• Journal of the Institute of Convergence Signal Processing
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• v.22 no.4
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• pp.179-184
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• 2021

Along with the development of the fourth industry, the public sector has increasingly paid more attention to search using drones and real-time monitoring, for various goals. The drones are used and researched to complete a variety of searching and monitoring missions, including search for missing persons, security, coastal patrol and monitoring, speed enforcement, highway and urban traffic monitoring, fire and wildfire monitoring, monitoring of illegal fishing in reservoirs and protest rally monitoring. Police stations, fire departments and military authorities, however, concentrate on the hardware part, so there are little research on efficient communication systems for the real-time monitoring of data collected from high-performance resolution and infrared thermal imagining cameras, and analysis programs suitable for special missions. In order to increase the efficiency of drones with the searching mission, this paper, therefore, attempts to propose an image analysis technique to increase the precision of search by producing image data suitable for searching missions, based on images obtained from drones and provide the foundation for improving relevant policies and establishing proper platforms, based on actual field cases and experiments.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.373-380
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• 2022

This paper presents a deep learning based group synchronization that supports networked immersive interactions between remote users. The goal of group synchronization is to enable all participants to synchronously interact with others for increasing user presence Most previous methods focus on NTP-based clock synchronization to enhance time accuracy. Moving average filters are used to control media playout time on the synchronization server. As an example, the exponentially weighted moving average(EWMA) would be able to track and estimate accurate playout time if the changes in input data are not significant. However it needs more time to be stable for any given change over time due to codec and system loads or fluctuations in network status. To tackle this problem, this work proposes the Deep Group Synchronization(DeepGroupSync), a group synchronization based on deep learning that models important features from the data. This model consists of two Gated Recurrent Unit(GRU) layers and one fully-connected layer, which predicts an optimal playout time by utilizing the sequential playout delays. The experiments are conducted with an existing method that uses the EWMA and the proposed method that uses the DeepGroupSync. The results show that the proposed method are more robust against unpredictable or rapid network condition changes than the existing method.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.4
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• pp.20-29
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• 2022

The operational modal analysis (OMA) technique, which extracts the modal parameters of a structural system using ambient vibrations, has been actively developed as a field of structural health monitoring of cable-supported bridges. In this paper, the short and long-term modal parameters of a cable-stayed bridge were evaluated using the acceleration data obtained from the two ambient vibration tests (AVTs) and three years of continuous measurements. A total of 27 vertical modes and 1 lateral mode in the range 0.1 ~ 2.5 Hz were extracted from the high-resolution AVTs which were conducted in the 6th and 19th years after its completion. Existing OMA methods such as Peak-Picking (PP), Eigensystem Realization Algorithm with Data Correlation (ERADC), Frequency Domain Decomposition (FDD) and Time Domain Decomposition (TDD) were applied for modal parameters extraction, and it was confirmed that there was no significant difference between the applied methods. From the correlation analysis between long-term natural frequencies and environmental factors, it was confirmed that temperature change is the dominant factor influencing natural frequency fluctuations. It was revealed that the decreased natural frequencies of the bridge were not due to changes in structural performance and integrity, but to the environmental effects caused by the temperature difference between the two AVTs. In addition, when the TDD technique is applied, the accuracy of extracted mode shapes is improved by adding a proposed algorithm that normalizes the sequence so that the autocorrelations at zero lag equal 1.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.5
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• pp.127-134
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• 2022

In this study, RCPTs (Rapid Chloride Penetration Test) were performed for fly ash concrete with curing age of 4 ~ 6 years. The concrete mixtures were prepared with 3 levels of water to binder ratio (0.37, 0.42, and 0.47) and 2 levels of substitution ratio of fly ash (0 and 30%), and the improved passed charges of chloride ion behavior were quantitatively analyzed. Additionally, the results were trained through the univariate time series models consisted of GRU (Gated Recurrent Unit) algorithm and those from the models were evaluated. As the result of the RCPT, fly ash concrete showed the reduced passed charges with period and an more improved resistance to chloride penetration than OPC concrete. At the final evaluation period (6 years), fly ash concrete showed 'Very low' grade in all W/B (water to binder) ratio, however OPC concrete showed 'Moderate' grade in the condition with the highest W/B ratio (0.47). The adopted algorithm of GRU for this study can analyze time series data and has the advantage like operation efficiency. The deep learning model with 4 hidden layers was designed, and it provided a reasonable prediction results of passed charge. The deep learning model from this study has a limitation of single consideration of a univariate time series characteristic, but it is in the developing process of providing various characteristics of concrete like strength and diffusion coefficient through additional studies.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.47-58
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• 2022

In recent years, navigation aids, which plays as sea traffic lights, have been digitized, and are developing beyond simple sign purpose to provide various functions such as marine information collection, supervision, control, etc. For example, Busan Port which is located in South Korea is leading the application of the advanced technologies by installing cameras on buoys and recording video images to supervise maritime accidents. However, there are difficulties to perform their major functions since the advanced technologies require long-term battery operation and also management and maintenance of them are hampered by marine characteristics. This study proposes a system that can automatically notify maritime objects passing around buoys by analyzing image information. In the existing sensor-based accident prevention systems, the alarms are generated by a collision detection sensor. The system can identify the cause of the accident whilst even though it is difficult not possible to fundamentally prevent the accidents. Therefore, in order to overcome these limitations, the proposed a maritime object detection system is based on marine characteristics. The experiments demonstrate that the proposed system shows about 5 times faster processing speed than other existing algorithms.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.5
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• pp.449-456
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• 2022

This study proposes a deep learning algorithm to predict crop yield using GIS (Geographic Information System) to extract soil properties from Soilgrids and soil suitability class maps. The proposed model modified the structure of a published CNN-RNN (Convolutional Neural Network-Recurrent Neural Network) based crop yield prediction model suitable for the domestic crop environment. The existing model has two characteristics. The first is that it replaces the original yield with the average yield of the year, and the second is that it trains the data of the predicted year. The new model uses the original field value to ensure accuracy, and the network structure has been improved so that it can train only with data prior to the year to be predicted. The proposed model predicted the yield per unit area of autumn cabbage for kimchi by region based on weather, soil, soil suitability classes, and yield data from 1980 to 2020. As a result of computing and predicting data for each of the four years from 2018 to 2021, the error amount for the test data set was about 10%, enabling accurate yield prediction, especially in regions with a large proportion of total yield. In addition, both the proposed model and the existing model show that the error gradually decreases as the number of years of training data increases, resulting in improved general-purpose performance as the number of training data increases.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.35-43
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• 2022

Makeup is the most common way to improve a person's appearance. However, since makeup styles are very diverse, there are many time and cost problems for an individual to apply makeup directly to himself/herself.. Accordingly, the need for makeup automation is increasing. Makeup transfer is being studied for makeup automation. Makeup transfer is a field of applying makeup style to a face image without makeup. Makeup transfer can be divided into a traditional image processing-based method and a deep learning-based method. In particular, in deep learning-based methods, many studies based on Generative Adversarial Networks have been performed. However, both methods have disadvantages in that the resulting image is unnatural, the result of makeup conversion is not clear, and it is smeared or heavily influenced by the makeup style face image. In order to express the clear boundary of makeup and to alleviate the influence of makeup style facial images, this study divides the makeup area and calculates the loss function using HoG (Histogram of Gradient). HoG is a method of extracting image features through the size and directionality of edges present in the image. Through this, we propose a makeup transfer network that performs robust learning on edges.By comparing the image generated through the proposed model with the image generated through BeautyGAN used as the base model, it was confirmed that the performance of the model proposed in this study was superior, and the method of using facial information that can be additionally presented as a future study.

• Journal of the Korean Institute of Gas
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• v.25 no.2
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• pp.34-41
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• 2021

In order to cope with the problems that may occur when the natural gas used in Korea becomes low in calories, the problems that may have to the domestic industrial gas equipment must be identified in advance, and based on this, countermeasures for efficient use of energy must be preceded. In this study, in order to solve the problem of deterioration of engine output performance and efficiency due to the introduction of low calorific gas when using a lean-burning natural gas engine that complies with the EURO-6 regulation, specific control plans and results based on the experiment are intended to be presented. In order to identify the improvement effect by the control variable represented by the ignition timing under the full load condition at the engine speed of 1,400 rpm and 550 Nm, 2,100 rpm, which is the engine speed at the rated operation condition, the thermal efficiency and exhaust gas characteristics were identified and optimized by changing the ignition timing for each gas fuel. In the case of pure methane, which shows the lowest value based on the torque under the full load condition, if the ignition timing is advanced by about 2 CAD from the reference ignition timing, the torque can be compensated without a large increase in NOx emission.

• Journal of the Society of Disaster Information
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• v.17 no.1
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• pp.184-193
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• 2021

Purpose: In this study, since the operation principle of the CCFL Exit light is the same as that of general lighting equipment, the characteristics of the CCFL Exit light were analyzed by deriving test items that can affect the characteristics of the light source from the KS standard, which is the standard for lamp ballast performance certification of general lighting equipment. Method: The samples used in the experiment were performed on products of two manufacturers for each size, such as large, medium, and small, and the test items were power factor, crest factor, and current harmonic distortion. Result: As a result of the experiment, the power factor showed a value between 0.4 and 0.6 in all samples, which was smaller than the 0.9 value set by KS. The crest factor ranged from 3.6 to 3.7 for large, 4.4 to 4.7 for medium, and 3.5 to 3.7 for small. It showed a value more than two times higher than the KS standard of 1.7. Current total harmonic distortion ranged from 81% to 110%, and considering that the KS standard was less than 20%, it could be confirmed that all samples had a value significantly exceeding the KS standard. Conclusion: The crest factor and current total harmonic distortion may affect the temperature rise of the light source and the burnout of the device. When developing an exit light, if this item is developed within the scope of the KS standard, the quality improvement and maintenance of the exit light will be greatly improved.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.6
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• pp.825-836
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• 2022

Micropiles can be used to support additional load in extended building structures. However, their use brings about a risk of exceeding the bearing capacity of existing piles. In this study, pre-compression was applied to distribute the load of an existing building to micropiles, and an indoor loading test was performed to confirm the structural applicability of a wedge-type anchorage device designed to improve its capacity. According to the test results, the maximum strain of the anchorage device was 0.63 times that of the yield strain, and the amount of slip generated at the time of anchorage was 0.11 mm, satisfying structural standards. In addition, using MIDAS GTS, a geotechnical finite element analysis software, the effect of the size of the pre-compression, the thickness of the soil layer, and the ground conditions around the tip on the reaction force of the existing piles and micropiles were analyzed. From the numerical analysis, as the size of the pre-compression load increased, the reaction force of the existing pile decreased, resulting in a reduction rate of up to 36 %. In addition, as the soil layer increased by 5 m, the reduction rate decreased by 4 %, and when the ground condition at the tip of the micropile was weathered rock, the reduction rate increased by 14 % compared with that of weathered soil.