude which are influenced by a direction of e-WOM. The research also approved Moderating Effect of Brand Reputation regarding research in WOM effectiveness of e-WOM travel information. As a result, the research drew a fact that there are differences in WOM effect between positive and negative WOM. On the other hand, the research shows, different from previous studies, positive WOM information causes more influence on WOM effectiveness. In addition, by studying Moderating Effect between e-WOM travel information and WOM effectiveness, the research shows that there is Moderating Effect between WOM Acceptance and WOM Activity besides Brand Attitude.

온라인 구전 여행정보의 방향성과 구전효과

최근 소비자들은 온라인상에서 다양한 온라인 구전정보를 접하고 있으며, 이러한 온라인 구전정보는 소 비자 태도(구매의도)에 중요한 영향을 미치고 있다. 본 연구에서는 특정 대상이 아닌 일반적인 온라인 구전(eWOM) 특성이 신뢰에 미치는 영향과 신뢰가 구전수용, 구전수용이 구매의도에 미치는 영향을 알아보는데 목적이 있다. 본 연구에서는 온라인 구전 특 성으로써 정보 특성을 동의성과 최신성, 커뮤니티 특성은 유대강도와 상호작용으로 구분하였다. 본 연구의 공헌은 첫째, 정보동의성은 신뢰에 유의한 영향을 미치지 않았다. 둘째, 정보최신성과 커뮤니 티 특성(유대강도, 상호작용)은 신뢰에 유의한 영향을 미치고 있다. 셋째, 신뢰는 구전수용에 유의한 영향 을 미치고 있다. 마지막으로 구전수용은 구매의도에 유의한 영향을 미치는 것으로 나타났다.

The effects of eWOM Characteristics on Credibility, eWOM Acceptance and Purchase Intentions

In data mining or machine learning, one of the most commonly used feature extraction techniques is principal component analysis (PCA) However, it performs poorly on a large dataset In this paper, we propose a new method of accelerating conventional PCA, named hash-tree PCA It samples the objects that are similar to each other without losing the original data distribution First, it explores similar objects and stores them in hash tables Afterward, it samples a certain number of the objects from each hash table and creates a new dataset with a reduced number of objects Finally, it executes PCA on the sampled dataset Experimental results show that our method outperforms the PCA and fast PCA methods

Sang-Hyun Lee

Papers

The effects of eWOM Characteristics on Credibility, eWOM Acceptance and Purchase Intentions

Hash-tree PCA: accelerating PCA with hash-based grouping

Detection of the Defected Regions in Manufacturing Process Data using DBSCAN