• 대한의용생체공학회:의공학회지
• /
• 제15권4호
• /
• pp.465-474
• /
• 1994

This study is concerned with a method which helps human to generate EMG signals accurately and consistently to make reliable design samples of function discriminator for prosthetic arm control. We intend to ensure a signal accuracy and consistency by training human as a signal generation source. For the purposes, we construct a human training system using a digital computer, which generates visual graphes to compare real target motion trajectory with the desired one, to observe EMG signals and their features. To evaluate the effect which affects a feature variance and a feature separability between motion classes by the human training system, we select 4 features such as integral absolute value, zero crossing counts, AR coefficients and LPC cepstrum coefficients. We perform a experiment four times during 2 months. The experimental results show that the hu- man training system is effective for accurate and consistent EMG signal generation and reduction of a feature variance, but is not correlated for a feature separability, The cepstrum coefficient is the most preferable among the used features for reduction of variance, class separability and robustness to a time varing property of EMG signals.

• 대한원격탐사학회:학술대회논문집
• /
• 대한원격탐사학회 2008년도 춘계학술대회 논문집
• /
• pp.195-200
• /
• 2008

The accuracy of classifying pixels in HIRIS images is usually degraded by noisy bands since noisy bands may deform the typical shape of spectral reflectance. Proposed in this paper is a statistical method for noisy band removal which mainly makes use of the correlation coefficients between bands. Considering each band as a random variable, the correlation coefficient measures the strength and direction of a linear relationship between two random variables. While the correlation between two signal bands is high, existence of a noisy band will produce a low correlation due to ill-correlativeness and undirectedness. The application of the correlation coefficient as a measure for detecting noisy bands is under a two-pass screening scheme. This method is independent of the prior knowledge of the sensor or the cause resulted in the noise. The classification in this experiment uses the unsupervised k-nearest neighbor algorithm in accordance with the well-accepted Euclidean distance measure and the spectral angle mapper measure. This paper also proposes a hierarchical combination of these measures for spectral matching. Finally, a separability assessment based on the between-class and within-class scatter matrices is followed to evaluate the performance.

• 대한원격탐사학회지
• /
• 제25권3호
• /
• pp.263-270
• /
• 2009

Noise band removal is a crucial step before spectral matching since the noise bands can distort the typical shape of spectral reflectance, leading to degradation on the matching results. This paper proposes a statistical noise band removal method for hyperspectral data using the correlation coefficient between two bands. The correlation coefficient measures the strength and direction of a linear relationship between two random variables. Considering each band of the hyperspectral data as a random variable, the correlation between two signal bands is high; existence of a noisy band will produce a low correlation due to ill-correlativeness and undirected ness. The unsupervised k-nearest neighbor clustering method is implemented in accordance with three well-accepted spectral matching measures, namely ED, SAM and SID in order to evaluate the validation of the proposed method. This paper also proposes a hierarchical scheme of combining those measures. Finally, a separability assessment based on the between-class and the within-class scatter matrices is followed to evaluate the applicability of the proposed noise band removal method. Also, the paper brings out a comparison for spectral matching measures. The experimental results conducted on a 228-band hyperspectral data show that while the SAM measure is rather resistant, the performance of SID measure is more sensitive to noise.

• 대한원격탐사학회지
• /
• 제25권1호
• /
• pp.11-20
• /
• 2009

우리나라는 황사발생 빈도가 증가하고 특히 하절기에 강우 및 구름 발생이 잦아 위성원격탐사영상의 대기보정처리를 필요로 한다. 본 연구에서는 대기보정 전후의 클래스별 화소값 분포 변화를 비교하여 대기보정이 영상화소분류에 미치는 영향을 분석하였다. 실험에 사용된 영상은 LANDSAT-5 TM이고, 대기보정 모듈로는 상용 소프트웨어인 ATCOR, FLAASH와 인터넷에 공개된 COST 모델 3가지를 적용하였다. 실험 결과, 건물밀집 지역 영역에서 클래스 분리도가 향상되는 것으로 나타났다.

• 한국지능시스템학회:학술대회논문집
• /
• 한국지능시스템학회 2007년도 추계학술대회 학술발표 논문집
• /
• pp.59-62
• /
• 2007

본 논문에서는 불균형 및 Under-sampling된 바이오 데이터에 대하여 클래스 구분력이 없는 특징의 소거를 통해 이후 이어질 FLDA 둥 다양한 방법론올 적용할 수 있는 방법을 제안하고자 한다. 제안하는 알고리즘은 평균과 분산을 통해 클래스의 형태를 결정하는 기존 방법론의 문제점을 회피할 수 있는 방법을 제공하며, 클래스 구분력에 중점을 두어 특정을 선별하였을 경우 선별된 특정들의 상관 계수가 높은 문제를 극복할 수 있도록 한다. 이에 따라 알고리즘이 선택한 특정집합은 서로의 특징에 대해 상관계수가 낮으며, 클래스의 구분력이 높은 특정을 갖게 된다.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2000년도 하계종합학술대회 논문집(4)
• /
• pp.85-87
• /
• 2000

In pattern classification, the Bhattacharyya distance has been used as a class separability measure and provides useful information for feature selection and extraction. In this paper, we propose a method to predict the classification error for multimodal data based on the Bhattacharyya distance. In our approach, we first approximate the pdf of multimodal distribution with a Gaussian mixture model and find the bhattacharyya distance and classification error. Exprimental results showed that there is a strong relationship between the Bhattacharyya distance and the classification error for multimodal data.

• 대한원격탐사학회지
• /
• 제19권2호
• /
• pp.170-179
• /
• 2003

다중분광 영상의 정확한 지형지물 분류를 수행할 때 고려해야 할 중요한 요소중에 적절한 분류 클래스의 선정과 선정된 클래스의 분리도가 높아지도록 트레이닝 지역(training fields)을 잡는 것은 특히 중요하다. 최근에 이용되고 있는 위성탑재 하이퍼스펙트럴(hyperspectral) 영상은 많은 밴드를 포함하고 있기 때문에 데이터 처리가 어렵고, 잡음(noise)으로 인하여 다중분광 영상보다 분류 결과가 나쁜 경우도 나타난다. 특히 대상지역의 클래스에 따른 트레이닝 지역의 선정시 일부 클래스에서 하이퍼스펙트럴 밴드수에 비해 상대적으로 적은 수의 트레이닝 샘플로 인하여 공분산 행렬의 계산에 어려움이 따른다. 따라서 본 연구에서는 Hyperion 데이터를 이용한 분류를 수행하기 위하여 밴드 추출 방식을 알아보고, 분류영상의 정확도 평가를 통하여 밴드 추출의 효용성을 시험하였다. 밴드를 줄이는 또 다른 방법인 클래스간 분리도에 따른 최적 밴드를 추출하여 분류정확도를 평가하였다. 실험 결과, 밴드 추출이나 클래스 분리도에 따라 선택된 영상의 분류 정확도는 분류자(classifier)에 상관없이 전체 밴드를 사용한 원영상과 유사하게 나타났지만, 사용된 밴드수와 계산 시간은 단축되었다. 분류자는 MLC, SAM, ECHO의 3종류가 사용되었다.

• 한국중재학회지:중재연구
• /
• 제20권2호
• /
• pp.151-171
• /
• 2010

This paper investigates the consumer arbitration practices In the U.S. The key issue in consumer arbitration is how to protect the individual consumers from the loss of their legal rights stemming from the arbitration agreement with the business. In the U.S., the major legal doctrines to protect individual consumer include the voluntary-knowing-intelligent doctrine, unconscionability doctrine, and void contract. Even though the US courts are favorable to the enforceability of arbitration agreement, they strictly apply the contract law theories in deciding the existence of arbitration agreement, providing a strong common law protection for the consumers in arbitration. However, the practices for protection of consumers in arbitration in Korea are not mature yet. If consumer arbitration is widely adopted into B to C contracts, a protective measure for individual consumer can be found in the Act of Clause Regulation providing that the business has duty to explain the relevant clause in the adhesive contracts.

• 대한원격탐사학회:학술대회논문집
• /
• 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
• /
• pp.111-114
• /
• 2008

The existence of noise bands may deform the typical shape of the spectrum, making the accuracy of clustering degraded. This paper proposes a statistical approach to remove noise bands in hyperspectral data using the correlation coefficient of bands as an indicator. Considering each band as a random variable, two adjacent signal bands in hyperspectral data are highly correlative. On the contrary, existence of a noise band will produce a low correlation. For clustering, the unsupervised ${\kappa}$-nearest neighbor clustering method is implemented in accordance with three well-accepted spectral matching measures, namely ED, SAM and SID. Furthermore, this paper proposes a hierarchical scheme of combining those measures. Finally, a separability assessment based on the between-class and the within-class scatter matrices is followed to evaluate the applicability of the proposed noise band removal method. Also, the paper brings out a comparison for spectral matching measures.

• 대한원격탐사학회지
• /
• 제37권4호
• /
• pp.719-731
• /
• 2021

This study proposes a two-stage hybrid classification model for crop classification using multi-temporal remote sensing images; the model combines feature embedding by using an autoencoder (AE) with a convolutional neural network (CNN) classifier to fully utilize features including informative temporal and spatial signatures. Long short-term memory (LSTM)-based AE (LAE) is fine-tuned using class label information to extract latent features that contain less noise and useful temporal signatures. The CNN classifier is then applied to effectively account for the spatial characteristics of the extracted latent features. A crop classification experiment with multi-temporal unmanned aerial vehicle images is conducted to illustrate the potential application of the proposed hybrid model. The classification performance of the proposed model is compared with various combinations of conventional deep learning models (CNN, LSTM, and convolutional LSTM) and different inputs (original multi-temporal images and features from stacked AE). From the crop classification experiment, the best classification accuracy was achieved by the proposed model that utilized the latent features by fine-tuned LAE as input for the CNN classifier. The latent features that contain useful temporal signatures and are less noisy could increase the class separability between crops with similar spectral signatures, thereby leading to superior classification accuracy. The experimental results demonstrate the importance of effective feature extraction and the potential of the proposed classification model for crop classification using multi-temporal remote sensing images.