• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.4
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• pp.95-105
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• 2021

Road surface damage detection is essential for a comfortable driving environment and the prevention of safety accidents. Road management institutes are using automated technology-based inspection equipment and systems. As one of these automation technologies, a sensor to detect road surface damage plays an important role. For this purpose, several studies on sensors using deep learning have been conducted in recent years. Road images and label images are needed to develop such deep learning algorithms. On the other hand, considerable time and labor will be needed to secure label images. In this paper, the adversarial learning method, one of the semi-supervised learning techniques, was proposed to solve this problem. For its implementation, a lightweight deep neural network model was trained using 5,327 road images and 1,327 label images. After experimenting with 400 road images, a model with a mean intersection over a union of 80.54% and an F1 score of 77.85% was developed. Through this, a technology that can improve recognition performance by adding only road images was developed to learning without label images and is expected to be used as a technology for road surface management in the future.

• Journal of the Earthquake Engineering Society of Korea
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• v.26 no.1
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• pp.13-22
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• 2022

This study proposes a low-cycle fatigue life derived from measurement points on pipe elbows, which are components that are vulnerable to seismic load in the interface piping systems of nuclear power plants that use seismic isolation systems. In order to quantitatively define limit states regarding leakage, i.e., actual failure caused by low-cycle fatigue, in-plane cyclic loading tests were performed using a sine wave of constant amplitude. The test specimens consisted of SCH40 6-inch carbon steel pipe elbows and straight pipes, and an image processing method was used to measure the nonlinear behavior of the test specimens. The leakage lines caused by low-cycle fatigue and the low-cycle fatigue curves were compared and analyzed using the relationship between the relative deformation angles, which were measured based on each of the measurement points on the straight pipe, and the moment, which was measured at the center of the pipe elbow. Damage indices based on the combination of ductility and dissipation energy at each measurement point were used to quantitatively express the time at which leakage occurs due to through-wall cracking in the pipe elbow.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.75-82
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• 2022

There are various exhibition halls, shopping malls, theme parks around us and analysis of interest in exhibits or contents is mainly done through questionnaires. These questionnaires are mainly depend on the subjective memory of the person being investigated, resulting in incorrect statistical results. Therefore, it is possible to identify an exhibition space with low interest by tracking the movement and counting the number of visitors. Based on this, it can be used as quantitative data for exhibits that need replacement. In this paper, we use deep learning-based artificial intelligence algorithms to recognize visitors, assign IDs to the recognized visitors, and continuously track them to identify the movement path. When visitors pass the counting line, the system is designed to count the number and transmit data to the server for integrated management.

• Journal of Biomedical Engineering Research
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• v.43 no.5
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• pp.361-368
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• 2022

The purpose of this study was to develop a hand rehabilitation training system for hemiplegic patients. We also tried to find out five hand postures (WF: Wrist Flexion, WE: Wrist Extension, BG: Ball Grip, HG: Hook Grip, RE: Rest) in real-time using multi-channel EMG-based deep learning. We performed a pre-processing method that converts to Spider Chart image data for the classification of hand movement from five test subjects (total 1,500 data sets) using Convolution Neural Networks (CNN) deep learning with an 8-channel armband. As a result of this study, the recognition accuracy was 92% for WF, 94% for WE, 76% for BG, 82% for HG, and 88% for RE. Also, ten physical therapists participated for the usability evaluation. The questionnaire consisted of 7 items of acceptance, interest, and satisfaction, and the mean and standard deviation were calculated by dividing each into a 5-point scale. As a result, high scores were obtained in immersion and interest in game (4.6±0.43), convenience of the device (4.9±0.30), and satisfaction after treatment (4.1±0.48). On the other hand, Conformity of intention for treatment (3.90±0.49) was relatively low. This is thought to be because the game play may be difficult depending on the degree of spasticity of the hemiplegic patient, and compensation may occur in patient with weakened target muscles. Therefore, it is necessary to develop a rehabilitation program suitable for the degree of disability of the patient.

• Nuclear Engineering and Technology
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• v.53 no.4
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• pp.1189-1198
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• 2021

When performing remote tasks using robots in nuclear power plants, a 3D shape measurement system is advantageous in improving the efficiency of remote operations by easily identifying the current state of the target object for example, size, shape, and distance information. Nuclear power plants have high-radiation and underwater environments therefore the electronic parts that comprise 3D shape measurement systems are prone to degradation and thus cannot be used for a long period of time. Also, given the refraction caused by a medium change in the underwater environment, optical design constraints and calibration methods for them are required. The present study proposed a method for developing an underwater 3D shape measurement system with improved radiation tolerance, which is composed of commercial electric parts and a stereo camera while being capable of easily and readily correcting underwater refraction. In an effort to improve its radiation tolerance, the number of parts that are exposed to a radiation environment was minimized to include only necessary components, such as a line beam laser, a motor to rotate the line beam laser, and a stereo camera. Given that a signal processing circuit and control circuit of the camera is susceptible to radiation, an image sensor and lens of the camera were separated from its main body to improve radiation tolerance. The prototype developed in the present study was made of commercial electric parts, and thus it was possible to improve the overall radiation tolerance at a relatively low cost. Also, it was easy to manufacture because there are few constraints for optical design.

• Particle and aerosol research
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• v.18 no.4
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• pp.137-145
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• 2022

As many studies on the harmfulness of fine dust have been reported, awareness of its seriousness is spreading. Recently, interest in indoor air quality as well as air pollution is increasing, and research on measures to block fine dust flowing into the room from the outside is being conducted. The clothing company is launching functional clothing to prevent fine dust attached to clothing from entering the room through outdoor activities. However, it is difficult to confirm whether there is actually fine-dust blocking performance, and there is no evaluation standard. In this study, the contamination rate caused by fine dust was quantitatively compared through image processing after contamination of the outer fabric for 4 types of commercially available functional clothing with fine-dust blocking effect. The difference in particle contamination according to the material of the outer fabric was analyzed by comparing the surface resistance, and it was found that the higher the surface resistance of the outer fabric material, the more fine dust was attached. The analysis method of this study is expected to be able to quantitatively compare and evaluate the fine-dust blocking performance of functional clothing.

• Composites Research
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• v.35 no.6
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• pp.469-476
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• 2022

In this paper, the microwave absorbing characteristics after the impact of the radar-absorbing structure (RAS) consisting of periodic pattern sheet (PPS) and glass fiber-reinforced plastic (GFRP) were experimentally investigated. The fabricated RAS effectively absorbed the microwave in the X-band (8.2-12.4 GHz). In order to induce the damage to the RAS, a low-velocity impact test with various impact energy of 15, 40, and 60 J was conducted. Afterward, the impact damage was observed by using visual inspection, non-destructive test, and image processing method. Moreover, the absorbing performance of intact and damaged RAS was measured by the free-space measurement system. The experiment results revealed that the delamination damage from the impact energy of 15 J did not considerably affect the microwave absorbing performance of the RAS. However, fiber breakage and penetration damage with a relatively large damaged area were occuured when the impact energy was increased up to 40 J and 60 J, and these failures significantly degraded the microwave absorbing characteristics of the RAS.

• Journal of the Korean Society of Propulsion Engineers
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• v.26 no.1
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• pp.47-59
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• 2022

Four swirl coaxial injectors with different recess lengths were manufactured using an additive manufacturing method. Single-injection and bi-injection cold-flow experiments were performed using water and air as simulated propellants in an atmospheric pressure environment. According to the recess length and propellant flow conditions, the injection pressure drop and discharge coefficient were investigated, and the breakup length and spray angle were measured using an image processing technique. In the bi-injection pressure drop and discharge coefficient results, the liquid-side injector was not affected by the recess. For the gas-side injector, however, the injection pressure drop increased and the discharge coefficient decreased as the recess length increased. The breakup length in the single-injection increased with the increase of the recess, but decreased in the bi-injection.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.11
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• pp.305-310
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• 2021

Artificial intelligence technology in the medical field initially focused on analysis and algorithm development, but it is gradually changing to web application development for service as a product. This paper describes a Urinary Stone segmentation model in abdominal CT images and an artificial intelligence web application based on it. To implement this, a model was developed using U-Net, a fully-convolutional network-based model of the end-to-end method proposed for the purpose of image segmentation in the medical imaging field. And for web service development, it was developed based on AWS cloud using a Python-based micro web framework called Flask. Finally, the result predicted by the urolithiasis segmentation model by model serving is shown as the result of performing the AI web application service. We expect that our proposed AI web application service will be utilized for screening test.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.12
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• pp.561-568
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• 2021

In recent years, autonomous driving technologies have become a high-value-added technology that attracts attention in the fields of science and industry. For smooth Self-driving, it is necessary to accurately detect an object and estimate its movement speed in real time. CNN-based deep learning algorithms and conventional dense optical flows have a large consumption time, making it difficult to detect objects and estimate its movement speed in real time. In this paper, using a single camera image, fast object detection was performed using the YOLOv5 algorithm, a deep learning algorithm, and fast estimation of the speed of the object was performed by using a local dense optical flow modified from the existing dense optical flow based on the detected object. Based on this algorithm, we present a system that can predict the collision time and probability, and through this system, we intend to contribute to prevent tram accidents.