• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5A
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• pp.737-746
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• 2008

Recently, NDTs (Non-Destructive Techniques) using electromagnetic(EM) properties are applied to the performance evaluation for RC (Reinforced Concrete) structures. Since nonmetallic materials which are cement-based system have their unique dielectric constant and conductivity, they can be characterized and changed with different mixture conditions like W/C (water to cement) ratios and unit cement weight. In a room condition, cement mortar is generally dry so that porosity plays a major role in EM properties, which is determined at early-aged stage and also be affected by curing condition. In this paper, EM properties (dielectric constant and conductivity) in cement mortar specimens with 4 different W/C ratios are measured in the wide region of 0.2 GHz~20 GHz. Each specimen has different submerged curing period from 0 to 28 days and then EM measurement is performed after 4 weeks. Furthermore, porosity at the age of 28 days is measured through MIP (Mercury Intrusion Porosimeter) and saturation is also measured through amount of water loss in room condition. In order to evaluate the porosity from the initial curing stage, numerical analysis based on the modeling for the behavior in early-aged concrete is performed and the calculated results of porosity and measured EM properties are analyzed. For the convenient comparison with influencing parameters like W/C ratios and curing period, EM properties from 5 GHz to 15 GHz are averaged as one value. For 4 weeks, the averaged dielectric constant and conductivity in cement mortar are linearly decrease with higher W/C ratios and they increase in proportion to the square root of curing period regardless of W/C ratios.

• Journal of The Korean Society of Grassland and Forage Science
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• v.43 no.2
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• pp.95-102
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• 2023

This study was to investigate the cultivation technique previously conducted cultivation research for the stable production of alfalfa and to present further research. The data used in the study were 270 alfalfa cultivation experimental data from 1983 to 2008, indicating the cultivation region, field type, variety, sowing, cutting frequency, fertilization, and dry matter yield (DMY). The average DMY of alfalfa in the Republic of Korea was 12,536 kg/ha, which differed greatly depending on the cultivated region. Most of the field type was cultivated in upland. In order to increase alfalfa production, it is necessary to use reclaimed and unused land, and research on the soil amendment matter to improve the soil condition is needed. Alfalfa varieties cultivated an amount of 53, but collected data no studies considered fall dormancy, the criteria for selecting alfalfa varieties, so further research is required. The fertilizer did not consider each component at various levels, and research is needed as the demand for fertilizer. In particular, research on potassium is needed considering the increase in alfalfa production. The alfalfa cutting frequency differed in the estimated pasture production period depending on the region, and the DMY tended to increase with increasing cutting frequency. This suggests that the alfalfa DMY can be increased according to the cutting frequency in the Republic of Korea, so research is needed to present the appropriate cutting frequency.

• Convergence Security Journal
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• v.22 no.3
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• pp.25-32
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• 2022

Recently, the image processing industry has been activated as deep learning-based technology is introduced in the image recognition and detection field. With the development of deep learning technology, learning model vulnerabilities for adversarial attacks continue to be reported. However, studies on countermeasures against poisoning attacks that inject malicious data during learning are insufficient. The conventional countermeasure against poisoning attacks has a limitation in that it is necessary to perform a separate detection and removal operation by examining the training data each time. Therefore, in this paper, we propose a technique for reducing the attack success rate by applying modifications to the training data and inference data without a separate detection and removal process for the poison data. The One-shot kill poison attack, a clean label poison attack proposed in previous studies, was used as an attack model. The attack performance was confirmed by dividing it into a general attacker and an intelligent attacker according to the attacker's attack strategy. According to the experimental results, when the proposed defense mechanism is applied, the attack success rate can be reduced by up to 65% compared to the conventional method.

• Smart Media Journal
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• v.12 no.10
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• pp.71-84
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• 2023

Paralichthys olivaceus accounts for a large proportion, accounting for more than half of Korea's aquaculture industry. However, about 25-30% of the total breeding volume throughout the year occurs due to diseases, which has a very bad impact on the economic feasibility of fish farms. For the economic growth of Paralichthys olivaceus farms, it is necessary to quickly and accurately diagnose disease symptoms by automating the diagnosis of Paralichthys olivaceus diseases. In this study, we create training data using innovative data collection methods, refining data algorithms, and techniques for partitioning dataset, and compare the Paralichthys olivaceus disease symptom detection performance of four object detection deep learning models(such as YOLOv8, Swin, Vitdet, MvitV2). The experimental findings indicate that the YOLOv8 model demonstrates superiority in terms of average detection rate (mAP) and Estimated Time of Arrival (ETA). If the performance of the AI model proposed in this study is verified, Paralichthys olivaceus farms can diagnose disease symptoms in real time, and it is expected that the productivity of the farm will be greatly improved by rapid preventive measures according to the diagnosis results.

• Journal of the Korean Geotechnical Society
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• v.39 no.10
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• pp.41-48
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• 2023

The presence of abnormalities in the subgrade of roads poses safety risks to users and results in significant maintenance costs. In this study, we aimed to experimentally evaluate the temperature distributions in abnormal areas of subgrade materials using infrared cameras and analyze the data with machine learning techniques. The experimental site was configured as a cubic shape measuring 50 cm in width, length, and depth, with abnormal areas designated for water and air. Concrete blocks covered the upper part of the site to simulate the pavement layer. Temperature distribution was monitored over 23 h, from 4 PM to 3 PM the following day, resulting in image data and numerical temperature values extracted from the middle of the abnormal area. The temperature difference between the maximum and minimum values measured 34.8℃ for water, 34.2℃ for air, and 28.6℃ for the original subgrade. To classify conditions in the measured images, we employed the image analysis method of a convolutional neural network (CNN), utilizing ResNet-101 and SqueezeNet networks. The classification accuracies of ResNet-101 for water, air, and the original subgrade were 70%, 50%, and 80%, respectively. SqueezeNet achieved classification accuracies of 60% for water, 30% for air, and 70% for the original subgrade. This study highlights the effectiveness of CNN algorithms in analyzing subgrade properties and predicting subsurface conditions.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.1
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• pp.69-75
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• 2023

In recent years, there has been a growing interest in deep learning-based animal pose estimation, especially in the areas of animal behavior analysis and healthcare. However, existing animal pose estimation techniques do not perform well when body parts are occluded or not present. In particular, the occlusion of dog tail or ear might lead to a significant degradation of performance in pet behavior and emotion recognition. In this paper, to solve this intractable problem, we propose a simple yet novel framework for pet pose estimation where pet pose is predicted on an outpainted image where some body parts hidden outside the input image are reconstructed by the image inpainting network preceding the pose estimation network, and we performed a preliminary study to test the feasibility of the proposed approach. We assessed CE-GAN and BAT-Fill for image outpainting, and evaluated SimpleBaseline for pet pose estimation. Our experimental results show that pet pose estimation on outpainted images generated using BAT-Fill outperforms the existing methods of pose estimation on outpainting-less input image.

• Journal of The Korean Astronomical Society
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• v.56 no.2
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• pp.213-224
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• 2023

Type II solar radio bursts show frequency drifts from high to low over time. They have been known as a signature of coronal shock associated with Coronal Mass Ejections (CMEs) and/or flares, which cause an abrupt change in the space environment near the Earth (space weather). Therefore, early detection of type II bursts is important for forecasting of space weather. In this study, we develop a deep-learning (DL) model for the automatic detection of type II bursts. For this purpose, we adopted a 1-D Convolution Neutral Network (CNN) as it is well-suited for processing spatiotemporal information within the applied data set. We utilized a total of 286 radio burst spectrum images obtained by Hiraiso Radio Spectrograph (HiRAS) from 1991 and 2012, along with 231 spectrum images without the bursts from 2009 to 2015, to recognizes type II bursts. The burst types were labeled manually according to their spectra features in an answer table. Subsequently, we applied the 1-D CNN technique to the spectrum images using two filter windows with different size along time axis. To develop the DL model, we randomly selected 412 spectrum images (80%) for training and validation. The train history shows that both train and validation losses drop rapidly, while train and validation accuracies increased within approximately 100 epoches. For evaluation of the model's performance, we used 105 test images (20%) and employed a contingence table. It is found that false alarm ratio (FAR) and critical success index (CSI) were 0.14 and 0.83, respectively. Furthermore, we confirmed above result by adopting five-fold cross-validation method, in which we re-sampled five groups randomly. The estimated mean FAR and CSI of the five groups were 0.05 and 0.87, respectively. For experimental purposes, we applied our proposed model to 85 HiRAS type II radio bursts listed in the NGDC catalogue from 2009 to 2016 and 184 quiet (no bursts) spectrum images before and after the type II bursts. As a result, our model successfully detected 79 events (93%) of type II events. This results demonstrates, for the first time, that the 1-D CNN algorithm is useful for detecting type II bursts.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.2
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• pp.207-216
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• 2024

As big data was built due to the 4th Industrial Revolution, personalized services increased rapidly. As a result, the amount of personal information collected from online services has increased, and concerns about users' personal information leakage and privacy infringement have increased. Online service providers provide privacy policies to address concerns about privacy infringement of users, but privacy policies are often misused due to the long and complex problem that it is difficult for users to directly identify risk items. Therefore, there is a need for a method that can automatically check whether the privacy policy is safe. However, the safety verification technique of the conventional blacklist and machine learning-based privacy policy has a problem that is difficult to expand or has low accessibility. In this paper, to solve the problem, we propose a safety verification technique for the privacy policy using the GPT-3.5 API, which is a generative artificial intelligence. Classification work can be performed evenin a new environment, and it shows the possibility that the general public without expertise can easily inspect the privacy policy. In the experiment, how accurately the blacklist-based privacy policy and the GPT-based privacy policy classify safe and unsafe sentences and the time spent on classification was measured. According to the experimental results, the proposed technique showed 10.34% higher accuracy on average than the conventional blacklist-based sentence safety verification technique.

• Journal of Korean Tunnelling and Underground Space Association
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• v.25 no.6
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• pp.479-493
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• 2023

The need for safety management has arisen due to the increasing number of years of operated underground structures, such as tunnels and utility tunnels, and accidents caused by those aging infrastructures. However, in the case of privately managed underground utility ducts, there is a lack of detailed guidelines for facility safety and maintenance, resulting in inadequate safety management. Furthermore, the absence of basic design information and the limited space for safety assessments make applying currently used non-destructive testing methods challenging. Therefore, this study suggests non-destructive inspection methods using ultrasonic and impact-echo techniques to assess the quality of underground structures. Thickness, presence of rebars, depth of rebars, and the presence and depth of internal defects are assessed to provide fundamental data for the safety assessment of box-type general underground structures. To validate the proposed methodology, different conditions of concrete specimens are designed and cured to simulate actual field conditions. Applying ultrasonic and impact signals and collecting data through multi-channel accelerometers determine the thickness of the simulated specimens, the depth of embedded rebar, and the extent of defects. The predicted results are well agreed upon compared with actual measurements. The proposed methodology is expected to contribute to developing safety diagnostic methods applicable to general underground structures in practical field conditions.

• Journal of the Korea Institute of Building Construction
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• v.24 no.1
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• pp.67-75
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• 2024

In the context of Korean residential heating systems, Ondol pipelines are a prevalent choice. However, the maintenance of these pipelines becomes a complex task once they are embedded within concrete structures. As time progresses, the accumulation of sludge, corrosive oxides, and microorganisms on the inner surfaces of these pipelines diminishes their heating efficiency. In extreme scenarios, this accumulation can induce corrosion and scale formation, compromising the system's integrity. Consequently, this research introduces an ultrasonic generation system tailored for the upkeep of Ondol pipelines, with the objective of empirically assessing its practicality. This investigation delineates three variants of ultrasonic generating apparatuses: those employing surface vibration, external generation, and internal generation techniques. To emulate the presence of contaminants within the pipelines, substances in powder, slurry, and liquid forms were employed. The efficacy of the cleaning process post-ultrasonic wave application was scrutinized over time, with image analysis methodologies being utilized to evaluate the outcomes. The findings indicate that ultrasonic waves, whether generated externally or internally, exert a beneficial effect on the cleanliness of the pipelines. Given the inherent characteristics of Ondol pipelines, external generation proves impractical, thereby rendering internal generation a more viable solution for pipeline maintenance. It is anticipated that future endeavors will pave the way for innovative maintenance strategies for Ondol pipelines, particularly through the advancement of internal generation technologies for pipeline applications.