• 한국전기전자재료학회논문지
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• 제19권12호
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• pp.1099-1105
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• 2006

In this paper, the equivalent circuit model of a Rosen-type multilayer piezoelectric transformer(MPT) has been proposed based on the Mason's equivalent circuit model and the principle of single layer piezoelectric plate. From the piezoelectric direct and converse effects, the symbolic expressions between the electric inputs and outputs of the MPT have been derived from the equivalent circuit model. A simplified equivalent circuit model of the MPT whose driving part has a single input form has been proposed. The symbolic expressions of the driving part have been derived from the simplified equivalent circuit model and the model was compared with the multi-input equivalent circuit model through the simulation. In the comparisons between the simulation results and the experimental data, output voltage is 630 Vp-p in case of 11-layered MPT and 670 Vp-p for 13-layered MPT over the experiment range. As the load resistance increases, output voltage increases and saturates over $300k{\Omega}$ and the resonant frequency changes from 102 kHz to 103 kHz. The simulation and the experimental results agree well over different load resistances and frequencies.

• 전기학회논문지
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• 제57권9호
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• pp.1507-1515
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• 2008

It is essential for electric power companies to have a quick restoration system of the faulted pole transformers which occupy most of transformers to supply stable electricity. However, it takes too much time to restore it when a transformer is out of order suddenly because we now count on operator in investigating causes of failure and making decision of recovery methods. This paper presents the concept of 'Fault pattern analysis and Restoration prediction model using Data mining techniques’, which is based on accumulated fault record of pole transformers in the past. For this, it also suggests external and internal causes of fault which influence the fault pattern of pole transformers. It is expected that we can reduce not only defects in manufacturing procedure by upgrading quality but also the time of predicting fault patterns and recovering when faults occur by using the result.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 춘계학술발표대회
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• pp.687-688
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• 2023

Bone age assessment is a crucial task in pediatric radiology for assessing growth and development in children. In this paper, we explore the potential of Vision Transformer, a state-of-the-art deep learning model, for bone age assessment using X-ray images. We generate heatmap outputs using a pre-trained Vision Transformer model on a publicly available dataset of hand X-ray images and show that the model tends to focus on the overall hand and only the bone part of the image, indicating its potential for accurately identifying the regions of interest for bone age assessment without the need for pre-processing to remove background noise. We also suggest two methods for extracting the region of interest from the heatmap output. Our study suggests that Vision Transformer holds great potential for bone age assessment using X-ray images, as it can provide accurate and interpretable output that may assist radiologists in identifying potential abnormalities or areas of interest in the X-ray image.

• 로봇학회논문지
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• 제18권4호
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• pp.436-443
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• 2023

With the advancement of robot intelligence, the concept of man and unmanned teaming (MUM-T) has garnered considerable attention in military research. In this paper, we present a transformer-based architecture for predicting the health status of agents, with the help of multi-head attention mechanism to effectively capture the dynamic interaction between friendly and enemy forces. To this end, we first introduce a framework for generating a dataset of battlefield situations. These situations are simulated on a virtual simulator, allowing for a wide range of scenarios without any restrictions on the number of agents, their missions, or their actions. Then, we define the crucial elements for identifying the battlefield, with a specific emphasis on agents' status. The battlefield data is fed into the transformer architecture, with classification headers on top of the transformer encoding layers to categorize health status of agent. We conduct ablation tests to assess the significance of various factors in determining agents' health status in battlefield scenarios. We conduct 3-Fold corss validation and the experimental results demonstrate that our model achieves a prediction accuracy of over 98%. In addition, the performance of our model are compared with that of other models such as convolutional neural network (CNN) and multi layer perceptron (MLP), and the results establish the superiority of our model.

• Journal of Electrical Engineering and Technology
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• 제1권3호
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• pp.302-307
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• 2006

The purpose of this paper is to develop an improved three-phase transformer model for ATP and parameter estimation methods that can efficiently utilize the limited available information such as factory test reports. In this paper, improved topologically-correct duality-based models are developed for three-phase autotransformers having shell-form cores. The problem in the implementation of detailed models is the lack of complete and reliable data. Therefore, parameter estimation methods are developed to determine the parameters of a given model in cases where available information is incomplete. The transformer nameplate data is required and relative physical dimensions of the core are estimated. The models include a separate representation of each segment of the core, including hysteresis of the core, ${\lambda}-i$ saturation characteristic and core loss.

• 대한전기학회논문지:전력기술부문A
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• 제55권6호
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• pp.221-226
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• 2006

The current flowing though a power line is measured by a current transformer (CT). Since a CT is a kind of transformer, saturation of magnetic flux in the core may occur when a large primary current flows. This saturation makes the secondary current of a CT distorted and causes problems in the protection point of view. Because of the current distortion, a protection relay cannot collect the correct information showing how the primary power system changed. Consequently, the current distortion may cause the mal-operation or operation time delay of protective relay. In this paper, an algorithm based on AR model and LSQ is proposed to compensate the saturated CT secondary currents. Various test results indicate that the proposed algorithm can accurately compensate a severely distorted secondary current and is not affected by remanence.

• Journal of Advanced Marine Engineering and Technology
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• 제28권5호
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• pp.837-846
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• 2004

This paper presents a screening technique for greatly reducing the computation involved in determining the optimal location and types of Flexible AC Transmission System (FACTS) devices in a large power system. The first-order sensitivities of the generation cost for various FACTS devices are derived. This technique requires solving only one optimal power flow (OPF) to obtain sensitivities with respect to FACTS device control variables for every transmission line To implement a sensitivity-based screening technique, we used a new UPFC model, which consists of an ideal transformer with a complex turns ratio and a variable shunt admittance. A S-bus system based on the IEEE 14-bus system was used to illustrate the technique.

• Journal of Magnetics
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• 제22권2호
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• pp.196-202
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• 2017

The occurrence of ferroresonance in electrical systems including nonlinear inductors such as transformers will bring a lot of malicious damages. The intense ferromagnetic saturation of the iron core is the most influential factor in ferroresonance that makes nonsinusoidal current and voltage. So the nonlinear behavior modeling of the magnetic core is the most important challenge in the study of ferroresonance. In this paper, the ferroresonance phenomenon is investigated in a single phase transformer using the finite element method and considering the hysteresis loop. Jiles-Atherton (JA) inverse vector model is used for modeling the hysteresis loop, which provides the accurate nonlinear model of the transformer core. The steady-state analysis of ferroresonance is done while considering different capacitors in series with the no-load transformer. The accurate results from copper losses and iron losses are extracted as the most important specifications of transformers. The validity of the simulation results is confirmed by the corresponding experimental measurements.

• 전기학회논문지P
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• 제65권3호
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• pp.188-193
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• 2016

It has been proven that the dissolved gas analysis (DGA) is the most effective and convenient method to diagnose the transformers. The DGA is a simple, inexpensive, and non intrusive technique. Among the various diagnosis methods, IEC 60599 has been widely used in transformer in service. But this method cannot offer accurate diagnosis for all the faults. This paper proposes a fault diagnosis method of oil-filled power transformers using DGA and Intelligent Probability Model. To demonstrate the validity of the proposed method, experiment is performed and its results are illustrated.

• 산업경영시스템학회지
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• 제46권3호
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• pp.123-138
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• 2023

The Fourth Industrial Revolution and sensor technology have led to increased utilization of sensor data. In our modern society, data complexity is rising, and the extraction of valuable information has become crucial with the rapid changes in information technology (IT). Recurrent neural networks (RNN) and long short-term memory (LSTM) models have shown remarkable performance in natural language processing (NLP) and time series prediction. Consequently, there is a strong expectation that models excelling in NLP will also excel in time series prediction. However, current research on Transformer models for time series prediction remains limited. Traditional RNN and LSTM models have demonstrated superior performance compared to Transformers in big data analysis. Nevertheless, with continuous advancements in Transformer models, such as GPT-2 (Generative Pre-trained Transformer 2) and ProphetNet, they have gained attention in the field of time series prediction. This study aims to evaluate the classification performance and interval prediction of remaining useful life (RUL) using an advanced Transformer model. The performance of each model will be utilized to establish a health index (HI) for cutting blades, enabling real-time monitoring of machine health. The results are expected to provide valuable insights for machine monitoring, evaluation, and management, confirming the effectiveness of advanced Transformer models in time series analysis when applied in industrial settings.