• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1490-1499
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• 2022

Arrhythmia is a condition in which the heart has an irregular rhythm or abnormal heart rate, early diagnosis and management is very important because it can cause stroke, cardiac arrest, or even death. In this paper, we propose arrhythmia classification using hybrid combination model of CNN-BLSTM. For this purpose, the QRS features are detected from noise removed signal through pre-processing and a single bit segment was extracted. In this case, the GAN oversampling technique is applied to solve the data imbalance problem. It consisted of CNN layers to extract the patterns of the arrhythmia precisely, used them as the input of the BLSTM. The weights were learned through deep learning and the learning model was evaluated by the validation data. To evaluate the performance of the proposed method, classification accuracy, precision, recall, and F1-score were compared by using the MIT-BIH arrhythmia database. The achieved scores indicate 99.30%, 98.70%, 97.50%, 98.06% in terms of the accuracy, precision, recall, F1 score, respectively.

• The KIPS Transactions:PartD
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• v.14D no.4 s.114
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• pp.381-388
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• 2007

Consumers changed to the subject to produce a digital contents as data transmission technique is advanced and a digital machine is diffused variously. Users are interested greatly in a user creative movie (UCM) production among various online contents. The UCM production method which uses the music and picture is the method that users make the UCM more easily. However, the UCM production service has the problem that any association does not exist in the music and picture and that the picture changes according to fixed time interval without the relation at a music rhythm. To solve this problem, we propose the UCM production method which uses a music analysis and picture analysis in the paper. A music analysis finds a picture change time according to the rhythm and a picture analysis finds the association of the picture. A music analysis finds strong parts of the sound which uses Root-Mean-Square (RMS). And a picture analysis classifies the picture as a scenery picture and people picture which uses structure simplicity of the picture(SSP) and face region detection. A picture analysis got correct result of 86.4% in the experiment and we can finds the association at each picture and arranges the sequence which the picture appears. Therefore, if we use a music and picture analysis at the UCM production, users may make natural and efficient movie.

• Phonetics and Speech Sciences
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• v.14 no.3
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• pp.51-66
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• 2022

This study aims to help Korean English learners to learn English pronunciation by revealing which suprasegmentals affect the implementation of English sentences closer to native English speakers when they read English sentences. To this end, Korean middle school English learners were selected as subjects and research data were gathered through sentence types (declarative, interrogative, imperative, and exclamative), as well as syllables. Speech rate, pause frequency, pause duration, F0 range, and rhythm among suprasegmentals were used for analysis of these English sentence utterances. Mean analysis, correlation analysis, and regression analysis were performed. The results showed that speech rate, pause frequency, pause duration, and F0 range affected the evaluation of fluency levels. In the regression analysis between all suprasegmentals and fluency levels, the suprasegmentals that most affected fluency levels were speech rate and F0 range. Rhythm had no meaningful relation with fluency levels. Therefore, when teaching English pronunciation, it is necessary to teach students to increase their speech rate and F0 range. In addition, students should be trained to reduce both the number and the duration of pauses during utterance to improve their fluency. It is noteworthy that of the four sentence types, exclamative sentences were produced with faster speech rate, fewer pauses, shorter pause duration, and higher rhythm values.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.8C
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• pp.1159-1174
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• 2004

In this paper, we present an efficient algorithm for region analysis of business card images acquired in a PDA by using DCT and information pixel density. The proposed method consists of three parts: region segmentation, information region classification, and text region classification. In the region segmentation, an input business card image is partitioned into 8 f8 blocks and the blocks are classified into information and background blocks using the normalized DCT energy in their low frequency bands. The input image is then segmented into information and background regions by region labeling on the classified blocks. In the information region classification, each information region is classified into picture region or text region by using a ratio of the DCT energy of horizontal and vertical edge components to that in low frequency band and a density of information pixels, that are black pixels in its binarized region. In the text region classification, each text region is classified into large character region or small character region by using the density of information pixels and an averaged horizontal and vertical run-lengths of information pixels. Experimental results show that the proposed method yields good performance of region segmentation, information region classification, and text region classification for test images of several types of business cards acquired by a PDA under various surrounding conditions. In addition, the error rates of the proposed region segmentation are about 2.2-10.1% lower than those of the conventional region segmentation methods. It is also shown that the error rates of the proposed information region classification is about 1.7% lower than that of the conventional information region classification method.

• The Journal of the Korea Contents Association
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• v.22 no.5
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• pp.600-610
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• 2022

While Korean piano pedagogy has seen a remarkable growth, the relatively weak attention to intermediate level has emerged as a pending problem. The limited literature review, specifically the lack of playing Korean original works, has been considered as a serious issue. To emphasize the usefulness of Kim Kukjin's "Pieces of Korean Melody for Piano" as an intermediate work, this study presents practical teaching guidelines by classifying of difficulty in his 25 pieces and providing step by step learning goals and teaching plan. The difficulty stage was based on Jane Magret's 10-step classification table for comparison with other intermediate piano literature, and this study more specifically classified Kim's pieces according to Korean melody, rhythm, and texture. As a result of the difficulty classification, it was found that the pieces from stages 4 to 10 was organized to systematically and comprehensively learn step by step from the most basic progression to Korean 'Jangdan' rhythm patterns, various articulations and decorations that express 'Sigimsae' of Korean Traditional Music, and heterophony texture. In addition, this study proposed the order of pieces for the effective teaching according to the characteristics and difficulty of the pieces. This study suggests that the findings lead to the expansion of Korean intermediate literature study and the revitalization of Korean original works teaching method.

• Journal of the Korean Society of Radiology
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• v.17 no.5
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• pp.735-742
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• 2023

Non-alcoholic fatty liver disease is an independent risk factor for the development of cardiovascular disease, diabetes, hypertension, and kidney disease, and the clinical importance of non-alcoholic fatty liver disease has recently been increasing. In this study, we aim to extract feature values by applying GLCM, a texture analysis method, to ultrasound images of patients with non-alcoholic fatty liver disease. By applying an artificial neural network model using extracted feature values, we would like to classify the degree of fat deposition in non-alcoholic fatty liver into normal liver, mild fatty liver, moderate fatty liver, and severe fatty liver. As a result of applying the GLCM algorithm, the parameters Autocorrelation, Sum of squares, Sum average, and sum variance showed a tendency for the average value of the feature values to increase as it progressed from mild fatty liver to moderate fatty liver to severe fatty liver. The four parameters of Autocorrelation, Sum of squares, Sum average, and sum variance extracted by applying the GLCM algorithm to ultrasound images of non-alcoholic fatty liver disease were applied as inputs to the artificial neural network model. The classification accuracy was evaluated by applying the GLCM algorithm to the ultrasound images of non-alcoholic fatty liver disease and applying the extracted images to an artificial neural network, showing a high accuracy of 92.5%. Through these results, we would like to present the results of this study as basic data when conducting a texture analysis GLCM study on ultrasound images of patients with non-alcoholic fatty liver disease.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.4
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• pp.399-407
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• 2002

In this paper, a novel algorithm is proposed to reduce the blocking artifacts of block-based coded images by using block classification and MLP. In the proposed algorithm, we classify the block into four classes based on a characteristic of DCT coefficients. And then, according to the class information of neighborhood block, adaptive neural network filter is performed in horizontal and vertical block boundary. That is, for smooth region, horizontal edge region, vertical edge region, and complex region, we use a different two-layer neural network filter to remove blocking artifacts. Experimental results show that the proposed algorithm gives better results than the conventional algorithms both subjectively and objectively.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.3
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• pp.13-20
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• 1990

This paper describes an interframe image coding using motion compensated and classified vector quantizer (MC-CVQ). It is essential to carefully encode blocks with significant pels in motion compensated vector quantizers (MCVQ). In this respect, we propose a new CVQ algorithm which is appropriate to the coding of interframe prediction error after motion compensation. In order to encode an image efficiently at a low bit rate, we partition each block, which is the processing element in MC, into equally sized 4 vectors, and classify vectors into 15 classes according to the position of significant pels. Vectors in each class are then encoded by the vector quantizer with the codebook independently designed for the class. The computer simulation shows that the signal-to-noise ratio and the average bit rate of MC-CVQ are 35-37dB and 0.2-0.25bit/pel, respectively, for the videophone or video conference type image.

• Journal of Energy Engineering
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• v.23 no.4
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• pp.263-271
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• 2014

Corrective thermal performance analysis is required for thermal power plants to determine performance status of turbine cycle. We developed classification method for main feed water flow to make precise correction for performance analysis based on ASME (American Society of Mechanical Engineers) PTC (Performance Test Code). The classification is based on feature identification of status of main water flow. Also we developed predictive algorithms for corrected main feed-water through Support Vector Machine (SVM) Model for each classified feature area. The results was compared to estimations using Neural Network(NN) and Kernel Regression(KR). The feature classification and predictive model of main feed-water flow provides more practical methods for corrective thermal performance analysis of turbine cycle.

• 전자공학회논문지 IE
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• v.43 no.4
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• pp.43-51
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• 2006

Segmentation method using RSST has many advantages in extracting of accurate region boundaries and controlling the resolution of segmented result and so on. In this paper, we propose three fast RSST algorithms for image segmentation. In first method, we classify links according to weight size for fast link search. In the second method, very similar links before RSST construction are eliminated. In third method, the links of very small regions which are not important for human eye are eliminated. As a result, the total times elapsed for segmentation are reduced by about 10 $\sim$ 40 times, and reconstructed images based on the segmentation results show little degradation of PSNR and visual quality.