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

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A Novel SOC Estimation Method for Multiple Number of Lithium Batteries Using a Deep Neural Network (딥 뉴럴 네트워크를 이용한 새로운 리튬이온 배터리의 SOC 추정법)

  • Khan, Asad;Ko, Young-Hwi;Choi, Woo-Jin
    • The Transactions of the Korean Institute of Power Electronics
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    • v.26 no.1
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    • pp.1-8
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    • 2021
  • For the safe and reliable operation of lithium-ion batteries in electric vehicles or energy storage systems, having accurate information of the battery, such as the state of charge (SOC), is essential. Many different techniques of battery SOC estimation have been developed, such as the Kalman filter. However, when this filter is applied to multiple batteries, it has difficulty maintaining the accuracy of the estimation over all cells owing to the difference in parameter values of each cell. The difference in the parameter of each cell may increase as the operation time accumulates due to aging. In this paper, a novel deep neural network (DNN)-based SOC estimation method for multi-cell application is proposed. In the proposed method, DNN is implemented to determine the nonlinear relationships of the voltage and current at different SOCs and temperatures. In the training, the voltage and current data obtained at different temperatures during charge/discharge cycles are used. After the comprehensive training with the data obtained from the cycle test with a cell, the resulting algorithm is applied to estimate the SOC of other cells. Experimental results show that the mean absolute error of the estimation is 1.213% at 25℃ with the proposed DNN-based SOC estimation method.

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 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 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
    • The Journal of Korean Institute of Information Technology
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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
    • 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.

Generation of optical fringe patterns using deep learning (딥러닝을 이용한 광학적 프린지 패턴의 생성)

  • Kang, Ji-Won;Kim, Dong-Wook;Seo, Young-Ho
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.24 no.12
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    • pp.1588-1594
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    • 2020
  • In this paper, we discuss a data balancing method for learning a neural network that generates digital holograms using a deep neural network (DNN). Deep neural networks are based on deep learning (DL) technology and use a generative adversarial network (GAN) series. The fringe pattern, which is the basic unit of a hologram to be created through a deep neural network, has very different data types depending on the hologram plane and the position of the object. However, because the criteria for classifying the data are not clear, an imbalance in the training data may occur. The imbalance of learning data acts as a factor of instability in learning. Therefore, it presents a method for classifying and balancing data for which the classification criteria are not clear. And it shows that learning is stabilized through this.

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.

Recovery the Missing Streamflow Data on River Basin Based on the Deep Neural Network Model

  • Le, Xuan-Hien;Lee, Giha
    • Proceedings of the Korea Water Resources Association Conference
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    • pp.156-156
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    • 2019
  • In this study, a gated recurrent unit (GRU) network is constructed based on a deep neural network (DNN) with the aim of restoring the missing daily flow data in river basins. Lai Chau hydrological station is located upstream of the Da river basin (Vietnam) is selected as the target station for this study. Input data of the model are data on observed daily flow for 24 years from 1961 to 1984 (before Hoa Binh dam was built) at 5 hydrological stations, in which 4 gauge stations in the basin downstream and restoring - target station (Lai Chau). The total available data is divided into sections for different purposes. The data set of 23 years (1961-1983) was employed for training and validation purposes, with corresponding rates of 80% for training and 20% for validation respectively. Another data set of one year (1984) was used for the testing purpose to objectively verify the performance and accuracy of the model. Though only a modest amount of input data is required and furthermore the Lai Chau hydrological station is located upstream of the Da River, the calculated results based on the suggested model are in satisfactory agreement with observed data, the Nash - Sutcliffe efficiency (NSE) is higher than 95%. The finding of this study illustrated the outstanding performance of the GRU network model in recovering the missing flow data at Lai Chau station. As a result, DNN models, as well as GRU network models, have great potential for application within the field of hydrology and hydraulics.

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