• Journal of Technologic Dentistry
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• v.42 no.1
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• pp.1-8
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• 2020

Purpose: Among the virtual teeth generated by Deep Convolutional Generative Adversarial Networks (DCGAN), the optimal data was analyzed for the number of learning. Methods: We extracted 50 mandibular first molar occlusal surfaces and trained 4,000 epoch with DCGAN. The learning screen was saved every 50 times and evaluated on a Likert 5-point scale according to five classification criteria. Results were analyzed by one-way ANOVA and tukey HSD post hoc analysis (α = 0.05). Results: It was the highest with 83.90±6.32 in the number of group3 (2,050-3,000) learning and statistically significant in the group1 (50-1,000) and the group2 (1,050-2,000). Conclusion: Since there is a difference in the optimal virtual tooth generation according to the number of learning, it is necessary to analyze the learning frequency section in various ways.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.4
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• pp.845-852
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• 2000

In the 21s1 century, it may be possible to offer comprehensive service by integrating all communication media under the development of digital technology without the classification of broadcasting and communication. In addition, it will be begun to perform HDTV broadcasting by focusing on the Metropolitan area in 2001yr and will permit over 100 channels for satellite communication, thus multi-channel TV broadcasting age has come in Korea. Therefore, this study is to identify the future environment of TV broadcasting and service area in ISDB age and to present new interactive type publication satellite TV transmission system, which provides customizing type digital screen book publication, by analyzing satellite broadcasting technology to create Korean cultural area around the Korean peninsula.

• Journal of Information Processing Systems
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• v.16 no.3
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• pp.718-732
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• 2020

Conducting sentiment analysis and opinion mining are challenging tasks in natural language processing. Many of the sentiment analysis and opinion mining applications focus on product reviews, social media reviews, forums and microblogs whose reviews are topic-similar and opinion-rich. In this paper, we try to analyze the sentiments of sentences from online webcast reviews that scroll across the screen, which we call live barrages. Contrary to social media comments or product reviews, the topics in live barrages are more fragmented, and there are plenty of invalid comments that we must remove in the preprocessing phase. To extract evaluative sentiment sentences, we proposed a novel approach that clusters the barrages from the same commenter to solve the problem of scattering the information for each barrage. The method developed in this paper contains two subtasks: in the data preprocessing phase, we cluster the sentences from the same commenter and remove unavailable sentences; and we use a semi-supervised machine learning approach, the naïve Bayes algorithm, to analyze the sentiment of the barrage. According to our experimental results, this method shows that it performs well in analyzing the sentiment of online webcast barrages.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.4076-4092
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• 2019

Nowadays, most users access internet through mobile applications. The common way to authenticate users through websites forms is using passwords; while they are efficient procedures, they are subject to guessed or forgotten and many other problems. Additional multi modal authentication procedures are needed to improve the security. Behavioral authentication is a way to authenticate people based on their typing behavior. It is used as a second factor authentication technique beside the passwords that will strength the authentication effectively. Keystroke dynamic rhythm is one of these behavioral authentication methods. Keystroke dynamics relies on a combination of features that are extracted and processed from typing behavior of users on the touched screen and smart mobile users. This Research presents a novel analysis in the keystroke dynamic authentication field using two features categories: timing and no timing combined features. The proposed model achieved lower error rate of false acceptance rate with 0.1%, false rejection rate with 0.8%, and equal error rate with 0.45%. A comparison in the performance measures is also given for multiple datasets collected in purpose to this research.

• Journal of Korea Multimedia Society
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• v.21 no.11
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• pp.1305-1316
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• 2018

Graphical user interface(GUI) has a very important role to interact with software users. However, designing and coding of GUI are tedious and pain taking processes. In many studies, the researchers are trying to convert GUI elements or widgets to code or describe formally their structures by help of domain knowledge of stochastic methods. In this paper, we propose the GUI elements detection approach based on object detection strategy using deep neural networks(DNN). Object detection with DNN is the approach that integrates localization and classification techniques. From the experimental result, if we selected the appropriate object detection model, the results can be used for automatic code generation from the sketch or capture images. The successful GUI elements detection can describe the objects as hierarchical structures of elements and transform their information to appropriate code by object description translator that will be studied at future.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.56 no.1
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• pp.72-79
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• 2011

Here we report a simple screening system using hematoxylin staining (HS) of the root apex. It allowed rapid classification into different aluminum (Al) tolerance from 65 cultivars within one week. Using this system, we selected the most Al-tolerant (Jayae-2) and-sensitive (Pum-2) barley (Hordeum vulgare L.) The results show that the different responses in Al-induced growth inhibition, Al accumulation and expression of plasma membrane (PM) $H^+$-ATPase in root apices of selected two cultivars. It showed strongly Al-induced growth inhibition in a dosedependant manner only in Pum-2 but not in Jayae-2. Aluminum accumulation in root apices (10 mm) was significantly higher in Pum-2 only. The $H^+$-ATPase expression of PM vesicles by western blotting was decreased in Pum-2 but not in Jayae-2 treated with $20{\mu}M$ Al for 24 h. These finding indicate to screen from our system is rapid and reliable and to sustain the expression of PM $H^+$-ATPase at translational level is an important role in root growth as affected by Al.

• Science of Emotion and Sensibility
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• v.23 no.4
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• pp.21-32
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• 2020

The perception of a fashion product may vary depending on the texture and color of its material. Additionally, the product may appear differently in person versus on a digital screen. Therefore, in the present study, we sought to investigate the differences in visual sensibility evaluation between materials in person and on digital screens. In this study, three pairs of visual sensibility adjectives were tested for 60 samples selected as fashion materials. Fashion materials were divided into colors, embossings, and visual clarity categories. Results showed that each color had the same sense during in-person and digital evaluation. In terms of visual sensibility according to embossing, both in-person and digital evaluations of materials with embossings were found to have the same visual sense, whereas those without embossings looked different between in-person and digital evaluations. Assessments based on visual classification showed that both in-person and digital evaluations had the same sensibility. This study is meaningful in suggesting that when evaluating the visual sense of fashion material, the sensation for the digital screen versus in person may be different in some cases.

• The Journal of Korean Medicine
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• v.41 no.2
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• pp.58-79
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• 2020

Objectives: The purpose of this study is to predict the weight loss by applying machine learning using real-world clinical data from overweight and obese adults on weight loss program in 4 Korean Medicine obesity clinics. Methods: From January, 2017 to May, 2019, we collected data from overweight and obese adults (BMI≥23 kg/m2) who registered for a 3-month Gamitaeeumjowi-tang prescription program. Predictive analysis was conducted at the time of three prescriptions, and the expected reduced rate and reduced weight at the next order of prescription were predicted as binary classification (classification benchmark: highest quartile, median, lowest quartile). For the median, further analysis was conducted after using the variable selection method. The data set for each analysis was 25,988 in the first, 6,304 in the second, and 833 in the third. 5-fold cross validation was used to prevent overfitting. Results: Prediction accuracy was increased from 1^st to 2^nd and 3^rd analysis. After selecting the variables based on the median, artificial neural network showed the highest accuracy in 1^st (54.69%), 2^nd (73.52%), and 3^rd (81.88%) prediction analysis based on reduced rate. The prediction performance was additionally confirmed through AUC, Random Forest showed the highest in 1^st (0.640), 2^nd (0.816), and 3^rd (0.939) prediction analysis based on reduced weight. Conclusions: The prediction of weight loss by applying machine learning showed that the accuracy was improved by using the initial weight loss information. There is a possibility that it can be used to screen patients who need intensive intervention when expected weight loss is low.

• Journal of the Ergonomics Society of Korea
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• v.31 no.3
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• pp.445-453
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• 2012

The aim of this paper was to prove that if the risk level in combined tasks was improved through evaluation of postural load of liquid weight measurement process, the workload level and ratio of exposure time would be changed, and the time of process would be seen concurrently. Background: According to results of epidemiological studies conducted by Korea Occupational Safety & Health Agency, 122 musculoskeletal disorders occurred during 1992 to 2008, in which manufacturing industry covers 96(78.7%) of total. However, this is an insufficient level and only occupies 39% based on the South Korea's manufacturing standard industrial classification(246 industries). Method: Firstly, the number of batches weighed on one day(460min) was investigated based on the work performed and Weight measured weekly. VCR recording was taken based on the level of liquid ingredients prescribed for 1batch using the Camcorder. After dividing a 356 sec video into 1 sec using the screen capture function in Gom player, the job classification was performed by analyzing the change of working postures, which revealed 148 working postures. Time measurement was decided by time of the postures was being maintained. Then, the REBA analysis was performed for the working postures. The ratio of Exposure time was calculated based on the measurement time and REBA Score. In addition, the recommendations were designed and implementation was carried out for the working postures with REBA Score higher than 3. Finally, after the intervention, REBA measurement, time measurement, and ratio of exposure time were calculated for the comparison of works before and after improvement. Results: The number of work elements was decreased by 30.4% from 148 to 103 after improvement. The results of time measurement showed that the time was reduced by 46.3% from 356 sec to 191 sec. And the ratio of exposure time was also improved by 52.1% from 0% to 52.1% after improvement. Conclusion: The reduction of time was found to improve the productivity of management. Furthermore, because the reduction of ratio of exposure time and the improvement of workload level are the improvement of discomfort, it would contribute to the improvement of the worker's psychological working posture. Application: These results would contribute to musculoskeletal disease prevention and management performance. Further studies for other industries would be needed based on this case study.

• Korean Journal of Remote Sensing
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• v.25 no.5
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• pp.411-421
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• 2009

This study refers to develop a semi-automatic extraction of agricultural land use and vegetation information using high resolution satellite images. Data of IKONOS-2 satellite images (May 25 of 2001, December 25 of 2001, and October 23 of 2003), QuickBird-2 satellite images (May 1 of 2006 and November 17 of 2004) and KOMPSAT-2 satellite image (September 17 of 2007) which resemble with the spatial resolution and spectral characteristics of KOMPSAT-3 were used. The precise agricultural land use classification was tried using ISODATA unsupervised classification technique, and the result was compared with on-screen digitizing land use accompanying with field investigation. For the extraction of crop growth information, three crops of paddy, com and red pepper were selected, and the spectral characteristics were collected during each growing period using ground spectroradiometer. The vegetation indices viz. RVI, NDVI, ARVI, and SAVI for the crops were evaluated. The evaluation process was developed using the ERDAS IMAGINE Spatial Modeler Tool.