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

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Automatic Parameter Acquisition of 12 leads ECG Using Continuous Data Processing Deep Neural Network (연속적 데이터 처리 심층신경망을 이용한 12 lead 심전도 파라미터의 자동 획득)

  • Kim, Ji Woon;Park, Sung Min;Choi, Seong Wook
    • Journal of Biomedical Engineering Research
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    • v.41 no.2
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    • pp.107-119
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    • 2020
  • The deep neural networks (DNN) that can replicate the behavior of the human expert who recognizes the characteristics of ECG waveform have been developed and studied to analyze ECG. However, although the existing DNNs can not provide the explanations for their decisions, those trials have attempted to determine whether patients have certain diseases or not and those decisions could not be accepted because of the absence of relating theoretical basis. In addition, these DNNs required a lot of training data to obtain sufficient accuracy in spite of the difficulty in the acquisition of relating clinical data. In this study, a small-sized continuous data processing DNN (C-DNN) was suggested to determine the simple characteristics of ECG wave that were not required additional explanations about its decisions and the C-DNN can be easily trained with small training data. Although it can analyze small input data that was selected in narrow region on whole ECG, it can continuously scan all ECG data and find important points such as start and end points of P, QRS and T waves within a short time. The star and end points of ECG waves determined by the C-DNNs were compared with the results performed by human experts to estimate the accuracies of the C-DNNs. The C-DNN has 150 inputs, 51 outputs, two hidden layers and one output layer. To find the start and end points, two C-DNNs were trained through deep learning technology and applied to a parameter acquisition algorithms. 12 lead ECG data measured in four patients and obtained through PhysioNet was processed to make training data by human experts. The accuracy of the C-DNNs were evaluated with extra data that were not used at deep learning by comparing the results between C-DNNs and human experts. The averages of the time differences between the C-DNNs and experts were 0.1 msec and 13.5 msec respectively and those standard deviations were 17.6 msec and 15.7 msec. The final step combining the results of C-DNN through the waveforms of 12 leads was successfully determined all 33 waves without error that the time differences of human experts decision were over 20 msec. The reliable decision of the ECG wave's start and end points benefits the acquisition of accurate ECG parameters such as the wave lengths, amplitudes and intervals of P, QRS and T waves.

Deep Learning based Emotion Classification using Multi Modal Bio-signals (다중 모달 생체신호를 이용한 딥러닝 기반 감정 분류)

  • Lee, JeeEun;Yoo, Sun Kook
    • Journal of Korea Multimedia Society
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    • v.23 no.2
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    • pp.146-154
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    • 2020
  • Negative emotion causes stress and lack of attention concentration. The classification of negative emotion is important to recognize risk factors. To classify emotion status, various methods such as questionnaires and interview are used and it could be changed by personal thinking. To solve the problem, we acquire multi modal bio-signals such as electrocardiogram (ECG), skin temperature (ST), galvanic skin response (GSR) and extract features. The neural network (NN), the deep neural network (DNN), and the deep belief network (DBN) is designed using the multi modal bio-signals to analyze emotion status. As a result, the DBN based on features extracted from ECG, ST and GSR shows the highest accuracy (93.8%). It is 5.7% higher than compared to the NN and 1.4% higher than compared to the DNN. It shows 12.2% higher accuracy than using only single bio-signal (GSR). The multi modal bio-signal acquisition and the deep learning classifier play an important role to classify emotion.

Performance Evaluation of Deep Neural Network (DNN) Based on HRV Parameters for Judgment of Risk Factors for Coronary Artery Disease (관상동맥질환 위험인자 유무 판단을 위한 심박변이도 매개변수 기반 심층 신경망의 성능 평가)

  • Park, Sung Jun;Choi, Seung Yeon;Kim, Young Mo
    • Journal of Biomedical Engineering Research
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    • v.40 no.2
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    • pp.62-67
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    • 2019
  • The purpose of this study was to evaluate the performance of deep neural network model in order to determine whether there is a risk factor for coronary artery disease based on the cardiac variation parameter. The study used unidentifiable 297 data to evaluate the performance of the model. Input data consists of heart rate parameters, which are SDNN (standard deviation of the N-N intervals), PSI (physical stress index), TP (total power), VLF (very low frequency), LF (low frequency), HF (high frequency), RMSSD (root mean square of successive difference) APEN (approximate entropy) and SRD (successive R-R interval difference), the age group and sex. Output data are divided into normal and patient groups, and the patient group consists of those diagnosed with diabetes, high blood pressure, and hyperlipidemia among the various risk factors that can cause coronary artery disease. Based on this, a binary classification model was applied using Deep Neural Network of deep learning techniques to classify normal and patient groups efficiently. To evaluate the effectiveness of the model used in this study, Kernel SVM (support vector machine), one of the classification models in machine learning, was compared and evaluated using same data. The results showed that the accuracy of the proposed deep neural network was train set 91.79% and test set 85.56% and the specificity was 87.04% and the sensitivity was 83.33% from the point of diagnosis. These results suggest that deep learning is more efficient when classifying these medical data because the train set accuracy in the deep neural network was 7.73% higher than the comparative model Kernel SVM.

Multiple Discriminative DNNs for I-Vector Based Open-Set Language Recognition (I-벡터 기반 오픈세트 언어 인식을 위한 다중 판별 DNN)

  • Kang, Woo Hyun;Cho, Won Ik;Kang, Tae Gyoon;Kim, Nam Soo
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.41 no.8
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    • pp.958-964
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    • 2016
  • In this paper, we propose an i-vector based language recognition system to identify the spoken language of the speaker, which uses multiple discriminative deep neural network (DNN) models analogous to the multi-class support vector machine (SVM) classification system. The proposed model was trained and tested using the i-vectors included in the NIST 2015 i-vector Machine Learning Challenge database, and shown to outperform the conventional language recognition methods such as cosine distance, SVM and softmax NN classifier in open-set experiments.

Applying feature normalization based on pole filtering to short-utterance speech recognition using deep neural network (심층신경망을 이용한 짧은 발화 음성인식에서 극점 필터링 기반의 특징 정규화 적용)

  • Han, Jaemin;Kim, Min Sik;Kim, Hyung Soon
    • The Journal of the Acoustical Society of Korea
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    • v.39 no.1
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    • pp.64-68
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    • 2020
  • In a conventional speech recognition system using Gaussian Mixture Model-Hidden Markov Model (GMM-HMM), the cepstral feature normalization method based on pole filtering was effective in improving the performance of recognition of short utterances in noisy environments. In this paper, the usefulness of this method for the state-of-the-art speech recognition system using Deep Neural Network (DNN) is examined. Experimental results on AURORA 2 DB show that the cepstral mean and variance normalization based on pole filtering improves the recognition performance of very short utterances compared to that without pole filtering, especially when there is a large mismatch between the training and test conditions.

Deep Learning Model for Prediction Rate Improvement of Stock Price Using RNN and LSTM (RNN과 LSTM을 이용한 주가 예측율 향상을 위한 딥러닝 모델)

  • Shin, Dong-Ha;Choi, Kwang-Ho;Kim, Chang-Bok
    • 한국정보기술학회논문지
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    • v.15 no.10
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    • pp.9-16
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    • 2017
  • Recently, stock price prediction using deep learning has basically used assistance index as a prediction factors. However assistance index is necessary to examine whether it is suitable as prediction factors because it is subjective viewpoint of researcher. In this study, we examine the suitability as prediction factors with various combinations of existing assistance indexes through the R neural network package, and studied the optimal combinations of assistance indexes and environmental prediction factors like exchange rate, exchange rate moving average, and whole industrial production index in order to improve the prediction rate. In addition, we proposed a deep learning model like DNN, RNN, LSTM which have input-output with extracted prediction factors. As a result, most of the assistance indexes decreased the prediction rate and the prediction rate was improved through additional environmental prediction factors. Also, RNN and LSTM, which are time series deep learning networks, were learned quickly and steadily compared to DNN. Although there is a difference by items, the prediction rate improvement is about 15%.

Prediction of Short and Long-term PV Power Generation in Specific Regions using Actual Converter Output Data (실제 컨버터 출력 데이터를 이용한 특정 지역 태양광 장단기 발전 예측)

  • Ha, Eun-gyu;Kim, Tae-oh;Kim, Chang-bok
    • The Journal of Advanced Navigation Technology
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    • v.23 no.6
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    • pp.561-569
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    • 2019
  • Solar photovoltaic can provide electrical energy with only radiation, and its use is expanding rapidly as a new energy source. This study predicts the short and long-term PV power generation using actual converter output data of photovoltaic system. The prediction algorithm uses multiple linear regression, support vector machine (SVM), and deep learning such as deep neural network (DNN) and long short-term memory (LSTM). In addition, three models are used according to the input and output structure of the weather element. Long-term forecasts are made monthly, seasonally and annually, and short-term forecasts are made for 7 days. As a result, the deep learning network is better in prediction accuracy than multiple linear regression and SVM. In addition, LSTM, which is a better model for time series prediction than DNN, is somewhat superior in terms of prediction accuracy. The experiment results according to the input and output structure appear Model 2 has less error than Model 1, and Model 3 has less error than Model 2.

Improvement of PM10 Forecasting Performance using DNN and Secondary Data (DNN과 2차 데이터를 이용한 PM10 예보 성능 개선)

  • Yu, SukHyun;Jeon, YoungTae
    • Journal of Korea Multimedia Society
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    • v.22 no.10
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    • pp.1187-1198
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    • 2019
  • In this study, we propose a new $PM_{10}$ forecasting model for Seoul region using DNN(Deep Neural Network) and secondary data. The previous numerical and Julian forecast model have been developed using primary data such as weather and air quality measurements. These models give excellent results for accuracy and false alarms, but POD is not good for the daily life usage. To solve this problem, we develop four secondary factors composed with primary data, which reflect the correlations between primary factors and high $PM_{10}$ concentrations. The proposed 4 models are A(Anomaly), BT(Back trajectory), CB(Contribution), CS(Cosine similarity), and ALL(model using all 4 secondary data). Among them, model ALL shows the best performance in all indicators, especially the PODs are improved.

Indoor Space Recognition using Super-pixel and DNN (DNN과 슈퍼픽셀을 이용한 실내 공간 인식)

  • Kim, Kisang;Choi, Hyung-Il
    • Journal of Internet Computing and Services
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    • v.19 no.3
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    • pp.43-48
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    • 2018
  • In this paper, we propose an indoor-space recognition using DNN and super-pixel. In order to recognize the indoor space from the image, segmentation process is required for dividing an image Super-pixel is performed algorithm which can be divided into appropriate sizes. In order to recognize each segment, features are extracted using a proposed method. Extracted features are learned using DNN, and each segment is recognized using the DNN model. Experimental results show the performance comparison between the proposed method and existing algorithms.

A Deep Neural Network Model Based on a Mutation Operator (돌연변이 연산 기반 효율적 심층 신경망 모델)

  • Jeon, Seung Ho;Moon, Jong Sub
    • KIPS Transactions on Software and Data Engineering
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    • v.6 no.12
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    • pp.573-580
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    • 2017
  • Deep Neural Network (DNN) is a large layered neural network which is consisted of a number of layers of non-linear units. Deep Learning which represented as DNN has been applied very successfully in various applications. However, many issues in DNN have been identified through past researches. Among these issues, generalization is the most well-known problem. A Recent study, Dropout, successfully addressed this problem. Also, Dropout plays a role as noise, and so it helps to learn robust feature during learning in DNN such as Denoising AutoEncoder. However, because of a large computations required in Dropout, training takes a lot of time. Since Dropout keeps changing an inter-layer representation during the training session, the learning rates should be small, which makes training time longer. In this paper, using mutation operation, we reduce computation and improve generalization performance compared with Dropout. Also, we experimented proposed method to compare with Dropout method and showed that our method is superior to the Dropout one.