• Journal of IKEEE
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• v.27 no.3
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• pp.340-344
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• 2023

In this paper, we propose a study on the development of deep learning structure to secure visibility of outdoor LED display board according to weather change. The proposed technique secures the visibility of the outdoor LED display board by automatically adjusting the LED luminance according to the weather change using deep learning using an imaging device. In order to automatically adjust the LED luminance according to weather changes, a deep learning model that can classify the weather is created by learning it using a convolutional network after first going through a preprocessing process for the flattened background part image data. The applied deep learning network reduces the difference between the input value and the output value using the Residual learning function, inducing learning while taking the characteristics of the initial input value. Next, by using a controller that recognizes the weather and adjusts the luminance of the outdoor LED display board according to the weather change, the luminance is changed so that the luminance increases when the surrounding environment becomes bright, so that it can be seen clearly. In addition, when the surrounding environment becomes dark, the visibility is reduced due to scattering of light, so the brightness of the electronic display board is lowered so that it can be seen clearly. By applying the method proposed in this paper, the result of the certified measurement test of the luminance measurement according to the weather change of the LED sign board confirmed that the visibility of the outdoor LED sign board was secured according to the weather change.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.27-30
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• 2022

The plasma process, which has many advantages in terms of efficiency and environment compared to conventional process methods, is widely used in semiconductor manufacturing. Plasma Sheath is a dark region observed between the plasma bulk and the chamber wall surrounding it or the electrode. The Plasma Sheath Monitoring Sensor (PSMS) measures the difference in voltage between the plasma and the electrode and the RF power applied to the electrode in real time. The PSMS data, therefore, are expected to have a high correlation with the state of plasma in the plasma chamber. In this study, a model for predicting the state of nitrogen ions in the plasma chamber is training by a deep learning machine learning techniques using PSMS data. For the data used in the study, PSMS data measured in an experiment with different power and pressure settings were used as training data, and the ratio, flux, and density of nitrogen ions measured in plasma bulk and Si substrate were used as labels. The results of this study are expected to be the basis of artificial intelligence technology for the optimization of plasma processes and real-time precise control in the future.

• Journal of the Korean Geotechnical Society
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• v.26 no.12
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• pp.5-17
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• 2010

Coarse granular material is used as important fill material in most of large embankments such as railway, road, dam and so on. Therefore, the accurate design parameters of the coarse granular material are necessarily required in design and construction. The behavior of the coarse granular material was not well understood because of the lack of large testing equipment capable of coarse granular material. A large triaxial testing system was developed in this research, capable of large specimens of 500 mm, 300 mm and 150 mm in diameter. In the new large triaxial testing system, the load cell is installed inside the triaxial cell and axial displacement is measured locally on a specimen in order to improve control and measurement in small strain level. Urethane specimens of 300 mm and 50 mm in diameter were prepared. The large triaxial tests were performed on the 300 mm diameter urethane specimens while RC/TS and impact echo tests on the 50 mm diameter urethane specimens to verify this testing system. In this verification test results, we could ascertain the reasonable test results of the KRRI large triaxial testing system.

• The Journal of the Acoustical Society of Korea
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• v.41 no.2
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• pp.184-191
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• 2022

Underwater acoustic wave in shallow water is propagated through multipath that has a large delay spread causing Inter-Symbol Interference (ISI) and these characteristics deteriorate the performance in the communication system. In order to analyze the communication performance and investigate the correlation with multipath delay spread in a reverberant environment, an underwater acoustic communication experiment using Binary Phase-Shift Keying (BPSK) signals with symbol rates from 100 sym/s to 8000 sym/s was conducted in a 5 × 5 × 5 m³ water tank. The acoustic channels in a well-controlled tank environment had the characteristics of dense multipath delay spread due to multiple reflections from the interfaces and walls within the tank and showed the maximum excess delay of 40 ms or less, and the Root Mean Squared (RMS) delay spread of 8 ms or less. In this paper, the performances of Bit Error Rate (BER) and output Signal-to-Noise Ratio (SNR) were analyzed using four types of communication demodulation techniques. And the parameter, Symbol interval to Delay spread Ratio in reverberant environment (SDR_rev), which is the ratio of symbol interval to RMS delay spread in the reverberant environment is defined. Finally, the SDR_rev was compared to the BER and the output SNR. The results present the reference symbol rate in which high communication performance can be guaranteed.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.6
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• pp.124-131
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• 2017

Recently, drone is extending its range of usability. For example, the delivery, agriculture, industry, and entertainment area take advantage of drone mobilities. To control real drones, it needs huge amount of drone control training steps. However, it is risky; falling down, missing, destroying. The virtual drone system can avoid such risks. We reason that what kinds of technologies are required for building the virtual drone system. First, it needs that the virtual drone authoring tool that can assemble drones with the physical restriction in the virtual environment. We suggest that the drone assembly method that can fulfill physical restrictions in the virtual environment. Next, we introduce the virtual drone simulator that can simulate the assembled drone moves physically right in the virtual environment. The simulator produces a high quality rendering results more than 60 frames per second. In addition, we develop the physics engine based on SILS(Software in the loop simulation) framework to perform more realistic drone movement. Last, we suggest the virtual drone controller that can interact with real drone controllers which are commonly used to control real drones. Our virtual drone system earns 7.64/10.0 user satisfaction points on human test: the test is done by one hundred persons.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.5A
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• pp.433-442
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• 2010

A control of chloride diffusion coefficient is very essential for service life of reinforced concrete (RC) structures exposed to chloride attack so that much studies have been focused on this work. The purpose of this study is to derive the intended diffusion coefficient which satisfies intended service life and propose a technique for optimum concrete mixture through genetic algorithm(GA). For this study, 30 data with mixture proportions and related diffusion coefficients are analyzed. Utilizing 27 data, fitness function for diffusion coefficient is obtained with variables of water to binder ratio(W/B), weight of cement, mineral admixture(slag, flay ash, and silica fume), sand, and coarse aggregate. 3 data are used for verification of the results from GA. Average error from fitness function is observed to 18.7% for 27 data for diffusion coefficient with 16.0% of coefficient of variance. For the verification using 3 data, a range of error for mixture proportions through GA is evaluated to 0.3~9.3% in 3 given diffusion coefficients. Assuming the durability design parameters like intended service life, cover depth, surface chloride content, and replacement ratio of mineral admixture, target diffusion coefficient, where exterior conditions like relative humidity(R.H.) and temperature, is derived and optimum design mixtures for concrete are proposed. In this paper, applicability of GA is attempted for durability mixture design and the proposed technique would be improved with enhancement of comprehensive data set including wider range of diffusion coefficients.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6B
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• pp.697-707
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• 2008

The grid-based KIneMatic wave STOrm Runoff Model (KIMSTORM) by Kim (1998) predicts the temporal variation and spatial distribution of overland flow, subsurface flow and stream flow in a watershed. The model programmed with C++ language on Unix operating system adopts single flowpath algorithm for water balance simulation of flow at each grid element. In this study, we attempted to improve the model by converting the code into FORTRAN 90 on MS Windows operating system and named as ModKIMSTORM. The improved functions are the addition of GAML (Green-Ampt & Mein-Larson) infiltration model, control of paddy runoff rate by flow depth and Manning's roughness coefficient, addition of baseflow layer, treatment of both spatial and point rainfall data, development of the pre- and post-processor, and development of automatic model evaluation function using five evaluation criteria (Pearson's coefficient of determination, Nash and Sutcliffe model efficiency, the deviation of runoff volume, relative error of the peak runoff rate, and absolute error of the time to peak runoff). The modified model adopts Shell Sort algorithm to enhance the computational performance. Input data formats are accepted as raster and MS Excel, and model outputs viz. soil moisture, discharge, flow depth and velocity are generated as BSQ, ASCII grid, binary grid and raster formats.

• Journal of the Korean Society of Radiology
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• v.17 no.7
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• pp.1067-1073
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• 2023

Breast cancer is one of the top three most common cancers in modern women, and the incidence rate is increasing rapidly. Breast cancer has a high family history and a mortality rate of about 15%, making it a high-risk group. Therefore, breast cancer needs constant management after an early examination. Among the various equipment that can diagnose cancer, ultrasound has the advantage of low risk and being able to diagnose in real time. In addition, breast ultrasound will be more useful because Asian women's breasts are denser and less sensitive. However, the results of ultrasound examinations vary greatly depending on the technology of the examiner. To compensate for this, we intend to incorporate motion tracking technology. Motion tracking is a technology that specifies and analyzes a location according to the movement of an object in a three-dimensional space. Therefore, real-time control is possible, and complex and fast movements can be recorded in real time. We would like to present the production of an ultrasound examination guide using these advantages.

• Smart Media Journal
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• v.13 no.3
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• pp.18-26
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• 2024

Road surface monitoring is essential for maintaining road environment safety through managing risk factors like rutting and crack detection. Using autonomous driving-based UGVs with high-performance 2D laser sensors enables more precise measurements. However, the increased energy consumption of these sensors is limited by constrained battery capacity. In this paper, we propose a fusion sensor system for efficient surface monitoring with UGVs. The proposed system combines color information from cameras and depth information from line laser sensors to accurately detect surface displacement. Furthermore, a dynamic sampling algorithm is applied to control the scanning frequency of line laser sensors based on the detection status of monitoring targets using camera sensors, reducing unnecessary energy consumption. A power consumption model of the fusion sensor system analyzes its energy efficiency considering various crack distributions and sensor characteristics in different mission environments. Performance analysis demonstrates that setting the power consumption of the line laser sensor to twice that of the saving state when in the active state increases power consumption efficiency by 13.3% compared to fixed sampling under the condition of λ=10, µ=10.

• Information Systems Review
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• v.21 no.1
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• pp.23-38
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• 2019

The social network service (SNS) is one of the important marketing channels, so many companies actively exploit SNSs by posting SNS messages with appropriate content and style for their customers. In this paper, we focused on the psychological distances embedded in the SNS messages and developed a method to measure the psychological distance in SNS message by mixing a traditional content analysis, natural language processing (NLP), and machine learning. Through a traditional content analysis by human coding, the psychological distance was extracted from the SNS message, and these coding results were used for input data for NLP and machine learning. With NLP, word embedding was executed and Bag of Word was created. The Support Vector Machine, one of machine learning techniques was performed to train and test the psychological distance in SNS message. As a result, sensitivity and precision of SVM prediction were significantly low because of the extreme skewness of dataset. We improved the performance of SVM by balancing the ratio of data by upsampling technique and using data coded with the same value in first content analysis. All performance index was more than 70%, which showed that psychological distance can be measured well.