• ETRI Journal
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• v.39 no.5
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• pp.643-651
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• 2017

Human age estimation is one of the key factors in the field of Human-Robot Interaction/Human-Computer Interaction (HRI/HCI). Owing to the development of deep-learning technologies, age recognition has recently been attempted. In general, however, deep learning techniques require a large-scale database, and for age learning with variations, a conventional database is insufficient. For this reason, we propose an age estimation method using artificially generated data. Image data are artificially generated through 3D information, thus solving the problem of shortage of training data, and helping with the training of the deep-learning technique. Augmentation using 3D has advantages over 2D because it creates new images with more information. We use a deep architecture as a pre-trained model, and improve the estimation capacity using artificially augmented training images. The deep architecture can outperform traditional estimation methods, and the improved method showed increased reliability. We have achieved state-of-the-art performance using the proposed method in the Morph-II dataset and have proven that the proposed method can be used effectively using the Adience dataset.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.206-214
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• 2022

The way to measure the extent of damage from floods and droughts is to identify changes in the extent of water systems. In order to effectively grasp this at a glance, satellite images are used. KOMPSAT-5 uses Synthetic Aperture Radar (SAR) to capture images regardless of weather conditions such as clouds and rain. In this paper, various deep learning models are applied to perform semantic segmentation of the water system in this SAR image and the performance is compared. The models used are U-net, V-Net, U2-Net, UNet 3+, PSPNet, Deeplab-V3, Deeplab-V3+ and PAN. In addition, performance comparison was performed when the data was augmented by applying elastic deformation to the existing SAR image dataset. As a result, without data augmentation, U-Net was the best with IoU of 97.25% and pixel accuracy of 98.53%. In case of data augmentation, Deeplab-V3 showed IoU of 95.15% and V-Net showed the best pixel accuracy of 96.86%.

• The Journal of Korea Robotics Society
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• v.18 no.4
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• pp.376-384
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• 2023

In this paper, we introduce an upper extremity rehabilitation robot, NREH (NRC End-effector based Rehabilitation arm at Home). Through NREH, stroke survivors could continuously exercise their upper extremities at home. NREH allows a user to hold the handle of the end-effector of the robot arm. NREH is a end-effector-based robot that moves the arm on a two-dimensional plane, but the tilt angle can be adjusted to mimic a movement similar to that in a three-dimensional space. Depending on the tilting angle, it is possible to perform customized exercises that can adjust the difficulty for each user. The user can sit down facing the robot and perform exercises such as arm reaching. When the user sits 90 degrees sideways, the user can also exercise their arms on a plane parallel to the sagittal plane. NREH was designed to be as simple as possible considering its use at home. By applying error augmentation, the exercise effect can be increased, and assistance force or resistance force can be applied as needed. Using an encoder on two actuators and a force/torque sensor on the end-effector, NREH can continuously collect and analyze the user's movement data.

• Journal of Broadcast Engineering
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• v.25 no.2
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• pp.275-278
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• 2020

In this paper, we proposed a deep learning based character recognition system through virtual data generation. In order to secure the learning data that takes the largest weight in supervised learning, virtual data was created. Also, after creating virtual data, data generalization was performed to cope with various data by using augmentation parameter. Finally, the learning data composition generated data by assigning various values to augmentation parameter and font parameter. Test data for measuring the character recognition performance was constructed by cropping the text area from the actual image data. The test data was augmented considering the image distortion that may occur in real environment. Deep learning algorithm uses YOLO v3 which performs detection in real time. Inference result outputs the final detection result through post-processing.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.1
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• pp.23-35
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• 2023

Korea Augmentation Satellite System (KASS) is a satellite-based augmentation system (SBAS) that provides approach procedure with vertical guidance-I (APV-I) level corrections and integrity information to Korea territory. KASS is used to monitor navigation performance in real-time, and this paper introduces the design, implementation, and verification process of mission monitoring (MIMO) in KASS. MIMO was developed in compliance with the Minimum Operational Performance Standards of the Radio Technical Commission for Aeronautics for Global Positioning System (GPS)/SBAS airborne equipment. In this study, the MIMO system was verified by comparing and analyzing the outputs of reference tools. Additionally, the definition and derivation method of accuracy, integrity, continuity, and availability subject to MIMO were examined. The internal and external interfaces and functions were then designed and implemented. The GPS data pre-processing was minimized during the implementation to evaluate the navigation performance experienced by general users. Subsequently, tests and verification methods were used to compare the obtained results based on reference tools. The test was performed using the KASS dataset, which included GPS and SBAS observations. The decoding performance of the developed MIMO was identical to that of the reference tools. Additionally, the navigation performance was verified by confirming the similarity in trends. As MIMO is a component of KASS used for real-time monitoring of the navigation performance of SBAS, the KASS operator can identify whether an abnormality exists in the navigation performance in real-time. Moreover, the preliminary identification of the abnormal point during the post-processing of data can improve operational efficiency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2304-2320
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• 2021

Intelligently detecting anomalies in health sensor data streams (e.g., Electrocardiogram, ECG) can improve the development of E-health industry. The physiological signals of patients are collected through sensors. Timely diagnosis and treatment save medical resources, promote physical health, and reduce complications. However, it is difficult to automatically classify the ECG data, as the features of ECGs are difficult to extract. And the volume of labeled ECG data is limited, which affects the classification performance. In this paper, we propose a Generative Adversarial Network (GAN)-based deep learning framework (called CAB) for heart arrhythmia classification. CAB focuses on improving the detection accuracy based on a small number of labeled samples. It is trained based on the class-imbalance ECG data. Augmenting ECG data by a GAN model eliminates the impact of data scarcity. After data augmentation, CAB classifies the ECG data by using a Bidirectional Long Short Term Memory Recurrent Neural Network (Bi-LSTM). Experiment results show a better performance of CAB compared with state-of-the-art methods. The overall classification accuracy of CAB is 99.71%. The F1-scores of classifying Normal beats (N), Supraventricular ectopic beats (S), Ventricular ectopic beats (V), Fusion beats (F) and Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively. Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively.

• Smart Structures and Systems
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• v.29 no.1
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• pp.237-250
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• 2022

Structural health monitoring (SHM) plays an important role in ensuring the safety and functionality of critical civil infrastructure. In recent years, numerous researchers have conducted studies to develop computer vision and machine learning techniques for SHM purposes, offering the potential to reduce the laborious nature and improve the effectiveness of field inspections. However, high-quality vision data from various types of damaged structures is relatively difficult to obtain, because of the rare occurrence of damaged structures. The lack of data is particularly acute for fatigue crack in steel bridge girder. As a result, the lack of data for training purposes is one of the main issues that hinders wider application of these powerful techniques for SHM. To address this problem, the use of synthetic data is proposed in this article to augment real-world datasets used for training neural networks that can identify fatigue cracks in steel structures. First, random textures representing the surface of steel structures with fatigue cracks are created and mapped onto a 3D graphics model. Subsequently, this model is used to generate synthetic images for various lighting conditions and camera angles. A fully convolutional network is then trained for two cases: (1) using only real-word data, and (2) using both synthetic and real-word data. By employing synthetic data augmentation in the training process, the crack identification performance of the neural network for the test dataset is seen to improve from 35% to 40% and 49% to 62% for intersection over union (IoU) and precision, respectively, demonstrating the efficacy of the proposed approach.

• Journal of Positioning, Navigation, and Timing
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• v.2 no.1
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• pp.41-48
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• 2013

When the ground subsystem of Ground Based Augmentation System(GBAS) is installed at the airport, the functions and performance of subsystem should be evaluated through ground and flight testing at the pre-commissioning phase. In the case of GBAS flight testing, it can be conducted by the existing flight check aircraft, but the GBAS ground testing requires the development of specially customized equipment to perform the ground testing. Therefore, this paper describes the preliminary design of GBAS onboard test equipment which can be independently used for the GBAS ground testing and flight testing on a car and an aircraft.

• Journal of Environmental Science International
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• v.14 no.11
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• pp.995-1004
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• 2005

As a research establish reservoir safety operation for small dam systems. This study presents hydrologic analysis conducted in the Duckdong and Bomun dam watershed based on various rainfall data and increase inflow. Especially the Duckdong dam without flood control feature are widely exposed to the risk of flooding, thus it is constructed emergency gate at present. In this study reservoir routing program was simulation for basin runoff estimating using HEC-HMS model, the model simulation the reservoir condition of emergency Sate with and without. At the reservoir analysis results is the Duckdong dam average storage decrease $20\%$ with emergency gate than without emergency gate. Also, the Bomun dam is not affected by the Duckdong flood control augmentation.

• 한국전산유체공학회:학술대회논문집
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• 2001.10a
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• pp.138-143
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• 2001

An experimental model to enhance the mixing of parallel supersonic-subsonic jet ($M_1$=1.78 and $M_2$=0.30) is simulated with a numerical technique by modeling the wall-mounted cavity to a boundary condition of oscillating pressure. The computed pilot pressure distributions along three representative cross sections show a good agreement with the equivalent experimental data. The irradiation of acoustic wave in the ultrasonic range causes the mixing augmentation of jet and wake due to the transfer of vibration energy between fluid particles.