• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.13-21
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• 2020

The Android author identification study can be interpreted as a method for revealing the source in a narrow range, but if viewed in a wide range, it can be interpreted as a study to gain insight to identify similar works through known works. The problem found in the Android author identification study is that it is an important code on the Android system, but it is difficult to find the important feature of the author due to the meaningless codes. Due to this, legitimate codes or behaviors were also incorrectly defined as malicious codes. To solve this, we introduced the concept of survival network to solve the problem by removing the features found in various Android apps and surviving unique features defined by authors. We conducted an experiment comparing the proposed framework with a previous study. From the results of experiments on 440 authors' identified apps, we obtained a classification accuracy of up to 92.10%, and showed a difference of up to 3.47% from the previous study. It used a small amount of learning data, but because it used unique features without duplicate features for each author, it was considered that there was a difference from previous studies. In addition, even in comparative experiments with previous studies according to the feature definition method, the same accuracy can be shown with a small number of features, and this can be seen that continuously overlapping meaningless features can be managed through the concept of a survival network.

• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.33-39
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• 2021

Speech recognition technology is being combined with deep learning and is developing at a rapid pace. In particular, voice recognition services are connected to various devices such as artificial intelligence speakers, vehicle voice recognition, and smartphones, and voice recognition technology is being used in various places, not in specific areas of the industry. In this situation, research to meet high expectations for the technology is also being actively conducted. Among them, in the field of natural language processing (NLP), there is a need for research in the field of removing ambient noise or unnecessary voice signals that have a great influence on the speech recognition recognition rate. Many domestic and foreign companies are already using the latest AI technology for such research. Among them, research using a convolutional neural network algorithm (CNN) is being actively conducted. The purpose of this study is to determine the non-voice section from the user's speech section through the convolutional neural network. It collects the voice files (wav) of 5 speakers to generate learning data, and utilizes the convolutional neural network to determine the speech section and the non-voice section. A classification model for discriminating speech sections was created. Afterwards, an experiment was conducted to detect the non-speech section through the generated model, and as a result, an accuracy of 94% was obtained.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.3
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• pp.133-139
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• 2021

Currently, the process of constructing a high-definition road map has a high proportion of manual labor, so there are limitations in construction time and cost. Research to automate map production with high-definition road maps using artificial intelligence is being actively conducted, but since the construction of training data for the map construction is also done manually, there is a need to automatically build training data. Therefore, in this study, after converting to images using point clouds acquired by a mobile mapping system, the road marking areas were extracted through image reclassification and overlap analysis using thresholds. Then, a methodology was proposed to automatically construct training data for deep learning data for the high-definition road map through the classification of the polygon types in the extracted regions. As a result of training 2,764 lane data constructed through the proposed methodology on a deep learning-based PointNet model, the training accuracy was 99.977%, and as a result of predicting the lanes of three color types using the trained model, the accuracy was 99.566%. Therefore, it was found that the methodology proposed in this study can efficiently produce training data for high-definition road maps, and it is believed that the map production process of road markings can also be automated.

• The Journal of Korean Obstetrics and Gynecology
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• v.34 no.1
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• pp.1-14
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• 2021

Objectives: We studied for the adjustment of the patterns of 'The Diagnostic Tool for Climacteric and Postmenopausal Syndrome Pattern Identification (hereinafter CaPSPI)' (studyI) and the correlation between CaPSPI and Korean medicine doctors' diagnosis which was carried out without knowing the results of CaPSPI (studyII). Methods: The studyI followed the previous study method in 2018 (2018-3). The studyII was conducted from June 1, 2019 to July 10, 2020 with ◯◯ University Korean Medicine Hospital IRB's approval (2019-4). Doctors' diagnosis was conducted face-to-face with the subjects. Doctors' diagnosis was carried out based on the Kupperman's questionnaire, 'Diagnosis System of Oriental Medicine (hereinafter DSOM)' and four examinations (四診) records. The diagnosis was marked with 0 for 'no', 1 for 'somewhat', 2 for 'yes' and 3 for 'very yes'. The correlation between CaPSPI and the mean of doctors diagnostic scores were investigated statistically. Results: The studyI showed that heart-heat (心火) pattern was added. The Factor loading coefficient for heart-heat was 0.551 to 0.789, and the Cronbach's coefficient was 0.896. The studyII showed that the diagnosis (Kappa statistic) of two doctors showed statistically significant concordance (all eight patterns), with correlation of them were 0.3 or higher. And the correlation between the CaPSPI score and the mean of doctors' diagnostic score showed a statistically significant correlation, with liver qi depression (肝鬱) being the highest at 0.552 and dual deficiency of the heart-spleen (心脾兩虛) being the lowest at 0.301. Conclusions: Since the diagnosis results of CaPSPI showed a significant correlation with the diagnosis of Korean traditional medicine experts, it was believed that the CaPSPI results can be trusted and used for clinical purposes.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1523-1535
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• 2020

In order to produce crop information using remote sensing, we use classification and growth monitoring based on crop phenology. Therefore, time-series satellite images with a short period are required. However, there are limitations to acquiring time-series satellite data, so it is necessary to use fusion with other earth observation satellites. Before fusion of various satellite image data, it is necessary to overcome the inherent difference in radiometric characteristics of satellites. This study performed Korea Multi-Purpose Satellite-3 (KOMPSAT-3) cross calibration with Landsat-8 as the first step for fusion. Top of Atmosphere (TOA) Reflectance was compared by applying Spectral Band Adjustment Factor (SBAF) to each satellite using hyperspectral sensor band aggregation. As a result of cross calibration, KOMPSAT-3 and Landsat-8 satellites showed a difference in reflectance of less than 4% in Blue, Green, and Red bands, and 6% in NIR bands. KOMPSAT-3, without on-board calibrator, idicate lower radiometric stability compared to ladnsat-8. In the future, efforts are needed to produce normalized reflectance data through BRDF (Bidirectional reflectance distribution function) correction and SBAF application for spectral characteristics of agricultural land.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.577-582
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• 2021

Recently, with the development of AR, VR, and smart device technologies, the demand for services based on non-face-to-face environments is also increasing in the fitness industry. The non-face-to-face online home training service has the advantage of not being limited by time and place compared to the existing offline service. However, there are disadvantages including the absence of exercise equipment, difficulty in measuring the amount of exercise and chekcing whether the user maintains an accurate exercise posture or not. In this study, we develop a standard exercise program that can compensate for these shortcomings and propose a new non-face-to-face home training application by using a deep learning-based body posture estimation image processing algorithm. This application allows the user to directly watch and follow the trainer of the standard exercise program video, correct the user's own posture, and perform an accurate exercise. Furthermore, if the results of this study are customized according to their purpose, it will be possible to apply them to performances, films, club activities, and conferences

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1489-1503
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• 2022

Drone and sensor technologies are enabling digitalization of agricultural crop's growth information and accelerating the development of the precision agriculture. These technologies could be able to assess damage of crops when natural disaster occurs, and contribute to the scientification of the crop insurance assessment method, which is being conducted through field survey. This study was aimed to calculate lodged damage rate from the vegetation indices extracted by drone based RGB images for soybean. Support Vector Classifier (SVC) models were considered by adding vegetation indices to the Crop Surface Model (CSM) based lodged damage rate. Visible Atmospherically Resistant Index (VARI) and Green Red Vegetation Index (GRVI) based lodged damage rate classification were shown the highest accuracy score as 0.709 and 0.705 each. As a result of this study, it was confirmed that drone based RGB images can be used as a useful tool for estimating the rate of lodged damage. The result acquired from this study can be used to the satellite imagery like Sentinel-2 and RapidEye when the damages from the natural disasters occurred.

• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1871-1877
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• 2022

In the past, satellite development required enormous budget and time, so only some developed countries possessed satellites. However, with the recent emergence of low-budget satellites such as micro-satellites, many countries around the world are participating in satellite development. Low-orbit and geostationary-orbit satellites are used in various fields such as environment and weather monitoring, precise change detection, and disasters. Recently, it has been actively used for monitoring through deep learning-based object-of-interest detection. Until now, Korea has developed satellites for national demand according to the space development plan, and the satellite image obtained through this is used for various purpose in the public and private sectors. Interest in satellite image is continuously increasing in Korea, and various contests are being held to discover ideas for satellite image application and promote technology development. In this special issue, we would like to introduce the topics that participated in the recently held 2022 Satellite Information Application Contest and research on the processing and utilization of KOMPSAT image data.

• Analytical Science and Technology
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• v.35 no.3
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• pp.93-115
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• 2022

Potential impurities in pharmaceuticals could be produced during manufacture, distribution, and storage and affect quality and safety of pharmaceuticals. In particular, highly reactive impurities could result in carcinogenic (mutagenic) effects on human body. International Conference on Harmonisation (ICH) has provided M7(R1) guideline for "Assessment and Control of DNA Reactive (Mutagenic) Impurities in Pharmaceuticals to Limit Potential Carcinogenic Risk" and recommended an adoption of this guideline to the authorities. ICH M7(R1) guideline provides classification, accepted intakes, and controls of potential impurities in pharmaceuticals. However, since appropriate and unified analytical methods for impurities in pharmaceuticals have not been provided in this guideline, most potential impurities in pharmaceuticals are still difficult to manage and supervise by pharmaceutical companies and regulatory authorities, respectively. In this review, we briefly described definition of unintended mutagenic impurities, basic information in ICH M7(R1) guideline, and analytical methods to determine potential impurities. This review would be helpful to manage and supervise potential impurities in pharmaceuticals by pharmaceutical companies and regulatory authorities.

• Smart Media Journal
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• v.11 no.3
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• pp.54-61
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• 2022

With the increase in the types of information systems managed by public institutions and companies, a security certification system is being implemented in Korea to quickly respond to vulnerabilities that may arise due to insufficient security checks. The korea security evaluation system, such as ISMS-P, performs a systematic security evaluation for each category by dividing the categories for technical inspection items. NIST in the United States has developed SCAP that can create security checklists and automate vulnerability checks, and the security checklists used for SCAP can be written in OVAL. Each manufacturer prepares a security check list and shares it through the SCAP community, but it's difficult to use it in Korea because it is not categorized according to the korea security evaluation system. Therefore, in this paper, we present a mechanism to categorize the OVAL definition, which is an inspection item written in OVAL, to apply SCAP to the korea security evaluation system. It was shown that 189 out of 230 items of the Red Hat 8 STIG file could be applied to the korea security evaluation system, and the statistics of the categorized Redhat definition file could be analyzed to confirm the trend of system vulnerabilities by category.