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

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Prediction and Comparison of Electrochemical Machining on Shape Memory Alloy(SMA) using Deep Neural Network(DNN)

  • Song, Woo Jae;Choi, Seung Geon;Lee, Eun-Sang
    • Journal of Electrochemical Science and Technology
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    • v.10 no.3
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    • pp.276-283
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    • 2019
  • Nitinol is an alloy of nickel and titanium. Nitinol is one of the shape memory alloys(SMA) that are restored to a remembered form, changing the crystal structure at a given temperature. Because of these unique features, it is used in medical devices, high precision sensors, and aerospace industries. However, the conventional method of mechanical machining for nitinol has problems of thermal and residual stress after processing. Therefore, the electrochemical machining(ECM), which does not produce residual stress and thermal deformation, has emerged as an alternative processing technique. In addition, to replace the existing experimental planning methods, this study used deep neural network(DNN), which is the basis for AI. This method was shown to be more useful than conventional method of design of experiments(RSM, Taguchi, Regression) by applying deep neural network(DNN) to electrochemical machining(ECM) and comparing root mean square errors(RMSE). Comparison with actual experimental values has shown that DNN is a more useful method than conventional method. (DOE - RSM, Taguchi, Regression). The result of the machining was accurately and efficiently predicted by applying electrochemical machining(ECM) and deep neural network(DNN) to the shape memory alloy(SMA), which is a hard-mechinability material.

DNN-based acoustic modeling for speech recognition of native and foreign speakers (원어민 및 외국인 화자의 음성인식을 위한 심층 신경망 기반 음향모델링)

  • Kang, Byung Ok;Kwon, Oh-Wook
    • Phonetics and Speech Sciences
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    • v.9 no.2
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    • pp.95-101
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    • 2017
  • This paper proposes a new method to train Deep Neural Network (DNN)-based acoustic models for speech recognition of native and foreign speakers. The proposed method consists of determining multi-set state clusters with various acoustic properties, training a DNN-based acoustic model, and recognizing speech based on the model. In the proposed method, hidden nodes of DNN are shared, but output nodes are separated to accommodate different acoustic properties for native and foreign speech. In an English speech recognition task for speakers of Korean and English respectively, the proposed method is shown to slightly improve recognition accuracy compared to the conventional multi-condition training method.

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.

River streamflow prediction using a deep neural network: a case study on the Red River, Vietnam

  • Le, Xuan-Hien;Ho, Hung Viet;Lee, Giha
    • Korean Journal of Agricultural Science
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    • v.46 no.4
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    • pp.843-856
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    • 2019
  • Real-time flood prediction has an important role in significantly reducing potential damage caused by floods for urban residential areas located downstream of river basins. This paper presents an effective approach for flood forecasting based on the construction of a deep neural network (DNN) model. In addition, this research depends closely on the open-source software library, TensorFlow, which was developed by Google for machine and deep learning applications and research. The proposed model was applied to forecast the flowrate one, two, and three days in advance at the Son Tay hydrological station on the Red River, Vietnam. The input data of the model was a series of discharge data observed at five gauge stations on the Red River system, without requiring rainfall data, water levels and topographic characteristics. The research results indicate that the DNN model achieved a high performance for flood forecasting even though only a modest amount of data is required. When forecasting one and two days in advance, the Nash-Sutcliffe Efficiency (NSE) reached 0.993 and 0.938, respectively. The findings of this study suggest that the DNN model can be used to construct a real-time flood warning system on the Red River and for other river basins in Vietnam.

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.

A survey on parallel training algorithms for deep neural networks (심층 신경망 병렬 학습 방법 연구 동향)

  • Yook, Dongsuk;Lee, Hyowon;Yoo, In-Chul
    • The Journal of the Acoustical Society of Korea
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    • v.39 no.6
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    • pp.505-514
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    • 2020
  • Since a large amount of training data is typically needed to train Deep Neural Networks (DNNs), a parallel training approach is required to train the DNNs. The Stochastic Gradient Descent (SGD) algorithm is one of the most widely used methods to train the DNNs. However, since the SGD is an inherently sequential process, it requires some sort of approximation schemes to parallelize the SGD algorithm. In this paper, we review various efforts on parallelizing the SGD algorithm, and analyze the computational overhead, communication overhead, and the effects of the approximations.

A DNN-Based Personalized HRTF Estimation Method for 3D Immersive Audio

  • Son, Ji Su;Choi, Seung Ho
    • International Journal of Internet, Broadcasting and Communication
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    • v.13 no.1
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    • pp.161-167
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    • 2021
  • This paper proposes a new personalized HRTF estimation method which is based on a deep neural network (DNN) model and improved elevation reproduction using a notch filter. In the previous study, a DNN model was proposed that estimates the magnitude of HRTF by using anthropometric measurements [1]. However, since this method uses zero-phase without estimating the phase, it causes the internalization (i.e., the inside-the-head localization) of sound when listening the spatial sound. We devise a method to estimate both the magnitude and phase of HRTF based on the DNN model. Personalized HRIR was estimated using the anthropometric measurements including detailed data of the head, torso, shoulders and ears as inputs for the DNN model. After that, the estimated HRIR was filtered with an appropriate notch filter to improve elevation reproduction. In order to evaluate the performance, both of the objective and subjective evaluations are conducted. For the objective evaluation, the root mean square error (RMSE) and the log spectral distance (LSD) between the reference HRTF and the estimated HRTF are measured. For subjective evaluation, the MUSHRA test and preference test are conducted. As a result, the proposed method can make listeners experience more immersive audio than the previous methods.

Deep Neural Network Based Prediction of Daily Spectators for Korean Baseball League : Focused on Gwangju-KIA Champions Field (Deep Neural Network 기반 프로야구 일일 관중 수 예측 : 광주-기아 챔피언스 필드를 중심으로)

  • Park, Dong Ju;Kim, Byeong Woo;Jeong, Young-Seon;Ahn, Chang Wook
    • Smart Media Journal
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    • v.7 no.1
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    • pp.16-23
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    • 2018
  • In this paper, we used the Deep Neural Network (DNN) to predict the number of daily spectators of Gwangju - KIA Champions Field in order to provide marketing data for the team and related businesses and for managing the inventories of the facilities in the stadium. In this study, the DNN model, which is based on an artificial neural network (ANN), was used, and four kinds of DNN model were designed along with dropout and batch normalization model to prevent overfitting. Each of four models consists of 10 DNNs, and we added extra models with ensemble model. Each model was evaluated by Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The learning data from the model randomly selected 80% of the collected data from 2008 to 2017, and the other 20% were used as test data. With the result of 100 data selection, model configuration, and learning and prediction, we concluded that the predictive power of the DNN model with ensemble model is the best, and RMSE and MAPE are 15.17% and 14.34% higher, correspondingly, than the prediction value of the multiple linear regression model.

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.

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.