• International Journal of Advanced Culture Technology
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• 제7권4호
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• pp.327-333
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• 2019

Wind speed data constitute important weather information for aircrafts flying at low altitudes, such as drones. Currently, the accuracy of low altitude wind predictions is much lower than that of high-altitude wind predictions. Deep neural networks are proposed in this study as a method to improve wind speed forecast information. Deep neural networks mimic the learning process of the interactions among neurons in the brain, and it is used in various fields, such as recognition of image, sound, and texts, image and natural language processing, and pattern recognition in time-series. In this study, the deep neural network model is constructed using the wind prediction values generated by the numerical model as an input to improve the wind speed forecasts. Using the ground wind speed forecast data collected at the Boseong Meteorological Observation Tower, wind speed forecast values obtained by the numerical model are compared with those obtained by the model proposed in this study for the verification of the validity and compatibility of the proposed model.

• KEPCO Journal on Electric Power and Energy
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• 제8권2호
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• pp.137-141
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• 2022

To inspect the power facility faults using artificial intelligence, it need that improve the accuracy of the diagnostic model are required. Data augmentation skill using generative adversarial network (GAN) is one of the best ways to improve deep learning performance. GAN model can create realistic-looking fake images using two competitive learning networks such as discriminator and generator. In this study, we intend to verify the effectiveness of virtual data generation technology by including the fake image of power facility generated through GAN in the deep learning training set. The GAN-based fake image was created for damage of LP insulator, and ResNet based normal and defect classification model was developed to verify the effect. Through this, we analyzed the model accuracy according to the ratio of normal and defective training data.

• Journal of information and communication convergence engineering
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• 제21권1호
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• pp.17-23
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• 2023

Recently, several high-performance instance segmentation models have used the Mask R-CNN model as a baseline, which reached a historical peak in instance segmentation in 2017. There are numerous derived models using the Mask R-CNN model, and if the performance of Mask R-CNN is improved, the performance of the derived models is also anticipated to improve. The Mask R-CNN uses interpolation to adjust the image size, and the input differs depending on the interpolation method. Therefore, in this study, the performance change of Mask R-CNN was compared when various interpolation methods were applied to the transform layer to improve the performance of Mask R-CNN. To train and evaluate the models, this study utilized the PennFudan and Balloon datasets and the AP metric was used to evaluate model performance. As a result of the experiment, the derived Mask R-CNN model showed the best performance when bicubic interpolation was used in the transform layer.

• International journal of advanced smart convergence
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• 제12권3호
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• pp.51-60
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• 2023

This study focuses on the design of a GPT-based system for relatively rapid technology credit assessment of SMEs. This system addresses the limitations of traditional time-consuming evaluation methods and proposes a GPT-based model to comprehensively evaluate the technological capabilities of SMEs. This model fine-tunes the GPT model to perform fast technical credit assessment on SME-specific text data. Also, It presents a system that automates technical credit evaluation of SMEs using GPT and LLM-based chatbot technology. This system relatively shortens the time required for technology credit evaluation of small and medium-sized enterprises compared to existing methods. This model quickly assesses the reliability of the technology in terms of usability of the base model.

• Asian Journal of Business Environment
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• 제13권4호
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• pp.1-6
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• 2023

Purpose: To explore the application prospect of support vector machine (SVM) in supply chain management and its practical application in supply chain performance evaluation practice. Research design, data and methodology: This paper establishes the performance evaluation index system of supply chain management according to the balanced scorecard (BSC) theory, and establishes the SVM model of supply chain management performance evaluation based on the SVM principle. Results: The performance evaluation results of the supply chain of an electric power equipment Co., Ltd. in Harbin established by using the model are consistent with the actual situation, which indicates the nature and accuracy of the possible reflection of the established supply chain performance evaluation model. Conclusions: The results show that SVM model can be used to evaluate enterprise supply chain management performance indicators, and can improve enterprise supply chain management performance, thus demonstrating the effectiveness of the model.

• Journal of information and communication convergence engineering
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• 제22권1호
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• pp.23-32
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• 2024

This study aims to improve the quality of distance learning by developing webcast-based e-learning media and integrating it into an e-learning platform for functional job training purposes at the National Research and Innovation Agency, Indonesia. This study uses a Technology Acceptance Model (TAM) to assess and predict user perceptions of information systems using webcast platforms as an alternative to conventional applications. The research method was an online survey using Google Forms. Data collected from 136 respondents involved in practical job training were analyzed using structural equation modeling to test the technology acceptance model. The results showed that the proposed model effectively explained the variables associated with the adoption of web-based e-learning during the COVID-19 pandemic in Indonesia for participants engaged in functional job training. These findings suggest that users' perceptions of ease of use, usefulness, benefits, attitudes, intentions, and webcast usage significantly contribute to the acceptance and use of a more effective and efficient webcast-based e-learning platform.

• 한국농공학회논문집
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• 제49권3호
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• pp.21-31
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• 2007

This study is aimed at the development of a runoff forecasting model by using the Fuzzy inference system and Neural Network model to solve the uncertainties occurring in the process of rainfall-runoff modeling and improve the modeling accuracy of the stream runoff forecasting. The Neuro-Fuzzy (NF) model were used in this study. The NF model, recently received a great deal of attention, improve the existing Neural Networks by the aid of the Fuzzy theory applied to each node. The study area is the downstreams of Naeseung-chun. Therefore, time-dependent data was obtained from the Wolpo water level gauging station. 11 and 2 out of total 13 flood events were selected for the training and testing set of model respectively. The schematic diagram method and the statistical analysis are conducted to evaluate the feasibility of rainfall-runoff modeling. The model accuracy was rapidly decreased as the forecasting time became longer. The NF model can give accurate runoff forecasts up to 4 hours ahead in standard above the Determination coefficient $(R^2)$ 0.7. In the comparison of the runoff forecasting using the NF and TANK models, characteristics of peak runoff in the TANK model was higher than ones in the NF models, but peak values of hydrograph in the NF models were similar.

• 한국정보통신학회논문지
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• 제25권6호
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• pp.749-756
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• 2021

제조 생산공정에는 다양한 센서를 통해 실시간으로 양질의 데이터가 데이터베이스에 축적되고 있다. 이와 함께 통계적으로 접근하기 까다로운 데이터에 대해서 높은 수준의 정확도로 예측모델을 구축할 수 있는 머신러닝이 보급되면서 '4차 산업화 시대'를 맞이하고 있다. 본 논문에서는 이러한 제조업계의 흐름에 따라 업계의 주요 관심사인 제품의 품질특성을 예측하는 머신러닝 모델의 성능을 향상하는 방법을 제시한다. 머신러닝 모델의 성능을 향상하는데 일반적으로 사용되는 샘플 크기의 증가, Hyper-Parameter의 최적화 및 적절한 알고리즘 선택의 효과를 검증한다. 그리고, 새로운 성능향상 방법을 제시하고, 그 효과를 검증해본다. 논문에서 제시한 방법을 통해서 제조업에서는 더욱 향상된 성능의 예측모델을 구축, 품질예측과 관리에 크게 이바지할 수 있을 것이다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 추계학술발표대회
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• pp.625-627
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• 2022

In the construction and development of modern industrial production technology, the traditional technology management mode is faced with many problems such as low qualification rates and high application costs. In the research, an improved workpiece defect detection method based on deep learning is proposed, which can control the application cost and improve the detection efficiency of irregular defects. Based on the research of the current situation of deep learning applications, this paper uses the improved Faster R-CNN network structure model as the core detection algorithm to automatically locate and classify the defect areas of the workpiece. Firstly, the robustness of the model was improved by appropriately changing the depth and the number of channels of the backbone network, and the hyperparameters of the improved model were adjusted. Then the deformable convolution is added to improve the detection ability of irregular defects. The final experimental results show that this method's average detection accuracy (mAP) is 4.5% higher than that of other methods. The model with anchor size and aspect ratio (65,129,257,519) and (0.2,0.5,1,1) has the highest defect recognition rate, and the detection accuracy reaches 93.88%.

• 한국컴퓨터정보학회논문지
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• 제15권12호
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• pp.125-132
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• 2010

어휘 인식에서는 인식 학습 시 나타나지 않는 미 출현 트라이 폰이 존재하며, 이들 시스템에서는 모델 파라미터들의 초기 추정치를 생성하지 못하고 음소 데이터에 대한 모델을 구성할 수 없는 단점으로 인하여 가우시안 모델의 정확성을 확보하지 못하게 된다. 이를 개선하기 위하여 확률 분포를 이용한 모델 파라미터의 가우시안 모델 최적화 방법을 제안한다. 확률 분포의 가우시안 모델을 최적화하여 가우시안 모델의 정확성을 제공하고, 음소 단위로 데이터의 탐색을 지원하여 신뢰도가 향상되었다. 제안된 방법의 성능 평가를 위하여 실제 다양한 미등록어가 관측될 수 있는 대상으로 실험을 수행하였으며 본 연구에서 제안한 정규화 신뢰도를 이용한 미등록어 거절 알고리즘이 기존의 방법들에 비하여 평균 1.7%의 성능향상을 나타내었다.