• Title/Summary/Keyword: Multi-DNN

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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.

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.

Land Cover Classification Based on High Resolution KOMPSAT-3 Satellite Imagery Using Deep Neural Network Model (심층신경망 모델을 이용한 고해상도 KOMPSAT-3 위성영상 기반 토지피복분류)

  • MOON, Gab-Su;KIM, Kyoung-Seop;CHOUNG, Yun-Jae
    • Journal of the Korean Association of Geographic Information Studies
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    • v.23 no.3
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    • pp.252-262
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    • 2020
  • In Remote Sensing, a machine learning based SVM model is typically utilized for land cover classification. And study using neural network models is also being carried out continuously. But study using high-resolution imagery of KOMPSAT is insufficient. Therefore, the purpose of this study is to assess the accuracy of land cover classification by neural network models using high-resolution KOMPSAT-3 satellite imagery. After acquiring satellite imagery of coastal areas near Gyeongju City, training data were produced. And land cover was classified with the SVM, ANN and DNN models for the three items of water, vegetation and land. Then, the accuracy of the classification results was quantitatively assessed through error matrix: the result using DNN model showed the best with 92.0% accuracy. It is necessary to supplement the training data through future multi-temporal satellite imagery, and to carry out classifications for various items.

Study on Image Use for Plant Disease Classification (작물의 병충해 분류를 위한 이미지 활용 방법 연구)

  • Jeong, Seong-Ho;Han, Jeong-Eun;Jeong, Seong-Kyun;Bong, Jae-Hwan
    • The Journal of the Korea institute of electronic communication sciences
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    • v.17 no.2
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    • pp.343-350
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    • 2022
  • It is worth verifying the effectiveness of data integration between data with different features. This study investigated whether the data integration affects the accuracy of deep neural network (DNN), and which integration method shows the best improvement. This study used two different public datasets. One public dataset was taken in an actual farm in India. And another was taken in a laboratory environment in Korea. Leaf images were selected from two different public datasets to have five classes which includes normal and four different types of plant diseases. DNN used pre-trained VGG16 as a feature extractor and multi-layer perceptron as a classifier. Data were integrated into three different ways to be used for the training process. DNN was trained in a supervised manner via the integrated data. The trained DNN was evaluated by using a test dataset taken in an actual farm. DNN shows the best accuracy for the test dataset when DNN was first trained by images taken in the laboratory environment and then trained by images taken in the actual farm. The results show that data integration between plant images taken in a different environment helps improve the performance of deep neural networks. And the results also confirmed that independent use of plant images taken in different environments during the training process is more effective in improving the performance of DNN.

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.

Multi-level Skip Connection for Nested U-Net-based Speech Enhancement (중첩 U-Net 기반 음성 향상을 위한 다중 레벨 Skip Connection)

  • Seorim, Hwang;Joon, Byun;Junyeong, Heo;Jaebin, Cha;Youngcheol, Park
    • Journal of Broadcast Engineering
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    • v.27 no.6
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    • pp.840-847
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    • 2022
  • In a deep neural network (DNN)-based speech enhancement, using global and local input speech information is closely related to model performance. Recently, a nested U-Net structure that utilizes global and local input data information using multi-scale has bee n proposed. This nested U-Net was also applied to speech enhancement and showed outstanding performance. However, a single skip connection used in nested U-Nets must be modified for the nested structure. In this paper, we propose a multi-level skip connection (MLS) to optimize the performance of the nested U-Net-based speech enhancement algorithm. As a result, the proposed MLS showed excellent performance improvement in various objective evaluation metrics compared to the standard skip connection, which means th at the MLS can optimize the performance of the nested U-Net-based speech enhancement algorithm. In addition, the final proposed m odel showed superior performance compared to other DNN-based speech enhancement models.

Deep Learning-based Antenna Selection Scheme for Millimeter-wave Systems in Urban Micro Cell Scenario (도심 Micro 셀 시나리오에서 밀리미터파 시스템을 위한 딥러닝 기반 안테나 선택 기법)

  • Ju, Sang-Lim;Kim, Nam-Il;Kim, Kyung-Seok
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.20 no.5
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    • pp.57-62
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    • 2020
  • The millimeter wave that uses the spectrum in the 30GHz~300GHz band has a shorter wavelength due to its high carrier frequency, so it is suitable for Massive MIMO systems because more antennas can be equipped in the base station. However, since an RF chain is required per antenna, hardware cost and power consumption increase as the number of antennas increases. Therefore, in this paper, we investigate antenna selection schemes to solve this problem. In order to solve the problem of high computational complexity in the exhaustive search based antenna selection scheme, we propose a approach of applying deep learning technology. An best antenna combination is predicted using a DNN model capable of classifying multi-classes. By simulation tests, we compare and evaluate the existing antenna selection schemes and the proposed deep learning-based antenna selection scheme.

Development of machine learning model for reefer container failure determination and cause analysis with unbalanced data (불균형 데이터를 갖는 냉동 컨테이너 고장 판별 및 원인 분석을 위한 기계학습 모형 개발)

  • Lee, Huiwon;Park, Sungho;Lee, Seunghyun;Lee, Seungjae;Lee, Kangbae
    • Journal of the Korea Convergence Society
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    • v.13 no.1
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    • pp.23-30
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    • 2022
  • The failure of the reefer container causes a great loss of cost, but the current reefer container alarm system is inefficient. Existing studies using simulation data of refrigeration systems exist, but studies using actual operation data of refrigeration containers are lacking. Therefore, this study classified the causes of failure using actual refrigerated container operation data. Data imbalance occurred in the actual data, and the data imbalance problem was solved by comparing the logistic regression analysis with ENN-SMOTE and class weight with the 2-stage algorithm developed in this study. The 2-stage algorithm uses XGboost, LGBoost, and DNN to classify faults and normalities in the first step, and to classify the causes of faults in the second step. The model using LGBoost in the 2-stage algorithm was the best with 99.16% accuracy. This study proposes a final model using a two-stage algorithm to solve data imbalance, which is thought to be applicable to other industries.

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

  • Khan, Asad;Ko, Young-hwi;Choi, Woojin
    • Proceedings of the KIPE Conference
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    • 2019.11a
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    • pp.70-72
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    • 2019
  • For the safe and reliable operation of Lithium-ion batteries in Electric Vehicles (EVs) or Energy Storage Systems (ESSs), it is essential to have accurate information of the battery such as State of Charge (SOC). Many kinds of different techniques to estimate the SOC of the batteries have been developed so far such as the Kalman Filter. However, when it is applied to the multiple number of batteries it is difficult to maintain the accuracy of the estimation over all cells due to the difference in parameter value of each cell. Moreover the difference in the parameter of each cell may become larger 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 learn non-linear relationship of the voltage and current of the lithium-ion battery at different SOCs and different temperatures. In the training the voltage and current data of the Lithium battery at charge and discharge cycles obtained at different temperatures are used. After the comprehensive training with the data obtained with a cell resulting estimation algorithm is applied to the other cells. The experimental results show that the Mean Absolute Error (MAE) of the estimation is 0.56% at 25℃, and 3.16% at 60℃ with the proposed SOC estimation algorithm.

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Optimization of Multi-time Scale Loss Function Suitable for DNN-based Audio Coder (심층신경망 기반 오디오 부호화기를 위한 Multi-time Scale 손실함수의 최적화)

  • Shin, Seung-Min;Byun, Joon;Park, Young-Cheol;Beack, Seung-kwon;Sung, Jong-mo
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • 2022.06a
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    • pp.1315-1317
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    • 2022
  • 최근, 심층신경망 기반 오디오 부호화기가 활발히 연구되고 있다. 심층신경망 기반 오디오 부호화기는 기존의 전통적인 오디오 부호화기보다 구조적으로 간단하지만, 네트워크의 복잡도를 증가시키지 않고 인지적 성능향상을 기대하는 것은 어렵다. 이 문제를 해결하기 위하여 인간의 청각적 특성을 활용한 심리음향모델 기반 손실함수를 사용한 기법들이 소개되었다. 심리음향 모델 기반 손실함수를 사용한 오디오 부호화기는 양자화 잡음을 잘 제어하였지만, 여전히 지각적인 향상이 필요하다. 본 논문에서는 심층신경망 기반 오디오 부호화기를 위한 Multi-time Scale 손실함수의 지역 손실함수 윈도우 크기의 최적화 제안한다. Multi-time Scale 손실함수의 지역 손실함수 계산을 위한 윈도우 크기를 조절하며, 이를 통하여 오디오 부호화에 적합한 윈도우 사이즈를 결정한다. 실험을 통해 얻은 최적의 Multi-time Scale 손실함수를 사용하여 네트워크를 훈련하였고, 주관적 평가를 통해 기존의 심리음향모델 기반 손실함수보다 좋은 음성 품질을 보여주는 것을 확인하였다.

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