• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권7호
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• pp.2568-2584
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• 2015

Spectral clustering has become one of the most popular clustering approaches in recent years. Similarity graph constructed on the data is one of the key factors that influence the performance of spectral clustering. However, the similarity graphs constructed by existing methods usually contain some unreliable edges. To construct reliable similarity graph for spectral clustering, an efficient method based on Markov random walk (MRW) is proposed in this paper. In the proposed method, theMRW model is defined on the raw k-NN graph and the neighbors of each sample are determined by the probability of the MRW. Since the high order transition probabilities carry complex relationships among data, the neighbors in the graph determined by our proposed method are more reliable than those of the existing methods. Experiments are performed on the synthetic and real-world datasets for performance evaluation and comparison. The results show that the graph obtained by our proposed method reflects the structure of the data better than those of the state-of-the-art methods and can effectively improve the performance of spectral clustering.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 심포지엄 논문집 정보 및 제어부문
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• pp.99-100
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• 2008

This paper proposes a Gaussian Mixture Model (GMM) - based speech emotion recognition methods using four feature parameters; 1) Fast Fourier Transform(FFT) spectral entropy, 2) delta FFT spectral entropy, 3) Mel-frequency Filter Bank (MFB) spectral entropy, and 4) delta MFB spectral entropy. In addition, we use four emotions in a speech database including anger, sadness, happiness, and neutrality. We perform speech emotion recognition experiments using each pre-defined emotion and gender. The experimental results show that the proposed emotion recognition using FFT spectral-based entropy and MFB spectral-based entropy performs better than existing emotion recognition based on GMM using energy, Zero Crossing Rate (ZCR), Linear Prediction Coefficient (LPC), and pitch parameters. In experimental Results, we attained a maximum recognition rate of 75.1% when we used MFB spectral entropy and delta MFB spectral entropy.

• 한국대기환경학회지
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• 제7권3호
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• pp.189-196
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• 1991

In recent years spectral methods have been found to be a powerful tool for the numerical solution of hynamic differential equations. The main attraction of spectral method is accuracy even though it is generally difficult to implement and solve the complex problems using spectral method. We introduced diffusion equations describing the state of air pollution and solved by pseutospectral method in dimensionless form. The results were compared with both those of other numerical methods and analytical solutions. Comparing with finite difference method and finite element method, spectral method shows the highest accuracy for one dimension problem in this study. Also, the results of two dimensional diffusion problems show good agreement with analytical solutions.

• 한국산학기술학회논문지
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• 제7권4호
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• pp.649-655
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• 2006

본 논문은 화자 독립의 음성인식을 위한 연구로써, DMS 모델에 의한 DMSVQ(Dynamic Multi-Section Vector Quantization) 코드북과 이중 스펙트럼 특징을 이용한 HMM(Hidden Markov Model) 음성인식 방법을 제안한다. 정적 스펙트럼 특징으로서는 LPC ?S스트럼 계수를 이용하였고, 동적 스펙트럼 특징으로는 LPC ?S스트럼의 회귀계수를 사용하였다. 이들 두개의 스펙트럼 특징들을 각각 VQ 코드북으로 양자화되고, DMS 모델을 이용한 HMM은 입력으로써 정적 스펙트럼 특징과 동적 스펙트럼 특징을 받아드림으로써 모델링된다. 제안된 방법에 의한 인식 실험은 기존의 다양한 인식 방법에 의한 인식 실험들과 비교를 위해 동일한 데이터와 조건 하에서 수행하였다. 실험 결과, 본 연구에서 제안한 방법이 기존의 방법들보다 우수한 방법임을 입증하였다.

• 한국인쇄학회지
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• 제16권3호
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• pp.121-131
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• 1998

The object color does not look same under the different light source. It depends on the surface spectral reflectance and the spectral distribution of light source. Therefore we should find the surface spectral reflectance of object color and the spectral distribution of light source for color reproduction. Using Wiener estimation, we can estimate the spectral reflectance from low dimensional images obtained with multi-band image acquisition system. The kind and the number of imaging filters have the effect on the estimation of the spectral reflectance. Therefore it is important that optimal filters are selected to minimize the error of the result. In this paper, we describe methods to select optimal filters with minimum error between measured and estimated surface spectral reflectance and to estimate surface spectral reflectance of Munsell color chart from six multi-band images by using Wiener estimation.

• 한국인쇄학회:학술대회논문집
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• 한국인쇄학회 1998년도 추계 논문 발표회 논문집
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• pp.1-6
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• 1998

The object color does not look same under the different light source. It depends on the surface spectral reflectance and the spectral distribution of light source. Therefore we should find the surface spectral reflectance of object color and the spectral distribution of light source for color reproduction. Using Winer estimation, we can reconstruct the spectral reflectance from low dimensional images obtained with a few filters. The kind and the number of filters have the effect on the estimation of the spectral reflectance. Therefore it is important that optimal filters are selected to minimize the error of the result. In this paper, we describe methods to select optimal filters with minimum error between measured and estimated surface spectral reflectance and to estimate surface spectral reflectance of Munsell color from six band images by using Wiener estimation.

• 한국음향학회지
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• 제20권7호
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• pp.47-54
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• 2001

본 논문은 스펙트럴 크기 파라미터들에 대한 효율적인 가변 차원 양자화 기법을 제안한다. 특히, 하모닉 부호화 기에서의 스펙트럴 크기값 계수들은 가변차원이기 때문에 가변 차원의 양자화를 필요로 한다. 따라서, 본 논문에서는 스펙트럴 크기값 계수들에 대해 가변 이산 코사인 변환(DCT: Discrete Cosine Transform) 및 가변 차원에 적합한 훈련구조를 가지는 비정방형 변환 벡터 양자화 (NSTVQ: Nonsquare Transform Vector Quantization)를 홀수/짝수 구조 및 분할(Split) 구조 그리고 다단계(Multi-stage) 구조 등과 결합시킨 효율적인 양자화 기법을 제안한다. 제안된 양자화 기법의 성능평가는 스펙트럴의 크기값에 대한 주파수 왜곡(SD: Spectral Distortion) 값을 사용하였으며, 다단계 비정방형 변환 벡터 양자화(MSNSTVQ: Multi-Stage Nonsquare Transform Vector Quantization)가 가장 좋은 성능을 나타내었다.

• 한국ITS학회 논문지
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• 제8권3호
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• pp.64-70
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• 2009

본 논문은 잡음에 강인한 음성인식 성능을 획득하기 위해 generalized Gamma 분포기반의 음성향상 기법을 제안한다. 우수한 음성향상을 위해서 제안된 방식에서는 generalized Gamma분포와 spectral gain floor를 이용한 음성추적 기법에 스펙트럼 최소잡음성분에 의한 희귀적인 평균 스펙트럼 값으로부터 유도되는 잡음추정을 결합하여 음질을 향상시켜 음성인식에 적용하였다. Spectral component, spectral amplitude 그리고 log spectral amplitude에 기반하여 제안된 음성향상 기법을 잡음환경에서의 음성인식에 적용하여 그 성능을 측정하였다.

• 한국음향학회지
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• 제36권1호
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• pp.64-69
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• 2017

본 논문에서는 PTT(Push-To-Talk) 기반의 무선 통신을 위한 진폭 및 위상 복원 기반의 단일 채널 음성 향상 방식을 제안한다. 제안한 방식은 신호의 진폭만을 대상으로 음성 향상을 진행했던 기존의 방식들과 달리, 음성 신호의 진폭과 위상을 분리하여 각각 향상시켜 다시 결합함으로써 더욱 양질의 음성을 제공한다. 본 논문에서 제안하는 방식의 성능을 평가하기 위해 동적 잡음 환경에서의 단계별 비교 실험을 실시하였으며, 실험 결과를 통해 제안한 방식이 다양한 잡음 환경에서 양질의 음성을 제공하는 것을 확인할 수 있다.

• Structural Monitoring and Maintenance
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• 제9권2호
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• pp.179-200
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• 2022

This paper proposes a data-driven methodology for online early damage identification under changing environmental conditions. The proposed method relies on two data analysis methods: feature-based method and hybrid principal component analysis (PCA) and kernel PCA to separate damage from environmental influences. First, spectral sub-band features, namely, spectral sub-band centroids (SSCs) and log spectral sub-band energies (LSSEs), are proposed as damage-sensitive features to extract damage information from measured structural responses. Second, hybrid modeling by integrating PCA and kernel PCA is performed on the spectral sub-band feature matrix for data normalization to extract both linear and nonlinear features for nonlinear procedure monitoring. After feature normalization, suppressing environmental effects, the control charts (Hotelling T² and SPE statistics) is implemented to novelty detection and distinguish damage in structures. The hybrid PCA-KPCA technique is compared to KPCA by applying support vector machine (SVM) to evaluate the effectiveness of its performance in detecting damage. The proposed method is verified through numerical and full-scale studies (a Bridge Health Monitoring (BHM) Benchmark Problem and a cable-stayed bridge in China). The results demonstrate that the proposed method can detect the structural damage accurately and reduce false alarms by suppressing the effects and interference of environmental variations.