• 한국콘텐츠학회논문지
• /
• 제13권5호
• /
• pp.183-190
• /
• 2013

본 연구에서는 문화콘텐츠 경쟁력 강화 및 미래형 융합콘텐츠 기술 지원을 위한 필수 요소인 문화기술의 분류체계를 제안한다. 이를 위해 문화기술의 개념 및 현황에 대한 고찰을 통해 문화콘텐츠의 특수성을 도출하고, 기존의 문화기술 분류체계와 기타 기술 분류체계의 체계적 분석을 통해 현재 문화기술 분류체계의 문제점 및 향후 문화기술 분류체계의 반영을 위한 시사점을 도출한 후 문화콘텐츠의 특수성을 반영한 새로운 문화기술 분류체계를 개발하였다. 최종적으로 제안된 문화기술 분류체계 안은 문화콘텐츠의 기획, 생산/제작, 유통, 서비스의 모든 가치사슬 과정에 해당하는 관련 기술들을 포괄함과 동시에, 중분류 구성에 있어서 CT의 특성을 문화콘텐츠의 특수성을 고려하여 개편함으로써 산업 현장에서 보다 현실성 있고, 향후 정부 투자 및 제도 반영에 유연한 분류체계로서 그 의미를 갖는다.

• 한국문헌정보학회지
• /
• 제20권
• /
• pp.35-63
• /
• 1991

DDC, LCC, and CC can be said as the major schemes for mordern general library classification. Among these, DDC, since its publication in 1876, has been continuously studies and revised by many scholars and practitioners to publish 20th edition in 1989: LCC also has been studied and revised by the specialists in each subject, since 1904; and CC(first edition 1933) is now on the stage of 7th edition(1987). Even though studied, revised and developed by many classificationists, all these schemes maintain the general framework of the beginning, only with the partial revision and expansion to reflect the developments of the subjects. and antioipated tremendous amount of works resulted from reclassification also can be a reason that disturbs the full innovative revision of the scheme, because these are used in many libraries as a basic tools for the classification. But all these schemes mainly based on the state of the discipline at the time of their creation, the beginning of 20the century, and so in some aspect it is natural for them to have many problems. This study aims to investigate the problems in these major schemes, to find some ways to solve the problems, and to suggest the ideas for the basic design of a new modern library classification scheme. This plan is prepared to be applied to the situation of all countries equally without any revision. And in its notation, it uses two digits of Arabic numerals as centesimal, and so it is named provisionally to Plan for Centesimal Classification (PCC).

• 한국멀티미디어학회논문지
• /
• 제20권10호
• /
• pp.1646-1654
• /
• 2017

Pupil size variation can not be controlled intentionally by the user and includes various features such as the blinking frequency and the duration of a blink, so it is suitable for understanding the user's emotional state. In addition, an ocular feature based emotion classification method should be studied for virtual and augmented reality, which is expected to be applied to various fields. In this paper, we propose a novel emotion classification based on CNN with pupil size variation signals which include not only various ocular feature information but also time information. As a result, compared to previous studies using the same database, the proposed method showed improved results of 5.99% and 12.98% respectively from arousal and valence emotion classification.

• 정보관리학회지
• /
• 제30권3호
• /
• pp.49-70
• /
• 2013

본 연구는 인터넷 서점의 건강분야 분류체계의 개선방안으로 이를 위해 국내 외 8곳의 인터넷 서점의 건강분야 분류체계 현황을 비교분석하고, KDC, DDC의 해당 주제 분류항목과 비교 분석하였다. 그리고 인터넷 서점의 건강분야 분류체계의 이용에 대한 이용자 면담을 진행하였다. 그 결과를 바탕으로 설계원칙을 수립하고, 인터넷 서점의 건강분야 분류체계 설계안을 개발한 후, 실무자와 전문가 평가를 받아서 건강이란 대분류 아래 11개의 중분류 항목과 60개의 소분류 항목, 16개의 세분류 항목으로 제시하였다. 본 연구의 결과는 인터넷 서점은 물론 웹상에서 건강 관련 정보를 효율적으로 분류하는 기반이 될 것이다.

• 한국대기환경학회지
• /
• 제12권4호
• /
• pp.369-376
• /
• 1996

Major atmospheric stability classification methods were evaluated with meteorological data obtained by scoustic sounding profiler (SODAR/RASS) in Seoul. The Psequill classificatio method, the method most widely used because of its good agreement in respect of synoptic scope under the steady state, fails to describe the time lag, the response time on stability by heating or cooling caused by daily insolation or noctrunal surface radiation. Horizontal and vertical standard deviation of wind fluctuation $(\sigma_A and \sigma_E)$ method tend to classify night-time stable condition (E, F class) into unstable condition (A, B class). The classification matrix tables for Vogt's vertical temperature difference and wind speed using method ($\Delta$T $\cdot$ U) and bulk Richardson number (Rb) were amended for practical use over Seoul. The modified tables for $\Delta$T $\cdot$ U and Rb method were made by using comprehensive frequency distribution from Pasquill's method and other existing results, and the correlation coefficient(r) was equal to 0.829. It was confirmed that atmospheric stability could be changed with monitoring site characteristics, height and vertical difference between sensors of monitoring station, and classification method itself.

• Journal of information and communication convergence engineering
• /
• 제21권3호
• /
• pp.208-215
• /
• 2023

Deep learning techniques provide powerful solutions to several pattern-recognition problems, including Raman spectral classification. However, these networks require large amounts of labeled data to perform well. Labeled data, which are typically obtained in a laboratory, can potentially be alleviated by data augmentation. This study investigated various data augmentation techniques and applied multiple deep learning methods to Raman spectral classification. Raman spectra yield fingerprint-like information about chemical compositions, but are prone to noise when the particles of the material are small. Five augmentation models were investigated to build robust deep learning classifiers: weighted sums of spectral signals, imitated chemical backgrounds, extended multiplicative signal augmentation, and generated Gaussian and Poisson-distributed noise. We compared the performance of nine state-of-the-art convolutional neural networks with all the augmentation techniques. The LeNet5 models with background noise augmentation yielded the highest accuracy when tested on real-world Raman spectral classification at 88.33% accuracy. A class activation map of the model was generated to provide a qualitative observation of the results.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅲ
• /
• pp.1279-1282
• /
• 2003

Estimation of human motion states is important enabling technologies for realizing a pervasive computing environment. In this paper, an improved method fur estimating human motion state from accelerometer data is introduced. Our method fur estimating human motion state utilizes various statistics of accelerometer data, such as mean, standard variation, skewness, kurtosis, eccentricity, as features for classification, and therefore is expected to be more robust than other existing methods that rely on only a few simple statistics. A series of experiments fur testing the effectiveness of the proposed method has been performed, and its result is presented.

• 한국지하수토양환경학회:학술대회논문집
• /
• 한국지하수토양환경학회 2000년도 창립총회 및 춘계학술발표회
• /
• pp.24-27
• /
• 2000

The development of land classification and soil productivity rating systems (SPR) are examined for their application to valuation of agricultural land in South Dakota, USA. The application of SPR data to land valuation work conducted by real estate appraisers, tax assessors, and economists are discussed along with an assessment of its benefits and limitations.

• 전기전자학회논문지
• /
• 제24권1호
• /
• pp.97-105
• /
• 2020

열 영상은 온도에 따라 방출하는 에너지의 차이를 나타낸 영상이다. 주야간 사용이 가능하기 때문에 군사적인 용도로 많이 활용되고 있으나, 열 영상은 물체의 경계가 불명확하고 흐릿하게 표현되는 경우가 많으며 화염 등의 열기로 인해 경계부분이 변질되는 단점이 있다. 따라서, 열 영상을 이용하여 표적의 종류를 분류할 때 정확하게 분할된 경계선을 이용할 경우 효과적으로 분류 할 수 있지만, 물체의 경계가 잘못 추출되는 경우 분류의 정확도가 크게 감소한다. 본 논문에서는 이러한 단점을 극복하기 위해서 표적 영상의 분할 신뢰도에 따라 형태특징과 지역특징의 분류결과를 융합하는 계층적 분류기법을 제안하였으며, 연속 영상 기반으로 분류 결과를 갱신하는 기법을 새롭게 제안하여 차량 표적 분류 정확도를 개선하였다. 제안하는 방법은 실제 군용 표적 4종(전차, 장갑차, 상용차, 군용트럭)이 있는 다양한 자세의 열 영상 20,000장 이상을 이용하여 성능을 검증하였으며, 우수한 성능의 기존 방법 대비 정확도 개선에 효과가 있음을 확인하였다.

• 한국컴퓨터정보학회:학술대회논문집
• /
• 한국컴퓨터정보학회 2016년도 제54차 하계학술대회논문집 24권2호
• /
• pp.51-54
• /
• 2016

In computer vision especially in image processing, it has become popular to apply deep convolutional networks for supervised learning. Convolutional networks have shown a state of the art results in classification, object recognition, detection as well as semantic segmentation. However, supervised learning has two major disadvantages. One is it requires huge amount of labeled data to get high accuracy, the second one is to train so much data takes quite a bit long time. On the other hand, unsupervised learning can handle these problems more cheaper way. In this paper we show efficient way to learn features for classification in an unsupervised way. The network trained layer-wise, used backpropagation and our network learns features from unlabeled data. Our approach shows better results on Caltech-256 and STL-10 dataset.