• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2003년도 봄 학술발표논문집 Vol.30 No.1 (B)
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• pp.178-180
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• 2003

EEG-based brain computer interface(BCI) provides a new communication channel between human brain and computer. EEG data is a multivariate time series so that hidden Markov model (HMM) might be a good choice for classification. However EEG is very noisy data and contains artifacts, so useful features mr expected to improve the performance of HMM. In this paper we addresses the usefulness of principal component features with Hidden Markov model (HHM). We show that some selected principal component features can suppress small noises and artifacts, hence improves classification performance. Experimental study for the classification of EEG data during imagination of a left, right up or down hand movement confirms the validity of our proposed method.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 춘계학술대회논문집
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• pp.872-876
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• 2007

Road traffic noise map is effective method to save cost and time for environmental noise assessment. Generally, noise is calculated by using theoretical equation of noise prediction, and the calculated result can be influenced by various input factors. Especially, domestic vehicle classification method for traffic flow and heavy vehicle percentage is different from that of foreign countries. Thus, this can cause effect on the noise prediction results. In this study, noise prediction results by using domestic vehicle classification method are compared with those by foreign methods.

• 한국농공학회논문집
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• 제58권1호
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• pp.91-97
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• 2016

The objective of this study was to classify between radish and Chinese cabbage in autumn using hyperspectral images. The hyperspectral images were acquired by Compact Airborne Spectrographic Imager (CASI) with 1m spatial resolution and 48 bands covering the visible and near infrared portions of the solar spectrum from 370 to 1044 nm with a bandwidth of 14 nm. An object-based technique is used for classification of radish and Chinese cabbage. It was found that the optimum parameter values for image segmentation were scale 400, shape 0.1, color 0.9, compactness 0.5 and smoothness 0.5. As a result, the overall accuracy of classification was 90.7 % and the kappa coefficient was 0.71. The hyperspectral images can be used to classify other crops with higher accuracy than radish and Chines cabbage because of their similar characteristic and growth time.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2012년도 추계학술대회 논문집
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• pp.153-158
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• 2012

Noise maps are produced according to 'The Method of making a Noise Map' in order to noise control efficiently, and prediction model to predict road traffic noise which may apply to Korean situation, include CRTN, RLS 90, NMPB, Nord 2000 and ASJ 2003. Of them, ASJ 2003, Japan's prediction model has not been verified for the application to Korean situation according to the classification of vehicle. In addition, ASJ 2003 was revised to ASJ 2008 recently, a classification for motorcycle was added. This study attempts to check the classification of vehicle in ASJ 2008 and 'The Method of making a Noise Map' to confirm the suitability of the application of them to Korean situation.

• 한국소음진동공학회논문집
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• 제22권2호
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• pp.193-197
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• 2012

Road traffic noise map is effective method to save cost and time for environmental noise assessment. Generally, noise is calculated by using theoretical equation of noise prediction, and the calculated result can be influenced by various input factors. Especially, domestic vehicle classification method for traffic flow and heavy vehicle percentage is different from that of foreign countries. Thus, this can cause effect on the noise prediction results. In this study, noise prediction results by using domestic vehicle classification method are compared with those by foreign methods.

• 대한의용생체공학회:의공학회지
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• 제21권2호
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• pp.137-144
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• 2000

Clustered microcalcifications in X-ray mammograms are an important sign for the diagnosis of breast cancer. A shape-based method, which is based on the morphological features of clustered microcalcifications, is proposed for classifying clustered microcalcifications into benign or malignant categories. To verify the effectiveness of the proposed shape features, clinical mammograms were used to compare the classification performance of the proposed shape features with those of conventional textural features, such as the spatial gray-leve dependence method and the wavelet-based method. Image features extracted from these methods were used as inputs to a three-layer backpropagation neural network classifier. The classification performance of features extracted by each method was studied by using receiver operating-characteristics analysis. The proposed shape features were shown to be superior to the conventional textural features with respect to classification accuracy.

• 가정간호학회지
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• 제6권
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• pp.19-32
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• 1999

The purpose of this study was to develop of home nursing care classification and home health care costs of the free-standing home nursing care agency. This study was done through 3 steps The First stage, home nursing care classification was identified and classified by literature, review-committee and expert meeting. The second stage, cost elements for home nursing care visit were identified and accounted. That were divided into direct nursing care cost, indirect nursing care cost, management cost and transportation cost. Third stage, total cost of per visit was produced. Data were collected from 810 visits of 120 patients received home dare and from January. 1999 to November, 1999, and analysed with EXCEL program. The obtained results are as follows : 1. Home nursing care classification was consisted of 6 high level classification domain and 10 low level classification domain and 163 home nursing care behavior. 2. The cost of home nursing care per visit was 30,638 won which were direct and indirect nursing care cost(16.305won), management cost(5,255won) and transportation cost (9,098won). In conclusion. Home nursing behavior care classification developed in this study would be used as home health care standard. And the home nursing care costs can be used as a fundamental data for the further development of home health care costs in Korea.

• 한국지능시스템학회논문지
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• 제21권2호
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• pp.218-223
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• 2011

최근 음악을 장르로 분류하는 다양한 연구가 진행되고 있다. 하지만 이러한 분류는 전문가들 마다 분류하는 기준이 서로 상이하여 정확한 결과를 도출하기가 쉽지 않다. 또한 새로운 장르 출현 시, 새롭게 정의해야하는 번거로움이 발생한다. 따라서 음악을 장르로 구분하기 보다는 감정형용사들로 분류, 검색하여야 한다. 선행연구에서는 밝고 어두움을 기준으로 음악을 분류 하였다. 본 논문에서는 선행연구를 포함하여 사람이 느끼는 감정 중, 격렬함과 잔잔함, 그리고 웅장함과 가벼움 등, 3가지 분류 기준을 가지고 분위기에 알맞은 검색을 위한 감정 형용사 기반의 음악 분류 시스템을 제안한다. 분류 알고리즘으로는 Support Vector Machine을 개선한 알고리즘인 Variance Considered Machines을 이용하였으며, 총 525개의 곡을 분류 시도한 결과, 약 85%의 분류 정확도를 나타내었다.

• 한국문헌정보학회지
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• 제44권2호
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• pp.241-262
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• 2010

본 연구에서는 국내 주요 검색 포털들인 네이버, 네이트, 다음, 야후에서 제공하는 서비스들의 분류체계를 분류체계의 일관성, 분류체계의 논리성, 인터페이스의 일관성, 카테고리명의 명확성, 카테고리 및 사이트 배열 순서, 계층 구조 설계 등의 관점에서 비교, 분석하였다. 이러한 기준에 따라 조사한 결과, 동일한 포털에서 제공하는 서비스들이 공통점이 거의 없는 독자적인 분류체계를 구축, 운영하고 있는 것으로 나타났다. 따라서 향후 포털들의 통합 분류체계 구축과 인터페이스 표준화가 요구된다. 본 연구의 결과는 포털들의 분류체계의 개선에 활용될 수 있을 것으로 기대된다.

• 전기학회논문지
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• 제66권9호
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• pp.1402-1408
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• 2017

Recently, services for personal biometric data analysis based on real-time monitoring systems has been increasing and many of them have focused on recognition of emotions. In this paper, we propose a classification model to classify anxiety emotion using biometric data actually collected from people. We propose to deploy the support vector machine to build a classification model. In order to improve the classification accuracy, we propose two data pre-processing procedures, which are normalization and data deletion. The proposed algorithms are actually implemented based on Real-time Traffic Flow Measurement structure, which consists of data collection module, data preprocessing module, and creating classification model module. Our experiment results show that the proposed classification model can infers anxiety emotions of people with the accuracy of 65.18%. Moreover, the proposed model with the proposed pre-processing techniques shows the improved accuracy, which is 78.77%. Therefore, we can conclude that the proposed classification model based on the pre-processing process can improve the classification accuracy with lower computation complexity.