• 대한설비공학회:학술대회논문집
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• 대한설비공학회 2008년도 하계학술발표대회 논문집
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• pp.22-29
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• 2008

In order to improve the efficiency of a main transformer in a tilting train, the optimal operation of a cooling system is necessary. For the development of optimal control algorithms of a cooling system, mathematical models of a main transformer cooling system were developed. These include dynamic models of a main transformer, an oil pump, an oil cooler, a blower, and a pipe. Control algorithms for a blower and an oil pump were selected in order to identify the effectiveness of dynamic models. A simulation program was developed by using the developed dynamic models and the selected control algorithms. Simulation results showed good predictions of dynamic behaviors of a main transformer cooling system. Therefore, dynamic models, which were developed in this study, may be effectively used to develop control algorithms of a main transformer cooling system.

• 설비공학논문집
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• 제22권2호
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• pp.70-77
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• 2010

In order to improve the efficiency of a main transformer in a train, the optimal operation of a cooling system is necessary. For the development of optimal control algorithms of a cooling system, mathematical models of a main transformer cooling system were developed. These include static and dynamic models of a main transformer, an oil pump, an oil cooler, and a blower. Static models were used to find optimal oil temperatures of the inlet and the outlet of a transformer. Dynamic models were used to predict transient performances of control algorithms of a blower and an oil pump. Simulation results showed good predictions of the static and the dynamic behavior of a main transformer cooling system. Therefore, mathematical models developed in this study may be effectively used for the development of control algorithms of a main transformer cooling system.

• 대한설비공학회:학술대회논문집
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• 대한설비공학회 2009년도 하계학술발표대회 논문집
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• pp.955-960
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• 2009

In order to improve the efficiency of the main transformer in a tilting train, the optimal operation of a cooling system is necessary. Mathematical models of a main transformer cooling system were developed. These include models for the main transformer, the oil pump, the oil cooler, and the blower. The optimal oil temperature algorithm was also developed. This consists of the optimal setpoint algorithm and the control algorithm. A simulation program was developed by using mathematical models and the optimal oil temperature algorithm. Simulation results showed that the dynamic behavior of a main transformer cooling system was predicted well by mathematical models and a main transformer cooling system was controlled effectively by the optimal oil temperature algorithm.

• 한국지리정보학회지
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• 제27권1호
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• pp.115-127
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• 2024

본 연구는 Transformer 모듈을 기반으로 다양한 구조의 모델을 구성하고, 토지피복 분류를 수행하여 Transformer 모듈의 활용방안 검토를 목적으로 하였다. 토지피복 분류를 위한 딥러닝 모델은 CNN 구조를 가진 Unet 모델을 베이스 모델로 선정하였으며, 모델의 인코더 및 디코더 부분을 Transformer 모듈과 조합하여 총 4가지 딥러닝 모델을 구축하였다. 딥러닝 모델의 학습과정에서 일반화 성능 평가를 위해 같은 학습조건으로 10회 반복하여 학습을 진행하였다. 딥러닝 모델의 분류 정확도 평가결과, 모델의 인코더 및 디코더 구조 모두 Transformer 모듈을 활용한 D모델이 전체 정확도 평균 약 89.4%, Kappa 평균 약 73.2%로 가장 높은 정확도를 보였다. 학습 소요시간 측면에서는 CNN 기반의 모델이 가장 효율적이었으나 Transformer 기반의 모델을 활용할 경우, 분류 정확도가 Kappa 기준 평균 0.5% 개선되었다. 차후, CNN 모델과 Transformer의 결합과정에서 하이퍼파라미터 조절과 이미지 패치사이즈 조절 등 다양한 변수들을 고려하여 모델을 고도화 할 필요가 있다고 판단된다. 토지피복 분류과정에서 모든 모델이 공통적으로 발생한 문제점은 소규모 객체들의 탐지가 어려운 점이었다. 이러한 오분류 현상의 개선을 위해서는 고해상도 입력자료의 활용방안 검토와 함께 지형 정보 및 질감 정보를 포함한 다차원적 데이터 통합이 필요할 것으로 판단된다.

• 센서학회지
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• 제32권3호
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• pp.164-168
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• 2023

Accurate and reliable weather forecasts for temperature, relative humidity, and precipitation using advanced transformer models and IoT are essential in various fields related to global climate change. We propose a novel weather prediction device that integrates state-of-the-art transformer models and IoT techniques to improve prediction accuracy and real-time processing. The proposed system demonstrated high reliability and performance, offering valuable insights for industries and sectors that rely on accurate weather information, including agriculture, transportation, and emergency response planning. The integration of transformer models with the IoT signifies a substantial advancement in weather and climate modeling.

• 전력전자학회논문지
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• 제27권6호
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• pp.507-514
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• 2022

This study proposes an optimal design process for a high-frequency transformer that has a large leakage inductance for dual-active-bridge converters. Notably, conventional design processes have large errors in designing leakage transformers because mathematically modeling the leakage inductance of such transformers is difficult. In this work, the geometric parameters of a shell-type transformer are identified, and finite element analysis(FEA) simulation is performed to determine the magnetization inductance, leakage inductance, and copper loss of various shapes of shell-type transformers. Regression models for magnetization and leakage inductances and copper loss are established using the simulation results and the machine learning technique. In addition, to improve the regression models' performance, the regression models are tuned by adding featured parameters that consider the physical characteristics of the transformer. With the regression models, optimal high-frequency transformer designs and the Pareto front (in terms of volume and loss) are determined using NSGA-II. In the Pareto front, a desirable optimal design is selected and verified by FEA simulation and experimentation. The simulated and measured leakage inductances of the selected design match well, and this result shows the validity of the proposed design process.

• 대한임베디드공학회논문지
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• 제17권6호
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• pp.319-327
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• 2022

Transformers are the most famous deep learning models that has achieved great success in natural language processing and also showed good performance on computer vision. In this survey, we categorized transformer-based models for computer vision, particularly object detection tasks and perform comprehensive comparative experiments to understand the characteristics of each model. Next, we evaluated the models subdivided into standard transformer, with key point attention, and adding attention with coordinates by performance comparison in terms of object detection accuracy and real-time performance. For performance comparison, we used two metrics: frame per second (FPS) and mean average precision (mAP). Finally, we confirmed the trends and relationships related to the detection and real-time performance of objects in several transformer models using various experiments.

• Journal of Electrical Engineering and Technology
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• 제10권3호
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• pp.1093-1101
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• 2015

Partial Discharge (PD) is a physical phenomenon, which causes defects and damages to the insulation. This phenomenon is regarded as the most important source of fault and defect in power transformers. Therefore, methods of high speed and precision are considered of special importance for the maintenance of transformers in localization of the origin of partial discharge. In this paper, the transformer winding is first modeled in a transient state by using RLC ladder network and multiconductor transmission line (MTL) models. The parameters of the two models were calculated by Ansoft Maxwell software, and the simulations were performed by Matlab software. Then, the PD pulses were applied to the models with different widths of pulses. With regard to the fact that the signals received after the application of PD had a variable frequency nature over time, and based on the wavelet transform and signal energy, a new method was presented for the localization of PD. Ultimately; the mentioned method was implemented on a 20 kV winding distribution transformer. Then, the performances of the models used in this paper, including RLC and MTL models, were compared in different frequency bands for the correct distinction of partial discharge location.

• KIEE International Transactions on Power Engineering
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• 제5A권4호
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• pp.385-389
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• 2005

Power transformers would appear to be simple. However, due to their nonlinear and frequency-dependent behaviors, they can be one of the most complex system components to model. It is imperative that the applied models be appropriate for the range of frequencies and excitation levels that the system experiences. Transformer modeling is not a mature field and newer improved models must be made available in ATP packages. Further, there is a lack of published guidance on recommended modeling approaches. And there is typically not enough detailed design or test information available to determine the parameters for a given model. The purpose of this paper is to develop improved transformer core models for ATP and parameter estimation methods that can efficiently utilize the limited available information such as factory test reports.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 춘계학술발표대회
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• pp.624-626
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• 2023

Credit scoring is a technique used by financial institutions to assess the creditworthiness of potential borrowers. This involves evaluating a borrower's credit history to predict the likelihood of defaulting on a loan. This paper presents an ensemble of two Transformer based models within a framework for discriminating the default risk of loan applications in the field of credit scoring. The first model is FinBERT, a pretrained NLP model to analyze sentiment of financial text. The second model is FT-Transformer, a simple adaptation of the Transformer architecture for the tabular domain. Both models are trained on the same underlying data set, with the only difference being the representation of the data. This multi-modal approach allows us to leverage the unique capabilities of each model and potentially uncover insights that may not be apparent when using a single model alone. We compare our model with two famous ensemble-based models, Random Forest and Extreme Gradient Boosting.