• 전기의세계
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• v.20 no.6
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• pp.7-18
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• 1971

Secondary-side transfer phenomena of primary-side surge voltage in concentric-cylindrical transformers of a high turn-ratio still present a problem in transformer insulation design even in the case of a neutral solid-grounding type. The conventional methods of analyzing them so far are much complicated for practical applications. Therefore, this paper describes a new approach to the analysis of secondary-side transfer phenomena of surge in concentric-cylindrical transformers of high turn-ratio and solid-grounding type. This generalized approach is thought to be more simple with the use of minor network concepts than the conventional one by major network only. The result shows that the secondary-side transfer phenomena of surge voltage could not be neglected even in concentric-cylindrical transformer of high turn-ratio and solid-grounding type, and will be satisfactorily applicable to the design of neutral-solid-grounding type transformers.

• Journal of Electrical Engineering and information Science
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• v.1 no.2
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• pp.45-51
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• 1996

This paper presents a neural network approach for on-line estimation of partial discharge(PD) location using advanced correlation technique in power transformer. Ultrasonic sensors detect ultrasonic signals generated by a PD and the proposed method calculates time difference between the ultrasonic signals at each sensor pair using the cross-correlation technique applied by moving average and the Hamming window. The neural network takes distance difference as inputs converted from time difference, and estimates the PD location. Case studies showed that the proposed method using advanced correlation technique and a neural network estimated the PD location better than conventional methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1784-1789
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• 2021

Korean sentiment classification task is used in real-world services such as chatbots and analysis of user's purchase reviews. And due to the development of deep learning technology, neural network models with high performance are being applied. However, the neural network model is not easy to interpret what the input sentences are predicting due to which words, and recently, model interpretation methods for interpreting these neural network models have been popularly proposed. In this paper, we used the LIME algorithm among the model interpretation methods to interpret which of the words in the input sentences of the models learned with the korean sentiment classification dataset. As a result, the interpretation of the Bi-LSTM model with 85.24% performance included 25,283 words, but 84.20% of the transformer model with relatively low performance showed that the transformer model was more reliable than the Bi-LSTM model because it contains 26,447 words.

• The Journal of the Acoustical Society of Korea
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• v.28 no.6
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• pp.491-498
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• 2009

This paper presents the design method of an impedance matching network using an isolation transformer and the Chebyshev filter function for the high efficiency and the flat power driving of an underwater acoustic piezoelectric transducer. The proposed impedance matching network is designed for minimizing the reactance component of transducer and having the flat power response in the wide frequency range. We design a low pass filter with ladder-type circuit using the Chebyshev function as standard prototype filter function. In addition, we design the impedance matching network which is suitable for the equivalent circuit of transducer and the turn ratio of transformer through the bandpass frequency transformation. The proposed method is applied to the simulated dummy load of the tonpilz-type transducer operating in the middle frequency range. The simulation results are compared with the measured characteristics and the validity of the proposed method is verified.

• Journal of the Korea Computer Graphics Society
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• v.30 no.4
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• pp.11-18
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• 2024

In partial nephrectomy for kidney cancer treatment, accurate segmentation of the kidney tumor is crucial for surgical planning, as it provides essential information on the precise size and location of the tumor. However, it is challenging due to the tumor's similar intensity to surrounding organs and the variability in its location and size across patients. In this study, we propose a hybrid network that integrates a convolutional neural network and a transformer to capture both local and global features, aiming to improve the segmentation performance of kidney tumors. We validated our method through comparative experiments with UNETR++, outperforming it with a Dice Similarity Coefficient (DSC) of 78.54% and a precision of 85.0 7%. Moreover, in the analysis by tumor size, our method demonstrated improvements by reducing over-segmentation and outlier cases observed in UNETR++.

• Proceedings of the KIEE Conference
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• 2003.04a
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• pp.37-39
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• 2003

This paper Presents an effective modeling scheme of high temperature superconducting transformer. So far there were numerical modeling and designs for conventional transformers for various applications. Recently, the interest and the R&D in superconducting technology and devices such as superconducting generator, motor, cable, fault current limiter and transformer have been increased gradually. With those interests, this paper proposes a simulation model of high temperature superconducting transformer using PSCAD/EMTDC, which can be applied to the utility network simulation readily under various system conditions.

• Journal of the Semiconductor & Display Technology
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• v.6 no.2 s.19
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• pp.29-33
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• 2007

Low frequency (400 kHz) Ferrite ICP has been proposed for large area processing. Because the coupling coefficient is close to 1, the transformer matching can be adapted to Ferrite ICP. The transformer matching system is simple. In this paper the new matching system by controlling the turns of transformer using relays is proposed for impedance matching. We developed a simple matching system and characterized it when the turns were changed. It was observed that the 2-channel relay is available for transformer matching.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.6
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• pp.415-423
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• 2018

We herein investigate the E1 pulse for evaluating the conducted performance of transmission lines connected to the electromagnetic pulse protection facilities against a conducted high-altitude electromagnetic pulse threat exposed to an external electromagnetic environment. The existing E1 pulse generator uses the Marx generator high-voltage step-up method; however, in this research, we used the Tesla transformer method to easily change the broadband output voltage(30 to 350 kV). We also analyzed the controller, power supply, high-voltage booster, and pulse-shaping device. The E1 pulse performance using the Tesla transformer was predicted through simulations and validated by measurements.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.5
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• pp.624-631
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• 2012

This paper presents a high-efficiency current mode class-D(CMCD) power amplifier for the 13.56 MHz band using a Guanella's 1:1 transmission-line transformer and filtering circuits at the output network. The second and third s are filtered out in the load network of the class-D amplifier. The implemented CMCD power amplifier exhibited a power gain of 13.4 dB and a high power-added efficiency(PAE) of 84.6 % at an output power of 44.4 dBm using the 13.56 MHz CW input signal. The second and third distortion levels were -50.3 dBc and -46.4 dBc at the same output power level, respectively.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1743-1747
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• 2022

Sentinel-2 can be used as proxy data for the Korean Compact Advanced Satellite 500-4 (CAS500-4), also known as Agriculture and Forestry Satellite, in terms of spectral wavelengths and spatial resolution. This letter examined cloud detection for later use in the CAS500-4 based on deep learning technologies. DeepLabV3+, a traditional Convolutional Neural Network (CNN) model, and Shifted Windows (Swin) Transformer, a state-of-the-art (SOTA) Transformer model, were compared using 22,728 images provided by Radiant Earth Foundation (REF). Swin Transformer showed a better performance with a precision of 0.886 and a recall of 0.875, which is a balanced result, unbiased between over- and under-estimation. Deep learning-based cloud detection is expected to be a future operational module for CAS500-4 through optimization for the Korean Peninsula.