• Title, Summary, Keyword: Deep Neural Network(DNN)

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Nuclear reactor vessel water level prediction during severe accidents using deep neural networks

  • Koo, Young Do;An, Ye Ji;Kim, Chang-Hwoi;Na, Man Gyun
    • Nuclear Engineering and Technology
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    • v.51 no.3
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    • pp.723-730
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    • 2019
  • Acquiring instrumentation signals generated from nuclear power plants (NPPs) is essential to maintain nuclear reactor integrity or to mitigate an abnormal state under normal operating conditions or severe accident circumstances. However, various safety-critical instrumentation signals from NPPs cannot be accurately measured on account of instrument degradation or failure under severe accident circumstances. Reactor vessel (RV) water level, which is an accident monitoring variable directly related to reactor cooling and prevention of core exposure, was predicted by applying a few signals to deep neural networks (DNNs) during severe accidents in NPPs. Signal data were obtained by simulating the postulated loss-of-coolant accidents at hot- and cold-legs, and steam generator tube rupture using modular accident analysis program code as actual NPP accidents rarely happen. To optimize the DNN model for RV water level prediction, a genetic algorithm was used to select the numbers of hidden layers and nodes. The proposed DNN model had a small root mean square error for RV water level prediction, and performed better than the cascaded fuzzy neural network model of the previous study. Consequently, the DNN model is considered to perform well enough to provide supporting information on the RV water level to operators.

Performance analysis of local exit for distributed deep neural networks over cloud and edge computing

  • Lee, Changsik;Hong, Seungwoo;Hong, Sungback;Kim, Taeyeon
    • ETRI Journal
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    • v.42 no.5
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    • pp.658-668
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    • 2020
  • In edge computing, most procedures, including data collection, data processing, and service provision, are handled at edge nodes and not in the central cloud. This decreases the processing burden on the central cloud, enabling fast responses to end-device service requests in addition to reducing bandwidth consumption. However, edge nodes have restricted computing, storage, and energy resources to support computation-intensive tasks such as processing deep neural network (DNN) inference. In this study, we analyze the effect of models with single and multiple local exits on DNN inference in an edge-computing environment. Our test results show that a single-exit model performs better with respect to the number of local exited samples, inference accuracy, and inference latency than a multi-exit model at all exit points. These results signify that higher accuracy can be achieved with less computation when a single-exit model is adopted. In edge computing infrastructure, it is therefore more efficient to adopt a DNN model with only one or a few exit points to provide a fast and reliable inference service.

Development of Deep Learning Based Deterioration Prediction Model for the Maintenance Planning of Highway Pavement (도로포장의 유지관리 계획 수립을 위한 딥러닝 기반 열화 예측 모델 개발)

  • Lee, Yongjun;Sun, Jongwan;Lee, Minjae
    • Korean Journal of Construction Engineering and Management
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    • v.20 no.6
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    • pp.34-43
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    • 2019
  • The maintenance cost for road pavement is gradually increasing due to the continuous increase in road extension as well as increase in the number of old routes that have passed the public period. As a result, there is a need for a method of minimizing costs through preventative grievance preventive maintenance requires the establishment of a strategic plan through accurate prediction of road pavement. Hence, In this study, the deep neural network(DNN) and the recurrent neural network(RNN) were used in order to develop the expressway pavement damage prediction model. A superior model among these two network models was then suggested by comparing and analyzing their performance. In order to solve the RNN's vanishing gradient problem, the LSTM (Long short-term memory) circuits which are a more complicated form of the RNN structure were used. The learning result showed that the RMSE value of the RNN-LSTM model was 0.102 which was lower than the RMSE value of the DNN model, indicating that the performance of the RNN-LSTM model was superior. In addition, high accuracy of the RNN-LSTM model was verified through the comparison between the estimated average road pavement condition and the actually measured road pavement condition of the target section over time.

Korean and English Sentiment Analysis Using the Deep Learning

  • Ramadhani, Adyan Marendra;Choi, Hyung Rim;Lim, Seong Bae
    • Journal of the Korea Industrial Information Systems Research
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    • v.23 no.3
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    • pp.59-71
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    • 2018
  • Social media has immense popularity among all services today. Data from social network services (SNSs) can be used for various objectives, such as text prediction or sentiment analysis. There is a great deal of Korean and English data on social media that can be used for sentiment analysis, but handling such huge amounts of unstructured data presents a difficult task. Machine learning is needed to handle such huge amounts of data. This research focuses on predicting Korean and English sentiment using deep forward neural network with a deep learning architecture and compares it with other methods, such as LDA MLP and GENSIM, using logistic regression. The research findings indicate an approximately 75% accuracy rate when predicting sentiments using DNN, with a latent Dirichelet allocation (LDA) prediction accuracy rate of approximately 81%, with the corpus being approximately 64% accurate between English and Korean.

An Adaptation Method in Noise Mismatch Conditions for DNN-based Speech Enhancement

  • Xu, Si-Ying;Niu, Tong;Qu, Dan;Long, Xing-Yan
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.12 no.10
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    • pp.4930-4951
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    • 2018
  • The deep learning based speech enhancement has shown considerable success. However, it still suffers performance degradation under mismatch conditions. In this paper, an adaptation method is proposed to improve the performance under noise mismatch conditions. Firstly, we advise a noise aware training by supplying identity vectors (i-vectors) as parallel input features to adapt deep neural network (DNN) acoustic models with the target noise. Secondly, given a small amount of adaptation data, the noise-dependent DNN is obtained by using $L_2$ regularization from a noise-independent DNN, and forcing the estimated masks to be close to the unadapted condition. Finally, experiments were carried out on different noise and SNR conditions, and the proposed method has achieved significantly 0.1%-9.6% benefits of STOI, and provided consistent improvement in PESQ and segSNR against the baseline systems.

Bit Operation Optimization and DNN Application using GPU Acceleration (GPU 가속기를 통한 비트 연산 최적화 및 DNN 응용)

  • Kim, Sang Hyeok;Lee, Jae Heung
    • Journal of IKEEE
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    • v.23 no.4
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    • pp.1314-1320
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    • 2019
  • In this paper, we propose a new method for optimizing bit operations and applying them to DNN(Deep Neural Network) in software environment. As a method for this, we propose a packing function for bitwise optimization and a masking matrix multiplication operation for application to DNN. The packing function converts 32-bit real value to 2-bit quantization value through threshold comparison operation. When this sequence is over, four 32-bit real values are changed to one 8-bit value. The masking matrix multiplication operation consists of a special operation for multiplying the packed weight value with the normal input value. And each operation was then processed in parallel using a GPU accelerator. As a result of this experiment, memory saved about 16 times than 32-bit DNN Model. Nevertheless, the accuracy was within 1%, similar to the 32-bit model.

Pavement Crack Detection and Segmentation Based on Deep Neural Network

  • Nguyen, Huy Toan;Yu, Gwang Hyun;Na, Seung You;Kim, Jin Young;Seo, Kyung Sik
    • The Journal of Korean Institute of Information Technology
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    • v.17 no.9
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    • pp.99-112
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    • 2019
  • Cracks on pavement surfaces are critical signs and symptoms of the degradation of pavement structures. Image-based pavement crack detection is a challenging problem due to the intensity inhomogeneity, topology complexity, low contrast, and noisy texture background. In this paper, we address the problem of pavement crack detection and segmentation at pixel-level based on a Deep Neural Network (DNN) using gray-scale images. We propose a novel DNN architecture which contains a modified U-net network and a high-level features network. An important contribution of this work is the combination of these networks afforded through the fusion layer. To the best of our knowledge, this is the first paper introducing this combination for pavement crack segmentation and detection problem. The system performance of crack detection and segmentation is enhanced dramatically by using our novel architecture. We thoroughly implement and evaluate our proposed system on two open data sets: the Crack Forest Dataset (CFD) and the AigleRN dataset. Experimental results demonstrate that our system outperforms eight state-of-the-art methods on the same data sets.

Comparison of Audio Event Detection Performance using DNN (DNN을 이용한 오디오 이벤트 검출 성능 비교)

  • Chung, Suk-Hwan;Chung, Yong-Joo
    • The Journal of the Korea institute of electronic communication sciences
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    • v.13 no.3
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    • pp.571-578
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    • 2018
  • Recently, deep learning techniques have shown superior performance in various kinds of pattern recognition. However, there have been some arguments whether the DNN performs better than the conventional machine learning techniques when classification experiments are done using a small amount of training data. In this study, we compared the performance of the conventional GMM and SVM with DNN, a kind of deep learning techniques, in audio event detection. When tested on the same data, DNN has shown superior overall performance but SVM was better than DNN in segment-based F-score.

A study on recognition improvement of velopharyngeal insufficiency patient's speech using various types of deep neural network (심층신경망 구조에 따른 구개인두부전증 환자 음성 인식 향상 연구)

  • Kim, Min-seok;Jung, Jae-hee;Jung, Bo-kyung;Yoon, Ki-mu;Bae, Ara;Kim, Wooil
    • The Journal of the Acoustical Society of Korea
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    • v.38 no.6
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    • pp.703-709
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    • 2019
  • This paper proposes speech recognition systems employing Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) structures combined with Hidden Markov Moldel (HMM) to effectively recognize the speech of VeloPharyngeal Insufficiency (VPI) patients, and compares the recognition performance of the systems to the Gaussian Mixture Model (GMM-HMM) and fully-connected Deep Neural Network (DNNHMM) based speech recognition systems. In this paper, the initial model is trained using normal speakers' speech and simulated VPI speech is used for generating a prior model for speaker adaptation. For VPI speaker adaptation, selected layers are trained in the CNN-HMM based model, and dropout regulatory technique is applied in the LSTM-HMM based model, showing 3.68 % improvement in recognition accuracy. The experimental results demonstrate that the proposed LSTM-HMM-based speech recognition system is effective for VPI speech with small-sized speech data, compared to conventional GMM-HMM and fully-connected DNN-HMM system.

Method for Road Vanishing Point Detection Using DNN and Hog Feature (DNN과 HoG Feature를 이용한 도로 소실점 검출 방법)

  • Yoon, Dae-Eun;Choi, Hyung-Il
    • The Journal of the Korea Contents Association
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    • v.19 no.1
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    • pp.125-131
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    • 2019
  • A vanishing point is a point on an image to which parallel lines projected from a real space gather. A vanishing point in a road space provides important spatial information. It is possible to improve the position of an extracted lane or generate a depth map image using a vanishing point in the road space. In this paper, we propose a method of detecting vanishing points on images taken from a vehicle's point of view using Deep Neural Network (DNN) and Histogram of Oriented Gradient (HoG). The proposed algorithm is divided into a HoG feature extraction step, in which the edge direction is extracted by dividing an image into blocks, a DNN learning step, and a test step. In the learning stage, learning is performed using 2,300 road images taken from a vehicle's point of views. In the test phase, the efficiency of the proposed algorithm using the Normalized Euclidean Distance (NormDist) method is measured.