• Nuclear Engineering and Technology
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• 제54권10호
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• pp.3943-3948
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• 2022

Single-photon emission computed tomography is one of the reliable pin-by-pin verification techniques for spent-fuel assemblies. One of the challenges with this technique is to increase the total fuel assembly verification speed while maintaining high verification accuracy. The aim of the present study, therefore, was to develop an artificial intelligence (AI) algorithm-based tomographic image analysis technique for partial-defect verification of fuel assemblies. With the Monte Carlo (MC) simulation technique, a tomographic image dataset consisting of 511 fuel-rod patterns of a 3 × 3 fuel assembly was generated, and with these images, the VGG16, GoogLeNet, and ResNet models were trained. According to an evaluation of these models for different training dataset sizes, the ResNet model showed 100% pattern estimation accuracy. And, based on the different tomographic image qualities, all of the models showed almost 100% pattern estimation accuracy, even for low-quality images with unrecognizable fuel patterns. This study verified that an AI model can be effectively employed for accurate and fast partial-defect verification of fuel assemblies.

• 한국전자거래학회지
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• 제25권2호
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• pp.65-98
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• 2020

온라인 상점에서 인공 지능 기술의 채택이 지속적으로 증가하는 중이다. 그러나 각 인공지능 기능이 온라인 쇼핑에 대한 소비자의 지속사용의도에 어떠한 영향을 미치는지 여부를 실증분석한 연구는 거의 없다. 따라서 본 연구의 목적은 실증연구를 통해 온라인 상점의 지속사용의도에 인공지능의 주요 기능이 미치는 영향을 이해하는 것이다. 특히 온라인 상점 자원으로서의 인공지능 기능이 자원 기반관점에서 온라인 상점의 차별성에 어떠한 영향을 미치는지에 초점을 맞추고자 한다. 또한 인공 지능 기능과 지속사용의도 간의 매개 효과로서 온라인 상점 이미지를 고려하였다. 설문은 중국 소비자들을 대상으로 실시하였으며 분석 결과 온라인 상점에서 인공지능 기능의 존재가 자원 기반 관점에서 지속가능성에 긍정적인 영향을 미친다는 것을 알 수 있었다. 또한 인공지능 기능은 제품 및 서비스의 이미지에 긍정적인 영향을 미치며, 인공지능 기능에 의한 온라인 상점 사용 의도에 영향을 미치는 방식에 차이가 있음을 발견했다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2021년도 추계학술발표대회
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• pp.779-782
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• 2021

Survival time analysis is one of the main methods used by the pathologist to prognosis for cancer patients. In this paper, we strive to estimate the individual survival time of Adenocarcinoma (ADC) lung cancer patients from pathological images by adopting the convolutional neural network called the SurvPatchV1 model. First, we extracted tissue patches from the whole-slide images (WSI) to deal with extremely large dimensions of WSI. Then the survival time of each patch is estimated through the SurvPatchV1 model. Finally, the individual survival time of each patient is computed. The proposed method is trained and tested on the subset of the NLST dataset for ADC lung cancer. The result demonstrates that our model can obtain all tissue information in lieu of only tumor information in a whole pathological image to estimate the individual survival time.

• 한국멀티미디어학회논문지
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• 제24권2호
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• pp.178-185
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• 2021

Three-dimensional high-resolution magnetic resonance imaging (MRI) provides fine-level anatomical information for disease diagnosis. However, there is a limitation in obtaining high resolution due to the long scan time for wide spatial coverage. Therefore, in order to obtain a clear high-resolution(HR) image in a wide spatial coverage, a super-resolution technology that converts a low-resolution(LR) MRI image into a high-resolution is required. In this paper, we propose a super-resolution technique through filter learning based on information on the surrounding gradient information in 3D space from 3D MRI images. In the learning step, the gradient features of each voxel are computed through eigen-decomposition from 3D patch. Based on these features, we get the learned filters that minimize the difference of intensity between pairs of LR and HR images for similar features. In test step, the gradient feature of the patch is obtained for each voxel, and the filter is applied by selecting a filter corresponding to the feature closest to it. As a result of learning 100 T1 brain MRI images of HCP which is publicly opened, we showed that the performance improved by up to about 11% compared to the traditional interpolation method.

• International journal of advanced smart convergence
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• 제12권3호
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• pp.104-108
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• 2023

This paper deals with research on innovative systems using Python-based artificial intelligence technology in the field of plant growth monitoring. The importance of monitoring and analyzing the health status and growth environment of plants in real time contributes to improving the efficiency and quality of crop production. This paper proposes a method of processing and analyzing plant image data using computer vision and deep learning technologies. The system was implemented using Python language and the main deep learning framework, TensorFlow, PyTorch. A camera system that monitors plants in real time acquires image data and provides it as input to a deep neural network model. This model was used to determine the growth state of plants, the presence of pests, and nutritional status. The proposed system provides users with information on plant state changes in real time by providing monitoring results in the form of visual or notification. In addition, it is also used to predict future growth conditions or anomalies by building data analysis and prediction models based on the collected data. This paper is about the design and implementation of Python-based plant growth monitoring systems, data processing and analysis methods, and is expected to contribute to important research areas for improving plant production efficiency and reducing resource consumption.

• 한국ITS학회 논문지
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• 제22권3호
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• pp.38-50
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• 2023

최근 인공지능 (Artificial Intelligence: AI) 기술 발전으로 폐쇄회로 TV(Closed-Circuit television: CCTV)와 같은 영상 센서에 AI 기술을 도입하여 특정 돌발상황을 검지하고 있으나 대부분 고정식 장비 기반으로 돌발상황 검지가 진행되어왔다. 따라서 모든 도로 공간에 대한 돌발상황 검지에는 한계가 존재해왔다. 그러나 영상 센서와 edge-computing 기술 등의 발전으로 이동식 영상정보수집 및 분석 기술이 확산되고 있다. 본 연구는 이러한 이동식 영상 수집 및 분석 장비(커넥티드 차량)의 도입 수준에 따른 돌발상황 검지시간 감소효과를 추정하는 것이 목적이다. 이를 위해 2021년 경부고속도로 수원지사에서 수집된 돌발상황 발생 건수 자료를 활용하였다. 분석 결과 편도 2차로 고속도로는 커넥티드 차량의 보급률(Market Penetration Rate: MPR)이 4% 이상, 편도 3차로 고속도로는 3% 이상이면 돌발상황 검지 시간이 1분 이하로 나타났고, 편도 2차로와 편도 3차로 고속도로에서 MPR이 각각 0.4% 이상, 0.2% 이상이면 한국도로공사에서 발표한 평균 돌발상황 검지시간 보다 감소하는 것으로 나타났다.

• Journal of Information Processing Systems
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• 제15권4호
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• pp.717-723
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• 2019

Smart systems and services aim to facilitate growing urban populations and their prospects of virtual-real social behaviors, gig economies, factory automation, knowledge-based workforce, integrated societies, modern living, among many more. To satisfy these objectives, smart systems and services must comprises of a complex set of features such as security, ease of use and user friendliness, manageability, scalability, adaptivity, intelligent behavior, and personalization. Recently, artificial intelligence (AI) is realized as a data-driven technology to provide an efficient knowledge representation, semantic modeling, and can support a cognitive behavior aspect of the system. In this paper, an integration of AI with the smart systems and services is presented to mitigate the existing challenges. Several novel researches work in terms of frameworks, architectures, paradigms, and algorithms are discussed to provide possible solutions against the existing challenges in the AI-based smart systems and services. Such novel research works involve efficient shape image retrieval, speech signal processing, dynamic thermal rating, advanced persistent threat tactics, user authentication, and so on.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• 제3권2호
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• pp.67-72
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• 2022

Ecological research relies on the interpretation of large amounts of visual data obtained from extensive wildlife surveys, but such large-scale image interpretation is costly and time-consuming. Using an artificial intelligence (AI) machine learning model, especially convolution neural networks (CNN), it is possible to streamline these manual tasks on image information and to protect wildlife and record and predict behavior. Ecological research using deep-learning-based object recognition technology includes various research purposes such as identifying, detecting, and identifying species of wild animals, and identification of the location of poachers in real-time. These advances in the application of AI technology can enable efficient management of endangered wildlife, animal detection in various environments, and real-time analysis of image information collected by unmanned aerial vehicles. Furthermore, the need for school education and social use on biodiversity and environmental issues using AI is raised. School education and citizen science related to ecological activities using AI technology can enhance environmental awareness, and strengthen more knowledge and problem-solving skills in science and research processes. Under these prospects, in this paper, we compare the results of our early 2013 study, which automatically identified African cichlid fish species using photographic data of them, with the results of reanalysis by CNN deep learning method. By using PyTorch and PyTorch Lightning frameworks, we achieve an accuracy of 82.54% and an F1-score of 0.77 with minimal programming and data preprocessing effort. This is a significant improvement over the previous our machine learning methods, which required heavy feature engineering costs and had 78% accuracy.

• 한국가금학회지
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• 제49권3호
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• pp.157-165
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• 2022

RGB 조건에서 하나의 픽셀은 255의 세제곱 개 이상의 색상을 표현할 수 있다. 현재까지의 컴퓨터 비전 연구는 조류에서 나타나는 다양한 깃털색 표현형에 대해 세밀히 분석하여 종을 구분하였지만, GWAS에 이용될 목적을 위해 다양하게 유전되는 색상을 단순화하지 못하였다고 판단된다. 본 연구는 연산오계(YO)와 백색레그혼(WL) 상호역교배 F₂ 집단을 이용하였으며 이미지 양자화를 통하여 이미지의 크기를 줄이고 저장을 용이하게 하였으며 깃털색의 원인 유전자 탐색을 위한 기초 자료를 제공하기 위하여 육안으로 결정하였던 다양한 깃털색을 단순화하였다. 특히, GWAS 연구에 필요한 수치화된 표현형을 제시하였다는 측면에서 가치가 있다고 판단된다.

• 전기전자학회논문지
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• 제24권1호
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• pp.302-310
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• 2020

본 논문은 인공지능과 IoT 기반의 생활폐기물 모니터링 시스템을 구현하고, 이를 통하여 효율적인 쓰레기 처리와 관리 방안을 제안한다. 제주 지역은 타 지역에 비하여 대체로 정량적 추정과 운영 전략을 수립하는데 장점을 갖추고 있다. 특히, 상주인구 대비 사계절 관광객의 변화를 통하여 쓰레기 량의 변화를 알 수 있음으로써 쓰레기 종량제 연구의 좋은 사례가 되고 있다. 이에 클린하우스 현장에 기존 CCTV를 연동하여 정보를 제공할 수 있는 IoT 장치를 개발하고, 카메라의 쓰레기 영상데이터 셋을 분석하기 위하여 인공지능(AI) 알고리즘을 이용한다. 이를 통하여 쓰레기가 쓰레기통 밖에 잘못 투기되었는지, 쓰레기 처리를 위해 판단된 의사결정에 따라 쓰레기 차량이 수거해야 할지 정보를 전송함으로써 원활한 쓰레기 처리 및 홍보방송을 할 수 있도록 한다. 개발된 IoT 장치는 국가 공인 시험연구기관을 통해 전파적합성 시험과 환경시험을 하고, 다양한 쓰레기 대상을 제안한 인공지능 알고리즘으로 시뮬레이션하여 의사판단용 데이터 구축방법으로 활용하고자 한다.