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

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Design of Deep De-nosing Network for Power Line Artifact in Electrocardiogram (심전도 신호의 전력선 잡음 제거를 위한 Deep De-noising Network 설계)

  • Kwon, Oyun;Lee, JeeEun;Kwon, Jun Hwan;Lim, Seong Jun;Yoo, Sun Kook
    • Journal of Korea Multimedia Society
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    • v.23 no.3
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    • pp.402-411
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    • 2020
  • Power line noise in electrocardiogram signals makes it difficult to diagnose cardiovascular disease. ECG signals without power line noise are needed to increase the accuracy of diagnosis. In this paper, it is proposed DNN(Deep Neural Network) model to remove the power line noise in ECG. The proposed model is learned with noisy ECG, and clean ECG. Performance of the proposed model were performed in various environments(varying amplitude, frequency change, real-time amplitude change). The evaluation used signal-to-noise ratio and root mean square error (RMSE). The difference in evaluation metrics between the noisy ECG signals and the de-noising ECG signals can demonstrate effectiveness as the de-noising model. The proposed DNN model learning result was a decrease in RMSE 0.0224dB and a increase in signal-to-noise ratio 1.048dB. The results performed in various environments showed a decrease in RMSE 1.7672dB and a increase in signal-to-noise ratio 15.1879dB in amplitude changes, a decrease in RMSE 0.0823dB and a increase in signal-to-noise ratio 4.9287dB in frequency changes. Finally, in real-time amplitude changes, RMSE was decreased 0.3886dB and signal-to-noise ratio was increased 11.4536dB. Thus, it was shown that the proposed DNN model can de-noise power line noise in ECG.

Deep Learning Model for Electric Power Demand Prediction Using Special Day Separation and Prediction Elements Extention (특수일 분리와 예측요소 확장을 이용한 전력수요 예측 딥 러닝 모델)

  • Park, Jun-Ho;Shin, Dong-Ha;Kim, Chang-Bok
    • The Journal of Advanced Navigation Technology
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    • v.21 no.4
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    • pp.365-370
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    • 2017
  • This study analyze correlation between weekdays data and special days data of different power demand patterns, and builds a separate data set, and suggests ways to reduce power demand prediction error by using deep learning network suitable for each data set. In addition, we propose a method to improve the prediction rate by adding the environmental elements and the separating element to the meteorological element, which is a basic power demand prediction elements. The entire data predicted power demand using LSTM which is suitable for learning time series data, and the special day data predicted power demand using DNN. The experiment result show that the prediction rate is improved by adding prediction elements other than meteorological elements. The average RMSE of the entire dataset was 0.2597 for LSTM and 0.5474 for DNN, indicating that the LSTM showed a good prediction rate. The average RMSE of the special day data set was 0.2201 for DNN, indicating that the DNN had better prediction than LSTM. The MAPE of the LSTM of the whole data set was 2.74% and the MAPE of the special day was 3.07 %.

DNN Based Multi-spectrum Pedestrian Detection Method Using Color and Thermal Image (DNN 기반 컬러와 열 영상을 이용한 다중 스펙트럼 보행자 검출 기법)

  • Lee, Yongwoo;Shin, Jitae
    • Journal of Broadcast Engineering
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    • v.23 no.3
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    • pp.361-368
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    • 2018
  • As autonomous driving research is rapidly developing, pedestrian detection study is also successfully investigated. However, most of the study utilizes color image datasets and those are relatively easy to detect the pedestrian. In case of color images, the scene should be exposed by enough light in order to capture the pedestrian and it is not easy for the conventional methods to detect the pedestrian if it is the other case. Therefore, in this paper, we propose deep neural network (DNN)-based multi-spectrum pedestrian detection method using color and thermal images. Based on single-shot multibox detector (SSD), we propose fusion network structures which simultaneously employ color and thermal images. In the experiment, we used KAIST dataset. We showed that proposed SSD-H (SSD-Halfway fusion) technique shows 18.18% lower miss rate compared to the KAIST pedestrian detection baseline. In addition, the proposed method shows at least 2.1% lower miss rate compared to the conventional halfway fusion method.

Image Restoration using GAN (적대적 생성신경망을 이용한 손상된 이미지의 복원)

  • Moon, ChanKyoo;Uh, YoungJung;Byun, Hyeran
    • Journal of Broadcast Engineering
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    • v.23 no.4
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    • pp.503-510
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    • 2018
  • Restoring of damaged images is a fundamental problem that was attempted before digital image processing technology appeared. Various algorithms for reconstructing damaged images have been introduced. However, the results show inferior restoration results compared with manual restoration. Recent developments of DNN (Deep Neural Network) have introduced various studies that apply it to image restoration. However, if the wide area is damaged, it can not be solved by a general interpolation method. In this case, it is necessary to reconstruct the damaged area through contextual information of surrounding images. In this paper, we propose an image restoration network using a generative adversarial network (GAN). The proposed system consists of image generation network and discriminator network. The proposed network is verified through experiments that it is possible to recover not only the natural image but also the texture of the original image through the inference of the damaged area in restoring various types of images.

Motion Sickness Measurement and Analysis in Virtual Reality using Deep Neural Networks Algorithm (심층신경망 알고리즘을 이용한 가상환경에서의 멀미 측정 및 분석)

  • Jeong, Daekyo;Yoo, Sangbong;Jang, Yun
    • Journal of The Korea Computer Graphics Society
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    • v.25 no.1
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    • pp.23-32
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    • 2019
  • Cybersickness is a symptom of dizziness that occurs while experiencing Virtual Reality (VR) technology and it is presumed to occur mainly by crosstalk between the sensory and cognitive systems. However, since the sensory and cognitive systems cannot be measured objectively, it is difficult to measure cybersickness. Therefore, methodologies for measuring cybersickness have been studied in various ways. Traditional studies have collected answers to questionnaires or analyzed EEG data using machine learning algorithms. However, the system relying on the questionnaires lacks objectivity, and it is difficult to obtain highly accurate measurements with the machine learning algorithms. In this work, we apply Deep Neural Network (DNN) deep learning algorithm for objective cybersickness measurement from EEG data. We also propose a data preprocessing for learning and network structures allowing us to achieve high performance when learning EEG data with the deep learning algorithms. Our approach provides cybersickness measurement with an accuracy up to 98.88%. Besides, we analyze video characteristics where cybersickness occurs by examining the video segments causing cybersickness in the experiments. We discover that cybersickness happens even in unusually persistent changes in the darkness such as the light in a room keeps switching on and off.

LFMMI-based acoustic modeling by using external knowledge (External knowledge를 사용한 LFMMI 기반 음향 모델링)

  • Park, Hosung;Kang, Yoseb;Lim, Minkyu;Lee, Donghyun;Oh, Junseok;Kim, Ji-Hwan
    • The Journal of the Acoustical Society of Korea
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    • v.38 no.5
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    • pp.607-613
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    • 2019
  • This paper proposes LF-MMI (Lattice Free Maximum Mutual Information)-based acoustic modeling using external knowledge for speech recognition. Note that an external knowledge refers to text data other than training data used in acoustic model. LF-MMI, objective function for optimization of training DNN (Deep Neural Network), has high performances in discriminative training. In LF-MMI, a phoneme probability as prior probability is used for predicting posterior probability of the DNN-based acoustic model. We propose using external knowledges for training the prior probability model to improve acoustic model based on DNN. It is measured to relative improvement 14 % as compared with the conventional LF-MMI-based model.

Speech Recognition Accuracy Measure using Deep Neural Network for Effective Evaluation of Speech Recognition Performance (효과적인 음성 인식 평가를 위한 심층 신경망 기반의 음성 인식 성능 지표)

  • Ji, Seung-eun;Kim, Wooil
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.21 no.12
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    • pp.2291-2297
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    • 2017
  • This paper describe to extract speech measure algorithm for evaluating a speech database, and presents generating method of a speech quality measure using DNN(Deep Neural Network). In our previous study, to produce an effective speech quality measure, we propose a combination of various speech measures which are highly correlated with WER(Word Error Rate). The new combination of various types of speech quality measures in this study is more effective to predict the speech recognition performance compared to each speech measure alone. In this paper, we describe the method of extracting measure using DNN, and we change one of the combined measure from GMM(Gaussican Mixture Model) score used in the previous study to DNN score. The combination with DNN score shows a higher correlation with WER compared to the combination with GMM score.

Multiaspect-based Active Sonar Target Classification Using Deep Belief Network (DBN을 이용한 다중 방위 데이터 기반 능동소나 표적 식별)

  • Kim, Dong-wook;Bae, Keun-sung;Seok, Jong-won
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.22 no.3
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    • pp.418-424
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    • 2018
  • Detection and classification of underwater targets is an important issue for both military and non-military purposes. Recently, many performance improvements are being reported in the field of pattern recognition with the development of deep learning technology. Among the results, DBN showed good performance when used for pre-training of DNN. In this paper, DBN was used for the classification of underwater targets using active sonar, and the results are compared with that of the conventional BPNN. We synthesized active sonar target signals using 3-dimensional highlight model. Then, features were extracted based on FrFT. In the single aspect based experiment, the classification result using DBN was improved about 3.83% compared with the BPNN. In the case of multi-aspect based experiment, a performance of 95% or more is obtained when the number of observation sequence exceeds three.

Speech emotion recognition using attention mechanism-based deep neural networks (주목 메커니즘 기반의 심층신경망을 이용한 음성 감정인식)

  • Ko, Sang-Sun;Cho, Hye-Seung;Kim, Hyoung-Gook
    • The Journal of the Acoustical Society of Korea
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    • v.36 no.6
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    • pp.407-412
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    • 2017
  • In this paper, we propose a speech emotion recognition method using a deep neural network based on the attention mechanism. The proposed method consists of a combination of CNN (Convolution Neural Networks), GRU (Gated Recurrent Unit), DNN (Deep Neural Networks) and attention mechanism. The spectrogram of the speech signal contains characteristic patterns according to the emotion. Therefore, we modeled characteristic patterns according to the emotion by applying the tuned Gabor filters as convolutional filter of typical CNN. In addition, we applied the attention mechanism with CNN and FC (Fully-Connected) layer to obtain the attention weight by considering context information of extracted features and used it for emotion recognition. To verify the proposed method, we conducted emotion recognition experiments on six emotions. The experimental results show that the proposed method achieves higher performance in speech emotion recognition than the conventional methods.

A Study on the Analysis and Estimation of the Construction Cost by Using Deep learning in the SMART Educational Facilities - Focused on Planning and Design Stage - (딥러닝을 이용한 스마트 교육시설 공사비 분석 및 예측 - 기획·설계단계를 중심으로 -)

  • Jung, Seung-Hyun;Gwon, Oh-Bin;Son, Jae-Ho
    • The Journal of Korean Institute of Educational Facilities
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    • v.25 no.6
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    • pp.35-44
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    • 2018
  • The purpose of this study is to predict more accurate construction costs and to support efficient decision making in the planning and design stages of smart education facilities. The higher the error in the projected cost, the more risk a project manager takes. If the manager can predict a more accurate construction cost in the early stages of a project, he/she can secure a decision period and support a more rational decision. During the planning and design stages, there is a limited amount of variables that can be selected for the estimating model. Moreover, since the number of completed smart schools is limited, there is little data. In this study, various artificial intelligence models were used to accurately predict the construction cost in the planning and design phase with limited variables and lack of performance data. A theoretical study on an artificial neural network and deep learning was carried out. As the artificial neural network has frequent problems of overfitting, it is found that there is a problem in practical application. In order to overcome the problem, this study suggests that the improved models of Deep Neural Network and Deep Belief Network are more effective in making accurate predictions. Deep Neural Network (DNN) and Deep Belief Network (DBN) models were constructed for the prediction of construction cost. Average Error Rate and Root Mean Square Error (RMSE) were calculated to compare the error and accuracy of those models. This study proposes a cost prediction model that can be used practically in the planning and design stages.