• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.29-35
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• 2022

UV rays have beneficial or harmful effects on the human body depending on the degree of exposure. An accurate UV information is required for proper exposure to UV rays per individual. The UV rays' information is provided by the Korea Meteorological Administration as one component of daily weather information in Korea. However, it does not provide an accurate UVI at the user's location based on the region's Ultraviolet index. Some operate measuring instrument to obtain an accurate UVI, but it would be costly and inconvenient. Studies which assumed the UVI through environmental factors such as solar radiation and amount of cloud have been introduced, but those studies also could not provide service to individual. Therefore, this paper proposes a deep learning model to calculate UVI using solar object information and sunlight characteristics to provide an accurate UVI at individual location. After selecting the factors, which were considered as highly correlated with UVI such as location and size and illuminance of sun and which were obtained through the analysis of sky images and solar characteristics data, a data set for DNN model was constructed. A DNN model that calculates the UVI was finally realized by entering the solar object information and sunlight characteristics extracted through Mask R-CNN. In consideration of the domestic UVI recommendation standards, it was possible to accurately calculate UVI within the range of MAE 0.26 compared to the standard equipment in the performance evaluation for days with UVI above and below 8.

• Journal of Korea Multimedia Society
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• v.24 no.10
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• pp.1414-1424
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• 2021

In this paper, the importance of input factors of a DNN (Deep Neural Network) PM_2.5 forecasting model using LRP(Layer-wise Relevance Propagation) is analyzed, and forecasting performance is improved. Input factor importance analysis is performed by dividing the learning data into time and PM_2.5 concentration. As a result, in the low concentration patterns, the importance of weather factors such as temperature, atmospheric pressure, and solar radiation is high, and in the high concentration patterns, the importance of air quality factors such as PM_2.5, CO, and NO₂ is high. As a result of analysis by time, the importance of the measurement factors is high in the case of the forecast for the day, and the importance of the forecast factors increases in the forecast for tomorrow and the day after tomorrow. In addition, date, temperature, humidity, and atmospheric pressure all show high importance regardless of time and concentration. Based on the importance of these factors, the LRP_DNN prediction model is developed. As a result, the ACC(accuracy) and POD(probability of detection) are improved by up to 5%, and the FAR(false alarm rate) is improved by up to 9% compared to the previous DNN model.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.207-221
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• 2023

This study suggests deep neural network models for estimating air temperature with Level 1B (L1B) datasets of GEO-KOMPSAT-2A (GK-2A). The temperature at 1.5 m above the ground impact not only daily life but also weather warnings such as cold and heat waves. There are many studies to assume the air temperature from the land surface temperature (LST) retrieved from satellites because the air temperature has a strong relationship with the LST. However, an algorithm of the LST, Level 2 output of GK-2A, works only clear sky pixels. To overcome the cloud effects, we apply a deep neural network (DNN) model to assume the air temperature with L1B calibrated for radiometric and geometrics from raw satellite data and compare the model with a linear regression model between LST and air temperature. The root mean square errors (RMSE) of the air temperature for model outputs are used to evaluate the model. The number of 95 in-situ air temperature data was 2,496,634 and the ratio of datasets paired with LST and L1B show 42.1% and 98.4%. The training years are 2020 and 2021 and 2022 is used to validate. The DNN model is designed with an input layer taking 16 channels and four hidden fully connected layers to assume an air temperature. As a result of the model using 16 bands of L1B, the DNN with RMSE 2.22℃ showed great performance than the baseline model with RMSE 3.55℃ on clear sky conditions and the total RMSE including overcast samples was 3.33℃. It is suggested that the DNN is able to overcome cloud effects. However, it showed different characteristics in seasonal and hourly analysis and needed to append solar information as inputs to make a general DNN model because the summer and winter seasons showed a low coefficient of determinations with high standard deviations.

• The Journal of the Korea Contents Association
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• v.20 no.8
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• pp.10-18
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• 2020

Among various contents industry, this study especially focused on music industry and tried to develop a prediction model for music box office using deep learning. The deep learning prediction model designed to predict music chart-in period based on 17 variables -singer power, singer influence, featuring singer power, featuring singer influence, number of participating singers, gender of participating singers, lyric writer power, composer power, arranger power, production agency power, distributing agency power, title track, LIKEs on streaming platform, comments on streaming platform, pre-promotion article, teaser-video view, first-week performance. Additionally we conducted a linear regression analysis to sort out factors, and tried to compare the prediction performance between the original DNN prediction model and the DNN model made of sorted out factors.

• Journal of Advanced Navigation Technology
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• v.25 no.5
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• pp.409-414
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• 2021

The growing concerns on the emission of particulate matter has prompted a demand for highly reliable particulate matter forecasting. Currently, several studies on particulate matter prediction use various deep learning algorithms. In this study, we compared the predictive performances of typical neural networks used for particulate matter prediction. We used deep neural network(DNN), recurrent neural network, and long short-term memory algorithms to design an optimal predictive model on the basis of a hyperparameter search. The results of a comparative analysis of the predictive performances of the models indicate that the variation trend of the actual and predicted values generally showed a good performance. In the analysis based on the root mean square error and accuracy, the DNN-based prediction model showed a higher reliability for prediction errors compared with the other prediction models.

• Journal of Korea Multimedia Society
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• v.22 no.11
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• pp.1300-1312
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• 2019

BSTRACT In this paper, PM10 forecast model using DNN(Deep Neural Network) is developed for Seoul region. The previous Julian forecast model has been developed using weather and air quality data of Seoul region only. This model gives excellent results for accuracy and false alarm rates, but poor result for POD(Probability of Detection). To solve this problem, an WA(Wide Area) forecasting model that uses Chinese data is developed. The data is highly correlated with the emergence of high concentrations of PM10 in Korea. As a result, the WA model shows better accuracy, and POD improving of 3%(D+0), 21%(D+1), and 36%(D+2) for each forecast period compared with the Julian model.

• Annual Conference of KIPS
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• 2024.10a
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• pp.40-41
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• 2024

최근 딥러닝 연산의 고도화에 따라 희소성(Sparsity)을 효율적으로 처리할 수 있는 유연한 구조의 DNN 가속기가 중요해지고 있다. 그러나 기존의 가속기들은 유연성과 효율성 면에서 한계가 존재한다. 본 논문에서는 DNN 가속기의 기존 모델들과 최신 연구 동향에 대해 살펴본다. 특히 unstructured sparsity, structured sparsity, 그리고 최근 제안된 Hierarchical Structured Sparsity (HSS)를 적용한 가속기들을 분석하며, 각 접근 방식의 장단점을 비교한다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.3
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• pp.123-129
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• 2023

if we know what we're looking at, we can get a lot of information. Due to the development of eye tracking, Information on gaze point can be obtained through software provided by various eye tracking equipments. However, it is difficult to estimate accurate information such as the actual gaze depth. If it is possible to calibrate the eye tracker with the actual gaze depth, it will enable the derivation of realistic and accurate results with reliable validity in various fields such as simulation, digital twin, VR, and more. Therefore, in this paper, we experiment with acquiring and calibrating raw gaze depth using an eye tracker and software. The experiment involves designing a Deep Neural Network (DNN) model and then acquiring gaze depth values provided by the software for specified distances from 300mm to 10,000mm. The acquired data is trained through the designed DNN model and calibrated to correspond to the actual gaze depth. In our experiments with the calibrated model, we were able to achieve actual gaze depth values of 297mm, 904mm, 1,485mm, 2,005mm, 3,011mm, 4,021mm, 4,972mm, 6,027mm, 7,026mm, 8,043mm, 9,021mm, and 10,076mm for the specified distances from 300mm to 10,000mm.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.5
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• pp.317-323
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• 2022

In this paper, a deep neural network(DNN) model for predicting the vibration characteristics of the composite rotor blade with c-spar cross section was developed. Herein, the present DNN model is defined by using the natural frequencies obtained through the in-house code based on the nonlinear co-rotational(CR) shell element. For the present DNN model, the accuracy of the model was evaluated via the data with a random distribution of thickness and a tendency to decrease in thickness along the blade span.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.348-350
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• 2021

When predicting the concentration of fine dust using deep learning, there is a problem that the characteristics of a high concentration of 81㎍/m³ or more are not well reflected in the prediction model. In this paper, a comparison through predictive performance was conducted to confirm the results of reflecting the characteristics of fine dust in the high concentration area according to the deep learning algorithm. As a result of performance evaluation, overall, similar levels of results were shown, but the RNN model showed higher accuracy than other models at concentrations of "very bad" based on AQI. This confirmed that the RNN algorithm reflected the characteristics of the high concentration better than the DNN and LSTM algorithms.