• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2005년도 추계학술대회논문집
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• pp.127-130
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• 2005

There are many methods in feature extraction have been developed. Recently, principal components analysis (PCA) and independent components analysis (ICA) is introduced for doing feature extraction. PCA and ICA linearly transform the original input into new uncorrelated and independent features space respectively In this paper, the feasibility of using nonlinear feature extraction will be studied. This method will employ the PCA and ICA procedure and adopt the kernel trick to nonlinearly map the data into a feature space. The goal of this study is to seek effectively useful feature for faults classification.

• 정보과학회 컴퓨팅의 실제 논문지
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• 제23권5호
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• pp.322-327
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• 2017

문서 분류 문제는 오랜 기간 동안 자연어 처리 분야에서 연구되어 왔다. 우리는 기존 컨볼루션 신경망을 이용했던 연구에서 나아가, 순환 신경망에 기반을 둔 문서 분류를 수행하였고 그 결과를 종합하여 제시하려 한다. 컨볼루션 신경망은 단층 컨볼루션 신경망을 사용했으며, 순환 신경망은 가장 성능이 좋다고 알려져 있는 장기-단기 기억 신경망과 회로형 순환 유닛을 활용하였다. 실험 결과, 분류 정확도는 Multinomial Naïve Bayesian Classifier < SVM < LSTM < CNN < GRU의 순서로 나타났다. 따라서 텍스트 문서 분류 문제는 시퀀스를 고려하는 것 보다는 문서의 feature를 추출하여 분류하는 문제에 가깝다는 것을 확인할 수 있었다. 그리고 GRU가 LSTM보다 문서의 feature 추출에 더 적합하다는 것을 알 수 있었으며 적절한 feature와 시퀀스 정보를 함께 활용할 때 가장 성능이 잘 나온다는 것을 확인할 수 있었다.

• Journal of the Korean Data and Information Science Society
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• 제16권4호
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• pp.717-724
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• 2005

We often extract a new feature from the original features for the purpose of reducing the dimensions of feature space and better classification. In this paper, we show feature extraction method based on independent component analysis can be used for classification. Entropy and mutual information are used for the selection of ordered features. Performance of classification based on independent component analysis is compared with principal component analysis for three real data sets.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.653-655
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• 2005

More than 70 percent of terrestrial territory of Korea is mountainous areas where degradation becomes serious year by year due to illegal tombs, expanding golf courses and stone mine development. We elaborate the potential usage of high resolution image for the monitoring of the phenomena. We made the classification of tombs and the statistical radiometric characteristics of graves were identified from this project. The graves could be classified to 4 groups from the field survey. As compared with grouping data after clustering and discriminant analysis, the two results coincided with each other. Object-oriented classification algorithm for feature extraction was theoretically researched in this project. And we did a pilot project, which was performed with mixed methods. That is, the conventional methods such as unsupervised and supervised classification were mixed up with the new method for feature extraction, object-oriented classification method. This methodology showed about $60\%$ classification accuracy for extracting tombs from satellite imagery. The extraction of tombs' geographical coordinates and graves themselves from satellite image was performed in this project. The stone mines and golf courses are extracted by NDVI and GVI. The accuracy of classification was around 89 percent. The location accuracy showed extraction of tombs from one-meter resolution image is cheaper and quicker way than GPS method. Finally we interviewed local government officers and made analyses on the current situation of mountainous area management and potential usage of KOMPSAT-II images. Based on the requirement analysis, we developed software, which is to management and monitoring system for mountainous area for local government.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 제13회 신호처리 합동 학술대회 논문집
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• pp.915-918
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• 2000

In this paper, we propose an efficient feature vector extraction and decision fusion methods for the automatic classification of power system disturbances. Here, FFT and WPT(wavelet packet transform) are und to extract an appropriate feature for classifying power quality disturbances with variable properties. In particular, the WPT can be utilized to develop an adaptable feature extraction algorithm using best basis selection. Furthermore. the extracted feature vectors are applied as input to the decision fusion system which combines the decisions of several classifiers having complementary performances, leading to improvement of the classification performance. Finally, the applicability of the proposed approach is demonstrated using some simulations results obtained by analyzing power quality disturbances data generated by using Matlab.

• International Journal of Computer Science & Network Security
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• 제24권4호
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• pp.113-118
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• 2024

Pursuance Sentiment Analysis on Twitter is difficult then performance it's used for great review. The present be for the reason to the tweet is extremely small with mostly contain slang, emoticon, and hash tag with other tweet words. A feature extraction stands every technique concerning structure and aspect point beginning particular tweets. The subdivision in a aspect vector is an integer that has a commitment on ascribing a supposition class to a tweet. The cycle of feature extraction is to eradicate the exact quality to get better the accurateness of the classifications models. In this manuscript we proposed Term Frequency-Inverse Document Frequency (TF-IDF) method is to secure Principal Component Analysis (PCA) with Naïve Bayes Classifiers. As the classifications process, the work proposed can produce different aspects from wildly valued feature commencing a Twitter dataset.

• 대한원격탐사학회지
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• 제19권2호
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• pp.170-179
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• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권12호
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• pp.3383-3397
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• 2023

Scene graphs serve as semantic abstractions of images and play a crucial role in enhancing visual comprehension and reasoning. However, the performance of Scene Graph Generation is often compromised when working with biased data in real-world situations. While many existing systems focus on a single stage of learning for both feature extraction and classification, some employ Class-Balancing strategies, such as Re-weighting, Data Resampling, and Transfer Learning from head to tail. In this paper, we propose a novel approach that decouples the feature extraction and classification phases of the scene graph generation process. For feature extraction, we leverage a transformer-based architecture and design an adaptive calibration function specifically for predicate classification. This function enables us to dynamically adjust the classification scores for each predicate category. Additionally, we introduce a Distribution Alignment technique that effectively balances the class distribution after the feature extraction phase reaches a stable state, thereby facilitating the retraining of the classification head. Importantly, our Distribution Alignment strategy is model-independent and does not require additional supervision, making it applicable to a wide range of SGG models. Using the scene graph diagnostic toolkit on Visual Genome and several popular models, we achieved significant improvements over the previous state-of-the-art methods with our model. Compared to the TDE model, our model improved mR@100 by 70.5% for PredCls, by 84.0% for SGCls, and by 97.6% for SGDet tasks.

• 대한토목학회논문집
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• 제29권3D호
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• pp.419-431
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• 2009

본 연구의 핵심은 하이퍼스펙트럴영상에 정준상관분류기법을 적용할 때, 최적의 분광밴드를 찾아내는 유효밴드 선정 및 추출기법은 무엇인가를 알아내는 것이다. 본 연구에서는 미국의 Purdue University에서 개발된 Multispec$^{(C)}$ 소프트웨어를 사용하여 각각의 분리도 결정기법에 따른 최적의 유효밴드를 선정하였다. 사용된 분리도 결정기법은 Divergence, Transformed Divergence, Bhattacharyya, Mean Bhattacharyya, Covariance Bhattacharyya, Non Covariance Bhattacharyya로서 총 6가지이다. 특징추출을 위해 Erdas Imagine과 ENVI 소프트웨어를 사용하여 PCA 변환과 MNF 변환을 수행하였다. 유효밴드 선정 및 특징추출의 효과에 대한 비교평가를 위해, 정준상관분류기법에 의한 토지피복분류작업을 수행하였다. 1차 선별된 60개 밴드를 사용한 정준상관분류의 정확도는 71.8%이며, 정준상관분류를 사용하여 가장 높은 분류정확도를 얻은 방법은 Noncovariance Bhattacharyya 적용 후 정준상관분류를 수행한 경우로서 전체정확도 79.0% 이다. 결론적으로 정준상관분류에 의한 하이퍼스펙트럴영상 분류에서는 유효밴드선정기법으로 사실상 Noncovariance Bhattacharyya 기법만 유용하였으며, 나머지 유효밴드 선정기법(Divergence 제외)과 특징추출기법은 정준상관분류에서는 오히려 분류정확도가 하락함을 확인하였다.

• 한국지능시스템학회논문지
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• 제21권6호
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• pp.786-791
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• 2011

An optimal feature selection and extraction procedure is an important task that significantly affects the success of brain activity analysis in brain-computer interface (BCI) research area. In this paper, a novel method for extracting the optimal feature from electroencephalogram (EEG) signal is proposed. At first, a student's-t-statistic method is used to normalize and to minimize statistical error between EEG measurements. And, 2D time-frequency data set from the raw EEG signal was extracted using discrete wavelet transform (DWT) as a raw feature, standard deviations and mean of 2D time-frequency matrix were extracted as a optimal EEG feature vector along with other basis feature of sub-band signals. In the experiment, data set 1 of BCI competition IV are used and classification using SVM to prove strength of our new method.