• Title/Summary/Keyword: Deep Neural Network

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Implementation of Low-cost Autonomous Car for Lane Recognition and Keeping based on Deep Neural Network model

  • Song, Mi-Hwa
    • International Journal of Internet, Broadcasting and Communication
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    • v.13 no.1
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    • pp.210-218
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    • 2021
  • CNN (Convolutional Neural Network), a type of deep learning algorithm, is a type of artificial neural network used to analyze visual images. In deep learning, it is classified as a deep neural network and is most commonly used for visual image analysis. Accordingly, an AI autonomous driving model was constructed through real-time image processing, and a crosswalk image of a road was used as an obstacle. In this paper, we proposed a low-cost model that can actually implement autonomous driving based on the CNN model. The most well-known deep neural network technique for autonomous driving is investigated and an end-to-end model is applied. In particular, it was shown that training and self-driving on a simulated road is possible through a practical approach to realizing lane detection and keeping.

Network Traffic Classification Based on Deep Learning

  • Li, Junwei;Pan, Zhisong
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.14 no.11
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    • pp.4246-4267
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    • 2020
  • As the network goes deep into all aspects of people's lives, the number and the complexity of network traffic is increasing, and traffic classification becomes more and more important. How to classify them effectively is an important prerequisite for network management and planning, and ensuring network security. With the continuous development of deep learning, more and more traffic classification begins to use it as the main method, which achieves better results than traditional classification methods. In this paper, we provide a comprehensive review of network traffic classification based on deep learning. Firstly, we introduce the research background and progress of network traffic classification. Then, we summarize and compare traffic classification based on deep learning such as stack autoencoder, one-dimensional convolution neural network, two-dimensional convolution neural network, three-dimensional convolution neural network, long short-term memory network and Deep Belief Networks. In addition, we compare traffic classification based on deep learning with other methods such as based on port number, deep packets detection and machine learning. Finally, the future research directions of network traffic classification based on deep learning are prospected.

Genetic algorithm based deep learning neural network structure and hyperparameter optimization (유전 알고리즘 기반의 심층 학습 신경망 구조와 초모수 최적화)

  • Lee, Sanghyeop;Kang, Do-Young;Park, Jangsik
    • Journal of Korea Multimedia Society
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    • v.24 no.4
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    • pp.519-527
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    • 2021
  • Alzheimer's disease is one of the challenges to tackle in the coming aging era and is attempting to diagnose and predict through various biomarkers. While the application of various deep learning-based technologies as powerful imaging technologies has recently expanded across the medical industry, empirical design is not easy because there are various deep earning neural networks architecture and categorical hyperparameters that rely on problems and data to solve. In this paper, we show the possibility of optimizing a deep learning neural network structure and hyperparameters for Alzheimer's disease classification in amyloid brain images in a representative deep earning neural networks architecture using genetic algorithms. It was observed that the optimal deep learning neural network structure and hyperparameter were chosen as the values of the experiment were converging.

Deep Neural Network-Based Beauty Product Recommender (심층신경망 기반의 뷰티제품 추천시스템)

  • Song, Hee Seok
    • Journal of Information Technology Applications and Management
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    • v.26 no.6
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    • pp.89-101
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    • 2019
  • Many researchers have been focused on designing beauty product recommendation system for a long time because of increased need of customers for personalized and customized recommendation in beauty product domain. In addition, as the application of the deep neural network technique becomes active recently, various collaborative filtering techniques based on the deep neural network have been introduced. In this context, this study proposes a deep neural network model suitable for beauty product recommendation by applying Neural Collaborative Filtering and Generalized Matrix Factorization (NCF + GMF) to beauty product recommendation. This study also provides an implementation of web API system to commercialize the proposed recommendation model. The overall performance of the NCF + GMF model was the best when the beauty product recommendation problem was defined as the estimation rating score problem and the binary classification problem. The NCF + GMF model showed also high performance in the top N recommendation.

Improved Deep Learning Algorithm

  • Kim, Byung Joo
    • Journal of Advanced Information Technology and Convergence
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    • v.8 no.2
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    • pp.119-127
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    • 2018
  • Training a very large deep neural network can be painfully slow and prone to overfitting. Many researches have done for overcoming the problem. In this paper, a combination of early stopping and ADAM based deep neural network was presented. This form of deep network is useful for handling the big data because it automatically stop the training before overfitting occurs. Also generalization ability is better than pure deep neural network model.

Conversion Tools of Spiking Deep Neural Network based on ONNX (ONNX기반 스파이킹 심층 신경망 변환 도구)

  • Park, Sangmin;Heo, Junyoung
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.20 no.2
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    • pp.165-170
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    • 2020
  • The spiking neural network operates in a different mechanism than the existing neural network. The existing neural network transfers the output value to the next neuron via an activation function that does not take into account the biological mechanism for the input value to the neuron that makes up the neural network. In addition, there have been good results using deep structures such as VGGNet, ResNet, SSD and YOLO. spiking neural networks, on the other hand, operate more like the biological mechanism of real neurons than the existing activation function, but studies of deep structures using spiking neurons have not been actively conducted compared to in-depth neural networks using conventional neurons. This paper proposes the method of loading an deep neural network model made from existing neurons into a conversion tool and converting it into a spiking deep neural network through the method of replacing an existing neuron with a spiking neuron.

Prediction of fine dust PM10 using a deep neural network model (심층 신경망모형을 사용한 미세먼지 PM10의 예측)

  • Jeon, Seonghyeon;Son, Young Sook
    • The Korean Journal of Applied Statistics
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    • v.31 no.2
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    • pp.265-285
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    • 2018
  • In this study, we applied a deep neural network model to predict four grades of fine dust $PM_{10}$, 'Good, Moderate, Bad, Very Bad' and two grades, 'Good or Moderate and Bad or Very Bad'. The deep neural network model and existing classification techniques (such as neural network model, multinomial logistic regression model, support vector machine, and random forest) were applied to fine dust daily data observed from 2010 to 2015 in six major metropolitan areas of Korea. Data analysis shows that the deep neural network model outperforms others in the sense of accuracy.

Compressed Ensemble of Deep Convolutional Neural Networks with Global and Local Facial Features for Improved Face Recognition (얼굴인식 성능 향상을 위한 얼굴 전역 및 지역 특징 기반 앙상블 압축 심층합성곱신경망 모델 제안)

  • Yoon, Kyung Shin;Choi, Jae Young
    • Journal of Korea Multimedia Society
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    • v.23 no.8
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    • pp.1019-1029
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    • 2020
  • In this paper, we propose a novel knowledge distillation algorithm to create an compressed deep ensemble network coupled with the combined use of local and global features of face images. In order to transfer the capability of high-level recognition performances of the ensemble deep networks to a single deep network, the probability for class prediction, which is the softmax output of the ensemble network, is used as soft target for training a single deep network. By applying the knowledge distillation algorithm, the local feature informations obtained by training the deep ensemble network using facial subregions of the face image as input are transmitted to a single deep network to create a so-called compressed ensemble DCNN. The experimental results demonstrate that our proposed compressed ensemble deep network can maintain the recognition performance of the complex ensemble deep networks and is superior to the recognition performance of a single deep network. In addition, our proposed method can significantly reduce the storage(memory) space and execution time, compared to the conventional ensemble deep networks developed for face recognition.

Sound event classification using deep neural network based transfer learning (깊은 신경망 기반의 전이학습을 이용한 사운드 이벤트 분류)

  • Lim, Hyungjun;Kim, Myung Jong;Kim, Hoirin
    • The Journal of the Acoustical Society of Korea
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    • v.35 no.2
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    • pp.143-148
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    • 2016
  • Deep neural network that effectively capture the characteristics of data has been widely used in various applications. However, the amount of sound database is often insufficient for learning the deep neural network properly, so resulting in overfitting problems. In this paper, we propose a transfer learning framework that can effectively train the deep neural network even with insufficient sound event data by employing rich speech or music data. A series of experimental results verify that proposed method performs significantly better than the baseline deep neural network that was trained only with small sound event data.