• Journal of the Korean Data and Information Science Society
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• 제17권4호
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• pp.1151-1160
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• 2006

The similarity weight, the pearson's correlation coefficient, which is used in the recommender system has a weak point that it cannot predict all of the prediction value. The similarity weight, the vector similarity, has a weak point of the high MAE although the prediction coverage using the vector similarity is higher than that using the pearson's correlation coefficient. The purpose of this study is to suggest how to raise the prediction coverage. Also, the MAE using the suggested method in this study was compared both with the MAE using the pearson's correlation coefficient and with the MAE using the vector similarity, so was the prediction coverage. As a result, it was found that the low of the MAE in the case of using the suggested method was higher than that using the pearson's correlation coefficient. However, it was also shown that it was lower than that using the vector similarity. In terms of the prediction coverage, when the suggested method was compared with two similarity weights as I mentioned above, it was found that its prediction coverage was higher than that pearson's correlation coefficient as well as vector similarity.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2006년도 추계 학술발표회 논문집
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• pp.119-128
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• 2006

The similarity weight, the pearson's correlation coefficient, which is used in the recommender system has a weak point that it cannot predict all of the prediction value. The similarity weight, the vector similarity, has a weak point of the high MAE although the prediction coverage using the vector similarity is higher than that using the pearson's correlation coefficient. The purpose of this study is to suggest how to raise the prediction coverage. Also, the MAE using the suggested method in this study was compared both with the MAE using the pearson's correlation coefficient and with the MAE using the vector similarity, so was the prediction coverage. As a result, it was found that the low of the MAE in the case of using the suggested method was higher than that using the pearson's correlation coefficient. However, it was also shown that it was lower than that using the vector similarity In terms of the prediction coverage, when the suggested method was compared with two similarity weights as I mentioned above, it was found that its prediction coverage was higher than that pearson's correlation coefficient as well as vector similarity.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 1995년도 학술대회
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• pp.87-90
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• 1995

In this paper, we propose a new deinterlacing algorithm based on weighted wide vector correlations. This algorithm is developed mainly for the format conversion problem encountered in current HDTV system, but not limited to. By having wide vector correlations, visually annoying artifacts caused by interlacing, such as a serrate line, line crawling, a line flicker, and a large area flicker, can be remarkably reduced, since the use of wide vector correlation increases the detectability of edges in various orientations.

• 전자공학회논문지B
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• 제33B권6호
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• pp.64-72
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• 1996

In this paper, we propose predictive motion estimatin algorithm which can predict motion without additional side information considering spatio-tempral correlatio of motion vector. This method performs motion prediction of current block using correlation of the motion vector for two spatially adjacent blocks and a temporally adjacent block. Form predicted motion, the position of searhc area is determined. Then in this searhc area, we estimate motion vector of current block using block matching algoirthm. Considering spatial an temporal correlation of motion vector, the proposed method can predict motion precisely much more. Especially when the motion of objects is rapid, this method can estimate motion more precisely without reducing block size or increasing search area. Futhrmore, the proposed method has computation time the same as conventional block matching algorithm. And as it predicts motion from adjacent blocks, it does not require additional side information for adjacent block. Computer simulation results show that motion estimation of proposed method is more precise than that of conventioanl method.

• Computers and Concrete
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• 제5권5호
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• pp.461-473
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• 2008

The paper explores the potential of Support Vector Machines (SVM) approach in predicting 28-day compressive strength and slump flow of self-compacting concrete. Total of 80 data collected from the exiting literature were used in present work. To compare the performance of the technique, prediction was also done using a back propagation neural network model. For this data-set, RBF kernel worked well in comparison to polynomial kernel based support vector machines and provide a root mean square error of 4.688 (MPa) (correlation coefficient=0.942) for 28-day compressive strength prediction and a root mean square error of 7.825 cm (correlation coefficient=0.931) for slump flow. Results obtained for RMSE and correlation coefficient suggested a comparable performance by Support Vector Machine approach to neural network approach for both 28-day compressive strength and slump flow prediction.

• 제어로봇시스템학회논문지
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• 제6권5호
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• pp.390-395
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• 2000

We present a fingerprint identification algorithm using the wavelet transform and correlation. The wavelet transform is used because of its simple operation to extract fingerprint minutiaes features for fingerprint classification. We perform the rowwise 1-D wavelet transform for a $256\times256$ fingerprint image to get a $1\times256$ column vector using the Haar wavelet and repeat 1-D wavelet transform for a 1$\times$256 column vector to get a $1\times4$ feature vector. Using PNN(Probabilistic Neural Network), we select the possible candidates from the stored feature vectors for fingerprint images. For those candidates, we compute the correlation between the input binary image and the target binary image to find the most similar fingerprint image. The proposed algorithm may be the key to a low cost fingerprint identification system that can be operated on a small computer because it does not need a large memory size and much computation.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.2594-2599
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• 2003

We consider some problems of the Modified SPRT(Sequential Probability Ratio Test) method for fault detection and isolation of inertial redundant sensor systems and propose an Advanced SPRT method to solve the problems of the Modified SPRT method. One problem of the Modified SPRT method to apply to inertial sensor system comes from the effect of inertial sensor errors such as misalignment, scale factor error and sensor bias in the parity vector, which make the Modified SPRT method hard to be applicable. The other problem is due to the correlation of parity vector components which may induce false alarm. We use a two-stage Kalman filter to remove effects of the inertial sensor errors and propose the modified parity vector and the controlled parity vector which removes the effect of correlation of parity vector components. The Advanced SPRT method is derived form the modified parity vector and the controlled parity vector. Some simulation results are presented to show the usefulness of the Advanced SPRT method to redundant inertial sensor systems.

• 말소리와 음성과학
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• 제7권2호
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• pp.149-156
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• 2015

We propose a new method for automatic fluency scoring of English speaking tests spoken by nonnative speakers in a free-talking style. The proposed method is different from the previous methods in that it does not require the transcribed texts for spoken utterances. At first, an input utterance is segmented into a phone sequence by using a phone recognizer trained by using native speech databases. For each utterance, a feature vector with 6 features is extracted by processing the segmentation results of the phone recognizer. Then, fluency score is computed by applying support vector regression (SVR) to the feature vector. The parameters of SVR are learned by using the rater scores for the utterances. In computer experiments with 3 tests taken by 48 Korean adults, we show that speech rate, phonation time ratio, and smoothed unfilled pause rate are best for fluency scoring. The correlation of between the rater score and the SVR score is shown to be 0.84, which is higher than the correlation of 0.78 among raters. Although the correlation is slightly lower than the correlation of 0.90 when the transcribed texts are given, it implies that the proposed method can be used as a preprocessing tool for fluency evaluation of speaking tests.

• 제어로봇시스템학회논문지
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• 제16권3호
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• pp.269-276
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• 2010

In this paper, we present the detection method of moving objects based on two background models. These background models support to understand multi layered environment belonged in images taken by shaking camera and each model is MBM(Multiple Background Model) and TMBM (Temporal Median Background Model). Because two background models are Pixel-based model, it must have noise by camera movement. Therefore correlation coefficient calculates the similarity between consecutive images and measures camera motion vector which indicates camera movement. For the calculation of correlation coefficient, we choose the selected region and searching area in the current and previous image respectively then we have a displacement vector by the correlation process. Every selected region must have its own displacement vector therefore the global maximum of a histogram of displacement vectors is the camera motion vector between consecutive images. The MBM classifies the intensity distribution of each pixel continuously related by camera motion vector to the multi clusters. However, MBM has weak sensitivity for temporal intensity variation thus we use TMBM to support the weakness of system. In the video-based experiment, we verify the presented algorithm needs around 49(ms) to generate two background models and detect moving objects.

• 한국멀티미디어학회논문지
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• 제24권4호
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• pp.528-537
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• 2021

Face recognition has gained significant notice because of its application in many businesses: security, healthcare, and marketing. In this paper, we will present the recognition method using the combination of correlation filters (CF) and Support Vector Machine (SVM). Firstly, we evaluate the performance and compared four different correlation filters: minimum average correlation energy (MACE), maximum average correlation height (MACH), unconstrained minimum average correlation energy (UMACE), and optimal-tradeoff (OT). Secondly, we propose the machine learning approach by using the OT correlation filter for features extraction and SVM for classification. The numerical results on National Cheng Kung University (NCKU) and Pointing'04 face database show that the proposed method OT-SVM gets higher accuracy in face recognition compared to other machine learning methods. Our approach doesn't require graphics card to train the image. As a result, it could run well on a low hardware system like an embedded system.