• Title/Summary/Keyword: Deep Learning

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Deep Learning Structure Suitable for Embedded System for Flame Detection (불꽃 감지를 위한 임베디드 시스템에 적합한 딥러닝 구조)

  • Ra, Seung-Tak;Lee, Seung-Ho
    • Journal of IKEEE
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    • v.23 no.1
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    • pp.112-119
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    • 2019
  • In this paper, we propose a deep learning structure suitable for embedded system. The flame detection process of the proposed deep learning structure consists of four steps : flame area detection using flame color model, flame image classification using deep learning structure for flame color specialization, $N{\times}N$ cell separation in detected flame area, flame image classification using deep learning structure for flame shape specialization. First, only the color of the flame is extracted from the input image and then labeled to detect the flame area. Second, area of flame detected is the input of a deep learning structure specialized in flame color and is classified as flame image only if the probability of flame class at the output is greater than 75%. Third, divide the detected flame region of the images classified as flame images less than 75% in the preceding section into $N{\times}N$ units. Fourthly, small cells divided into $N{\times}N$ units are inserted into the input of a deep learning structure specialized to the shape of the flame and each cell is judged to be flame proof and classified as flame images if more than 50% of cells are classified as flame images. To verify the effectiveness of the proposed deep learning structure, we experimented with a flame database of ImageNet. Experimental results show that the proposed deep learning structure has an average resource occupancy rate of 29.86% and an 8 second fast flame detection time. The flame detection rate averaged 0.95% lower compared to the existing deep learning structure, but this was the result of light construction of the deep learning structure for application to embedded systems. Therefore, the deep learning structure for flame detection proposed in this paper has been proved suitable for the application of embedded system.

A Survey on Deep Reinforcement Learning Libraries (심층강화학습 라이브러리 기술동향)

  • Shin, S.J.;Cho, C.L.;Jeon, H.S.;Yoon, S.H.;Kim, T.Y.
    • Electronics and Telecommunications Trends
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    • v.34 no.6
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    • pp.87-99
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    • 2019
  • Reinforcement learning is a type of machine learning paradigm that forces agents to repeat the observation-action-reward process to assess and predict the values of possible future action sequences. This allows the agents to incrementally reinforce the desired behavior for a given observation. Thanks to the recent advancements of deep learning, reinforcement learning has evolved into deep reinforcement learning that introduces promising results in various control and optimization domains, such as games, robotics, autonomous vehicles, computing, industrial control, and so on. In addition to this trend, a number of programming libraries have been developed for importing deep reinforcement learning into a variety of applications. In this article, we briefly review and summarize 10 representative deep reinforcement learning libraries and compare them from a development project perspective.

A Model of Strawberry Pest Recognition using Artificial Intelligence Learning

  • Guangzhi Zhao
    • International Journal of Internet, Broadcasting and Communication
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    • v.15 no.2
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    • pp.133-143
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    • 2023
  • In this study, we propose a big data set of strawberry pests collected directly for diagnosis model learning and an automatic pest diagnosis model architecture based on deep learning. First, a big data set related to strawberry pests, which did not exist anywhere before, was directly collected from the web. A total of more than 12,000 image data was directly collected and classified, and this data was used to train a deep learning model. Second, the deep-learning-based automatic pest diagnosis module is a module that classifies what kind of pest or disease corresponds to when a user inputs a desired picture. In particular, we propose a model architecture that can optimally classify pests based on a convolutional neural network among deep learning models. Through this, farmers can easily identify diseases and pests without professional knowledge, and can respond quickly accordingly.

Bagging deep convolutional autoencoders trained with a mixture of real data and GAN-generated data

  • Hu, Cong;Wu, Xiao-Jun;Shu, Zhen-Qiu
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.11
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    • pp.5427-5445
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    • 2019
  • While deep neural networks have achieved remarkable performance in representation learning, a huge amount of labeled training data are usually required by supervised deep models such as convolutional neural networks. In this paper, we propose a new representation learning method, namely generative adversarial networks (GAN) based bagging deep convolutional autoencoders (GAN-BDCAE), which can map data to diverse hierarchical representations in an unsupervised fashion. To boost the size of training data, to train deep model and to aggregate diverse learning machines are the three principal avenues towards increasing the capabilities of representation learning of neural networks. We focus on combining those three techniques. To this aim, we adopt GAN for realistic unlabeled sample generation and bagging deep convolutional autoencoders (BDCAE) for robust feature learning. The proposed method improves the discriminative ability of learned feature embedding for solving subsequent pattern recognition problems. We evaluate our approach on three standard benchmarks and demonstrate the superiority of the proposed method compared to traditional unsupervised learning methods.

Image analysis technology with deep learning for monitoring the tidal flat ecosystem -Focused on monitoring the Ocypode stimpsoni Ortmann, 1897 in the Sindu-ri tidal flat - (갯벌 생태계 모니터링을 위한 딥러닝 기반의 영상 분석 기술 연구 - 신두리 갯벌 달랑게 모니터링을 중심으로 -)

  • Kim, Dong-Woo;Lee, Sang-Hyuk;Yu, Jae-Jin;Son, Seung-Woo
    • Journal of the Korean Society of Environmental Restoration Technology
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    • v.24 no.6
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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.

Predicting the Real Estate Price Index Using Deep Learning (딥 러닝을 이용한 부동산가격지수 예측)

  • Bae, Seong Wan;Yu, Jung Suk
    • Korea Real Estate Review
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    • v.27 no.3
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    • pp.71-86
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    • 2017
  • The purpose of this study was to apply the deep running method to real estate price index predicting and to compare it with the time series analysis method to test the possibility of its application to real estate market forecasting. Various real estate price indices were predicted using the DNN (deep neural networks) and LSTM (long short term memory networks) models, both of which draw on the deep learning method, and the ARIMA (autoregressive integrated moving average) model, which is based on the time seies analysis method. The results of the study showed the following. First, the predictive power of the deep learning method is superior to that of the time series analysis method. Second, among the deep learning models, the predictability of the DNN model is slightly superior to that of the LSTM model. Third, the deep learning method and the ARIMA model are the least reliable tools for predicting the housing sales prices index among the real estate price indices. Drawing on the deep learning method, it is hoped that this study will help enhance the accuracy in predicting the real estate market dynamics.

A Comparison of Deep Reinforcement Learning and Deep learning for Complex Image Analysis

  • Khajuria, Rishi;Quyoom, Abdul;Sarwar, Abid
    • Journal of Multimedia Information System
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    • v.7 no.1
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    • pp.1-10
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    • 2020
  • The image analysis is an important and predominant task for classifying the different parts of the image. The analysis of complex image analysis like histopathological define a crucial factor in oncology due to its ability to help pathologists for interpretation of images and therefore various feature extraction techniques have been evolved from time to time for such analysis. Although deep reinforcement learning is a new and emerging technique but very less effort has been made to compare the deep learning and deep reinforcement learning for image analysis. The paper highlights how both techniques differ in feature extraction from complex images and discusses the potential pros and cons. The use of Convolution Neural Network (CNN) in image segmentation, detection and diagnosis of tumour, feature extraction is important but there are several challenges that need to be overcome before Deep Learning can be applied to digital pathology. The one being is the availability of sufficient training examples for medical image datasets, feature extraction from whole area of the image, ground truth localized annotations, adversarial effects of input representations and extremely large size of the digital pathological slides (in gigabytes).Even though formulating Histopathological Image Analysis (HIA) as Multi Instance Learning (MIL) problem is a remarkable step where histopathological image is divided into high resolution patches to make predictions for the patch and then combining them for overall slide predictions but it suffers from loss of contextual and spatial information. In such cases the deep reinforcement learning techniques can be used to learn feature from the limited data without losing contextual and spatial information.

Research Trends for Deep Learning-Based High-Performance Face Recognition Technology (딥러닝 기반 고성능 얼굴인식 기술 동향)

  • Kim, H.I.;Moon, J.Y.;Park, J.Y.
    • Electronics and Telecommunications Trends
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    • v.33 no.4
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    • pp.43-53
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    • 2018
  • As face recognition (FR) has been well studied over the past decades, FR technology has been applied to many real-world applications such as surveillance and biometric systems. However, in the real-world scenarios, FR performances have been known to be significantly degraded owing to variations in face images, such as the pose, illumination, and low-resolution. Recently, visual intelligence technology has been rapidly growing owing to advances in deep learning, which has also improved the FR performance. Furthermore, the FR performance based on deep learning has been reported to surpass the performance level of human perception. In this article, we discuss deep-learning based high-performance FR technologies in terms of representative deep-learning based FR architectures and recent FR algorithms robust to face image variations (i.e., pose-robust FR, illumination-robust FR, and video FR). In addition, we investigate big face image datasets widely adopted for performance evaluations of the most recent deep-learning based FR algorithms.

Deep Neural Net Machine Learning and Manufacturing (제조업의 심층신경망 기계학습(딥러닝))

  • CHO, Mann;Lee, Mingook
    • Journal of Energy Engineering
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    • v.26 no.3
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    • pp.11-29
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    • 2017
  • In recent years, the use of artificial intelligence technology such as deep neural net machine learning(deep learning) is becoming an effective and practical option in industrial manufacturing process. This study focuses on recent deep learning development environments and their applications in the manufacturing field.

Recent R&D Trends for Lightweight Deep Learning (경량 딥러닝 기술 동향)

  • Lee, Y.J.;Moon, Y.H.;Park, J.Y.;Min, O.G.
    • Electronics and Telecommunications Trends
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    • v.34 no.2
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    • pp.40-50
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    • 2019
  • Considerable accuracy improvements in deep learning have recently been achieved in many applications that require large amounts of computation and expensive memory. However, recent advanced techniques for compacting and accelerating the deep learning model have been developed for deployment in lightweight devices with constrained resources. Lightweight deep learning techniques can be categorized into two schemes: lightweight deep learning algorithms (model simplification and efficient convolutional filters) in nature and transferring models into compact/small ones (model compression and knowledge distillation). In this report, we briefly summarize various lightweight deep learning techniques and possible research directions.