• Journal of Veterinary Science
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• v.22 no.1
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• pp.2.1-2.10
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• 2021

Background: Diseases related to cerebrospinal fluid flow, such as hydrocephalus, syringomyelia, and Chiari malformation, are often found in small dogs. Although studies in human medicine have revealed a correlation with cerebrospinal fluid flow in these diseases by magnetic resonance imaging, there is little information and no standard data for normal dogs. Objectives: The purpose of this study was to obtain cerebrospinal fluid flow velocity data from the cerebral aqueduct and subarachnoid space at the foramen magnum in healthy beagle dogs. Methods: Six healthy beagle dogs were used in this experimental study. The dogs underwent phase-contrast and time-spatial labeling inversion pulse magnetic resonance imaging. Flow rate variations in the cerebrospinal fluid were observed using sagittal time-spatial labeling inversion pulse images. The pattern and velocity of cerebrospinal fluid flow were assessed using phase-contrast magnetic resonance imaging within the subarachnoid space at the foramen magnum level and the cerebral aqueduct. Results: In the ventral aspect of the subarachnoid space and cerebral aqueduct, the cerebrospinal fluid was characterized by a bidirectional flow throughout the cardiac cycle. The mean ± SD peak velocities through the ventral and dorsal aspects of the subarachnoid space and the cerebral aqueduct were 1.39 ± 0.13, 0.32 ± 0.12, and 0.76 ± 0.43 cm/s, respectively. Conclusions: Noninvasive visualization of cerebrospinal fluid flow movement with magnetic resonance imaging was feasible, and a reference dataset of cerebrospinal fluid flow peak velocities was obtained through the cervical subarachnoid space and cerebral aqueduct in healthy dogs.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.45-53
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• 2020

The purpose of this paper is to reduce the ratio of the patient accidents that may occur in nursing hospitals. In other words, it determines whether the person approaching the dangerous area is a elderly (patient uniform) group or a practitioner(Casual Clothing) group, based on the clothing displayed by CCTV. We collected the basic learning data from web crawling techniques and nursing hospitals. Then model training data was created with Image Generator and Labeling program. Due to the limited performance of CCTV, it is difficult to create a good model with both high accuracy and speed. Therefore, we implemented the ResNet model with relatively excellent accuracy and the YOLO3 model with relatively excellent speed. Then we wanted to allow nursing hospitals to choose a model that they wanted. As a result of the study, we implemented a model that can distinguish patient and casual clothes with appropriate accuracy. Therefore, it is believed that it will contribute to the reduction of safety accidents in nursing hospitals by preventing the elderly from accessing the danger zone.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.111-118
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• 2022

In computer vision research, the two-dimensional human pose is a very extensive research direction, especially in pose tracking and behavior recognition, which has very important research significance. The acquisition of human pose targets, which is essentially the study of how to accurately identify human targets from pictures, is of great research significance and has been a hot research topic of great interest in recent years. Human pose recognition is used in artificial intelligence on the one hand and in daily life on the other. The excellent effect of pose recognition is mainly determined by the success rate and the accuracy of the recognition process, so it reflects the importance of human pose recognition in terms of recognition rate. In this human body gesture recognition, the human body is divided into 17 key points for labeling. Not only that but also the key points are segmented to ensure the accuracy of the labeling information. In the recognition design, use the comprehensive data set MS COCO for deep learning to design a neural network model to train a large number of samples, from simple step-by-step to efficient training, so that a good accuracy rate can be obtained.

• Journal of IKEEE
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• v.26 no.4
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• pp.647-652
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• 2022

In this paper, we propose a prediction system for skin pore labeling based on a CNN(Convolution Neural Network) model, where a data set is constructed by processing skin images taken by users, and a pore feature image is generated by the proposed image processing algorithm. The skin image data set was labeled for pore characteristics based on the visual classification criteria of skin beauty experts. The proposed image processing algorithm was applied to generate pore feature images from skin images and to train a CNN model that predicts pore feature ratings. The prediction results with pore features by the proposed CNN model is similar to experts visual classification results, where less learning time and higher prediction results were obtained than the results by the comparison model (Resnet-50). In this paper, we describe the proposed image processing algorithm and CNN model, the results of the prediction system and future research plans.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.696-701
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• 2002

This paper describes some results of change detection in Tokyo metropolitan area, Japan , using the Landsat TM data, and methods to quantify the ground cover classes. The changes are analyzed using the measures of not only conventional spectral classes but also a set of landscape indices to describe spatial properties of ground cove types using fractal dimension of objects, entropy in the specific windows defining the neighbors of focusing locations. In order eliminate the seasonal radiometric effects on TM data, an automated class labeling method is also attempted. Urban areas are also delineated automatically by defining the boundaries of the urban area. These procedures for urban change detection were implemented by the unified image computing methods proposed by the author, they can be automated in coherent and systematic ways, and it is anticipated to automate the whole procedures. The results of this analysis suggest that Tokyo metropolitan area was extended to the suburban areas along the new transportation networks and the high density area of Tokyo were also very much extended during the period between 1985 and 1995.

• Progress in Medical Physics
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• v.30 no.2
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• pp.39-48
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• 2019

Deep learning has been applied to various medical data. In particular, current deep learning models exhibit remarkable performance at specific tasks, sometimes offering higher accuracy than that of experts for discriminating specific diseases from medical images. The current status of deep learning applications to molecular imaging can be divided into a few subtypes in terms of their purposes: differential diagnostic classification, enhancement of image acquisition, and image-based quantification. As functional and pathophysiologic information is key to molecular imaging, this review will emphasize the need for accurate biomarker acquisition by deep learning in molecular imaging. Furthermore, this review addresses practical issues that include clinical validation, data distribution, labeling issues, and harmonization to achieve clinically feasible deep learning models. Eventually, deep learning will enhance the role of theranostics, which aims at precision targeting of pathophysiology by maximizing molecular imaging functional information.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1825-1844
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• 2019

Political campaigns circulate manipulative opinions in online communities to implant false beliefs and eventually win elections. Not only is this type of manipulation unfair, it also has long-lasting negative impacts on people's lives. Existing tools detect political manipulation based on a supervised classifier, which is accurate when trained with large labeled data. However, preparing this data becomes an excessive burden and must be repeated often to reflect changing manipulation tactics. We propose a practical detection system that requires moderate groundwork to achieve a sufficient level of accuracy. The proposed system groups opinions with similar properties into clusters, and then labels a few opinions from each cluster to build a classifier. It also models each opinion with features deduced from raw data with no additional processing. To validate the system, we collected over a million opinions during three nation-wide campaigns in South Korea. The system reduced groundwork from 200K to nearly 200 labeling tasks, and correctly identified over 90% of manipulative opinions. The system also effectively identified transitions in manipulative tactics over time. We suggest that online communities perform periodic audits using the proposed system to highlight manipulative opinions and emerging tactics.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.6
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• pp.527-536
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• 2016

This study aims to generate baseline data for vitalizing the sales of functional cosmetics through an Importance-Performance Analysis (IPA) of the selection attributes of functional cosmetics. From the analysis of consumers' selection criteria, the study will assist functional cosmetics companies in reflecting consumer demands and therefore securing competitiveness. For this, general consumers aged over 20 years were surveyed for 5 weeks from Feb 23 through Mar 30, 2015, and 447 empirical data (response rate 88.9%) were processed through SPSS WIN 21.0 program for analysis. To conduct gender difference analysis on the IPA of the selection attributes of functional cosmetics, 17 selection attributes were categorized into 4 factors: functionality, labeling, popularity, and product. Cronbach's alpha for all factors was 0.5, proving the internal consistency and reliability of the survey. The survey results showed that while the entire average came out significantly higher for females (5.89/7points) than for males (5.66/7points) (p<0.001), the selection attributes 'anti-wrinkling', 'whitening function', 'functionality', 'expiration date', 'full ingredient labeling system' and 'various promotional events' showed significant gender differences. IPA results pertaining to gender showed 'price', 'functionality', 'spreadability' and 'full ingredient labeling system' as 2nd quadrant attributes, whereas female consumers selected 'price', 'whitening function', 'anti-wrinkling', 'functionality' and 'full ingredient labeling system' as attributes. Results show that businesses in the field of cosmetics and related areas need to prioritize improving the following factors that received low satisfaction from all consumers: 'price', 'functionality', and 'total labeling.' In particular, the 'price' aspects are considered to require reasonable and affordable pricing.

• The Journal of the Acoustical Society of Korea
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• v.15 no.2
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• pp.61-71
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• 1996

In this paper, dialogic triphone data base construction for triphone synthesis system is discussed. Particularly, in this work, dialogic speech data is collected from the broadcast media, and three different transcription steps are taken. Total 10 hours of speech data are collected. Among them, six hours of speech data are used for the triphone data base construction, and the rest four hours of data are reserved. Dialogic speech data base construction is far different from the reciting speech data base construction. This paper describes various steps that necessary for the dialogic triphone data base construction from collecting speech data to triphone unit labeling.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.3
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• pp.129-138
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• 2022

Collecting and labeling sufficient training data, which is essential to deep learning-based visual inspection, is difficult for manufacturers to perform because it is very expensive. This paper presents a steel plate surface defect detection system with industrial-grade detection performance by training a small amount of steel plate surface images consisting of labeled and non-labeled data. To overcome the problem of lack of training data, we propose two data augmentation techniques: program-based augmentation, which generates defect images in a geometric way, and generative model-based augmentation, which learns the distribution of labeled data. We also propose a 4-step semi-supervised learning using pseudo labels and consistency training with fixed-size augmentation in order to utilize unlabeled data for training. The proposed technique obtained about 99% defect detection performance for four defect types by using 100 real images including labeled and unlabeled data.