• 융합보안논문지
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• 제5권2호
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• pp.37-42
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• 2005

정보통신기술의 발전과 지식정보 사회의 등장은 교육 및 훈련분야에도 거대한 변화를 가져왔다. 특히, 유비쿼터스 시대가 다가옴에 따라 e-Learning 또한 u-Learning으로 진화하려 하고 있다. 이는 지금까지와는 또 다른 형태로 교수-학습자 환경이 변화함을 말한다. 본 논문에서는 교육환경의 발전에 따른 다양한 학습 콘텐츠의 관리 방법을 제안, 구현하고 운영플랫폼 분석을 통하여 콘텐츠의 활용을 극대화 할 수 있는 LCMS를 제안하였다.

• International Journal of Advanced Culture Technology
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• 제10권1호
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• pp.302-309
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• 2022

Occupational safety accidents are caused by various factors, and it is difficult to predict when and why they occur, and it is directly related to the lives of workers, so the interest in safety accidents is increasing every year. Therefore, in order to reduce safety accidents at industrial fields, workers are required to wear personal protective equipment. In this paper, we proposes a method to automatically check whether workers are wearing safety helmets among the protective equipment in the industrial field. It detects whether or not the helmet is worn using YOLOv5, a computer vision-based deep learning object detection algorithm. We transfer learning the s model among Yolov5 models with different learning rates and epochs, evaluate the performance, and select the optimal model. The selected model showed a performance of 0.959 mAP.

• International Journal of Computer Science & Network Security
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• 제24권4호
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• pp.211-221
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• 2024

Pakistan is a top producer and exporter of high-quality rice, but traditional methods are still being used for detecting rice diseases. This research project developed an automated rice blast disease diagnosis technique based on deep learning, image processing, and transfer learning with pre-trained models such as Inception V3, VGG16, VGG19, and ResNet50. The modified connection skipping ResNet 50 had the highest accuracy of 99.16%, while the other models achieved 98.16%, 98.47%, and 98.56%, respectively. In addition, CNN and an ensemble model K-nearest neighbor were explored for disease prediction, and the study demonstrated superior performance and disease prediction using recommended web-app approaches.

• International Journal of Computer Science & Network Security
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• 제21권2호
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• pp.198-204
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• 2021

Glioma is one of the common types of brain tumors starting in the brain's glial cell. These tumors are classified into low-grade or high-grade tumors. Physicians analyze the stages of brain tumors and suggest treatment to the patient. The status of the tumor has an importance in the treatment. Nowadays, computerized systems are used to analyze and classify brain tumors. The accurate grading of the tumor makes sense in the treatment of brain tumors. This paper aims to develop a classification of low-grade glioma and high-grade glioma using a deep learning algorithm. This system utilizes four transfer learning algorithms, i.e., AlexNet, GoogLeNet, ResNet18, and ResNet50, for classification purposes. Among these algorithms, ResNet18 shows the highest classification accuracy of 97.19%.

• 한국IT서비스학회지
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• 제22권4호
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• pp.97-110
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• 2023

In this study, we propose a technique to automatically generate transfer documents using sensor data from livestock manure transfer systems. The research involves analyzing sensor data and applying machine learning techniques to derive optimized outcomes for livestock manure transfer documents. By comparing and contrasting with existing documents, we present a method for automatic document generation. Specifically, we propose the utilization of Gradient Boosting, a machine learning algorithm. The objective of this research is to enhance the efficiency of livestock manure and liquid byproduct management. Currently, stakeholders including producers, transporters, and processors manually input data into the livestock manure transfer management system during the disposal of manure and liquid byproducts. This manual process consumes additional labor, leads to data inconsistency, and complicates the management of distribution and treatment. Therefore, the aim of this study is to leverage data to automatically generate transfer documents, thereby increasing the efficiency of livestock manure and liquid byproduct management. By utilizing sensor data from livestock manure and liquid byproduct transport vehicles and employing machine learning algorithms, we establish a system that automates the validation of transfer documents, reducing the burden on producers, transporters, and processors. This efficient management system is anticipated to create a transparent environment for the distribution and treatment of livestock manure and liquid byproducts.

• 국제지역연구
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• 제12권3호
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• pp.391-415
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• 2008

본 연구는 중국의 특별한 국정과 기업 성장환경에서 존재하는 조직문화 유형을 법리형 조직문화, 윤리형 조직문화, 발전추구형 조직문화로 구별하여 각각이 개인차원의 학습환경과 조직차원의 학습환경에 어떠한 영향을 미치는 지, 그리고 그러한 학습환경이 학습전이 의도와 직무만족에 어떤 영향을 미치는지를 실증연구 하였다. 이를 위해 선행연구를 검토하여 각 변수들의 정의와 변수들 간의 상관관계를 이론적으로 검토하여 연구모형과 연구가설을 설정하였고, 중국 현지에서 설문조사를 실시하여 얻은 통계적 결과를 구조방정식 모형으로 처리하였다. 가설 검증한 결과 법리형 조직문화는 개인차원의 학습환경과 조직차원의 학습환경에 모두 유의한 영향을 미치는 것으로 나타났다. 뿐만 아니라 학습환경과 학습태도를 통하여 학습전이 의도와 직무만족에 미치는 영향도 윤리형 기업문화나 발전추구형 기업문화의 그것 보다 훨씬 큰 것으로 나타났다. 이는 법리형 기업문화가 다른 두 가지 기업문화보다 조직성과가 높다는 것을 보여주는 것이다.

• 대한원격탐사학회지
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• 제38권5_2호
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• pp.695-706
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• 2022

중, 고해상도 광학위성은 산불발생지역의 탐지에 대해 그 효용성이 입증되었다. 그러나 산불과 함께 발생하는 연기는 지표에 입사하는 가시광선을 산란시키므로 산불발생지역의 모니터링에 방해가 되며 따라서 연기를 사전에 추출하는 기술이 필요하다. 딥러닝 기술은 연기추출의 정확도를 향상시킬 수 있으나, 학습용 데이터셋의 부족으로 인해 적용에 한계가 있다. 반면에 연기와 유사하게 가시광선을 산란시키는 성질을 지닌 구름은 현재까지 다량의 학습용 데이터셋이 축적되었다. 본 연구는 딥러닝을 활용하여 연기추출을 고도화하는 것이 그 목적이며, 그 과정에서 데이터셋의 부족에 따른 연기추출의 한계점을 구름을 활용한 전이학습으로 해결했다. 전이학습의 효율성 확인을 위해 본 연구에서는 Landsat-8 위성영상을 기반으로 연기추출 학습용 데이터셋을 소규모로 제작한 후, 공공 구름 데이터셋을 활용하여 전이학습을 적용하기 전과 후의 연기추출 성능을 비교하였다. 그 결과 가시광선 파장대역 뿐만이 아니라 근적외선(NIR)과 단파장 적외선(SWIR) 영역에도 전이학습시 성능이 뚜렷하게 향상됨을 확인할 수 있었다. 본 연구결과를 통해서 연기추출의 데이터셋의 부족을 해결할 수 있을 것으로 보이며, 더 나아가 연기추출의 고도화를 통해서 산불발생지역의 모니터링에 이점을 제시할 수 있을 것이다.

• 영어어문교육
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• 제6호
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• pp.35-47
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• 2000

This study questions the validity of Crosslinguistic Influence (CLI) in EFL Learning. A ten-minute grammaticality judgement test involving resumptive pronouns in English relative clauses was given to 15 female subjects. The research results, which were analysed in terns of language transfer and universalist arguments, support the existence of a universal process that guides L2 learning, and some common developmental patterns between the two processes of L1 and L2 learning. Hence, the universalist view should be given at least equal Weight as the CLI approach.

• ETRI Journal
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• 제46권1호
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• pp.59-70
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• 2024

We introduce a high-performance named entity recognition (NER) model for written and spoken language. To overcome challenges related to labeled data scarcity and domain shifts, we use transfer learning to leverage our previously developed KorBERT as the base model. We also adopt a meta-pseudo-label method using a teacher/student framework with labeled and unlabeled data. Our model presents two modifications. First, the student model is updated with an average loss from both human- and pseudo-labeled data. Second, the influence of noisy pseudo-labeled data is mitigated by considering feedback scores and updating the teacher model only when below a threshold (0.0005). We achieve the target NER performance in the spoken language domain and improve that in the written language domain by proposing a straightforward rollback method that reverts to the best model based on scarce human-labeled data. Further improvement is achieved by adjusting the label vector weights in the named entity dictionary.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1994년도 Proceedings of the Korea Automatic Control Conference, 9th (KACC) ; Taejeon, Korea; 17-20 Oct. 1994
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• pp.703-707
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• 1994

This paper proposes a method of learning control in DC servomotor using a neural network. First we estimate the pulse transfer function of the servo system with an unknown load, then we determine the best gains of I-PD control system using a neural network. Each time the load changes, its best gains of the I-PD control system is computed by the neural network. And the best gains and its pulse transfer function for the case are stored in the memory. According the increase of the set of gains and its pulse transfer function, the learning control system can afford the most suitable I-PD gains instantly.