• 스마트미디어저널
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• 제11권2호
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• pp.39-52
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• 2022

In the context of the evolution of automation and intelligence, deep learning and machine learning algorithms have been widely applied in aquaculture in recent years, providing new opportunities for the digital realization of aquaculture. Especially, water quality management deserves attention thanks to its importance to food organisms. In this study, we proposed an end-to-end deep learning-based TabNet model for water quality prediction. From major indexes of water quality assessment, we applied novel deep learning techniques and machine learning algorithms in innovative fish aquaculture to predict the number of water cells counting. Furthermore, the application of deep learning in aquaculture is outlined, and the obtained results are analyzed. The experiment on in-house data showed an optimistic impact on the application of artificial intelligence in aquaculture, helping to reduce costs and time and increase efficiency in the farming process.

• International journal of advanced smart convergence
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• 제9권4호
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• pp.115-119
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• 2020

Recently, CCTV installations are rapidly increasing in the public and private sectors to prevent various crimes. In accordance with the increasing number of CCTVs, video-based abnormal behavior detection in control systems is one of the key technologies for safety. This is because it is difficult for the surveillance personnel who control multiple CCTVs to manually monitor all abnormal behaviors in the video. In order to solve this problem, research to recognize abnormal behavior using deep learning is being actively conducted. In this paper, we propose a model for detecting abnormal behavior based on the deep learning model that is currently widely used. Based on the abnormal behavior video data provided by AI Hub, we performed a comparative experiment to detect anomalous behavior through violence learning and fainting in videos using 2D CNN-LSTM, 3D CNN, and I3D models. We hope that the experimental results of this abnormal behavior learning model will be helpful in developing intelligent CCTV.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2018년도 춘계학술발표대회
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• pp.350-351
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• 2018

최근 의료 현장에 인공지능 기술의 도입이 가속화 되고 있다. 특히, 의료영상 분석 분야의 관련된 기 시스템 및 소프트웨어의 패러다임을 변화시키고 있다. 본 연구는 인공지능 기술을 적용하기 위한 학습의료영상 구성을 제안하고 이를 기반으로 X-ray 영상 중 손부위에 적용하여 오른손과 왼손을 판별하는 응용에 적용하였다. 그리고 Deep Learning Algorithm의 CNN을 개선하여 개발한 Advanced GoogLeNet를 적용하여 97%이상의 정확도를 보였다. 본 연구를 통해 얻어진 인공지능에 적용하기 위한 학습데이터 셋 구성과 개선된 알고리즘은 다양한 의료영상분석에 적용하고자 한다.

• 한국멀티미디어학회논문지
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• 제22권2호
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• pp.136-145
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• 2019

The crowded environment of a domestic pig farm is highly vulnerable to the spread of infectious diseases such as foot-and-mouth disease, and studies have been conducted to automatically analyze behavior of pigs in a crowded pig farm through a video surveillance system using a camera. Although it is required to correctly separate occluding pigs for tracking each individual pigs, extracting the boundaries of the occluding pigs fast and accurately is a challenging issue due to the complicated occlusion patterns such as X shape and T shape. In this study, we propose a fast and accurate method to separate occluding pigs not only by exploiting the characteristics (i.e., one of the fast deep learning-based object detectors) of You Only Look Once, YOLO, but also by overcoming the limitation (i.e., the bounding box-based object detector) of YOLO with the test-time data augmentation of rotation. Experimental results with two-pigs occlusion patterns show that the proposed method can provide better accuracy and processing speed than one of the state-of-the-art widely used deep learning-based segmentation techniques such as Mask R-CNN (i.e., the performance improvement over Mask R-CNN was about 11 times, in terms of the accuracy/processing speed performance metrics).

• 융합정보논문지
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• 제12권3호
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• pp.116-125
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• 2022

본 연구의 목적은 COVID-19 팬데믹 국면에서 코로나 발생과 확산에 따른 투자자 불안심리가 암호화폐 가격에 영향을 미치는지를 분석하고, 딥러닝 모형에 기반하여 암호화폐의 가격 예측을 실험하는 것이다. 투자자 불안심리는 네이버의 코로나 검색지수와 코로나 확진자 정보를 결합하여 산출하며, 암호화폐 가격과의 그랜저 인과성을 분석하고 딥러닝모형을 이용하여 암호화폐 가격을 예측한다. 실험 결과는 다음과 같다. 첫째, CCI 지표는 비트코인, 이더리움, 라이트코인의 수익률에 유의적인 그랜저 인과성을 보여주었다. 둘째, CCI를 입력변수로 하는 LSTM은 높은 예측성과를 보여주었다. 셋째, 암호화폐 사이의 비교에서는 비트코인의 가격 예측 성과가 가장 높게 나타났다. 본 연구는 코로나 국면에서 네이버 코로나 검색 정보와 암호화폐 가격과의 관련성을 분석한 첫 시도라는 점에서 학술적 의의를 찾을 수 있다. 향후 연구에서는 가격 예측 정확성을 높이기 위하여 다양한 딥러닝 모형으로의 확장 연구가 필요하다.

• 문화기술의 융합
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• 제7권2호
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• pp.437-442
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• 2021

본 연구에서는 소비자 데이터를 딥러닝 기반의 분류(Classification) 모델을 학습 시켜 추천 알고리즘을 구현하였다. 이를 위하여 사용자 데이터를 이미지로 변환 시켜 분류 과제에서 보편적으로 사용되는 ResNet50을 사용하여 학습한 결과로서 유의미한 결과에 대하여 제시함

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2021년도 추계학술발표대회
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• pp.805-807
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• 2021

In-hospital cardiac arrest is a significant problem for medical systems. Although the traditional early warning systems have been widely applied, they still contain many drawbacks, such as the high false warning rate and low sensitivity. This paper proposed a strategy that involves a deep learning approach based on a novel interpretable deep tabular data learning architecture, named TabNet, for the Rapid Response System. This study has been processed and validated on a dataset collected from two hospitals of Chonnam National University, Korea, in over 10 years. The learning metrics used for the experiment are the area under the receiver operating characteristic curve score (AUROC) and the area under the precision-recall curve score (AUPRC). The experiment on a large real-time dataset shows that our method improves compared to other machine learning-based approaches.

• International journal of advanced smart convergence
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• 제12권2호
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• pp.56-66
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• 2023

The goal of deep learning is to extract complex features from multidimensional data use the features to create models that connect input and output. Deep learning is a process of learning nonlinear features and functions from complex data, and the user data that is employed to train deep learning models has become the focus of privacy concerns. Companies that collect user's sensitive personal information, such as users' images and voices, own this data for indefinite period of times. Users cannot delete their personal information, and they cannot limit the purposes for which the data is used. The study has designed a deep learning method that employs privacy protection technology that uses distributed collaborative learning so that multiple participants can use neural network models collaboratively without sharing the input datasets. To prevent direct leaks of personal information, participants are not shown the training datasets during the model training process, unlike traditional deep learning so that the personal information in the data can be protected. The study used a method that can selectively share subsets via an optimization algorithm that is based on modified distributed stochastic gradient descent, and the result showed that it was possible to learn with improved learning accuracy while protecting personal information.

• 문화기술의 융합
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• 제10권1호
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• pp.471-476
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• 2024

다양한 스마트 기기 및 컴퓨팅 디바이스의 보급에 따라 빅데이터 생성이 광범위하게 일어나고 있다. 기계학습은 데이터의 패턴을 학습하여 추론을 수행하는 알고리즘이다. 다양한 기계학습 알고리즘 중에서 주목을 받는 알고리즘은 신경망 기반의 딥러닝 학습이다. 딥러닝은 다양한 응용이 발표되면서 빠른 성능 향상을 달성하고 있다. 최근 딥러닝 알고리즘 중에서 그래프 구조를 활용하여 데이터를 분석하려는 시도가 증가하고 있다. 본 연구에서는 그래프 구조를 활용하여 딥러닝 네트워크에 전달하기 위한 그래프 생성 방법을 제시한다. 본 논문은 그래프 생성 과정에서 노드의 속성과 간선의 가중치를 일반화하고 행렬화 과정을 제시하여 딥러닝 입력에 필요한 구조로 전환하는 방법을 제시한다. 그래프 생성 과정에서 속성과 가중치 정보를 보전할 수 있는 선형변환 매트릭스 적용 방법을 제시한다. 마지막으로 일반 그래프의 딥러닝 입력 구조를 제시하고 성능 분석을 위한 접근법을 제시한다.

• Journal of information and communication convergence engineering
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• 제16권4호
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• pp.235-241
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• 2018

Recently, deep learning has been actively studied and applied in various fields even to novel writing and painting in ways we could not imagine before. A key feature is that high-performance computing device, especially CUDA-enabled GPU, supports this trend. Researchers who have difficulty accessing such systems fall behind in this fast-changing trend. In this study, we propose and implement a library called Emulearner that helps users to utilize Emulab with ease. Emulab is a research framework equipped with up to thousands of nodes developed by the University of Utah. To use Emulab nodes for deep learning requires a lot of human interactions, however. To solve this problem, Emulearner completely automates operations from authentication of Emulab log-in, node creation, configuration of deep learning to training. By installing Emulearner with a legitimate Emulab account, users can focus on their research on deep learning without hassle.