• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.44-45
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• 2007

This paper deals with error compensation in current transformers. Since the exciting current can be considered as the main error source, its evaluation can allow the compensation of its detrimental effects to be obtained. The exciting current required by the transformer in every king of steady state operation can be determined by simply acquiring the secondary current, provided that the examined CT has been preliminarily identified. This paper also proposed a new approach to the model of the exciting branch. The exciting branch can be divided into a non-linear core loss resistor, and a non-linear magnetizing inductor whose flux and current characteristic is not the same as the characteristic shown by the joined tips of the first quadrant of a family of hysteresis loops. The performance of the proposed algorithm was validated under various conditions using EMTP generated data. Test result show, in all cases an improvement in primary current reproduction accuracy, compared with that achieved using CT's ratio.

• Journal of Korean Society for Quality Management
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• v.49 no.3
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• pp.327-340
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• 2021

Purpose: The purpose of this study is to propose a system predicting whether an electricity distribution system is abnormal by analyzing the temperature of the deteriorated system. Traditional electricity distribution system abnormality diagnosis was mainly limited to post-inspection. This research presents a remote monitoring system for detecting thermal images of the deteriorated electricity distribution system efficiently hereby providing safe and efficient abnormal diagnosis to electricians. Methods: In this study, an object detection algorithm (YOLOv5) is performed using 16,866 thermal images of electricity distribution systems provided by KEPCO(Korea Electric Power Corporation). Abnormality/Normality of the extracted system images from the algorithm are classified via the limit temperature. Each classification model, Random Forest, Support Vector Machine, XGBOOST is performed to explore 463,053 temperature datasets. The process capability index is employed to indicate the quality of the electricity distribution system. Results: This research performs case study with transformers representing the electricity distribution systems. The case study shows the following states: accuracy 100%, precision 100%, recall 100%, F1-score 100%. Also the case study shows the process capability index of the transformers with the following states: steady state 99.47%, caution state 0.16%, and risk state 0.37%. Conclusion: The sum of caution and risk state is 0.53%, which is higher than the actual failure rate. Also most transformer abnormalities can be detected through this monitoring system.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.31-38
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• 2023

Style transfer is one of deep learning-based image processing techniques that has been actively researched recently. These research efforts have led to significant improvements in the quality of result images. Style transfer is a technology that takes a content image and a style image as inputs and generates a transformed result image by applying the characteristics of the style image to the content image. It is becoming increasingly important in exploiting the diversity of digital content. To improve the usability of style transfer technology, ensuring stable performance is crucial. Recently, in the field of natural language processing, the concept of Transformers has been actively utilized. Attention maps, which forms the basis of Transformers, is also being actively applied and researched in the development of style transfer techniques. In this paper, we analyze the representative techniques SANet and AdaAttN and propose a novel attention map-based structure which can generate improved style transfer results. The results demonstrate that the proposed technique effectively preserves the structure of the content image while applying the characteristics of the style image.

• Journal of information and communication convergence engineering
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• v.22 no.1
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• pp.33-43
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• 2024

Automated text summarization (ATS) systems rely on language resources as datasets. However, creating these datasets is a complex and labor-intensive task requiring linguists to extensively annotate the data. Consequently, certain public datasets for ATS, particularly in languages such as Thai, are not as readily available as those for the more popular languages. The primary objective of the ATS approach is to condense large volumes of text into shorter summaries, thereby reducing the time required to extract information from extensive textual data. Owing to the challenges involved in preparing language resources, publicly accessible datasets for Thai ATS are relatively scarce compared to those for widely used languages. The goal is to produce concise summaries and accelerate the information extraction process using vast amounts of textual input. This study introduced ThEconSum, an ATS architecture specifically designed for Thai language, using economy-related data. An evaluation of this research revealed the significant remaining tasks and limitations of the Thai language.

• Korean Journal of Radiology
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• v.25 no.3
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• pp.224-242
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• 2024

The emergence of Chat Generative Pre-trained Transformer (ChatGPT), a chatbot developed by OpenAI, has garnered interest in the application of generative artificial intelligence (AI) models in the medical field. This review summarizes different generative AI models and their potential applications in the field of medicine and explores the evolving landscape of Generative Adversarial Networks and diffusion models since the introduction of generative AI models. These models have made valuable contributions to the field of radiology. Furthermore, this review also explores the significance of synthetic data in addressing privacy concerns and augmenting data diversity and quality within the medical domain, in addition to emphasizing the role of inversion in the investigation of generative models and outlining an approach to replicate this process. We provide an overview of Large Language Models, such as GPTs and bidirectional encoder representations (BERTs), that focus on prominent representatives and discuss recent initiatives involving language-vision models in radiology, including innovative large language and vision assistant for biomedicine (LLaVa-Med), to illustrate their practical application. This comprehensive review offers insights into the wide-ranging applications of generative AI models in clinical research and emphasizes their transformative potential.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.6
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• pp.2739-2746
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• 2011

This paper proposes the method for more effectively calculating X/R which is the ratio of equivalent reactance(X) and resistance(R) of the bulk power system and analyses the characteristic of X/R values by applying the proposed method to the real bulk power systems. X/R is used to determine the rating of the relay in the bulk power systems and its value has been accepted to be big enough to ignore the equivalent resistance of the bulk power systems. However, X/R is calculated as a big number when only the upper transformer and transmission line are considered. The correct approach to calculating X/R needs to consider all the parameters including generators, transformers, lines and loads. This paper calculates X/R of the bulk power systems using dynamic models which have been used to analyse the power system stability. The effectiveness of the proposed method is verified by applying it to the test system and X/R values of the real bulk power systems are analyzed. In addition, the dependence of X/R on the closeness of its calculating locations to the generator is verified by using the marginal loss factor which has been used in the electricity market.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.5
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• pp.775-782
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• 2022

In this paper, a novel small-diameter cylindrical post capacitor inserted into an evanescent-mode rectangular waveguide (EMRWG) is proposed for easier tuning. In order to feed the EMRWG, the proposed structure uses a single ridge rectangular waveguide with the same width and height as the waveguide at the input and output ends. The inserted post capacitor are made up a circular groove formed in the center of the lower part of the broad wall of the EMRWG, and a concentric cylindrical post inserted into the upper part. First, the equivalent circuit model for the proposed structure is presented. When the EMRWG and the single ridge waveguide are combined, the joint susceptance and the turns ratio of the ideal transformer are calculated by two simulations using HFSS (3d fullwave simulator, Ansoft Co.) respectively. The susceptance and resonance characteristics of the inserted post were analyzed by using the obtained parameters and the characteristics of the EMRWG. A 2-post filter with a center frequency of 4.5 GHz and a bandwidth of 170 MHz was designed using a WR-90 waveguide, and the simulation results by using the HFSS and CST, equivalent circuit model were in good agreement.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1413-1425
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• 2023

The increasing frequency of wildfires due to climate change is causing extreme loss of life and property. They cause loss of vegetation and affect ecosystem changes depending on their intensity and occurrence. Ecosystem changes, in turn, affect wildfire occurrence, causing secondary damage. Thus, accurate estimation of the areas affected by wildfires is fundamental. Satellite remote sensing is used for forest fire detection because it can rapidly acquire topographic and meteorological information about the affected area after forest fires. In addition, deep learning algorithms such as convolutional neural networks (CNN) and transformer models show high performance for more accurate monitoring of fire-burnt regions. To date, the application of deep learning models has been limited, and there is a scarcity of reports providing quantitative performance evaluations for practical field utilization. Hence, this study emphasizes a comparative analysis, exploring performance enhancements achieved through both model selection and data design. This study examined deep learning models for detecting wildfire-damaged areas using Landsat 8 satellite images in California. Also, we conducted a comprehensive comparison and analysis of the detection performance of multiple models, such as U-Net and High-Resolution Network-Object Contextual Representation (HRNet-OCR). Wildfire-related spectral indices such as normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as input channels for the deep learning models to reflect the degree of vegetation cover and surface moisture content. As a result, the mean intersection over union (mIoU) was 0.831 for U-Net and 0.848 for HRNet-OCR, showing high segmentation performance. The inclusion of spectral indices alongside the base wavelength bands resulted in increased metric values for all combinations, affirming that the augmentation of input data with spectral indices contributes to the refinement of pixels. This study can be applied to other satellite images to build a recovery strategy for fire-burnt areas.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.309-328
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• 2022

This study empirically analyzed a Korean pre-trained language models (PLMs) designed for natural language generation. The performance of two PLMs - BART and GPT - at the task of abstractive text summarization was compared. To investigate how performance depends on the characteristics of the inference data, ten different document types, containing six types of informational content and creation content, were considered. It was found that BART (which can both generate and understand natural language) performed better than GPT (which can only generate). Upon more detailed examination of the effect of inference data characteristics, the performance of GPT was found to be proportional to the length of the input text. However, even for the longest documents (with optimal GPT performance), BART still out-performed GPT, suggesting that the greatest influence on downstream performance is not the size of the training data or PLMs parameters but the structural suitability of the PLMs for the applied downstream task. The performance of different PLMs was also compared through analyzing parts of speech (POS) shares. BART's performance was inversely related to the proportion of prefixes, adjectives, adverbs and verbs but positively related to that of nouns. This result emphasizes the importance of taking the inference data's characteristics into account when fine-tuning a PLMs for its intended downstream task.