• 한국화재소방학회논문지
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• 제34권3호
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• pp.91-99
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• 2020

최근 해외 신종 감염병 코로나19가 우리나라뿐만 아니라 세계적으로 확산되고 있다. 이로 인해 정부는 2020년 2월 23일 감염병 재난 위기경보 수준을 심각단계로 격상했다. 이 연구는 공중상황이론을 적용하여 위기경보 발령에 따른 공중을 유형별로 분류해 보고, 위기경보체계 개선방안을 제시하는 것이다. 이를 위해 위기경보에 관한 국민의 인식정도를 확인하였다. 검증을 통해 공중상황이론이 위기경보에 대한 공중의 정보행위 의도를 분석하기에 적합한 이론적 틀이라는 것을 확인했다. 공중을 유형별로 분류한 결과 42.7%가 활동공중으로 분류되었다. 이를 토대로 위기경보체계를 국민과 소통할 수 있는 체계로 재정비할 것을 제안했다.

• Journal of Information Processing Systems
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• 제17권5호
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• pp.892-904
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• 2021

An image semantic segmentation model is proposed based on improved ENet network in order to achieve the low accuracy of image semantic segmentation in complex environment. Firstly, this paper performs pruning and convolution optimization operations on the ENet network. That is, the network structure is reasonably adjusted for better results in image segmentation by reducing the convolution operation in the decoder and proposing the bottleneck convolution structure. Squeeze-and-excitation (SE) module is then integrated into the optimized ENet network. Small-scale targets see improvement in segmentation accuracy via automatic learning of the importance of each feature channel. Finally, the experiment was verified on the public dataset. This method outperforms the existing comparison methods in mean pixel accuracy (MPA) and mean intersection over union (MIOU) values. And in a short running time, the accuracy of the segmentation and the efficiency of the operation are guaranteed.

• Journal of Animal Science and Technology
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• 제66권1호
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• pp.167-177
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• 2024

Iris segmentation is an initial step for identifying the biometrics of animals when establishing a traceability system for livestock. In this study, we propose a deep learning framework for pixel-wise segmentation of bovine iris with a minimized use of annotation labels utilizing the BovineAAEyes80 public dataset. The proposed image segmentation framework encompasses data collection, data preparation, data augmentation selection, training of 15 deep neural network (DNN) models with varying encoder backbones and segmentation decoder DNNs, and evaluation of the models using multiple metrics and graphical segmentation results. This framework aims to provide comprehensive and in-depth information on each model's training and testing outcomes to optimize bovine iris segmentation performance. In the experiment, U-Net with a VGG16 backbone was identified as the optimal combination of encoder and decoder models for the dataset, achieving an accuracy and dice coefficient score of 99.50% and 98.35%, respectively. Notably, the selected model accurately segmented even corrupted images without proper annotation data. This study contributes to the advancement of iris segmentation and the establishment of a reliable DNN training framework.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권3호
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• pp.678-700
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• 2023

Diagnosing breast diseases using ultrasound (US) images remains challenging because it is time-consuming and requires expert radiologist knowledge. As a result, the diagnostic performance is significantly biased. To assist radiologists in this process, computer-aided diagnosis (CAD) systems have been developed and used in practice. This type of system is used not only to assist radiologists in examining breast ultrasound images (BUS) but also to ensure the effectiveness of the diagnostic process. In this study, we propose a new approach for breast lesion localization and segmentation using a multi-scale pyramid of the ultrasound image of a breast organ and a convolutional semantic segmentation network. Unlike previous studies that used only a deep detection/segmentation neural network on a single breast ultrasound image, we propose to use multiple images generated from an input image at different scales for the localization and segmentation process. By combining the localization/segmentation results obtained from the input image at different scales, the system performance was enhanced compared with that of the previous studies. The experimental results with two public datasets confirmed the effectiveness of the proposed approach by producing superior localization/segmentation results compared with those obtained in previous studies.

• 한국산학기술학회논문지
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• 제16권7호
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• pp.4641-4650
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• 2015

본 연구는 지방의료원의 고객세분화를 통하여 향후 전문화된 의료기관으로 진료전문성을 강화하여 경쟁력을 확보할 수 있는 진료전문화 전략을 제시하는데 목적이 있다. 조사기간은 2013년 1월부터 12월까지 입원한 환자 26,658명을 연구대상을 선정하였다. 분석방법은 군집분석과 의사결정나무분석을 이용하였다. 결론을 보면, 성별은 여자, 연령은 60세 이상, 질환별로는 근 골격계 및 결합조직의 질환이 충성고객으로 선정되었다. 이들은 지방의료원의 고객관리측면에서 향후 구전의 효과가 높은 고객 군으로 금전적인 소비규모가 높은 점을 고려하여 이들에게 제공된 의료서비스에 대한 모니터링과 커뮤니케이션을 통해 지속적인 관계를 유지하는 것이 중요하다. 앞으로 전문 분야의 전문의와 전문적 시설 확보 등의 적합한 조직구조와 환경을 갖추는 것이 중요하며, 지역 내 개원의, 유관기관간의 전략적 제휴 통한 진료협력 및 의뢰, 의료서비스 범위의 집중화가 필요하다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권2호
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• pp.471-485
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• 2023

As data sharing increases explosively, such information encoded in QR code is completely public as private messages are not securely protected. This paper proposes a new 'PROMISE' framework for hiding information based on the QR code projection matrix by using image segmentation without modifying the essential QR code characteristics. Projection matrix mapping, matrix scrambling, fusion image segmentation and steganography with SEL(secret embedding logic) are part of the PROMISE framework. The QR code could be mapped to determine the segmentation site of the fusion image as a binary information matrix. To further protect the site information, matrix scrambling could be adopted after the mapping phase. Image segmentation is then performed on the fusion image and the SEL module is applied to embed the secret message into the fusion image. Matrix transformation and SEL parameters should be uploaded to the server as the secret key for authorized users to decode the private message. And it was possible to further obtain the private message hidden by the framework we proposed. Experimental findings show that when compared to some traditional information hiding methods, better anti-detection performance, greater secret key space and lower complexity could be obtained in our work.

• 지역연구
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• 제12권2호
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• pp.37-57
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• 1996

Intra-metropolitan spatial segmentation of the labor marker requires barriers of mobility on both supply and demand side of the local labor marker. The phenomena of spatial segmentation of the labor market are particularly applied to the secondary workers rather than to the primary workers. Supply side barriers include the costs of obtaining job information regarding jobs outside of the immediate area, commuting costs, and barriers to residential mobility. Demand side barriers include site-specific technology and product demand, and discrimination. In this paper, I discuss these barriers and examine their implications for differences in segmentation by demographic and skill groups at the intra-metropolitan scale. In particular, I apply a job search model to examine supply side barriers such as information and commuting costs, and an implicit contract model to explain demand side barriers such as dual/internal labor market and firms' (re) location strategies.

• 통상정보연구
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• 제11권4호
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• pp.129-149
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• 2009

The interest in wine has been increasing because of raised standard of living, increased leisure time, raised interest in health. Therefore, a few wine related tourism product has introduced to public including wine train to Young-dong region and overseas wine tour package. This study focused on motivation to visit overseas wine tour package in order to segment target wine tourism countries. As a result, three different markets were segmented and they showed different characteristics with regard to demographics, tourism behavior, and preferred wine tourism countries.

• Smart Structures and Systems
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• 제29권1호
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• pp.207-220
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• 2022

Structural health monitoring (SHM) plays a vital role in the maintenance and operation of constructions. In recent years, autonomous inspection has received considerable attention because conventional monitoring methods are inefficient and expensive to some extent. To develop autonomous inspection, a potential approach of crack identification is needed to locate defects. Therefore, this study exploits two deep learning-based segmentation models, DeepLabv3+ and Mask R-CNN, for crack segmentation because these two segmentation models can outperform other similar models on public datasets. Additionally, impacts of label quality on model performance are explored to obtain an empirical guideline on the preparation of image datasets. The influence of image cropping and label refining are also investigated, and different strategies are applied to the dataset, resulting in six alternated datasets. By conducting experiments with these datasets, the highest mean Intersection-over-Union (mIoU), 75%, is achieved by Mask R-CNN. The rise in the percentage of annotations by image cropping improves model performance while the label refining has opposite effects on the two models. As the label refining results in fewer error annotations of cracks, this modification enhances the performance of DeepLabv3+. Instead, the performance of Mask R-CNN decreases because fragmented annotations may mistake an instance as multiple instances. To sum up, both DeepLabv3+ and Mask R-CNN are capable of crack identification, and an empirical guideline on the data preparation is presented to strengthen identification successfulness via image cropping and label refining.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권1호
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• pp.15-29
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• 2024

A fetoscope is an optical endoscope, which is often applied in fetoscopic laser photocoagulation to treat twin-to-twin transfusion syndrome. In an operation, the clinician needs to observe the abnormal placental vessels through the endoscope, so as to guide the operation. However, low-quality imaging and narrow field of view of the fetoscope increase the difficulty of the operation. Introducing an accurate placental vessel segmentation of fetoscopic images can assist the fetoscopic laser photocoagulation and help identify the abnormal vessels. This study proposes a method to solve the above problems. A novel encoder-decoder network with a dual-path structure is proposed to segment the placental vessels in fetoscopic images. In particular, we introduce a channel attention mechanism and a continuous convolution structure to obtain multi-scale features with their weights. Moreover, a switching connection is inserted between the corresponding blocks of the two paths to strengthen their relationship. According to the results of a set of blood vessel segmentation experiments conducted on a public fetoscopic image dataset, our method has achieved higher scores than the current mainstream segmentation methods, raising the dice similarity coefficient, intersection over union, and pixel accuracy by 5.80%, 8.39% and 0.62%, respectively.