• Journal of Broadcast Engineering
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• v.28 no.2
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• pp.238-241
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• 2023

Recently deep learning models are widely used for various tasks such as facial recognition and face editing. Their training process often involves a dataset with imbalanced age distribution. It is because some age groups (teenagers and middle age) are more socially active and tends to have more data compared to the less socially active age groups (children and elderly). This imbalanced age distribution may negatively impact the deep learning training process or the model performance when tested against those age groups with less data. To this end, we propose an age-controllable face synthesis technique using a feature decomposition to classify age from facial images which can be utilized to synthesize novel data to balance out the age distribution. We perform extensive qualitative and quantitative evaluation on our proposed technique using the FFHQ dataset and we show that our method has better performance than existing method.

• Journal of Platform Technology
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• v.11 no.4
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• pp.3-12
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• 2023

In the post-COVID era, the importance of quarantine measures is greatly emphasized, and accordingly, research related to the detection of mask wearing conditions and prevention of other infectious diseases using deep learning is being conducted. However, research on the detection and tracking of visitors to cultural facilities to prevent the spread of diseases is equally important, so research on this should be conducted. In this paper, a convolutional neural network-based object detection model is trained through transfer learning using a pre-collected dataset. The weights of the trained detection model are then applied to a multi-object tracking model to monitor visitors. The visitor detection model demonstrates results with a precision of 96.3%, recall of 85.2%, and an F1-score of 90.4%. Quantitative results of the tracking model include a MOTA (Multiple Object Tracking Accuracy) of 65.6%, IDF1 (ID F1 Score) of 68.3%, and HOTA (Higher Order Tracking Accuracy) of 57.2%. Furthermore, a qualitative comparison with other multi-object tracking models showcased superior results for the model proposed in this paper. The research of this paper can be applied to the hygiene systems within cultural facilities in the post-COVID era.

• Proceedings of the ESK Conference
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• 1997.04a
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• pp.56-60
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• 1997

The study of human erroneous actions has traditionally taken place along two different lines of approach. One has been concerned with finding and explaining the causes of erroneous actions, such as studies in the psychology of "error". The other has been concerned with the qualitative and quantitative prediction of possible erroneous actions, exemplified by the field of human reliability analysis (HRA). Another distinction is also that the former approach has been dominated by an academic point of view, hence emphasising theories, models, and experiments, while the latter has been of a more pragmatic nature, hence putting greater emphasis on data and methods. We have been developing a method to make predictions about error modes. The input to the method is a detailed task description of a set of scenarios for an experiment. This description is then analysed to characterise thd nature of the individual task steps, as well as the conditions under which they must be carried out. The task steps are expressed in terms of a predefined set of cognitive activity types. Following that each task step is examined in terms of a systematic classification of possible error modes and the likely error modes are identified. This effectively constitutes a qualitative analysis of the possibilities for erroneous action in a given task. In order to evaluate the accuracy of the predictions, the data from a large scale experiment were analysed. The experiment used the full-scale nuclear power plant simulator in the Halden Man-Machine Systems Laboratory (HAMMLAB) and used six crews of systematic performance observations by experts using a pre-defined task description, as well as audio and video recordings. The purpose of the analysis was to determine how well the predictions matiched the actually observed performance failures. The results indicated a very acceptable rate of accuracy. The emphasis in this experiment has been to develop a practical method for qualitative performance prediction, i.e., a method that did not require too many resources or specialised human factors knowledge. If such methods are to become practical tools, it is important that they are valid, reliable, and robust.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.1
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• pp.23-30
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• 2022

Recently, there are various studies underway on the deep learning-used image matting methods. Even in the field of photogrammetry, a process of extracting information about relics from images photographed is essential to produce a high-quality realistic model. Such a process requires a great deal of time and manpower, so chroma-key has been used for extraction so far. This method is low in accuracy of sub-classification, however, it is difficult to apply the existing method to high-quality realistic models. Thus, this study attempted to remove background information from high-resolution relic images by using prior background information and trained learning data and evaluate both qualitative and quantitative results of the relic images extracted. As a result, this proposed method with FBA(manual trimap) showed quantitatively better results, and even in the qualitative evaluation, it was high in accuracy of classification around relics. Accordingly, this study confirmed the applicability of the proposed method in the indoor relic photography since it showed high accuracy and fast processing speed by acquiring prior background information when classifying high-resolution relic images.

• Earthquakes and Structures
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• v.22 no.5
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• pp.503-515
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• 2022

This paper presents the development of seismic fragility curves for a precast reinforced concrete bridge instrumented with a structural health monitoring (SHM) system. The bridge is located near an active seismic fault in the Dominican Republic (DR) and provides the only access to several local communities in the aftermath of a potential damaging earthquake; moreover, the sample bridge was designed with outdated building codes and uses structural detailing not adequate for structures in seismic regions. The bridge was instrumented with an SHM system to extract information about its state of structural integrity and estimate its seismic performance. The data obtained from the SHM system is integrated with structural models to develop a set of fragility curves to be used as a quantitative measure of the expected damage; the fragility curves provide an estimate of the probability that the structure will exceed different damage limit states as a function of an earthquake intensity measure. To obtain the fragility curves a digital twin of the bridge is developed combining a computational finite element model and the information extracted from the SHM system. The digital twin is used as a response prediction tool that minimizes modeling uncertainty, significantly improving the predicting capability of the model and the accuracy of the fragility curves. The digital twin was used to perform a nonlinear incremental dynamic analysis (IDA) with selected ground motions that are consistent with the seismic fault and site characteristics. The fragility curves show that for the maximum expected acceleration (with a 2% probability of exceedance in 50 years) the structure has a 62% probability of undergoing extensive damage. This is the first study presenting fragility curves for civil infrastructure in the DR and the proposed methodology can be extended to other structures to support disaster mitigation and post-disaster decision-making strategies.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.42.1-42.1
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• 2021

In this study, we present a visual explanation of a deep learning solar flare forecast model and its relationship to physical parameters of solar active regions (ARs). For this, we use full-disk magnetograms at 00:00 UT from the Solar and Heliospheric Observatory/Michelson Doppler Imager and the Solar Dynamics Observatory/Helioseismic and Magnetic Imager, physical parameters from the Space-weather HMI Active Region Patch (SHARP), and Geostationary Operational Environmental Satellite X-ray flare data. Our deep learning flare forecast model based on the Convolutional Neural Network (CNN) predicts "Yes" or "No" for the daily occurrence of C-, M-, and X-class flares. We interpret the model using two CNN attribution methods (guided backpropagation and Gradient-weighted Class Activation Mapping [Grad-CAM]) that provide quantitative information on explaining the model. We find that our deep learning flare forecasting model is intimately related to AR physical properties that have also been distinguished in previous studies as holding significant predictive ability. Major results of this study are as follows. First, we successfully apply our deep learning models to the forecast of daily solar flare occurrence with TSS = 0.65, without any preprocessing to extract features from data. Second, using the attribution methods, we find that the polarity inversion line is an important feature for the deep learning flare forecasting model. Third, the ARs with high Grad-CAM values produce more flares than those with low Grad-CAM values. Fourth, nine SHARP parameters such as total unsigned vertical current, total unsigned current helicity, total unsigned flux, and total photospheric magnetic free energy density are well correlated with Grad-CAM values.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.1
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• pp.1-12
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• 2023

It is very time-intensive to obtain data with labels on anomaly detection tasks for multivariate time series. Therefore, several studies have been conducted on unsupervised learning that does not require any labels. However, a well-done integrative survey has not been conducted on in-depth discussion of learning architecture and property for multivariate time series anomaly detection. This study aims to explore the characteristic of well-known architectures in anomaly detection of multivariate time series. Additionally, architecture was categorized by using top-down and bottom-up approaches. In order toconsider real-world anomaly detection situation, we trained models with dataset such as power grids or Cyber Physical Systems that contains realistic anomalies. From experimental results, we compared and analyzed the comprehensive performance of each architecture. Quantitative performance were measured using precision, recall, and F1 scores.

• The Journal of the Convergence on Culture Technology
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• v.9 no.2
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• pp.475-481
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• 2023

Softwares, especially like machine learning applications, affect human's life style tremendously. Accordingly, the importance of the cost model for softwares increases rapidly. As cost models, LOC(Line of Code) and M/M(Man-Month) estimates the quantitative aspects of the software. Differently from them, FP(Function Point) focuses on estimating the functional characteristics of software. FP is efficient in the aspect that it estimates qualitative characteristics. FP, however, has a limit for evaluating machine learning softwares because FP does not evaluate the critical factors of machine learning software. In this paper, we propose an extended function point(ExFP) that extends FP to adopt hyper parameter and the complexity of its optimization as the characteristics of the machine learning applications. In the evaluation reflecting the characteristics of machine learning applications. we reveals the effectiveness of the proposed ExFP.

• Analytical Science and Technology
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• v.20 no.4
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• pp.339-346
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• 2007

To search the ninhydrin derivatives that have high chromogenic and fluorogenic properties, molecular holographic quantitative structure property relationship (HQSPR) models on the reactivity between glycine and ninhydrin analogues as latent fingerprint detector were derived and investigated quantitatively. The ${\varepsilon}LUMO$ (e.v.) energy of ninhydrin molecule was an important factor to reactivity of ninhydrin. And, it is suggested that the nucleophilic reaction by orbital-controlled reaction from the frontier molecular orbital (FMO) interaction between glycine and ninhydrin derivatives was more superior than that of electrophilic reaction by charged controlled reaction. The analytical results in atomic contribution maps also shows that the reactivity of ninhydrin was increased by meta-substituents as strong electron withdrawing groups on the benzo ring. Therefore, it is sugested by HQSPR and QSPR model that the 5,6-dinitroninhydrin molecule would increase the reactivity as much as three times as compared to none substituted ninhydrin molecule.

• The Korean Journal of Physiology and Pharmacology
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• v.27 no.4
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• pp.333-344
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• 2023

Hepatocellular carcinoma (HCC) is a prevalent malignant tumor with high fatality. It has yet to be reported whether circ-SNX27 can affect the progression of HCC. This study attempted to analyze circ-SNX27's precise role and underlying mechanisms in HCC. HCC cell lines and tumor specimens from HCC patients were analyzed using quantitative real-time PCR and Western blotting to quantify the expressions of circ-SNX27, miR-375, and ribophorin I (RPN1). Cell invasion and cell counting kit 8 experiments were conducted for the evaluation of HCC cell invasion and proliferation. Caspase-3 Activity Assay Kit was utilized to gauge the caspase-3 activity. Luciferase reporter and RNA immunoprecipitation assays were executed to ascertain the relationships among miR-375, circ-SNX27, and RPN1. To determine how circ-SNX27 knockdown affects the growth of HCC xenografts in vivo, tumor-bearing mouse models were constructed. Elevated expressions of circ-SNX27 and RPN1 as well as a reduced miR-375 expression were observed among HCC cells and HCC patient tumor specimens. Knocking-down circ-SNX27 in HCC cells abated their proliferative and invasive abilities but raised their caspase-3 activity. Moreover, the poor levels of circ-SNX27 inhibited HCC tumor growth among the mice. Circ-SNX27 enhanced RPN1 by competitively binding with miR-375. Silencing miR-375 in HCC cells promoted their malignant phenotypes. Nonetheless, the promotive effect of miR375 silencing was reversible via the knockdown of circ-SNX27 or RPN1. This research demonstrated that circ-SNX27 accelerated the progression of HCC by modulating the miR-375/RPN1 axis. This is indicative of circ-SNX27's potential as a target for the treatment of HCC.