• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권10호
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• pp.5078-5094
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• 2019

In recent years, the development of drone technologies has promoted the widespread commercial application of drones. However, the ability of drone to carry explosives and other destructive materials may bring serious threats to public safety. In order to reduce these threats from illegal drones, acoustic feature extraction and classification technologies are introduced for drone sound identification. In this paper, we introduce the acoustic feature vector extraction method of harmonic line association (HLA), and subband power feature extraction based on wavelet packet transform (WPT). We propose a feature vector extraction method based on combined HLA and WPT to extract more sophisticated characteristics of sound. Moreover, to identify drone sounds, support vector machine (SVM) classification with the optimized parameter by genetic algorithm (GA) is employed based on the extracted feature vector. Four drones' sounds and other kinds of sounds existing in outdoor environment are used to evaluate the performance of the proposed method. The experimental results show that with the proposed method, identification probability can achieve up to 100 % in trials, and robustness against noise is also significantly improved.

• 전기학회논문지
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• 제61권5호
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• pp.757-762
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• 2012

In this paper, the multi feature extraction algorithm for estimation of wrist movements based on Electromyogram(EMG) is proposed. For the extraction of precise features from the EMG signals, the difference absolute mean value(DAMV), the mean absolute value(MAV), the root mean square(RMS) and the difference absolute standard deviation value(DASDV) to consider amplitude characteristic of EMG signals are used. We figure out a more accurate feature-set by combination of two features out of these, because of multi feature extraction algorithm is more precise than single feature method. Also, for the motion classification based on EMG, the linear discriminant analysis(LDA), the quadratic discriminant analysis(QDA) and k-nearest neighbor(k-NN) are used. We implemented a test targeting twenty adult male to identify the accuracy of EMG pattern classification of wrist movements such as up, down, right, left and rest. As a result of our study, the LDA, QDA and k-NN classification method using feature-set with MAV and DASDV showed respectively 87.59%, 89.06%, 91.75% accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권7호
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• pp.3093-3115
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• 2020

Network anomaly detection system plays an essential role in detecting network anomaly and ensuring network security. Anomaly detection system based machine learning has become an increasingly popular solution. However, due to the unbalance and high-dimension characteristics of network traffic, the existing methods unable to achieve the excellent performance of high accuracy and low false alarm rate. To address this problem, a new network anomaly detection method based on data balancing and recursive feature addition is proposed. Firstly, data balancing algorithm based on improved KNN outlier detection is designed to select part respective data on each category. Combination optimization about parameters of improved KNN outlier detection is implemented by genetic algorithm. Next, recursive feature addition algorithm based on correlation analysis is proposed to select effective features, in which a cross contingency test is utilized to analyze correlation and obtain a features subset with a strong correlation. Then, random forests model is as the classification model to detection anomaly. Finally, the proposed algorithm is evaluated on benchmark datasets KDD Cup 1999 and UNSW_NB15. The result illustrates the proposed strategies enhance accuracy and recall, and decrease the false alarm rate. Compared with other algorithms, this algorithm still achieves significant effects, especially recall in the small category.

• 대한전자공학회논문지SP
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• 제42권5호
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• pp.103-114
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• 2005

본 논문에서는 칼라 특징으로 칼라 오토코렐로그램(autocorrelogram)을 선택하고 질감 특징으로 BDIP(block difference inverse probabilities)와 BVLC(block variance of local correlation coefficient)를 선택하여 이들을 효율적으로 추출하고 결합한 다중 특징기반 영상검색 기법을 제안한다. 칼라 오토코렐로그램은 영상의 H(hue), S(saturation) 칼라 성분으로부터 추출 하였고, BDIP와 BVLC는 V(value) 성분으로부터 추출하였다. 이때 각 특징추출 시 계산량을 고려하여 간소화된 오토코렐로그램과 BVLC를 제안하여 사용하였으며, 추출한 특징들을 효율적으로 저장하기 위해 특징벡터성분들의 값을 그 분포에 따라 균등 또는 비균등 양자화 하여 사용하였다. Corel DB및 VisTex DB에 대한 실험 결과, 칼라 오토코렐로그램과 BDIP, BVLC 질감 특징을 결합함으로써 동일한 차원에서 오토코렐로그램만을 사용할 때보다 최대 9.5%, BDIP, BVLC만을 사용할 때보다 최대 4% 검색성능이 향상되었다. 또한 제안한 다중 특징은 웨이브렛 모멘트, CSD, 칼라 히스토그램에 비해 특징벡터의 저장공간을 약 3분의 1 정도 적게 차지하면서 검색성능이 각각 최대 12.6%, 14.6%, 27.9% 우수하게 나타남을 확인할 수 있었다.

• 반도체디스플레이기술학회지
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• 제14권2호
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• pp.53-59
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• 2015

Since the amount of digital image continues to grow in usage, users feel increased difficulty in finding specific images from the image collection. This paper proposes a novel image searching scheme that extracts the image feature using combination of Advanced SURF (Speed-Up Robust Feature) and DCD (Dominant Color Descriptor). The key point of this research is to provide a new feature extraction algorithm to improve the existing SURF method with removal of unnecessary feature in image retrieval, which can be adaptable to mobile system and efficiently run on the mobile environments. To evaluate the proposed scheme, we assessed the performance of simulation in term of average precision and F-score on two databases, commonly used in the field of image retrieval. The experimental results revealed that the proposed algorithm exhibited a significant improvement of over 14.4% in retrieval effectiveness, compared to OpenSURF. The main contribution of this paper is that the proposed approach achieves high accuracy and stability by using ASURF and DCD in searching for natural image on mobile platform.

• 정보관리학회지
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• 제40권1호
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• pp.1-21
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• 2023

본 연구는 텍스트 분류를 위한 효율적인 자질선정 방법으로 자질 순위화 기법의 성능을 구체적으로 검토하였다. 지금까지 자질 순위화 기법은 주로 문헌빈도에 기초한 경우가 대부분이며, 상대적으로 용어빈도를 사용한 경우는 많지 않았다. 따라서 텍스트 분류를 위한 자질선정 방법으로 용어빈도와 문헌빈도를 개별적으로 적용한 단일 순위화 기법들의 성능을 살펴본 다음, 양자를 함께 사용하는 조합 순위화 기법의 성능을 검토하였다. 구체적으로 두 개의 실험 문헌집단(Reuters-21578, 20NG)과 5개 분류기(SVM, NB, ROC, TRA, RNN)를 사용하는 환경에서 분류 실험을 진행하였고, 결과의 신뢰성 확보를 위해 5-fold cross validation과 t-test를 적용하였다. 결과적으로, 단일 순위화 기법으로는 문헌빈도 기반의 단일 순위화 기법(chi)이 전반적으로 좋은 성능을 보였다. 또한, 최고 성능의 단일 순위화 기법과 조합 순위화 기법 간에는 유의한 성능 차이가 없는 것으로 나타났다. 따라서 충분한 학습문헌을 확보할 수 있는 환경에서는 텍스트 분류의 자질선정 방법으로 문헌빈도 기반의 단일 순위화 기법(chi)을 사용하는 것이 보다 효율적이라 할 수 있다.

• International Journal of Control, Automation, and Systems
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• 제4권6호
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• pp.705-713
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• 2006

For the analysis of multiunit extracellular neural signals as multiple spike trains, neural spike sorting is essential. Existing algorithms for the spike sorting have been unsatisfactory when the signal-to-noise ratio(SNR) is low, especially for implementation of fully-automated systems. We present a novel method that shows satisfactory performance even under low SNR, and compare its performance with a recent method based on principal component analysis(PCA) and fuzzy c-means(FCM) clustering algorithm. Our system consists of a spike detector that shows high performance under low SNR, a feature extractor that utilizes projection pursuit based on negentropy maximization, and an unsupervised classifier based on Gaussian mixture model. It is shown that the proposed feature extractor gives better performance compared to the PCA, and the proposed combination of spike detector, feature extraction, and unsupervised classification yields much better performance than the PCA-FCM, in that the realization of fully-automated unsupervised spike sorting becomes more feasible.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
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• pp.145-148
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• 1996

In moving object tracking based on the visual sensory feedback, a prerequisite is to determine which feature or which object is to be tracked and then the feature or the object identification precedes the tracking. In this paper, we focus on the object identification not image feature identification. The target identification is realized by finding out corresponding line segments to the hypothesized model segments of the target. The key idea is the combination of the Mahalanobis distance with the geometrica relationship between model segments and extracted line segments. We demonstrate the robustness and feasibility of the proposed target identification algorithm by a moving vehicle identification and tracking in the video traffic surveillance system over images of a road scene.

• 한국ITS학회 논문지
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• 제8권1호
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• pp.127-135
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• 2009

This paper presents a new technique for corner shape based object retrieval from a database. The proposed feature matrix consists of values obtained through a neighborhood operation of detected corners. This results in a significant small size feature matrix compared to the algorithms using color features and thus is computationally very efficient. The corners have been extracted by finding the intersections of the detected lines found using Hough transform. As the affine transformations preserve the co-linearity of points on a line and their intersection properties, the resulting corner features for image retrieval are robust to affine transformations. Furthermore, the corner features are invariant to noise. It is considered that the proposed algorithm will produce good results in combination with other algorithms in a way of incremental verification for similarity.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제10권1호
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• pp.7-11
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• 2010

In this paper, we propose an improved speech/music discrimination method based on a feature combination and dimensionality reduction approach. To improve discrimination ability, we use a feature based on spectral duration analysis and employ the hierarchical dimensionality reduction (HDR) method to reduce the effect of correlated features. Through various kinds of experiments on speech and music, it is shown that the proposed method showed high discrimination results when compared with conventional methods.