• Journal of Korean Home Economics Education Association
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• v.18 no.2 s.40
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• pp.15-27
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• 2006

The purpose of this research was to develop and analyze the effects of ICT based teaching learning process plans for 'Designing My Home' unit of Technology Home Economics subject in High School. The seven housing contents were selected from 8 textbooks and 8 teaching resources at the analyses stage. A specific homepage(ieduhome.cafe.com) was built to utilize the eight ICT teaching learning process plan as well as many other resources at the planning & development stages. The number of 68 highschool students have participated for the application stage during September 4-26, 2003 and the same number have studied the same contents through regular teaching learning plans as a comparison group. Experimental groups have significantly more increased in the knowledge and understanding of the housing contents than have comparison groups. The same results occurred in the interests in Home Economics, Housing, and Internet utilized study. The Design reports were not statistically differed between two groups based on the objective evaluation criteria. The results of this study generally supported previous research and showed that the In teaching learning plans were more effective in various aspects than were the regular plans.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.301-304
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• 2000

본 논문에서는 디지털 영상에서 얼굴 영상 검출을 위해 픽셀의 퍼지 소속도를 이용하여 신경망으로 학습하는 퍼지 신경망을 이용한 얼굴영상 검출을 제안한다. 입력 영상의 피라미드 영상에서 추출된 20$\times$20 윈도우 템플릿 영상안의 각 픽셀의 소속도로 얼굴 영상 패턴을 학습하여 얼굴 영상을 검출하는 방법은 단순히 영상의 픽셀 값 하나씩만을 고려해서 각 픽셀의 소속도를 고려하여 수행하는 얼굴 영상 분할보다 얼굴 영상을 훨씬 더 정확하고 인식률이 높게 검출해 낼 수 있다.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.1
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• pp.39-44
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• 2001

본 논문에서는 디지털 영상에서 얼굴 영상 검출을 위해 픽셀의 퍼지 소속도를 이용하여 신경망으로 학습하는 퍼지 신경망을 이용한 얼굴영상 검출을 제안한다. 입력 영상의 피라미드 영상에서 추출된 20$\times$20 윈도우 영상 안의 각 픽셀의 소속도로 얼굴 영상 패턴을 학습하여 얼굴 영상을 검출하는 방법은 단순히 영상의 픽셀 값 하나씩만을 고려해서 각 픽셀의 소속도를 고려하여 수행하는 얼굴 영상 분할보다 얼굴 영상을 더 정확하고 인식률이 높게 검출해 낼 수 있다.

• The Journal of the Korea Contents Association
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• v.21 no.3
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• pp.23-32
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• 2021

Semantic image segmentation technology in the field of computer vision is a technology that classifies an image by dividing it into pixels. This technique is also rapidly improving performance using a machine learning method, and a high possibility of utilizing information in units of pixels is drawing attention. However, this technology has been raised from the early days until recently for 'lack of detailed segmentation' problem. Since this problem was caused by increasing the size of the label map, it was expected that the label map could be improved by using the edge map of the original image with detailed edge information. Therefore, in this paper, we propose a post-processing algorithm that maintains semantic image segmentation based on learning, but modifies the resulting label map based on the edge map of the original image. After applying the algorithm to the existing method, when comparing similar applications before and after, approximately 1.74% pixels and 1.35% IoU (Intersection of Union) were applied, and when analyzing the results, the precise targeting fine segmentation function was improved.

• Journal of Digital Convergence
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• v.12 no.12
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• pp.365-370
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• 2014

The existing system consumes a considerable amount time and cost for extracting the patterns from whole sequences or misaligned sequences. In this paper, We propose the classification system, which creates the partition sequence sections using multiple sequence alignment method and extracts the sequential patterns from these section. These extracted patterns are accumulated motif candidate sets and then used the training sets of SVM classifier. This proposed system predicts a HPV-type(high/low) using the learned knowledges from known/unknown protein sequences and shows more improved precision, recall than previous system in 30% minimum support.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.481-486
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• 2021

Machine learning algorithms adopt criterion function as a key component to measure the quality of their model derived from data. Cluster analysis also uses this function to rate the clustering result. All the criterion functions have in general certain types of favoritism in producing high quality clusters. These clusters are then described by attributes and their values. Category utility and partition utility play an important role in cluster analysis. These are fully analyzed in this research particularly in terms of how they are related to the favoritism in the final results. In this research, several data sets are selected and analyzed to show how different results are induced from these criterion functions.

• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.13-19
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• 2022

Video Object Segmentation (VOS) task requires an amount of labeled sequence data, which limits the performance of the current VOS methods trained with public datasets. In this paper, we propose two effective data augmentation schemes for VOS. The first augmentation method is to swap the background segment to the background from another image, and the other method is to play the sequence in reverse. The two augmentation schemes for VOS enable the current VOS methods to robustly predict the segmentation labels and improve the performance of VOS.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.5
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• pp.23-29
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• 2020

The National Institute of Agricultural Sciences of the Rural Developement Administration (NAS, RDA) is conducting various studies on various crops, such as monitoring the cultivation environment and analyzing harvested seeds for high-throughput phenotyping. In this paper, we propose a deep learning-based rice seed segmentation method to analyze the seeds of various crops owned by the NAS. Using Mask-RCNN deep learning model, we perform the rice seed segmentation from manually taken images under specific environment (constant lighting, white background) for analyzing the seed characteristics. For this purpose, we perform the parameter tuning process of the Mask-RCNN model. By the proposed method, the results of the test on seed object detection showed that the accuracy was 82% for rice stem image and 97% for rice grain image, respectively. As a future study, we are planning to researches of more reliable seeds extraction from cluttered seed images by a deep learning-based approach and selection of high-throughput phenotype through precise data analysis such as length, width, and thickness from the detected seed objects.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.11a
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• pp.587-590
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• 2003

얼굴영역을 분할하기 위해서 Watershed Algorithm 와 Object Grouping 을 이용한 얼굴영역 분할기법을 제안한다. 영상분할에 단점은 단일 알고리즘으로 영역분할이 어렵고, 또한 복잡한 영상에서 정확한 영역을 분할하기가 어렵다는 것이다. 그래서 본 논문에서는 Watershed Segmentation 기법과 Grouping 작업을 통한 병합, 그리고 색상의 선형회귀분석을 이용한 분석법을 적용하여 분할하고자 한다. 얼굴영역 분할방법을 전처리 과정과 영역 병합 그리고 얼굴 부분을 추출하는 3 단계의 과정으로 나누고, 전처리 과정에서는 수리형태학적(Mophological) 연산자를 이용한 영상 분할기법을 이용하여 분할한 후 얼굴 후보 영역을 검출, 영역병합과정에서 기존의 학습데이터와의 유사도를 측정, 얼굴객체추출 조건에 맞지 않는 객체들을 모두 제거함으로써, 정확한 얼굴부분을 분할해 낸다. 실험결과 제안한 방법을 통해 비교적 정확한 얼굴영역을 분할 할 수 있었다.

• The Transactions of the Korea Information Processing Society
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• v.5 no.11
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• pp.2897-2907
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• 1998

The production-rule generation from training examples is a hard problem that has large space and many local optimal solutions. Many learning methods are proposed for production-rule generation and genetic algorithms is an alternative learning method. However, traditional genetic algorithms has been known to have an obstacle in converging at the global solution area and show poor efficiency of production-rules generated. In this paper, we propose a production-rule generating method which uses genetic algorithm learning. By analyzing optimal sub-solutions captured by genetic algorithm learning, our method takes advantage of its schema structure and thus generates relatively small rule set.