• 전자통신동향분석
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• 제33권4호
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• pp.54-60
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• 2018

In this paper, we analyze the trends of deep-learning based plant data processing technologies. In recent years, the deep-learning technology has been widely applied to various AI tasks, such as vision (image classification, image segmentation, and so on) and natural language processing because it shows a higher performance on such tasks. The deep-leaning method is also applied to plant data processing tasks and shows a significant performance. We analyze and show how the deep-learning method is applied to plant data processing tasks and related industries.

• Communications for Statistical Applications and Methods
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• 제26권6호
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• pp.591-610
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• 2019

Deep Learning is one of the machine learning methods to find features from a huge data using non-linear transformation. It is now commonly used for supervised learning in many fields. In particular, Convolutional Neural Network (CNN) is the best technique for the image classification since 2012. For users who consider deep learning models for real-world applications, Keras is a popular API for neural networks written in Python and also can be used in R. We try examine the parameter estimation procedures of Deep Neural Network and structures of CNN models from basics to advanced techniques. We also try to figure out some crucial steps in CNN that can improve image classification performance in the CIFAR10 dataset using Keras. We found that several stacks of convolutional layers and batch normalization could improve prediction performance. We also compared image classification performances with other machine learning methods, including K-Nearest Neighbors (K-NN), Random Forest, and XGBoost, in both MNIST and CIFAR10 dataset.

• 한국멀티미디어학회논문지
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• 제24권11호
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• pp.1508-1517
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• 2021

In CCTV environments, a lot of learning image data is required to monitor illegal dumping of garbage with a typical image-based object detection using deep learning method. In this paper, we propose a system to monitor unauthorized dumping of garbage by learning the articular points of the person using only a small number of images without immediate use of the image for deep learning. In experiment, the proposed system showed 74.97% of garbage dumping detection performance with only a relatively small amount of image data in CCTV environments.

• 한국소프트웨어감정평가학회 논문지
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• 제15권1호
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• pp.63-70
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• 2019

최근 딥 러닝(Deep leaning)을 이용한 이미지 인식 분야의 연구가 활발히 진행되고 있다. 본 연구에서는 육안으로 관찰하여 분류하기 어려운 암석을 이미지만으로 분류하기 위해 딥 러닝 오픈 소스 프레임워크인 Tensorflow 기반의 CNN모델을 사용하여 고등학교 교육과정에서 다루는 암석 18종(화성암 6종, 변성암 6종, 퇴적암 6종)의 이미지를 통해 암석을 분류하는 시스템을 제안한다. 암석의 이미지를 학습시켜 암석을 구별하는 분류기를 개발하여 분류 성능을 확인하였으며 최종적으로 구현한 모바일 어플리케이션을 통해 교실 내 학습 또는 현장체험학습 등에서 학생들의 학습 보조도구로서 사용할 수 있다.

• 반도체디스플레이기술학회지
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• 제19권2호
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• pp.88-91
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• 2020

Image segmentation is the task of partitioning an image into multiple sets of pixels based on some characteristics. The objective is to simplify the image into a representation that is more meaningful and easier to analyze. In this paper, we apply deep-learning to pre-train the learning model, and implement an algorithm that performs image segmentation in real time by extracting frames for the stream input from the Android device. Based on the open source of DeepLab-v3+ implemented in Tensorflow, some convolution filters are modified to improve real-time operation on the Android platform.

• 한국멀티미디어학회논문지
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• 제25권2호
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• pp.145-156
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• 2022

Nowadays, the complexity of object tracking models among hardware applications has become a more in-demand duty to complete in various indeterminable environment tracking situations with multifunctional algorithm skills. In this paper, we propose a virtual city environment using AirSim (Aerial Informatics and Robotics Simulation - AirSim, CityEnvironment) and use the DQN (Deep Q-Learning) model of deep reinforcement learning model in the virtual environment. The proposed object tracking DQN network observes the environment using a deep reinforcement learning model that receives continuous images taken by a virtual environment simulation system as input to control the operation of a virtual drone. The deep reinforcement learning model is pre-trained using various existing continuous image sets. Since the existing various continuous image sets are image data of real environments and objects, it is implemented in 3D to track virtual environments and moving objects in them.

• 한국환경복원기술학회지
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• 제24권6호
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• pp.89-96
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• 2021

In this study, a deep-learning image analysis model was established and validated for AI-based monitoring of the tidal flat ecosystem for marine protected creatures Ocypode stimpsoni and their habitat. The data in the study was constructed using an unmanned aerial vehicle, and the U-net model was applied for the deep learning model. The accuracy of deep learning model learning results was about 0.76 and about 0.8 each for the Ocypode stimpsoni and their burrow whose accuracy was higher. Analyzing the distribution of crabs and burrows by putting orthomosaic images of the entire study area to the learned deep learning model, it was confirmed that 1,943 Ocypode stimpsoni and 2,807 burrow were distributed in the study area. Through this study, the possibility of using the deep learning image analysis technology for monitoring the tidal ecosystem was confirmed. And it is expected that it can be used in the tidal ecosystem monitoring field by expanding the monitoring sites and target species in the future.

• 스마트미디어저널
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• 제11권11호
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• pp.25-31
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• 2022

자율주행 자동차에서 딥러닝 기반 영상처리는 매우 중요하다. 자동차를 비롯한 SoC(System on Chip) 환경에서 실시간으로 도로 영상을 처리하기 위해서는 영상처리 모델을 딥러닝 연산에 특화된 NPU(Neural Processing Unit) 상에서 실행해야 한다. 본 연구에서는 GPU 서버 환경에서 개발된 7종의 오픈소스 딥러닝 영상처리 모델들을 TIDL (Texas Instrument Deep Learning) NPU 환경에 이식하였다. 성능 평가와 시각화를 통해 본 연구에서 이식한 모델들이 SoC 가상환경에서 정상 작동함을 확인하였다. 본 논문은 NPU 환경의 제약으로 인해 이식 과정에 발생한 문제들과 그 해결 방법을 소개함으로써 딥러닝 모델을 SoC 환경에 이식하려는 개발자 및 연구자가 참고할 만한 사례를 제시한다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.183-183
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• 2021

Deep learning methods and their application have become an essential part of prediction and modeling in water-related research areas, including hydrological processes, climate change, etc. It is known that application of deep learning leads to high availability of data sources in hydrology, which shows its usefulness in analysis of precipitation, runoff, groundwater level, evapotranspiration, and so on. However, there is still a limitation on microclimate analysis and prediction with deep learning methods because of deficiency of gauge-based data and shortcomings of existing technologies. In this study, a real-time rainfall prediction model was developed from a sky image data set with convolutional neural networks (CNNs). These daily image data were collected at Chung-Ang University and Korea University. For high accuracy of the proposed model, it considers data classification, image processing, ratio adjustment of no-rain data. Rainfall prediction data were compared with minutely rainfall data at rain gauge stations close to image sensors. It indicates that the proposed model could offer an interpolation of current rainfall observation system and have large potential to fill an observation gap. Information from small-scaled areas leads to advance in accurate weather forecasting and hydrological modeling at a micro scale.

• 전기전자학회논문지
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• 제22권4호
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• pp.933-940
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• 2018

본 논문에서는 얼굴 인식률 향상을 위한 멀티 블록 방식의 딥러닝 구조를 제안한다. 제안하는 딥러닝의 인식 구조는 입력된 이미지의 멀티 블록화, 특징 수치 분석을 통한 멀티 블록 선정, 선정된 멀티 블록의 딥러닝 수행 등의 3가지 과정으로 구성된다. 첫 번째로 입력된 이미지의 멀티 블록화는 입력된 이미지를 4등분하여 멀티 블록화 시킨다. 두 번째로 특징 수치분석을 통한 멀티 블록 선정에서는 4등분된 멀티 블록들의 특징 수치를 확인하고 특징이 많이 부각되는 블록만을 선정하여 얼굴 인식에 방해가 되는 요소를 사전에 제거한 블록들을 선정한다. 세 번째로 선정된 멀티 블록으로 딥러닝 수행은 선정된 멀티 블록 부위가 학습되어진 딥러닝 모델에 인식을 수행하여 특징 수치가 높은 효율적인 블록으로 얼굴 인식의 결과를 도출한다. 제안된 딥러닝 구조의 성능을 평가하기 위하여 CAS-PEAL 얼굴 데이터베이스를 사용하여 실험 하였다. 실험 결과, 제안하는 멀티 블록 방식의 딥러닝 구조가 기존의 딥러닝 구조보다 평균 약 2.3% 향상된 얼굴 인식률을 나타내어 그 효용성이 입증됨을 확인하였다.