• Proceedings of the KOSOMBE Conference
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• v.1991 no.11
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• pp.25-29
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• 1991

In the field of prosthesis arm control, tile pattern classification of the EMG signal is a required basis process and also the estimation of force from col looted EMG data is another necessary duty. But unfortunately, what we've got is not real force but an EMG signal which contains the information of force. This is the reason why he estimate the force from the EMG data. In this paper, when we handle the EMG signal to estimate the force, spatial prewhitening process is applied from which the spatial correlation between the channels are removed. And after the orthogonal transformation, which is used in the force estimation process the transformed signal is inputed into the probabilistic model for pattern classification. To verify the different results of the multiple channels, SNR(signal to noire ratio) function is introduced.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.3
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• pp.978-983
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• 2010

Acoustic target classification in wireless sensor network is important research at environmental surveillance, invasion surveillance, multiple target separation. General sensor node signal processing methods concentrated on received signal energy based target detection and received raw signal compression. The former is not suited to target classification because of almost every target information are lost except target energy. The latter bring down life-time of sensor node owing to high computational complexity and transmission energy. In this paper, we introduce an feature extraction algorithm for acoustic target classification in wireless sensor network which has time and frequency information. The proposed method extracts time information and de-noised target classification information using wavelet decomposition step. This method reduces communication energy by 28% of original signal and computational complexity.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.3
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• pp.252-259
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• 2019

Jet engine modulation(JEM) signals are widely used in the field of target recognition along with high-range resolution profile and inverse synthetic aperture radar because they provide specific information of the jet engine. To obtain the number of blades of the jet engine, the chopping frequency proportional to the number of blades must be extracted. In the conventional chopping frequency extraction method, an initial threshold value is defined and a method of detecting the chopping peak is used. However, this detection method takes time depending on the signal due to repetitive detection. Thus, in this study, we proposed to extract the chopping frequency using MUltiple SIgnal Classification(MUSIC) algorithm. We applied the MUSIC algorithm to a given JEM signal to find the chopping frequency and determine the blade number candidates. We also applied the MUSIC algorithm to other chopping frequency extractions to determine the score of the candidate groups. Unlike the conventional detection algorithm, which requires repetitive frequency detection, MUSIC algorithm quickly detects the accurate chopping frequency and reduces the calculation time.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.11
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• pp.1506-1511
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• 2018

This study presents a human sensibility evaluation method using neural network and multiple-template method on electroencephalogram(EEG). We used a multi-layer perceptron type neural network as the sensibility classifier using EEG signal. For our research objective, 10-channel EEG signals are collected from the healthy subjects. After the necessary preprocessing is performed on the acquired signals, the various EEG parameters are estimated and their discriminating performance is evaluated in terms of pattern classification capability. In our study, Linear Prediction(LP) coefficients are utilized as the feature parameters extracting the characteristics of EEG signal, and a multi-layer neural network is used for indicating the degree of human sensibility. Also, the estimation for human comfortableness is performed by varying temperature and humidity environment factors and our results showed that the proposed scheme achieved good performances for evaluation of human sensibility.

• Journal of Positioning, Navigation, and Timing
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• v.10 no.2
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• pp.131-137
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• 2021

The Angle-of-Arrival (AOA) estimation in real time is one of core technologies for the real-time tracking system, such as a radar or a satellite. Although AOA estimation algorithms for various antenna types have been studied, most of them are for the single-shaped array antenna suitable to the specific frequency. In this paper, we propose the cascade AOA estimation algorithm for the combined array antenna with Uniform Rectangular Frame Array (URFA) and Uniform Circular Array (UCA), with the excellent performance for various frequencies. The proposed technique is consisted of Capon for roughly finding AOA groups with multiple signal AOAs and Beamspace Multiple Signal Classification (MUSIC) for estimating the detailed signal AOA in the AOA group, for the combined array antenna. In addition, we provide computer simulation results for verifying the estimation performance of the proposed algorithm.

• ETRI Journal
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• v.28 no.6
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• pp.761-769
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• 2006

In this paper, we address the problem of closely spaced source localization using sensor array processing. In particular, the performance efficiency (measured in terms of the root mean square error) of the unconditional maximum likelihood (UML) algorithm for estimating the direction of arrival (DOA) of near-field sources is evaluated. Four parameters are considered in this evaluation: angular separation among sources, signal-to-noise ratio (SNR), number of snapshots, and number of sources (multiple sources). Simulations are conducted to illustrate the UML performance to compute the DOA of sources in the near-field. Finally, results are also presented that compare the performance of the UML DOA estimator with the existing multiple signal classification approach. The results show the capability of the UML estimator for estimating the DOA when the angular separation is taken into account as a critical parameter. These results are consistent in both low SNR and multiple-source scenarios.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1991.10a
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• pp.123-126
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• 1991

A generalization of null-spectrum for use in the estimation of directions of arrival of signal sources is considered in this paper. The upper and lower bounds of the generalized null-spectrum, the maximum and minimum null-spectra, are also derived. We observed that the maximum null-spectrum has higher resolution capability than other null-spectra including the two well-known null-spectra, the multiple signal classification null-spectrum and the Min-Norm null-spectrum.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.12a
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• pp.105-108
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• 2000

본 논문에서는 텍스쳐가 지니고 있는 일반적인 속성 거침, 부드러움의 특성을 분석해서 영상에 내재된 텍스쳐를 자동으로 분석하고 분류하는 텍스쳐 인식 시스템을 제안한다. 본 연구는 텍스쳐 영상이 지닌 그레이 레벨의 공간적인 의존성을 이용한 통계적 분석에 기반 한 것으로 모멘트와 동차성의 차를 이용해서 텍스쳐의 일반적인 속성을 검출하기 때문에 텍스쳐의 구조형태에 크게 영향을 받지 않는 이점을 가진다. 제안한 시스템의 성능 평가를 위해서 다양한 텍스쳐 영상에 제안한 방법을 적용하고, 성공적인 결과를 보인다.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.2_2
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• pp.163-171
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• 2020

The condition of tire tread is a key parameter closely related to the driving safety of a vehicle, which affects the contact force of the tire for braking, accelerating and cornering. The major factor influencing the contact force is tread wear, and the more tire tread wears out, the higher risk of losing control of a vehicle exits. The tire tread condition is generally checked by visual inspection that can be easily forgotten. In this paper, we propose the intelligent tire (iTire) system that consists of an acceleration sensor, a wireless signal transmission unit and a tread classifier. In addition, we also presents classification algorithm that transforms the acceleration signal into the frequency domain and extracts the features of several frequency bands as inputs to an artificial neural network. The artificial neural network for classifying tire wear was designed with an Multiple Layer Perceptron (MLP) model. Experiments showed that tread wear classification accuracy was over 80%.

• Journal of Biomedical Engineering Research
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• v.29 no.4
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• pp.329-336
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• 2008

Recently, causality analysis of source time series extracted from EEG or MEG signals is becoming of great importance in human brain mapping studies and noninvasive diagnosis of various brain diseases. Two approaches have been widely used for the analyses: one is independent component analysis (ICA), and the other is multiple signal classification (MUSIC). To the best of our knowledge, however, any comparison studies to reveal the difference of the two approaches have not been reported. In the present study, we compared the performance of the two different techniques, ICA and MUSIC, especially focusing on how accurately they can estimate and separate various brain electrical signals such as linear, nonlinear, and chaotic signals without a priori knowledge. Results of the realistic simulation studies, adopting directed transfer function (DTF) and Granger causality (GC) as measures of the accurate extraction of source time series, demonstrated that the MUSIC-based approach is more reliable than the ICA-based approach.