• The Journal of Society for e-Business Studies
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• v.24 no.2
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• pp.1-14
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• 2019

Because of the characteristics of game software, it is important to quickly identify and reflect users' needs into game software after its launch. However, most sites such as the Google Play Store, where users can download games and post reviews, provide only very limited and ambiguous classification categories for game reviews. Therefore, in this paper, we develop an automatic classification system for game reviews that categorizes reviews into categories that are clearer and more useful for game providers. The developed system converts words in reviews into vectors using word2vec, which is a representative word embedding model, and classifies reviews into the most relevant categories by measuring the similarity between those vectors and each category. Especially, in order to choose the best similarity measure that directly affects the classification performance of the system, we have compared the performance of three representative similarity measures, the Euclidean similarity, cosine similarity, and the extended Jaccard similarity, in a real environment. Furthermore, to allow a review to be classified into multiple categories, we use a threshold-based multi-category classification method. Through experiments on real reviews collected from Google Play Store, we have confirmed that the system achieved up to 95% accuracy.

• Journal of Convergence for Information Technology
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• v.9 no.6
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• pp.194-201
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• 2019

This cohort study aimed to identify the effects of daily PM10 exposure on the hospital admission of patients with respiratory diseases, during the nine-year period (2002-2010), in Seoul, Korea. The research subjects were 13,974 patients who had been hospitalized with respiratory diseases, including chronic obstructive pulmonary disease (COPD), asthma, and pneumonia. During the follow-up period, an increase of 10 ug/m3 in PM10 under the threshold of 50 ug/m3 of PM10 led to hospital admission in 1.38% of the age group younger than 15 years, 1.62% in those 65 years or older, 2.87% in patients 75 years or older and in 1.50% of pneumonia patients, 1.51% of COPD patients, and 1.55% of pneumonia and asthma patients. Under the threshold of 80 ug/m3 of PM10, there was a 3.71% increase in new patients admitted in the age group 65 years or older and 4.25% in those at least 75 years old. Our study found that high PM10 was associated with increased risk of admission of respiratory patients, especially in the elderly. People who already have a respiratory disease should refrain from exposure to particulate matter when there is a high concentration of PM10, especially older patients.

• Journal of Digital Convergence
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• v.19 no.2
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• pp.245-251
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• 2021

As the trend of steadily increasing the number of single or double household, there is a growing demand to see who is the outsider visiting the home during the free time. Various models of face recognition technology have been proposed through many studies, and Harr Cascade of OpenCV and Hog of Dlib are representative open source models. Among the two modes, Dlib's Hog has strengths in front of the indoor and at a limited distance, which is the focus of this study. In this paper, a face recognition visitor access system based on Dlib was designed and implemented. The whole system consists of a front module, a server module, and a mobile module, and in detail, it includes face registration, face recognition, real-time visitor verification and remote control, and video storage functions. The Precision, Specificity, and Accuracy according to the change of the distance threshold value were calculated using the error matrix with the photos published on the Internet, and compared with the results of previous studies. As a result of the experiment, it was confirmed that the implemented system was operating normally, and the result was confirmed to be similar to that reported by Dlib.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.13 no.6
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• pp.33-43
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• 2003

In this paper, we proposed a real-time watermarking algorithm to be combined and to work with a DWT(Discrete Wavelet Transform)-based image compressor. To reduce the amount of computation in selecting the watermarking positions, the proposed algorithm uses a pre-established look-up table for critical values, which was established statistically by computing the correlation according to the energy values of the corresponding wavelet coefficients. That is, watermark is embedded into the coefficients whose values are greater than the critical value in the look-up table which is searched on the basis of the energy values of the corresponding level-1 subband coefficients. Therefore, the proposed algorithm can operate in a real-time because the watermarking process operates in parallel with the compression procession without affecting the operation of the image compression. Also it improved the property of losing the watermark and the efficiency of image compression by watermark inserting, which results from the quantization and Huffman-Coding during the image compression. Visual recognizable patterns such as binary image were used as a watermark The experimental results showed that the proposed algorithm satisfied the properties of robustness and imperceptibility that are the major conditions of watermarking.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.2
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• pp.78-82
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• 2022

Phenology determines the timing of crop development, and the timing of phenological events is strongly influenced by the temperature during the growing season. In process-based model, leaf area is simulated dynamically by coupling of morphology and phenology module. Therefore, the prediction of leaf appearance rate and final leaf number affects the performance of whole crop model. The dataset for the model equation was collected from SPA R chambers with five different temperature treatments. Beta distribution function (proposed by Yan and Hunt (1999)) was used for describing the leaf appearance rate as a function of temperature. The optimum temperature and the critical value were estimated to be 26.0℃ and 35.3℃, respectively. For evaluation of the model, the accumulated number of onion leaves observed in a temperature gradient chamber was compared with model estimates. The model estimate is the result of accumulating the daily increase in the number of onion leaves obtained by inputting the daily mean temperature during the growing season into the temperature model. In this study, the coefficient of determination (R²) and RMSE value of the model were 0.95 and 0.89, respectively.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1837-1846
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• 2022

The annual increase in chemical accidents is causing damage to life and the environment due to the spread and residual of substances. Environmental damage investigation is more difficult to determine the geographical scope and timing than human damage investigation. Considering the reality that there is a lack of professional investigation personnel, it is urgent to develop an efficient quantitative evaluation method. In order to improve this situation, this paper conducted a chemical accidents investigation using unmanned aerial vehicles(UAV) equipped with various sensors. The damaged area was calculated by Ortho-image and strength of agreement was calculated using the normalized difference vegetation index image. As a result, the Cohen's Kappa coefficient was 0.649 (threshold 0.7). However, there is a limitation in that analysis has been performed based on the pixel of the normalized difference vegetation index. Therefore, there is a need for a chemical accident investigation plan that overcomes the limitations.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.511-521
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• 2022

Cloud detection and removal from satellite images is an essential process for topographic observation and analysis. Threshold-based cloud detection techniques show stable performance because they detect using the physical characteristics of clouds, but they have the disadvantage of requiring all channels' images and long computational time. Cloud detection techniques using deep learning, which have been studied recently, show short computational time and excellent performance even using only four or less channel (RGB, NIR) images. In this paper, we confirm the performance dependence of the deep learning network according to the heterogeneous learning dataset with different resolutions. The DeepLabV3+ network was improved so that channel features of cloud detection were extracted and learned with two published heterogeneous datasets and mixed data respectively. As a result of the experiment, clouds' Jaccard index was low in a network that learned with different kind of images from test images. However, clouds' Jaccard index was high in a network learned with mixed data that added some of the same kind of test data. Clouds are not structured in a shape, so reflecting channel features in learning is more effective in cloud detection than spatial features. It is necessary to learn channel features of each satellite sensors for cloud detection. Therefore, cloud detection of heterogeneous sensors with different resolutions is very dependent on the learning dataset.

• The Journal of Engineering Geology
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• v.32 no.4
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• pp.643-659
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• 2022

Geologic sequestration technologies such as CCS (carbon capture and storage), EGS (enhanced geothermal systems), and EOR (enhanced oil recovery) have been widely implemented in recent years, prompting evaluation of the mechanical stability of storage sites. As fluid injection can stimulate mechanical instability in storage layers by perturbing the stress state and pore pressure, poroelastic models considering various injection scenarios are required. In this study, we calculate the pore pressure, stress distribution, and vertical displacement along a surface using commercial finite element software (COMSOL); fault slips are subsequently simulated using PyLith, an open-source finite element software. The displacement fields, are obtained from PyLith is transferred back to COMSOL to determine changes in coseismic stresses and surface displacements. Our sequential use of COMSOL-PyLith-COMSOL for poroelastic modeling of fluid-injection and induced-earthquakes reveals large variations of pore pressure, vertical displacement, and Coulomb failure stress change during injection periods. On the other hand, the residual stress diffuses into the remote field after injection stops. This flow pattern suggests the necessity of numerical modeling and long-term monitoring, even after injection has stopped. We found that the time at which the Coulomb failure stress reaches the critical point greatly varies with the hydraulic and poroelastic properties (e.g., permeability and Biot-Willis coefficient) of the fault and injection layer. We suggest that an understanding of the detailed physical properties of the surrounding layer is important in selecting the injection site. Our numerical results showing the surface displacement and deviatoric stress distribution with different amounts of fault slip highlight the need to test more variable fault slip scenarios.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.99-107
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• 2021

We live in a myriad of data. Various data are created in all situations in which we work, and we discover the meaning of data through big data technology. Many efforts are underway to find meaningful data. This paper introduces an analysis technique that enables humans to make better choices through the trend and prediction of time series data as a principal component analysis technique. Principal component analysis constructs covariance through the input data and presents eigenvectors and eigenvalues that can infer the direction of the data. The proposed method computes a reference axis in a time series data set having a similar directionality. It predicts the directionality of data in the next section through the angle between the directionality of each time series data constituting the data set and the reference axis. In this paper, we compare and verify the accuracy of the proposed algorithm with LSTM (Long Short-Term Memory) through cryptocurrency trends. As a result of comparative verification, the proposed method recorded relatively few transactions and high returns(112%) compared to LSTM in data with high volatility. It can mean that the signal was analyzed and predicted relatively accurately, and it is expected that better results can be derived through a more accurate threshold setting.

• Korean Journal of Clinical Laboratory Science
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• v.54 no.1
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• pp.28-37
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• 2022

Pregnancy requires an important interpretation of thyroid function tests. The presence of anti-thyroid antibodies and viral infectious agents affect the health of both the fetus and the mother. Hence, a selective evaluation of thyroid function in pregnancy is required. This study is a retrospective cross-sectional survey to examine the correlation between thyroid hormones and viral infections during pregnancy. The results showed that the triiodothyronine (T3) decreased with increasing age, especially in the hepatitis C virus (HCV)-positive group (P<0.01). In addition, although negative for the human immunodeficiency virus (HIV), thyroxine (FT4) showed a significant increase in near-threshold or twin pregnant women (P<0.05). The thyroid stimulating hormone (TSH) was highly distributed at the age of 30, and there was no statistically significant correlation with other viral infection factors. In addition, as a result of dividing and analyzing the result of TSH by the quantiles, FT4 and T3 showed a positive correlation but showed a negative correlation with TSH (P<0.05). Therefore, the evaluation of prenatal thyroid screening during pregnancy and viral infection factors should reflect the time of pregnancy, exposure to infection, and the quantitative values. Adequate thyroid hormone and viral infections availability is important for an uncomplicated pregnancy and optimal fetal development.