• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.96-98
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• 2005

In this paper, an algorithm to compensate the distorted signals due to Current Transformer(CT) saturation is suggested, First, DWT which can be easily realized by filter banks in real-time applications is used to detect a start point and an end point of the saturation. Secondly, For enough Datas those need to use the least-square curve fitting method, the distorted current signal is compensated by the AR(autoregressive) model using the data during the previous healthy section until pick point of Saturation. Thirdly, the least-square curve fitting method is used to restore the distorted section of the secondary current. Finaly, this algorithm had a Hadware test using DSP board(TMS320C32) with Doble test device. DWT has superior detection accuracy and the proposed compensation algorithm which shows very stable features under various levels of remanent flux in the CT core is also satisfactory. And this algorithm is more correct than a previous algorithm which is only using the LSQ fitting method. Also it can be used as a MU involving the compensation function that acquires the second data from CT and PT.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.15 no.10 s.89
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• pp.1005-1010
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• 2004

An analysis method of slot-coupled microstripline to waveguide transition is presented to developed a simple but accurate equivalent circuit model. The equivalent circuit consists of an ideal transformer, microstrip open stub, and admittance elements looking into a waveguide and a half space of feed side from a slot center. The related circuit element values are calculated by applying the reciprocity theorem, the Fourier transform and series representation, the complex power concept, and the spectral-domain immittance approach. The computed scattering parameters are compared with the measured, and good agreement validates the simplicity and accuracy of the proposed equivalent circuit model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.9
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• pp.3795-3801
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• 2011

The mathematical simulation of voltage and current waveform of the discharge lamp is useful for the analysis and design of ballasting circuits. This paper proposes a mathematical model which has lamp power and negative voltage drop in discharge lamp. Simulation applying the proposed model has been done, and the results are compared with the experimental results. Furthermore, in the paper, the ballast components(core, transformer) was designed such that high intensity discharge could be optimized for the automotive, by applying a method simulation based design.

• Journal of Power Electronics
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• v.19 no.5
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• pp.1087-1098
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• 2019

In this paper, a detuning factor (DeFac) method is proposed to design the key parameters for optimizing the transfer power and efficiency of an Inductively Coupled Power Transfer (ICPT) system with primary-secondary side compensation. Depending on the robustness of the system, the DeFac method can guarantee the stability of the transfer power and efficiency of an ICPT system within a certain range of resistive-capacitive or resistive-inductive loads. A MATLAB-Simulink model of a ICPT system was built to assess the system's main evaluation criteria, namely its maximum power ratio (PR) and efficiency, in terms of different approaches. In addition, a magnetic field simulation model was built using Ansoft to specify the leakage flux and current density. Simulation results show that both the maximum PR and efficiency of the ICPT system can reach almost 70% despite the severe detuning imposed by the DeFac method. The system also exhibited low levels of leakage flux and a high current density. Experimental results confirmed the validity and feasibility of an ICPT system using DeFac-designed parameters.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.101-101
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• 2023

지난 수십 년 동안 다양한 딥러닝 방법이 개발되고 있으며 수문 분야에서는 이러한 딥러닝 모형이 기존의 수문모형의 역할을 대체하여 사용할 수 있다는 가능성이 제시되고 있다. 본 연구에서는 딥러닝 모형 중에 트랜스포머 모형에 다중 인코더를 사용하여 중장기 기간 (1 ~ 10일)의 리드 타임에 대한 한국의 유출량 예측 전망의 가능성을 확인하고자 하였다. 트랜스포머 모형은 인코더와 디코더 구조로 구성되어 있으며 어텐션 (attention) 기법을 사용하여 기존 모형의 정보를 손실하는 단점을 보완한 모형이다. 본 연구에서 사용된 다중 인코더 기반의 트랜스포머 모델은 트랜스포머의 인코더와 디코더 구조에서 인코더를 하나 더 추가한 모형이다. 그리고 결과 비교를 위해 기존에 수문모형을 활용한 스태킹 앙상블 모형 (Stacking ensemble model) 기반의 예측모형을 추가로 구축하였다. 구축된 모형들은 남한 전체를 총 469개의 대규모 격자로 나누어 각 격자의 유출량을 비교하여 평가하였다. 결과적으로 수문모형보다 딥러닝 모형인 다중 인코더 기반의 트랜스포머 모형이 더 긴 리드 타임에서 높은 성능을 나타냈으며 이를 통해 수문모형의 역할을 딥러닝 모형이 어느 정도는 대신할 수 있고 높은 성능을 가질 수 있는 것을 확인하였다.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.15-24
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• 2023

One of the objectives and aspirations of scientists and engineers ever since the development of computers has been to interact naturally with machines. Hence features of artificial intelligence (AI) like natural language processing and natural language generation were developed. The field of AI that is thought to be expanding the fastest is interactive conversational systems. Numerous businesses have created various Virtual Personal Assistants (VPAs) using these technologies, including Apple's Siri, Amazon's Alexa, and Google Assistant, among others. Even though many chatbots have been introduced through the years to diagnose or treat psychological disorders, we are yet to have a user-friendly chatbot available. A smart generative cognitive behavioral therapy with spoken dialogue systems support was then developed using a model Persona Perception (P2) bot with Generative Pre-trained Transformer-2 (GPT-2). The model was then implemented using modern technologies in VPAs like voice recognition, Natural Language Understanding (NLU), and text-to-speech. This system is a magnificent device to help with voice-based systems because it can have therapeutic discussions with the users utilizing text and vocal interactive user experience.

• Geomechanics and Engineering
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• v.12 no.4
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• pp.595-609
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• 2017

Due to high in-situ stress and brittleness of rock mass, the surrounding rock masses of underground caverns are prone to appear splitting failure. In this paper, a kind of loading-unloading variable elastic modulus model has been initially proposed and developed based on energy dissipation principle, and the stress state of elements has been determined by a splitting failure criterion. Then the underground caverns of Dagangshan hydropower station is analyzed using the above model. For comparing with the monitoring results, the entire process of rock splitting failure has been achieved through monitoring the splitting failure on side walls of large-scale caverns in Dagangshan via borehole TV, micro-meter and deformation resistivity instrument. It shows that the maximum depth of splitting area in the downstream sidewall of the main power house is approximately 14 m, which is close to the numerical results, about 12.5 m based on the energy dissipation model. As monitoring result, the calculation indicates that the key point displacement of caverns decreases firstly with the distance from main powerhouse downstream side wall rising, and then increases, because this area gets close to the side wall of main transformer house and another smaller splitting zone formed here. Therefore it is concluded that the energy dissipation model can preferably present deformation and fracture zones in engineering, and be very useful for similar projects.

• Journal of IKEEE
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• v.28 no.3
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• pp.352-359
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• 2024

This paper proposes a CNN-ViT hybrid model that automatically evaluates the aesthetic quality of images by combining local and global features. In this approach, CNN is used to extract local features such as color and object placement, while ViT is employed to analyze the aesthetic value of the image by reflecting global features. Color composition is derived by extracting the primary colors from the input image, creating a color palette, and then passing it through the CNN. The Rule of Thirds is quantified by calculating how closely objects in the image are positioned near the thirds intersection points. These values provide the model with critical information about the color balance and spatial harmony of the image. The model then analyzes the relationship between these factors to predict scores that align closely with human judgment. Experimental results on the AADB image database show that the proposed model achieved a Spearman's Rank Correlation Coefficient (SRCC) of 0.716, indicating more consistent rank predictions, and a Pearson Correlation Coefficient (LCC) of 0.72, which is 2~4% higher than existing models.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.281-291
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• 2021

The Islamic jurisprudential legal system represents an essential component of the Islamic religion, that governs many aspects of Muslims' daily lives. This creates many questions that require interpretations by qualified specialists, or Muftis according to the main sources of legislation in Islam. The Islamic jurisprudence is usually classified into branches, according to which the questions can be categorized and classified. Such categorization has many applications in automated question-answering systems, and in manual systems in routing the questions to a specialized Mufti to answer specific topics. In this work we tackle the problem of automatic categorisation of Islamic jurisprudential legal questions using deep learning techniques. In this paper, we build a hierarchical deep learning model that first extracts the question text features at two levels: word and sentence representation, followed by a text classifier that acts upon the question representation. To evaluate our model, we build and release the largest publicly available dataset of Islamic questions and answers, along with their topics, for 52 topic categories. We evaluate different state-of-the art deep learning models, both for word and sentence embeddings, comparing recurrent and transformer-based techniques, and performing extensive ablation studies to show the effect of each model choice. Our hierarchical model is based on pre-trained models, taking advantage of the recent advancement of transfer learning techniques, focused on Arabic language.

• Journal of the Korean Society for information Management
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• v.39 no.3
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• pp.293-310
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• 2022

In this study, we analyzed the performance of the BERT-based document classification model by automatically classifying documents in the field of library and information science based on the KoBERT. For this purpose, abstract data of 5,357 papers in 7 journals in the field of library and information science were analyzed and evaluated for any difference in the performance of automatic classification according to the size of the learned data. As performance evaluation scales, precision, recall, and F scale were used. As a result of the evaluation, subject areas with large amounts of data and high quality showed a high level of performance with an F scale of 90% or more. On the other hand, if the data quality was low, the similarity with other subject areas was high, and there were few features that were clearly distinguished thematically, a meaningful high-level performance evaluation could not be derived. This study is expected to be used as basic data to suggest the possibility of using a pre-trained learning model to automatically classify the academic documents.