• The Journal of the Acoustical Society of Korea
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• v.22 no.5
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• pp.360-366
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• 2003

Although multilayer perceptrons (MLPs) present several advantages against other pattern recognition methods, MLP-based speaker verification systems suffer from slow enrollment speed caused by many background speakers to achieve a low verification error. To solve this problem, the quantitative discriminative cohort speakers (QnDCS) method, by introducing the cohort speakers method into the systems, reduced the number of background speakers required to enroll speakers. Although the QnDCS achieved the goal to some extent, the improvement rate for the enrolling speed was still unsatisfactory. To improve the enrolling speed, this paper proposes the qualitative DCS (QlDCS) by introducing a qualitative criterion to select less background speakers. An experiment for both methods is conducted to use the speaker verification system based on MLPs and continuants, and speech database. The results of the experiment show that the proposed QlDCS method enrolls speakers in two times shorter time than the QnDCS does over the online error backpropagation(EBP) method.

• The Journal of the Acoustical Society of Korea
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• v.15 no.4
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• pp.58-64
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• 1996

This paper was proposed the recognition method by using parameters which was estimated from the data on the estimated plane and a neural network. After the LPC estimated in each frame algorithm was mapped to the estimated plane by the optimum feature mapping function, we estimated the C-LPC and the maximum and minimum value and 3 divided power from the mapping data on the estimated plane. As a result of the experiment of the speech recognition that those parameters were applied to the input of a neural network, it was found that those parameters estimated from the estimated plane have the features of the original speech for a change in the time scale and that the recongnition rate by the proposed methods was 96.3 percent.

• Molecules and Cells
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• v.41 no.6
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• pp.591-602
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• 2018

Gastric cancer is the fifth most common type of malignancy worldwide, and the survival rate of patients with advanced-stage gastric cancer is low, even after receiving chemotherapy. Here, we validated neurotensin receptor 1 (NTSR1) as a potential therapeutic target in gastric cancer. We compared NTSR1 expression levels in sixty different gastric cancer-tissue samples and cells, as well as in other cancer cells (lung, breast, pancreatic, and colon), by assessing NTSR1 expression via semi-quantitative real-time reverse transcription polymerase chain reaction, immunocytochemistry and western blot. Following neurotensin (NT) treatment, we analyzed the expression and activity of matrix metalloproteinase-9 (MMP-9) and further determined the effects on cell migration and invasion via wound-healing and transwell assays. Our results revealed that NTSR1 mRNA levels were higher in gastric cancer tissues than non-cancerous tissues. Both of NTSR1 mRNA levels and expression were higher in gastric cancer cell lines relative to levels observed in other cancer-cell lines. Moreover, NT treatment induced MMP-9 expression and activity in all cancer cell lines, which was significantly decreased following treatment with the NTSR1 antagonist SR48692 or small-interfering RNA targeting NTSR1. Furthermore, NT-mediated metastases was confirmed by observing epithelial-mesenchymal transition markers SNAIL and E-cadherin in gastric cancer cells. NT-mediated invasion and migration of gastric cancer cells were reduced by NTSR1 depletion through the Erk signaling. These findings strongly suggested that NTR1 constitutes a potential therapeutic target for the inhibition of gastric cancer invasion and metastasis.

• Journal of Broadcast Engineering
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• v.8 no.2
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• pp.198-203
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• 2003

We develop the method of estimating the endpoints of speech by jointly using the lip motion (visual speech) and speech being included in multimedia data and then propose a new speech recognition system (SRS) based on that method. The endpoints of noisy speech are estimated as follows : For each test word, two kinds of endpoints are detected from visual speech and clean speech, respectively Their difference is made and then added to the endpoints of visual speech to estimate those for noisy speech. This estimation method for endpoints (i.e. speech interval) is applied to form a new SRS. The SRS differs from the convention alone in that each word model in the recognizer is provided an interval of speech not Identical but estimated respectively for the corresponding word. Simulation results show that the proposed method enables the endpoints to be accurately estimated regardless of the amount of noise and consequently achieves 8 o/o improvement in recognition rate.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.205-208
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• 2010

We introduce a novel feature extraction scheme for online handwritten digit based on utilizing Zernike moment and angulation feature. The time sequential signal from mouse movement on the writing pad is described as a sequence of consecutive points on the x-y plane. So, we can create data-set which are successive and time-sequential pixel position data by preprocessing. Data preprocessed is used for Zernike moment and angulation feature extraction. this feature is scale-, translation-, and rotation-invariant. The extracted specific feature is fed to a BP(backpropagation) neural network, which in turn classifies it as one of the nine digits. In this paper, proposed method not noly show high recognition rate but also need less learning data for 200 handwritten digit data.

• Journal of the Institute of Convergence Signal Processing
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• v.6 no.1
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• pp.15-22
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• 2005

In this paper, we propose a face recognition system by using the CCD color image. We first get the face candidate image by using YCbCr color model and adaptive skin color information. And we use it initial curve of active contour model to extract face region. We use the Eye map and mouth map using color information for extracting facial feature from the face image. To obtain center point of Log-polar image, we use extracted facial feature from the face image. In order to obtain feature vectors, we use extracted coefficients from DCT and wavelet transform. To show the validity of the proposed method, we performed a face recognition using neural network with BP learning algorithm. Experimental results show that the proposed method is robuster with higher recogntion rate than the conventional method for the rotation and scale variant.

• Journal of Korea Multimedia Society
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• v.6 no.2
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• pp.230-239
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• 2003

This paper presents face recognition algorithm using projection vector reflecting local feature for the area in contour lines. The outline shape of a face has many difficulties to distinguish people because human has similar face shape. For 3 dimensional(3D) face images include depth information, we can extract different face shapes from the nose tip using some depth values for a face image. In this thesis deals with 3D face image, because the extraction of contour lines from 2 dimensional face images is hard work. After finding nose tip, we extract two areas in the contour lilies from some depth values from 3D face image which is obtained by 3D laser scanner. And we propose a method of projection vector to localize the characteristics of image and reduce the number of index data in database. Euclidean distance is used to compare of similarity between two images. Proposed algorithm can be made recognition rate of 94.3% for face shapes using depth information.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.3
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• pp.232-247
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• 1991

This paper proposes a new classification algorithm using characteristic and structural information of printed Hanja as preliminary stages of Hanja-character recognition. Hanja is difficult for not only recognition but classification as many character and complicated structure. In this paper, to solve thie problem, extracted common subpattern in classified pattern after processing type classification fot Hanja pattern. First, we extracted subpattern, after we process preprecessing about input of character pattern, extracting directional segment, labeling on 4-directional pattern and 12 type classified using structural information based on the subpattern existing region of character pattern. Though the experiment, this study obtained that classified rate of Hanja is 93.07% on 1800 character of educational Hanja and 90.12% on 4888 character of KS C5601 standard TRIGEM LBP Hanja font and saw that as extracting subpattern at classified data was this paper possibly applied to the recognition.

• Journal of Convergence for Information Technology
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• v.9 no.11
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• pp.55-61
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• 2019

This study was conducted to identify the predictors of the choice of regional small and medium hospitals by identifying the job preference, recognition of small and medium hospitals. For this purpose, data were collected from September 2018 to October 2018 for nursing students attending 4 universities in Gwangju and Jeollanam - do, and total of 476 questionnaires were analyzed using the SPSS / WIN 24.0 program. The results showed that 66.0% of nursing students selected region local small and medium hospitals. The factors influencing the choice of region small and medium hospitals were high school region, nursing school performance and recognition of small and medium hospitals. In order to increase the employment rate of nursing students to the region small and medium hospitals, nursing educators should provide personalized career guidance to students who want to work in small and medium hospitals and hospital personnel should establish various public relations activities and marketing strategies to raise recognition of small and medium hospitals.

• Journal of the Korea Convergence Society
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• v.10 no.2
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• pp.159-165
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• 2019

Based on deep learning technology, speech recognition method has began to be applied to commercial products, but it is still difficult to be used in the area of VR contents, since there is no easy and efficient way to process the recognized text after the speech recognition module. In this paper, we propose a Korean Language Command System, which can efficiently recognize and respond to Korean speech commands. The system consists of two components. One is a morphological analyzer to analyze sentence morphemes and the other is a retrieval based model which is usually used to develop a chatbot system. Experimental results shows that the proposed system requires only 16% commands to achieve the same level of performance when compared with the conventional string comparison method. Furthermore, when working with Google Cloud Speech module, it revealed 60.1% of success rate. Experimental results show that the proposed system is more efficient than the conventional string comparison method.