• The Journal of the Acoustical Society of Korea
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• v.29 no.2
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• pp.133-140
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• 2010

We improve the performance of cardiac disorder classification by adding new temporal features extracted from continuous heart sound signals. We add three kinds of novel temporal features to a conventional feature based on mel-frequency cepstral coefficients (MFCC): Heart sound envelope, murmur probabilities, and murmur amplitude variation. In cardiac disorder classification and detection experiments, we evaluate the contribution of the proposed features to classification accuracy and select proper temporal features using the sequential feature selection method. The selected features are shown to improve classification accuracy significantly and consistently for neural network-based pattern classifiers such as multi-layer perceptron (MLP), support vector machine (SVM), and extreme learning machine (ELM).

• Journal of the Korea Society of Computer and Information
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• v.16 no.12
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• pp.83-92
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• 2011

Typographical errors by the author's mistyping occur frequently in a document being prepared with word processors contrary to usual publications. Preparing this online document, the most common orthographical errors are spelling errors resulting from incorrectly typing intent keys to near keys on keyboard. Typical spelling checkers detect and correct these errors by using morphological analyzer. In other words, the morphological analysis module of a speller tries to check well-formedness of input words, and then all words rejected by the analyzer are regarded as misspelled words. However, if morphological analyzer accepts even mistyped words, it treats them as correctly spelled words. In this paper, I propose a simple method capable of detecting and correcting errors that the previous methods can not detect. Proposed method is based on the characteristics that typographical errors are generally not repeated and so tend to have very low frequency. If words generated by operations of deletion, exchange, and transposition for each phoneme of a low frequency word are in the list of high frequency words, some of them are considered as correctly spelled words. Some heuristic rules are also presented to reduce the number of candidates. Proposed method is able to detect not syntactic errors but some semantic errors, and useful to scoring candidates.

• Journal of the Korean Society of Radiology
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• v.11 no.3
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• pp.171-175
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• 2017

In the non-destructive inspection field, we invest a lot of time and resources in developing the radiation source system to ensure the safety of the workers. However, the probability of accidents is still high. In order to prevent potential radiation accidents in advance, it is necessary to directly verify the position of the radiation source, but the research is still insufficient. In this study, we developed a monitoring system that can detect the position of the radiation source in the source guide tube in the gamma-ray irradiator. The characteristics of the radiation detector are estimated by monte carlo simulation. As a result, the radiation detector for Ir-192 gamma-ray energy was analyzed to have secondary electron equilibrium at $150{\mu}m$ regardless of the semiconductor material. Also, it is expected that the gamma ray response characteristic is the best in $HgI_2$. These results are expected to be used as a basis for determining the optimal thickness of the radiation detector located in the detection part of the future monitoring system. In addition, when developing a monitoring system based on this, radiation workers can easily recognize the danger and secure safety, as well as prevent and preemptively respond to potential radiation accidents.

• Food Science of Animal Resources
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• v.23 no.4
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• pp.327-332
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• 2003

This study was conducted to investigate the characteristics of strains which were isolated from market milk treated with ESL(extended shelf life) and conventional system, and to compare the microbiological quality of ESL milk with conventional milk. In order to characterize the isolated strains, purification, Gram staining, spore staining, catalase, oxidase, motility test, and identification by means of automatic identificator were performed. The results obtained are as follows: total 364 selected strains were analyzed in this study. Depending upon the isolated source, the number of strains from conventional milk was found to be Higher than ESL-milk. By means of grouping of total strains, Bacillus ssp. and Staphylococcus ssp. showed to be predominant. But most of strains were distributed with various groups except Lactobacillus ssp. When the isolates were compared with milk process methods, Enterococcus ssp. was detected much on market milk treated with LTLT pasteurization. Also, Pseudomonas ssp. was detected much on conventional milk treated with UHT pasteurization. By comparison with genus groups depending upon storage temperature of market milk, the higher milk storage temperature increased, the most frequency detected Bacillus ssp. increased. Also, Pseudomonas ssp. was detected most frequently at 10$^{\circ}C$ storage condition. Generally this genus derived from post-contamination during milk processing and related to the quality of market milk during chilled system. In conclusion, it was shown that ESL system reduced post-contamination during milk process, following the improvement of product quality and life cycle during the distribution of market milk.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.10C
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• pp.1017-1026
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• 2005

In burst OFDM system, the frame synchronization should be performed first for the acquisition of received frame and the estimation of the correct FFT-window position. The conventional frame synchronization algorithms using design features of the preamble symbol, the repetition pattern of the OFDM symbol by pilot sub-carrier allocation rule and Cyclic Prefix(CP), has difficulty in the detection of precise frame timing because its correlation characteristics would increase and decrease gradually. Also, the algorithm based on the correlation between the reference signal and the received signal has performance degradation due to frequency offset. Therefore, we adopt a differential correlation method that is robust to frequency offset and has the clear peak value at the correct frame timing for frame synchronization. However, performance improvement is essential for differential correlation methods, since it usually shows multiple peak values due to the repetition pattern. In this paper, we propose an enhanced frame synchronization algorithm based on the differential correlation method that shows a clear single peak value by using differential correlation between samples of identical repeating pattern. We also introduce a normalization scheme which normalizes the result of differential correlation with signal power to reduce the frame timing error in the high speed mobile channel environments.

• The Journal of the Acoustical Society of Korea
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• v.21 no.4
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• pp.380-386
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• 2002

Conventional post-processing as like confidence measure (CM) proposed by Rahim calculates phones' CM using the likelihood between phoneme model and anti-model, and then word's CM is obtained by averaging phone-level CMs[1]. In conventional method, CMs of some specific keywords are tory low and they are usually rejected. The reason is that statistics of phone-level CMs are not consistent. In other words, phone-level CMs have different probability density functions (pdf) for each phone, especially sri-phone. To overcome this problem, in this paper, we propose normalized confidence measure. Our approach is to transform CM pdf of each tri-phone to the same pdf under the assumption that CM pdfs are Gaussian. For evaluating our method we use common keyword spotting system. In that system context-dependent HMM models are used for modeling keyword utterance and contort-independent HMM models are applied to non-keyword utterance. The experiment results show that the proposed NCM reduced FAR (false alarm rate) from 0.44 to 0.33 FA/KW/HR (false alarm/keyword/hour) when MDR is about 8%. It achieves 25% improvement of FAR.

• Journal of IKEEE
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• v.23 no.1
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• pp.112-119
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• 2019

In this paper, we propose a deep learning structure suitable for embedded system. The flame detection process of the proposed deep learning structure consists of four steps : flame area detection using flame color model, flame image classification using deep learning structure for flame color specialization, $N{\times}N$ cell separation in detected flame area, flame image classification using deep learning structure for flame shape specialization. First, only the color of the flame is extracted from the input image and then labeled to detect the flame area. Second, area of flame detected is the input of a deep learning structure specialized in flame color and is classified as flame image only if the probability of flame class at the output is greater than 75%. Third, divide the detected flame region of the images classified as flame images less than 75% in the preceding section into $N{\times}N$ units. Fourthly, small cells divided into $N{\times}N$ units are inserted into the input of a deep learning structure specialized to the shape of the flame and each cell is judged to be flame proof and classified as flame images if more than 50% of cells are classified as flame images. To verify the effectiveness of the proposed deep learning structure, we experimented with a flame database of ImageNet. Experimental results show that the proposed deep learning structure has an average resource occupancy rate of 29.86% and an 8 second fast flame detection time. The flame detection rate averaged 0.95% lower compared to the existing deep learning structure, but this was the result of light construction of the deep learning structure for application to embedded systems. Therefore, the deep learning structure for flame detection proposed in this paper has been proved suitable for the application of embedded system.

• The KIPS Transactions:PartB
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• v.19B no.3
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• pp.201-208
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• 2012

Accurately segmenting lumen border in intravascular ultrasound images (IVUS) is very important to study vascular wall architecture for diagnosis of the cardiovascular diseases. After each of IVUS image is transformed to a polar coordinated image, initial points are detected using wavelet transform. Then, lumen border is initialized as the set of important points using non parametric probability density function and smoothing function by removing outlier initial points occurred by noises and artifacts. Finally, polynomial curve fitting is applied to obtain real lumen border using filtered important points. The evaluation of proposed method was performed with related method and the proposed method produced accurate lumen contour detection when compared to another method in most types of IVUS images.

• Journal of KIISE:Software and Applications
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• v.31 no.9
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• pp.1161-1170
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• 2004

This paper proposes to segment skin color areas using a clustering algorithm. Most of previously proposed clustering algorithms have some difficulties, since they generally detect hyperspherical clusters, run in a batch mode, and predefine a number of clusters. In this paper, we use a well-known elliptical clustering algorithm, an EM algorithm, and modify it to learn on-line and find automatically the number of clusters, called to an EAM algorithm. The effectiveness of the EAM algorithm is demonstrated on a task of skin color region segmentation. Experimental results present the EAM algorithm automatically finds a right number of clusters in a given image without any information on the number. Comparing with the EM algorithm, we achieved better segmentation results with the EAM algorithm. Successful results were achieved to detect and segment skin color regions using a conditional probability on a region. Also, we applied to classify images with persons and got good classification results.

• Analytical Science and Technology
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• v.7 no.3
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• pp.395-402
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• 1994

Benomyl(Methyl-1-(Butyl Carbamoyl)-Benzimidazole-2-yl-Carbamate) is widely used as pre- and post-harvest pesticide. It converts into MBC(Carbendarzime:Benzimidazole-2-yl-carbamate) and butyl-isocyanate in mild condition. In this study, three analytical methods for MBC were compared in view of detectability, correctness, and sensitivity. The first and second are HPLC analytical method employing the UV detection of MBC. Our new third method was modification of PFBB(pentafluoro-benzylbromide) derivatization method with GC-ECD & MSD. The average recoveries and detection limit of MBC in the newly modified method are 95% and $0.001{\mu}g/g$ in whole bean and bean sprouts respectively. This new method prevent pesticide analysis from misdetecting in bean and bean sprouts.