• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.334-337
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• 2019

In this study, we built the data collection system to learn user's habit data by deep learning and to create an indoor environment according to the situation. The system consists of a data collection server and several sensor nodes, which creates the environment according to the data collected. We used Google Inception v3 network to analyze the photographs and hand-designed second DNN (Deep Neural Network) to infer behaviors. As a result of the DNN learning, we gained 98.4% of Testing Accuracy. Through this results, we were be able to prove that DNN is capable of extrapolating the situation.

• Progress in Superconductivity and Cryogenics
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• v.11 no.2
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• pp.25-28
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• 2009

A 154kV class high-temperature superconducting (HTS) power cable system is developing in Korea. For insulation design of this cable, it is important that study on cryogenic electrical insulation design to develop the cold dielectric type HTS cable because the cable is operated under the high voltage environment in cryogenic temperature. Therefore, this paper describes a design method for the electrical insulation layer of the cold dielectric type HTS cable adopting the partial discharge-free design under ac stress, based on the experimental results such a ac breakdown strength, partial discharge inception stress, $V_{ac}$-t characteristics, $V_{imp}$-n characteristics, and impulse breakdown strength of liquid nitrogen/laminated polypropylene paper (LPP) composite insulation system in which the mini-model cable is immersed into pressurized liquid nitrogen.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.3
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• pp.385-396
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• 2024

To enhance the accuracy of sinkhole detection using GPR, this study derived a convolutional neural network that can optimally extract sinkhole characteristics from GPR B-scan grayscale images. The pre-trained convolutional neural network is evaluated to be more than twice as effective as the vanilla convolutional neural network. In pre-trained convolutional neural networks, fast feature extraction is found to cause less overfitting than feature extraction. It is analyzed that the top-1 verification accuracy and computation time are different depending on the type of architecture and simulation conditions. Among the pre-trained convolutional neural networks, InceptionV3 are evaluated as most robust for sinkhole detection in GPR B-scan grayscale images. When considering both top-1 verification accuracy and architecture efficiency index, VGG19 and VGG16 are analyzed to have high efficiency as the backbone for extracting sinkhole feature from GPR B-scan grayscale images. MobileNetV3-Large backbone is found to be suitable when mounted on GPR equipment to extract sinkhole feature in real time.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.2
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• pp.239-245
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• 2020

Recently, BIM (Building Information Modeling) are widely being utilized in Construction industry. However, most structures that have been constructed in the past do not have BIM. For structures without BIM, the use of SfM (Structure from Motion) techniques in the 2D image obtained from the camera allows the generation of 3D model point cloud data and BIM to be established. However, since these generated point cloud data do not contain semantic information, it is necessary to manually classify what elements of the structure. Therefore, in this study, deep learning was applied to automate the process of classifying structural components. In the establishment of deep learning network, Inception-ResNet-v2 of CNN (Convolutional Neural Network) structure was used, and the components of bridge structure were learned through transfer learning. As a result of classifying components using the data collected to verify the developed system, the components of the bridge were classified with an accuracy of 96.13 %.

• The Journal of Society for e-Business Studies
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• v.26 no.2
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• pp.19-43
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• 2021

The prediction of box office performance in performing arts institutions is an important issue in the performing arts industry and institutions. For this, traditional prediction methodology and data mining methodology using standardized data such as cast members, performance venues, and ticket prices have been proposed. However, although it is evident that audiences tend to seek out their intentions by the performance guide poster, few attempts were made to predict box office performance by analyzing poster images. Hence, the purpose of this study is to propose a deep learning application method that can predict box office success through performance-related poster images. Prediction was performed using deep learning algorithms such as Pure CNN, VGG-16, Inception-v3, and ResNet50 using poster images published on the KOPIS as learning data set. In addition, an ensemble with traditional regression analysis methodology was also attempted. As a result, it showed high discrimination performance exceeding 85% of box office prediction accuracy. This study is the first attempt to predict box office success using image data in the performing arts field, and the method proposed in this study can be applied to the areas of poster-based advertisements such as institutional promotions and corporate product advertisements.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.197-202
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• 2021

Biometric technology using finger veins is receiving a lot of attention due to its high security, convenience and accuracy. And the recent development of deep learning technology has improved the processing speed and accuracy for authentication. However, the training data is a subset of real data not in a certain order or method and the results are not constant. so the amount of data and the complexity of the artificial neural network must be considered. In this paper, the deep learning model of Inception-Resnet-v2 was used to improve the high accuracy of the finger vein recognizer and the performance of the authentication system, We compared and analyzed the performance of the deep learning model of DenseNet-201. The simulations used data from MMCBNU_6000 of Jeonbuk National University and finger vein images taken directly. There is no preprocessing for the image in the finger vein authentication system, and the results are checked through EER.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.73-88
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• 2023

Brain tumors can also be an abnormal collection or accumulation of cells in the brain that can be life-threatening due to their ability to invade and metastasize to nearby tissues. Accurate diagnosis is critical to the success of treatment planning, and resonant imaging is the primary diagnostic imaging method used to diagnose brain tumors and their extent. Deep learning methods for computer vision applications have shown significant improvements in recent years, primarily due to the undeniable fact that there is a large amount of data on the market to teach models. Therefore, improvements within the model architecture perform better approximations in the monitored configuration. Tumor classification using these deep learning techniques has made great strides by providing reliable, annotated open data sets. Reduce computational effort and learn specific spatial and temporal relationships. This white paper describes transfer models such as the MobileNet model, VGG19 model, InceptionResNetV2 model, Inception model, and DenseNet201 model. The model uses three different optimizers, Adam, SGD, and RMSprop. Finally, the pre-trained MobileNet with RMSprop optimizer is the best model in this paper, with 0.995 accuracies, 0.99 sensitivity, and 1.00 specificity, while at the same time having the lowest computational cost.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.24 no.11
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• pp.890-894
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• 2011

In this thesis, the silicone filler with a sample size of 0~75 phr and void size of 2~4.5 mm is prepared in order to diagnose the defect of void which exists in widely used insulation material, silicone rubber. In this silicone rubber sample, electrodes are connected and whilst the voltage changes, applied voltage 7 kV~9 kV is increased constantly over time and discharge quantity, discharge frequency and applied voltage (T-QNV) were measured. The discharge quantity of the applied voltage (VQ) is measured to estimate inception voltage and extinction voltage. In addition, under the condition of maintaining constant applied voltage, discharge quantity and discharge frequency (QN) are measured, and its characteristics are analyzed.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2005.07a
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• pp.542-543
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• 2005

In this paper, describes the fabrication and dielectric insulation characteristics experimental results of the model cable for the 22.9kV class HTS power cable. The model cable were tested with partial discharge(PD), AC and impulse withstand voltage in liquid nitrogen($LN_2$) and liquid nitrogen pressure. In these test results, PD inception stress and AC, impulse breakdown strength increase as the pressure of the liquid nitrogen increases.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.54 no.2
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• pp.69-75
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• 2005

In order to improve the reliability of high voltage rotating machines and mold transformers, it is necessary to understand the breakdown mechanism and life assessment of the high voltage winding parts. Especially the on-line PD test provides the ability to monitor effects, such as slot discharge, internal discharge, and end-winding discharge without interrupting the electrical machines, this method has been proven the major testing technology. Capacitive couplers have been the most widely used sensors for the on-line partial discharge detection in rotating machines nowadays. This paper deals with the electrical characteristics and long-term reliability of a ceramic coupler(CC), which can be easily mounted into high voltage input terminal part, has been developed and tested to continuously measure PD activity during operating condition. This paper presents electrical characteristics (dielectric loss angle, capacitance, PD inception level, breakdown voltage, and frequency response bandwidth) and long-term life test result of the developed 6.6 kV class on-line ceramic coupling sensor. It was found that this sensor had good electrical characteristics to detect PD activity during the operating condition with its detection frequency band is between several and several tens MHz. Also, the voltage life of the 6.6kV class ceramic coupler was calculated over 60 years.