• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.61-63
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• 1995

The main factors to bring out the power failure are trouble and operation power failure, the operation power failure of these includes about 85%. So, it is required that the without interruption of service method reduce the operation power failure. If we develop the without interruption of service method, the operation power failure will reduce greatly. It is necessary to develop the without interruption of service method which it solve various trobles (dissatisfacting solution of customer, sales revenue augmentation, and the reduction of the power failure negotiation work). The results of study are drawing up of the specification for temporary transmission methods, it of the working methods for them, and the scrutiny of the technology contents for them.

• The Korean Journal of Applied Statistics
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• v.35 no.2
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• pp.189-201
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• 2022

In this study, we propose factor augmentation to improve forecasting power of cryptocurrency return. We consider financial and economic variables as well as psychological aspect for possible factors. To be more specific, financial and economic factors are obtained by applying principal factor analysis. Psychological factor is summarized by news sentiment analysis. We also visualize such factors through impulse response analysis. In the modeling perspective, we consider ARIMAX as the classical model, and random forest and deep learning to accommodate nonlinear features. As a result, we show that factor augmentation reduces prediction error and the GRU performed the best amongst all models considered.

• Journal of Positioning, Navigation, and Timing
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• v.7 no.4
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• pp.267-275
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• 2018

The reference stations in a satellite-based augmentation system (SBAS) collect raw data from global navigation satellite system (GNSS) to generate correction and integrity information. The multipath signals degrade GNSS raw data quality and have adverse effects on the SBAS performance. The currently operating SBASs (WAAS and EGNOS, etc.) survey existing commercial equipment to perform multipath assessment around the antennas. For the multi-path assessment, signal power of GNSS and multipath at the MEDLL receiver of NovAtel were estimated and the results were replicated by a ratio of signal power estimated at NovAtel Multipath Assessment Tool (MAT). However, the same experiment environment used in existing systems cannot be configured in reference stations in Korean augmentation satellite system (KASS) due to the discontinued model of MAT and MEDLL receivers used in the existing systems. This paper proposes a test environment for multipath assessment around the antennas in KASS Multipath Assessment Tool (K-MAT) for multipath assessment. K-MAT estimates a multipath error contained in the code pseudorange using linear combination between the measurements and replicates the results through polar plot and histogram for multipath assessment using the estimated values.

• KEPCO Journal on Electric Power and Energy
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• v.8 no.2
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• pp.137-141
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• 2022

To inspect the power facility faults using artificial intelligence, it need that improve the accuracy of the diagnostic model are required. Data augmentation skill using generative adversarial network (GAN) is one of the best ways to improve deep learning performance. GAN model can create realistic-looking fake images using two competitive learning networks such as discriminator and generator. In this study, we intend to verify the effectiveness of virtual data generation technology by including the fake image of power facility generated through GAN in the deep learning training set. The GAN-based fake image was created for damage of LP insulator, and ResNet based normal and defect classification model was developed to verify the effect. Through this, we analyzed the model accuracy according to the ratio of normal and defective training data.

• Journal of the Korean Society of Propulsion Engineers
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• v.3 no.2
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• pp.66-74
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• 1999

In this study, the heat transfer augmentation without additional blower power was obtained by using punched plate. Though this scheme has some disadvantages such as decrease of momentum caused by the punched plate, it has significantly increased heat transfer coefficients in the stagnation region. Compared with the case of no punched plate, heat transfer coefficients were increased by punched plate more than 2 times near the optimum condition determined in this study. Liquid crystal and hue-capturing technique were used in this study to obtain heat transfer coefficients.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.33 no.8
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• pp.299-309
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• 1984

This paper proposes the adapter for interrupting capacity augmentation of circuit breaker which can be applied in case of shortage in a existing circuit breaker's interrupting capacity due to utility system extension. The adapter utilizes two winding type of reactor instead of single winding type of reactor and the control of 2ry circuit is excuted by a triac interlocked with the system protective relays actuation so as to cut out the reactor by short circuit of the 2ry winding in normal situation and to cut in the reactor by open circuit of the 2ry winding in abnomal situation such as short circuit accident. As a result of the theoritical analysis and experiment, it is proved that the adaptor can reduce the voltage crop and iron loss due to the reactor signigicantly in normal system condition and do a role of reactor upon the power system accident.

• Journal of information and communication convergence engineering
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• v.16 no.3
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• pp.173-178
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• 2018

Accurate classification of cloud images is a challenging task. Almost all the existing methods rely on hand-crafted feature extraction. Their limitation is low discriminative power. In the recent years, deep learning with convolution neural networks (CNNs), which can auto extract features, has achieved promising results in many computer vision and image understanding fields. However, deep learning approaches usually need large datasets. This paper proposes a deep learning approach for classification of cloud image patches on small datasets. First, we design a suitable deep learning model for small datasets using a CNN, and then we apply data augmentation and dropout regularization techniques to increase the generalization of the model. The experiments for the proposed approach were performed on SWIMCAT small dataset with k-fold cross-validation. The experimental results demonstrated perfect classification accuracy for most classes on every fold, and confirmed both the high accuracy and the robustness of the proposed model.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.2 no.3
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• pp.234-242
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• 1990

In this study, heat transfer characteristics of vertical fixed bed with air flowing through were experimentally investigated. The experiments were conducted for cases where heat was transfer steady state conditions from an immersed cylindrical heated to the surrounding fixed ved of steel balls. Based on the experimental data, the optimum conditions for heat transfer augmentation have been determined and shows in empirical forms. For the same power loss, comparison of heat transfer effect between the fixed bed and single phase forced convection device indicates that both miniaturization of heat exchange device and heat transfer augmentation at low flow velocity are possible by application of the fixed bed to heat exchangers. The present results could provide a useful design information for the heat exchangers where fixed beds are used.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.443-451
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• 2023

Machine Learning has introduced many solutions in data science, but its application in IoT faces significant challenges, due to the limitations in memory size and processing capability of constrained devices. In this paper we design an automatic gamma radiation detection and identification embedded system that exploits the power of TinyML in a SiPM micro radiation sensor leveraging the Edge Impulse platform. The model is trained using real gamma source data enhanced by software augmentation algorithms. Tests show high accuracy in real time processing. This design has promising applications in general-purpose radiation detection and identification, nuclear safety, medical diagnosis and it is also amenable for deployment in small satellites.

• The KSFM Journal of Fluid Machinery
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• v.15 no.3
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• pp.57-63
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• 2012

Effects of coolant pre-cooling and fuel pre-heating on the performance of a combined cycle using a F-class gas turbine were investigated. Coolant pre-cooling results in an increase of power output but a decrease in efficiency. Performance variation due to the fuel pre-heating depends on the location of the heat source for the pre-heating in the bottoming cycle (heat recovery steam generator). It was demonstrated that a careful selection of the heat source location would enhance efficiency with a minimal power penalty. The effect of combining the coolant pre-cooling and fuel pre-heating was also investigated. It was found that a favorable combination would yield power augmentation, while efficiency remains close to the reference value.