• Journal of radiological science and technology
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• v.40 no.2
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• pp.205-211
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• 2017

There is a need for aggressive diagnosis and treatment in middle-aged and high-risk individuals who are more likely to progress from nonalcoholic fatty liver to hepatitis. In this study, nonalcoholic fatty liver was divided into severe, moderate, and severe, and classified by quantitative method using computer analysis of GLCM algorithm. The purpose of this study was to evaluate the characteristics of ultrasound images in the local fat avoidance region. Normal, mild, moderate, severe fatty liver, and focal fat sparing area, 80 cases, respectively. Among the parameters of the GLCM algorithm, the values of the Autocorrelation, Square of the deviation, Sum of averages and Sum of variances with high recognition rate of the liver ultrasound image were calculated. The average recognition rate of the GLCM algorithm was 97.5%. The result of local fat avoidance image analysis showed the most similar value to the normal parenchyma. Ultrasonography can be easily accessed by primary screening, but there may be differences in the accuracy of the test method or the correspondence of results depending on proficiency. GLCM algorithm was applied to quantitatively classify the degree of fatty liver. Local fat avoidance region was similar to normal parenchyma, so it could be predicted to be homogeneous liver parenchyma without fat deposition. We believe that GLCM computer image analysis will provide important information for differentiating not only fatty liver but also other lesions.

• The Journal of the Acoustical Society of Korea
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• v.18 no.5
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• pp.12-20
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• 1999

In this paper, we compared speaker verification performance of the speech data collected in clean environment and in channel environment. For the improvement of the performance of speaker verification gathered in channel, we have studied on the efficient feature parameters in channel environment and on the preprocessing. Speech DB for experiment is consisted of Korean doublet of numbers, considering the text-prompted system. Speech features including LPCC(Linear Predictive Cepstral Coefficient), MFCC(Mel Frequency Cepstral Coefficient), PLP(Perceptually Linear Prediction), LSP(Line Spectrum Pair) are analyzed. Also, the preprocessing of filtering to remove channel noise is studied. To remove or compensate for the channel effect from the extracted features, cepstral weighting, CMS(Cepstral Mean Subtraction), RASTA(RelAtive SpecTrAl) are applied. Also by presenting the speech recognition performance on each features and the processing, we compared speech recognition performance and speaker verification performance. For the evaluation of the applied speech features and processing methods, HTK(HMM Tool Kit) 2.0 is used. Giving different threshold according to male or female speaker, we compare EER(Equal Error Rate) on the clean speech data and channel data. Our simulation results show that, removing low band and high band channel noise by applying band pass filter(150～3800Hz) in preprocessing procedure, and extracting MFCC from the filtered speech, the best speaker verification performance was achieved from the view point of EER measurement.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.3C
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• pp.374-386
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• 2004

The mammogram provides the way to observe detailed internal organization of breasts to radiologists for the early detection. This paper is mainly focused on efficiently detecting the Microcalcification's Region Of Interest(ROI)s. Breast cancers can be caused from either microcalcifications or masses. Microcalcifications are appeared in a digital mammogram as tiny dots that have a little higher gray levels than their surrounding pixels. We can roughly determine the area which possibly contain microcalifications. In general, it is very challenging to find all the microcalcifications in a digital mammogram, because they are similar to some tissue parts of a breast. To efficiently detect microcalcifications ROI, we used four sequential processes; preprocessing for breast area detection, modified multilevel thresholding, ROI selection using simple thresholding filters and final ROI selection with two stages of neural networks. The filtering process with boundary conditions removes easily-distinguishable tissues while keeping all microcalcifications so that it cleans the thresholded mammogram images and speeds up the later processing by the average of 86％. The first neural network shows the average of 96.66％ recognition rate. The second neural network performs better by showing the average recognition rate 98.26％. By removing all tissues while keeping microcalcifications as much as possible, the next parts of a CAD system for detecting breast cancers can become much simpler.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.4
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• pp.2749-2756
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• 2015

Recently, Depending on the quality of data increases, the problem of time-consuming to process the image is raised by being required to accelerate the image processing algorithms, in a traditional CPU and CUDA(Compute Unified Device Architecture) based recognition system for computing speed and performance gains compared to OpenMP When character recognition has been learned by the system to measure the input by the character data matching is implemented in an environment that recognizes the region of the well, so that the font of the characters image learning English alphabet are each constant and standardized in size and character an image matching method for calculating the matching has also been implemented. GPGPU (General Purpose GPU) programming platform technology when using the CUDA computing techniques to recognize and use the four cores of Intel i5 2500 with OpenMP to deal quickly and efficiently an algorithm, than the performance of existing CPU does not produce the rate of four times due to the delay of the data of the partition and merge operation proposed a method of improving the rate of speed of about 3.2 times, and the parallel processing of the video card that processes a result, the sequential operation of the process compared to CPU-based who performed the performance gain is about 21 tiems improvement in was confirmed.

• Journal of dental hygiene science
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• v.13 no.4
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• pp.410-417
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• 2013

This research surveyed 324 college students majored dental hygiene in Gwangju and Chonnam province to analyze their awareness and degree of practice about infection control. Through the study for recognition about infectious disease related with the grade, the sophomore students are the group who is the most aware. In addition, the study shows students who experienced teeth cleaning have high degree of awareness (p<0.05). The research of perception about infectious disease based on experiences for vaccinations and education about infection prevention says hepatitis B has the highest rate when it comes to level of occurrence risk and tuberculosis and acquired immune deficiency syndrome are the lowest (p<0.05). According to the research, the group who had vaccination, knowledge about infection prevention and experience for scaling shows high practice rate for hand washing according to whether students receive protective inoculation or not. Depending on what the result were, since student learned about infection control has high degree of recognition and practice about infection management if we could emphasize the importance to students through regulative education about infection control and then increase the degree of practice, it would make big contributions to the effective infection control.

• Resources Recycling
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• v.26 no.6
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• pp.73-83
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• 2017

Used small household appliances have a wide variety of product types and component materials, and contain high percentage of black plastics. However, they are not being recycled efficiently as conventional sensors such as near-infrared ray (NIR), etc. are not able to detect black plastic by types. In the present study, an automatic sorting system was developed based on laser-induced breakdown spectroscopy (LIBS) to promote the recycling of waste plastics. The system we developed mainly consists of sample feeder, automatic position recognition system, LIBS device, separator and control unit. By applying laser pulse on the target sample, characteristic spectral data can be obtained and analyzed by using CCD detectors. The obtained data was then treated by using a classifier, which was developed based on artificial intelligent algorithm. The separation tests on waste plastics also were carried out by using a lab-scale automatic sorting system and the test results will be discussed. The classification rate of the radial basis neural network (RBFNNs) classifier developed in this study was about > 97%. The recognition rate of the black plastic by types with the automatic sorting system was more than 94.0% and the sorting efficiency was more than 80.0%. Automatic sorting system based on LIBS technology is in its infant stage and it has a high potential for utilization in and outside Korea due to its excellent economic efficiency.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.1
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• pp.19-29
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• 2003

This paper presents a novel hybrid classifier for face recognition with artificially generated virtual training samples. We utilize both the nearest neighbor approach in feature angle space and a connectionist model to obtain a synergy effect by combining the results of two heterogeneous classifiers. First, a classifier called the nearest feature angle (NFA), based on angular information, finds the most similar feature to the query from a given training set. Second, a classifier has been developed based on the recall of stored frontal projection of the query feature. It uses a frontal recall network (FRN) that finds the most similar frontal one among the stored frontal feature set. For FRN, we used an ensemble neural network consisting of multiple multiplayer perceptrons (MLPs), each of which is trained independently to enhance generalization capability. Further, both classifiers used the virtual training set generated adaptively, according to the spatial distribution of each person's training samples. Finally, the results of the two classifiers are combined to comprise the best matching class, and a corresponding similarit measure is used to make the final decision. The proposed classifier achieved an average classification rate of 96.33% against a large group of different test sets of images, and its average error rate is 61.5% that of the nearest feature line (NFL) method, and achieves a more robust classification performance.

• Journal of Korean society of Dental Hygiene
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• v.17 no.5
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• pp.747-755
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• 2017

Objectives: The purpose of study is to investigate periodontal disease-related recognition and oral health-related behavior in orthodontic patients with fixed appliance. Methods: A self-reported questionnaire was completed by 286 orthodontic patients with fixed appliance in Gwangju, Jeonnam from September 1 to September 27, 2016. The questionnaire consisted of general characteristics (3 items), orthodontic related characteristics (3 items), knowledge of periodontal disease (3 items), and oral health-related behavior (4 items). The data were analyzed by frequency analysis, percentage and chi-square analysis using SPSS 21.0 program. Results: 62.8% had experiences of dental treatment and 67.5% had intention of involvement on incremental care program in orthodontic treatment periods. Accuracy rate of cause about periodontal disease was high in female and case of acquiring information experiences on periodontal disease (p<0.05). 67.2% performed correct toothbrushing for the management of periodontal disease in the experiences of acquiring information on periodontal disease in orthodontic treatment periods (p<0.05). The proportions of using interdental toothbrush and mouth rinsing solutions were high among those over 20 years old and students in the subjects (p<0.05). Conclusions:The accuracy rate were high in the answers about cause and management of periodontal disease in case of acquiring information experiences on periodontal disease in orthodontic treatment periods. Therefore, there is a need to further development and implementation of dental hygiene intervention program for periodontal disease care with fixed orthodontic appliances in that regard.

• Journal of Korean society of Dental Hygiene
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• v.18 no.4
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• pp.441-453
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• 2018

Objectives: The purpose of this study was to contribute to the development of basic data for establishing and expanding the dental health centers in the future by examining the awareness of the dental health center establishment and the level of dental health knowledge in the school. Methods: A survey was conducted from April 2017 with 336 students over 10 years old in South Korea to investigate the awareness of the dental health center establishment in the school and their dental health knowledge level. The results were summarized as follows. Statistical analysis of the collected data was performed using the SPSS WIN 21.0 statistical program. Results: It was necessary to establish the dental health centers in the school with their establishment rate of 82.8%, and the reason for the establishment of the dental health center was the possibility of regular checkup with the highest at 43.6%. In the school, 62.8% answered no experience of dental health education with the highest rate. Dental health education in the school was answered to be necessary with 91.0% the most prevalent one. Factors affecting the recognition of the expected effect of establishing the dental health center in the school, it is expected that if the establishment of the oral health center is needed and the oral health education is needed in the school, And the perception of the effect is increased. Conclusions: 91% of the respondents considered that dental health education was necessary and 83% answered they needed dental hygiene. However, only 37.2% of the students experienced dental health education in the school. Therefore, it is necessary to expand the dental health center establishment, and to develop the foundation of life dental health care through regular dental examination and proper brushing education.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.451-457
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• 2018

We propose an automatic detection and classification system of sewer damage database based on artificial intelligence and deep learning. In order to optimize the performance, we implemented a robust system against various environmental variations such as illumination and shadow changes. In our proposed system, a crack detection and damage classification method using a deep learning based Convolutional Neural Network (CNN) is implemented. For optimal results, 9,941 CCTV images with $256{\times}256$ pixel resolution were used for machine learning on the damaged area based on the CNN model. As a result, the recognition rate of 98.76% was obtained. Total of 646 images of $720{\times}480$ pixel resolution were extracted from various sewage DB for performance evaluation. Proposed system presents the optimal recognition rate for the automatic detection and classification of damage in the sewer DB constructed in various environments.