• Journal of the Korean Institute of Intelligent Systems
• /
• v.3 no.4
• /
• pp.3-20
• /
• 1993

본 연구에서는 퍼지사상화(fuzzy mapping)에 의한 사상된(mapped) 코드북을 사용하는 화자적은 음성합성 알고리즘을 제안한다. 입력화자와 기준화자의 코드북은 신경망 클러스터링 알고리즘인 자율경쟁 학습을 사용하여 작성된다. 사상된 코드북은 입력 음성벡터에 대한 두 화자의 대응 코드벡터의 소속갑(membership value)으로 퍼지 히스토그랩을 작성하여 이들을 1차 결합함으로써 얻어지는 퍼지사상화에 의하여 작성된다. 음성합성시에는 사상된 코드북을 사용하여 입력화자의 음것을 퍼지 벡터양자화한 다음, CFM 연산으로 합성함으로써 입력화자에 적응된 합성음을 얻는다. 실험에서 여러 입력화자로 30대의 남성, 20대의 여성음을 사용하였고 기준음석으로 입력음성과는 다른 20대의 여성음성을 사용하였다.실험에 사용된 음성데이타는 문장/안녕하십니까/와/굿모닝/이다. 실험결과는 각각의 입력화자에 기준화자 음성이 적응된 합성음을 얻었다.

• Journal of the Institute of Convergence Signal Processing
• /
• v.5 no.3
• /
• pp.205-209
• /
• 2004

The K-means algorithm is the most widely used method among the codebook generation algorithms in vector quantization. In this paper, we propose a codebook generation algorithm using a new updating condition to enhance the codebook performance. The conventional K-means algorithm uses a fixed weight of the distance for all training iterations, but the proposed method uses different weights according to the updating condition from the new codevectors for training iterations. Then, different weights can be applied to generate codevectors at each iteration according to this condition, and it can have a similar effect to variable weights. Experimental results show that the proposed algorithm has the better codebook performance than that of K-means algorithm.

• The KIPS Transactions:PartB
• /
• v.8B no.3
• /
• pp.237-242
• /
• 2001

고전적인 GLA 알고리즘과 마찬가지로 Kohonen 학습법은 경도 강하법으로 오차함수의 해에 접근해 나간다. 따라서 KLA의 이러한 문제를 극복하기 위해 모의 담금질법의 일종인 Cauchy 학습법을 응용을 제안한다. 그러나 이 방법은 학습시간이 느리다고 하는 단점이 있다. 본 논문 이 점을 개선시키기 위해 Cauchy 학습법과 Kohonen 학습법을 순차 결합시킨 또 다른 학습법을 제안한다. 그 결과 코시 학습법과 마찬가지로 국부최적 문제를 극복하면서도 삭습시간을 단축할 수 있었다.

• 유체기계공업학회:학술대회논문집
• /
• 1999.12a
• /
• pp.317-323
• /
• 1999

The necessity of diagnosis of the rotating machinery which is widely used in the industry is increasing. Many research has been conducted to manipulate field vibration signal data for diagnosing the fault of designated machinery. As the pattern recognition tool of that signal, neural network which use usually back-propagation algorithm was used in the diagnosis of rotating machinery. In this paper, self-organizing feature map(SOFM) which is unsupervised learning algorithm is used in the abnormal vibration diagnosis of rotating machinery and then learning vector quantization(LVQ) which is supervised teaming algorithm is used to improve the quality of the classifier decision regions.

• The Journal of the Acoustical Society of Korea
• /
• v.11 no.1
• /
• pp.35-41
• /
• 1992

A speaker identification scheme using the speaker-based VQ codecook of a sustained vowel is proposed and tested. With the pitch synchronous LPC vector of the sustained vowel /i/ as a feature vector, a VQ codebook size of 4 was found to be suitable to characterize each speaker's feature space. For 40 normal speakers (20 males, 20 females), we achieved the correct identification rate of 99.4% with a training data set, and 89.4% with a test data set with speech samples of only 50 pitch periods.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.7 no.4
• /
• pp.18-28
• /
• 1997

This paper presents image data compression using a classified vector quantization (CVQ) which categories edge blocks according to the energy distribution of subimages in the discrete cosine transform domain. Classifying the edge blocks enhances visual quality of the compressed images while maintaining a high compression ratio. The proposed classification method categories subimages into eight lypes of edge features according to an energy distribution. A neural network, trained with the data generated from the proposed classification method, can successfully classify subimages to eight edge categories. Experimental results are given to show how the (1VQ method incorporatd with a neural network can produce faithful compressed image quality for high compression ratios.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.14 no.3
• /
• pp.290-297
• /
• 2004

The control laws for cart-pole system are studied to see the schemes on which the control laws are made. Also, the odd symmetry of the relation between the output of the control laws and the system state vector is observed. Utilizing the symmetry in quantizing the system state variables and implementing the control laws into look-up table is discussed. Then, a CMAC is trained for a nonlinear control law for a cart-pole system such that the symmetry is conserved and its learning performance is evaluated. It is found that utilizing the symmetry is to reduce the memory requirement as well as the training period while improving the learning quality in terms of preserving the symmetry.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.49 no.3
• /
• pp.74-80
• /
• 2012

In resolution-constrained quantization, the size of Voronoi cell varies depending on probability density function of the input data, which causes large amount of distortion outliers. We propose a vector quantization method that reduces distortion outliers by combining the generalized Lloyd algorithm (GLA) and the cell-size constrained vector quantization (CCVQ) scheme. The training data are divided into the inside and outside regions according to the size of Voronoi cell, and consequently CCVQ and GLA are applied to each region, respectively. As CCVQ is applied to the densely populated region of the source instead of GLA, the number of centroids for the outside region can be increased such that distortion outliers can be decreased. In real-world environment, source mismatch between training and test data is inevitable. For the source mismatch case, the proposed algorithm improves performance in terms of average distortion and distortion outliers.

• Journal of Intelligence and Information Systems
• /
• v.15 no.2
• /
• pp.49-59
• /
• 2009

Contents-based image retrieval methods are in general more objective and effective than text-based image retrieval algorithms since they use color and texture in search and avoid annotating all images for search. SIM(Self-organizing Image browsing Map) is one of contents-based image retrieval algorithms that uses only browsable mapping results obtained by SOM(Self Organizing Map). However, SOM may have an error in selecting the right BMU in learning phase if there are similar nodes with distorted color information due to the intensity of light or objects' movements in the image. Such images may be mapped into other grouping nodes thus the search rate could be decreased by this effect. In this paper, we propose an improved SIM that uses HSV color model in extracting image features with color quantization. In order to avoid unexpected learning error mentioned above, our SOM consists of two layers. In learning phase, SOM layer 1 has the color feature vectors as input. After learning SOM Layer 1, the connection weights of this layer become the input of SOM Layer 2 and re-learning occurs. With this multi-layered SOM learning, we can avoid mapping errors among similar nodes of different color information. In search, we put the query image vector into SOM layer 2 and select nodes of SOM layer 1 that connects with chosen BMU of SOM layer 2. In experiment, we verified that the proposed SIM was better than the original SIM and avoid mapping error effectively.

• Journal of the Korean Institute of Telematics and Electronics C
• /
• v.34C no.1
• /
• pp.42-50
• /
• 1997

Vector quantization has shown to be useful for compressing data related with a wide rnage of applications such as image processing, speech processing, and weather satellite. Neural networks of images this paper propses a efficient neural network learning algorithm, called learning count control algorithm based on the frquency sensitive learning algorithm. This algorithm can train a results more codewords can be assigned to the sensitive region of the human visual system and the quality of the reconstructed imate can be improved. We use a human visual systrem model that is a cascade of a nonlinear intensity mapping function and a modulation transfer function with a bandpass characteristic.