• 한국의류학회지
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• 제27권2호
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• pp.239-249
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• 2003

The purpose of this research was to investigate influential factors for shopmaster's CRM (Customer Relationship Management) in the designer brand products, and to indicate the future fashion marketing strategies. The questionnaires were distributed to 74 shopmasters of the Designer shop in domestic L. S and H Department stores. Descriptive statistics, factor analysis, and path analysis from Lisrel program were used to analyze the data. The results were as follows; Firstly. for shopmaster's CRM variables, four factors of customer management variables were found and labeled as interest, DB construction, contact opportunity, and materials. Also, four (actors of shopmaster's knowledge regarding apparel materials were found and labeled as professionalism, manner, sense, and persuasion. For service variables, four factors such as precision, variety, rapidity, positiveness, and convenience of shopping were found. Secondly, for the results of hypothesis, all of the independent variables had direct influences on forming the relationship with customers. Therefore, it is concluded that the main elements of Shopmaster's CRM are highly important variables in customer relationship marketing strategy.

• Biotechnology and Bioprocess Engineering:BBE
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• 제11권3호
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• pp.179-186
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• 2006

End-Labeled Free-Solution Electrophoresis (ELFSE) is a new technique that is a promising bioconjugate method for DNA sequencing (or separation) and genotyping by both capillary and microfluidic device electrophoresis. Because ELFSE enables high-resolution electrophoretic separation in aqueous buffer alone (i.e., without a polymer matrix), it eliminates the need to load viscous polymer networks into electrophoresis microchannels. To achieve microchannel DNA separations with high performance, ELFSE requires monodisperse perturbing entities (i.e., drag-tags), which create a large amount of frictional drag when pulled behind DNA during free-solution electrophoresis, and which have other properties suitable for microchannel electrophoresis. In this article, the theoretical concepts of ELFSE and the required characteristics of the drag-tag molecules for the ultimate performance of ELFSE are reviewed. Additionally, the merits and limitations of current drag-tags are also discussed in the context of recent experimental data of ELFSE separation (or sequencing).

• Archives of Pharmacal Research
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• 제14권3호
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• pp.237-241
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• 1991

The tautomerism in 3-phenyl-4-arylazo-5-isoxazolones 1 was examined by $^1H-NMR$ spectra of $^15N-labeled$ compound and by HMO method. Both spectra data $(^1H-NMR\;and\;IR)$ and bonding energies are in support of the assignment of the hydrazone structure to such compounds. It is further shown that intermolecular and intramolecular hydrogen bondings favor the hydrazone tautomer.

• Management Science and Financial Engineering
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• 제16권1호
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• pp.81-117
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• 2010

Semi-supervised learning incorporates unlabeled examples, whose labels are unknown, as well as labeled examples into learning process. Although transductive support vector machine (TSVM), one of semi-supervised learning models, was proposed about a decade ago, its application to large-scaled data has still been limited due to its high computational complexity. Our previous research addressed this limitation by introducing a branch-and-bound algorithm for finding an optimal solution to TSVM. In this paper, we propose three new techniques to enhance the performance of the branch-and-bound algorithm. The first one tightens min-cut bound, one of two bounding strategies. Another technique exploits a graph-based approximation to a support vector machine problem to avoid the most time-consuming step. The last one tries to fix the labels of unlabeled examples whose labels can be obviously predicted based on labeled examples. Experimental results are presented which demonstrate that the proposed techniques can reduce drastically the number of subproblems and eventually computational time.

• 대한음성학회지:말소리
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• 제35_36호
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• pp.131-143
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• 1998

This study describes an algorithm for the F0 contour generation system for Korean sentences and its evaluation results. 400 K-ToBI labeled utterances were used which were read by one male and one female announcers. F0 contour generation system uses two classification trees for prediction of K-ToBI labels for input text and 11 regression trees for prediction of F0 values for the labels. Evaluation results of the system showed 77.2% prediction accuracy for prediction of IP boundaries and 72.0％ prediction accuracy for AP boundaries. Information of voicing and duration of the segments was not changed for F0 contour generation and its evaluation. Evaluation results showed 23.5Hz RMS error and 0.55 correlation coefficient in F0 generation experiment using labelling information from the original speech data.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2019년도 하계학술대회
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• pp.160-162
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• 2019

음향 이벤트 인식 기술은 오디오 신호에서 음향 이벤트를 예측하는 기술로, 최근 대용량 데이터베이스의 배포, 인식 알고리즘과 하드웨어의 발전, 관련 인식 대회 등에 힘입어 많은 연구가 이루어지고 있는 분야이다. 본 논문에서는 음향 장면 및 이벤트 인식 관련 대회인 DCASE 챌린지에 대하여 기술하고, 약한 레이블 기반의 데이터를 학습해 강한 레이블을 예측하는 DCASE 챌린지 과제 4에 대하여 설명한다. 또한 DCASE 챌린지 과제 4에 제출된 다양한 음향 이벤트 인식 알고리즘과 데이터베이스의 종류에 따른 성능을 비교하여 음향 이벤트 인식 성능을 분석한다.

• 한국컴퓨터정보학회논문지
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• 제19권9호
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• pp.125-139
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• 2014

본 논문에서는 준지도 지지 벡터 회귀 모델(semi-supervised support vector regression)을 이용한 반응 모델링(response modeling)을 제안한다. 반응 모델링의 성능 및 수익성을 높이기 위해, 고객 데이터 셋의 대부분을 차지하는 레이블이 존재하지 않는 데이터를 기존 레이블이 존재하는 데이터와 함께 학습에 이용한다. 제안하는 알고리즘은 학습 복잡도를 낮은 수준으로 유지하기 위해 일괄 학습(batch learning) 방식을 사용한다. 레이블 없는 데이터의 레이블 추정에서 불확실성(uncertainty)을 고려하기 위해, 분포추정(distribution estimation)을 하여 레이블이 존재할 수 있는 영역을 정의한다. 그리고 추정된 레이블 영역으로부터 오버샘플링(oversampling)을 통해 각 레이블이 없는 데이터에 대한 레이블을 복수 개 추출하여 학습 데이터 셋을 구성한다. 이 때, 불확실성의 정도에 따라 샘플링 비율을 다르게 함으로써, 불확실한 영역에 대해 더 많은 정보를 발생시킨다. 마지막으로 지능적 학습 데이터 선택 기법을 적용하여 학습 복잡도를 최종적으로 감소시킨다. 제안된 반응 모델링의 성능 평가를 위해, 실제 마케팅 데이터 셋에 대해 다양한 레이블 데이터 비율로 실험을 진행하였다. 실험 결과 제안된 준지도 지지 벡터 회귀 모델을 이용한 반응 모델이 기존 모델에 비해 더 높은 정확도 및 수익을 가질 수 있다는 점을 확인하였다.

• International Journal of Computer Science & Network Security
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• 제22권6호
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• pp.230-240
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• 2022

Sharing of online videos via internet is an emerging and important concept in different types of applications like surveillance and video mobile search in different web related applications. So there is need to manage personalized web video retrieval system necessary to explore relevant videos and it helps to peoples who are searching for efficient video relates to specific big data content. To evaluate this process, attributes/features with reduction of dimensionality are computed from videos to explore discriminative aspects of scene in video based on shape, histogram, and texture, annotation of object, co-ordination, color and contour data. Dimensionality reduction is mainly depends on extraction of feature and selection of feature in multi labeled data retrieval from multimedia related data. Many of the researchers are implemented different techniques/approaches to reduce dimensionality based on visual features of video data. But all the techniques have disadvantages and advantages in reduction of dimensionality with advanced features in video retrieval. In this research, we present a Novel Intent based Dimension Reduction Semi-Supervised Learning Approach (NIDRSLA) that examine the reduction of dimensionality with explore exact and fast video retrieval based on different visual features. For dimensionality reduction, NIDRSLA learns the matrix of projection by increasing the dependence between enlarged data and projected space features. Proposed approach also addressed the aforementioned issue (i.e. Segmentation of video with frame selection using low level features and high level features) with efficient object annotation for video representation. Experiments performed on synthetic data set, it demonstrate the efficiency of proposed approach with traditional state-of-the-art video retrieval methodologies.

• 대한지역사회영양학회지
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• 제1권1호
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• pp.28-40
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• 1996

This study was conducted to evaluate the nutrition quality of the commercial infant formulas and to seek the solution to the establishment of the standard of nutrient requirements for infant formula in Korea. Nutrition informations were obtained from the labels of nineteen commercial infant formulas manufactured by 3 different companies, and the actual amounts of some nutrients were analyzed and compared to the labeled amounts. In addition, the nutrient composition of the commercial infant formulas was compared to the composition of breast milk, RDA for infants, and Codex standard for infant formula. The kind of minerals, vitamins and special components added to the commercial infant formulas were the major differences among 3 manufacturers. For some nutrients, the analyzed amounts were lower than the labeled amounts. In addition when different batches of the same kind of infant formula were analyzed, the large variations in the amounts of certain nutrients were noted. These data suggest that the nutrition labeling informations need to be validated, and nutrients added to the formulas are to be homogenized thoroughly. In order to solve these problems, therefore, like other countries, we need to establish the standard of nutrient requirements for infant formulas. Considering the available data on breast milk composition, RDA for infants and coordination with the international standard, we suggest the adoption of the Codex standard for infant formula may be the best way to manage the nutrition quality of commercial infant formulas at the present time.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2007년도 추계학술대회
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• pp.439-443
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• 2007

Nowadays, activity recognition becomes a hot topic in context-aware computing. In activity recognition, machine learning techniques have been widely applied to learn the activity models from labeled activity samples. Most of the existing work uses only one learning method for activity learning and is focused on how to effectively utilize the labeled samples by refining the learning method. However, not much attention has been paid to the use of multiple classifiers for boosting the learning performance. In this paper, we use two methods to generate multiple classifiers. In the first method, the basic learning algorithms for each classifier are the same, while the training data is different (ASTD). In the second method, the basic learning algorithms for each classifier are different, while the training data is the same (ADTS). Experimental results indicate that ADTS can effectively improve activity recognition performance, while ASTD cannot achieve any improvement of the performance. We believe that the classifiers in ADTS are more diverse than those in ASTD.