• Journal of Advanced Research in Ocean Engineering
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• 제2권4호
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• pp.202-208
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• 2016

In this paper, we propose novel seabed sediment classification algorithm using feature obtained by continuous wavelet transform (CWT). Contrast to previous researches using direct reflection coefficient of seabed which is function of frequency and is highly influenced by sediment types, we develop an algorithm using both direct reflection signal and backscattering signal. In order to obtain feature vector, we employ CWT of the signal and obtain histograms extracted from local binary patterns of the scalogram. The proposed algorithm also adopts principal component analysis (PCA) to reduce dimension of the feature vector so that it requires low computational cost to classify seabed sediment. For training and classification, we adopts K-means clustering algorithm which can be done with low computational cost and does not require prior information of the sediment. To verify the proposed algorithm, we obtain field data measured at near Jeju island and show that the proposed classification algorithm has reliable discrimination performance by comparing the classification results with actual physical properties of the sediments.

• 한국정보통신학회논문지
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• 제14권5호
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• pp.1295-1302
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• 2010

본 논문에서는 LVQ(Learning Vector Quantization) 네트워크를 이용한 수신 신호 세기(Received Signal Strength Indication) 기반 실내 위치인식 시스템을 제안하였다. 제안한 방식의 유용성을 확인하기 위하여 실험을 수행하였고, 일반적인 삼각측량 방법과 비교하였다. 실험실을 40개의 영역으로 나누고 6개의 고정 노드를 설치하였다. 무선 채널의 대수-정규 경로 손실 모델을 구성하고, 수신 신호 강도를 거리로 변환하였다. 변환한 정보를 LVQ의 입력으로 사용하였다. LVQ 네트워크의 학습을 위해 영역의 인덱스를 목표값으로 설정하였다. 실험을 통해서 최적의 서브클래스 개수를 결정하였고, LVQ 네트워크의 훈련을 통해서는 96%, 테스트를 통해서는 91%의 성능을 확인하였다.

• 한국통신학회논문지
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• 제21권4호
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• pp.884-892
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• 1996

일반적으로 적응 블라인드 등화기는 등화기 입력과 전송된 데이타의 통계적 정보가 주어진 경우, 훈련 수열에 의존하지 않고 비이상적인 채널에 의한 왜곡들을 제거하는데 이용된다. 그러나 다중경로 채널(muItipath channel) 에서는 페이딩(fading)이라 불리우는 수선 선호의 시간 지연 및 감쇄를 발생 시키므로, 이러한 페이딩의 효과를 보상하기 위하여 다중 채널을 고려한 새로운 알고리듬이 요구된다. 본 논문에서는 다중 채널 시스템에 적용할 수 있도록 Stop-and-Go 알고리듬을 이용한 새로운 블라인드 동화 알고리듬을 제시하였다. 컴퓨터 모의실험 결과 다중 채널 Stop-and-Go 알고리듬이 기존의 다중 채널 알고리듬에서 계산량을 가중 시키는 메모리를 사용하지 않고도 보다 더 좋은 성능을 얻을 수 있었다.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2000년도 추계학술대회논문집
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• pp.796-801
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• 2000

The monitoring and diagnostics of the rotating machinery have been received considerable attention for many years. The objectives are to classify the machinery condition and to find out the cause of abnormal condition. This paper describes a signal classification method for diagnosing the rotating machinery using the artificial neural network and the wavelet transform. In order to extract salient features, the wavelet transform are used from primary noise signals. Since the wavelet transform decomposes raw time-waveform signals into two respective parts in the time space and frequency domain, more and better features can be obtained easier than time-waveform analysis. In the training phase for classification, self-organizing feature map(SOFM) and learning vector quantization(LVQ) are applied, and the accuracies of them are compared with each other. This paper is focused on the development of an advanced signal classifier to automatise the vibration signal pattern recognition. This method is verified by small reciprocating compressors, for refrigerator and normal and abnormal conditions are classified with high flexibility and reliability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권11호
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• pp.3761-3779
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• 2022

The performance of existing recognition algorithms for binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) signals degrade under conditions of low signal-to-noise ratios (SNR). Hence, a novel recognition algorithm based on features in the graph domain is proposed in this study. First, the power spectrum of the squared candidate signal is truncated by a rectangular window. Thereafter, the graph representation of the truncated spectrum is obtained via normalization, quantization, and edge construction. Based on the analysis of the connectivity difference of the graphs under different hypotheses, the sum of degree (SD) of the graphs is utilized as a discriminate feature to classify BPSK and QPSK signals. Moreover, we prove that the SD is a Schur-concave function with respect to the probability vector of the vertices (PVV). Extensive simulations confirm the effectiveness of the proposed algorithm, and its superiority to the listed model-driven-based (MDB) algorithms in terms of recognition performance under low SNRs and computational complexity. As it is confirmed that the proposed method reduces the computational complexity of existing graph-based algorithms, it can be applied in modulation recognition of radar or communication signals in real-time processing, and does not require any prior knowledge about the training sets, channel coefficients, or noise power.

• The Journal of the Acoustical Society of Korea
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• 제15권1E호
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• pp.34-38
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• 1996

In this paper, a new fast voronoi divider for vector quantization (VQ) is introduced, which results from Theorem that the nearest vectors in the sense of minimum mean square error(MMSE) have almost the same mean values of their elements. An improved splitting method for a VQ codebook design using the fast voronoi divider is also presented. Experimental results show that the new method reduces the complexity of training a VQ codebook several times with a high signal to noise ratio(SNR) using an appropriate extensive parameter of codebook.

• 전력전자학회:학술대회논문집
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• 전력전자학회 1998년도 전력전자학술대회 논문집
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• pp.197-199
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• 1998

A neuro-fuzzy based torque profile model of SRM with considerably improved accuracy is obtained using the measured data for training. The inferred torque profiles, which comprise magnetic non-linearities, represent the dynamic model of SRM. Then the reference torque signal with optimized waveform and switching angle are decided to control the torque directly. Hence, the presented scheme controls the torque in an instantaneous basis, allowing powerful torque control with minimum torque ripple even during the transient operation of the motor. Simulation and experimental results demonstrating the effectiveness of the proposed torque control scheme are presented.

• 대한의용생체공학회:의공학회지
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• 제11권1호
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• pp.75-82
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• 1990

The purpose of this study is to help the hearing Impaired's speech training with a visible speech processing system. In brief, this system converts the features of speech signals into graphics on monitor, and adjusts the features of hearing impaired to normal ones. There are formant and pitch in the features used for this system. They are extracted using the digital signal processing such as linear predictive method or AMDF(Average Magnitude Difference Function). In order to effectively train for the hearing impaired's abnormal speech, easilly visible feature has been being studied.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2006년도 학술대회
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• pp.175-177
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• 2006

This letter deals with non-pilot-aided symbol timing estimation methods in an orthogonal frequency division multiplexing (OFDM) system. To do this, OFDM system uses a frequency diversity scheme over two consecutive data symbols. Our approach can be viewed as an expansion of Schmidl's and Minn's correlation methods. Using the OFDM signal equipped with frequency diversity, however, symbol timing is accurately estimated without additional training symbol and a second-order diversity gain is achieved.

• 전자공학회논문지
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• 제49권12호
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• pp.64-72
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• 2012

주파수 옵셋 보상, 프레임 동기화, Timing Recovery를 포함하는 동기화는 모든 유/무선 통신 시스템에서 가장 중요한 신호 처리 블록이다. 대부분의 통신 시스템에서는 Training sequences 또는 프리앰블을 기반으로하는 동기화 방법이 사용된다. IEEE에서 제정한 802.11a/g/n의 무선랜 표준은 OFDM 시스템을 기반으로 한다. OFDM 시스템은 주파수와 타이밍 동기화 에러에 대해서 싱클캐리어 시스템보다 더 민감한 것으로 알려져 있다. 프레임의 시작점과 OFDM 심볼 및 훈련심볼의 시작점은 상관관계를 이용하여 추정될 수 있다. 상관관계를 처리 기능을 하는 블록은 일반적으로 많은 수의 곱셈기로 인하여 큰 복잡도를 갖게 된다. 본 논문에서는 IEEE 802.11a/g/n 시스템을 위한 훈련심볼 내의 심볼값이 반복되는 특성을 활용한 복잡도가 현저히 낮은 동기화 기법을 제안한다. 시뮬레이션과 구현결과 제안된 기법이 기존의 방법보다 성능저하는 없는 반면 훨씬 적은 복잡도를 갖는 결과를 보여준다.