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

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Estimation of Jamming Parameters based on Gaussian Kernel Function Networks (가우스 요소함수 망에 기초한 재밍 파라미터 추정)

  • Hwang, TaeHyun;Kil, Rhee Man;Lee, Hyun Ku;Kim, Jung Ho;Ko, Jae Heon;Jo, Jeil;Lee, Junghoon
    • Journal of the Korea Institute of Military Science and Technology
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    • v.23 no.1
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    • pp.1-10
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    • 2020
  • Effective jamming in electronic warfare depends on proper jamming technique selection and jamming parameter estimation. For this purpose, this paper proposes a new method of estimating jamming parameters using Gaussian kernel function networks. In the proposed approach, a new method of determining the optimal structure and parameters of Gaussian kernel function networks is proposed. As a result, the proposed approach estimates the jamming parameters in a reliable manner and outperforms other methods such as the DNN(Deep Neural Network) and SVM(Support Vector Machine) estimation models.

Photovoltaic Generation Forecasting Using Weather Forecast and Predictive Sunshine and Radiation (일기 예보와 예측 일사 및 일조를 이용한 태양광 발전 예측)

  • Shin, Dong-Ha;Park, Jun-Ho;Kim, Chang-Bok
    • The Journal of Advanced Navigation Technology
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    • v.21 no.6
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    • pp.643-650
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    • 2017
  • Photovoltaic generation which has unlimited energy sources are very intermittent because they depend on the weather. Therefore, it is necessary to get accurate generation prediction with reducing the uncertainty of photovoltaic generation and improvement of the economics. The Meteorological Agency predicts weather factors for three days, but doesn't predict the sunshine and solar radiation that are most correlated with the prediction of photovoltaic generation. In this study, we predict sunshine and solar radiation using weather, precipitation, wind direction, wind speed, humidity, and cloudiness which is forecasted for three days at Meteorological Agency. The photovoltaic generation forecasting model is proposed by using predicted solar radiation and sunshine. As a result, the proposed model showed better results in the error rate indexes such as MAE, RMSE, and MAPE than the model that predicts photovoltaic generation without radiation and sunshine. In addition, DNN showed a lower error rate index than using SVM, which is a type of machine learning.

Impurity profiling and chemometric analysis of methamphetamine seizures in Korea

  • Shin, Dong Won;Ko, Beom Jun;Cheong, Jae Chul;Lee, Wonho;Kim, Suhkmann;Kim, Jin Young
    • Analytical Science and Technology
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    • v.33 no.2
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    • pp.98-107
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    • 2020
  • Methamphetamine (MA) is currently the most abused illicit drug in Korea. MA is produced by chemical synthesis, and the final target drug that is produced contains small amounts of the precursor chemicals, intermediates, and by-products. To identify and quantify these trace compounds in MA seizures, a practical and feasible approach for conducting chromatographic fingerprinting with a suite of traditional chemometric methods and recently introduced machine learning approaches was examined. This was achieved using gas chromatography (GC) coupled with a flame ionization detector (FID) and mass spectrometry (MS). Following appropriate examination of all the peaks in 71 samples, 166 impurities were selected as the characteristic components. Unsupervised (principal component analysis (PCA), hierarchical cluster analysis (HCA), and K-means clustering) and supervised (partial least squares-discriminant analysis (PLS-DA), orthogonal partial least squares-discriminant analysis (OPLS-DA), support vector machines (SVM), and deep neural network (DNN) with Keras) chemometric techniques were employed for classifying the 71 MA seizures. The results of the PCA, HCA, K-means clustering, PLS-DA, OPLS-DA, SVM, and DNN methods for quality evaluation were in good agreement. However, the tested MA seizures possessed distinct features, such as chirality, cutting agents, and boiling points. The study indicated that the established qualitative and semi-quantitative methods will be practical and useful analytical tools for characterizing trace compounds in illicit MA seizures. Moreover, they will provide a statistical basis for identifying the synthesis route, sources of supply, trafficking routes, and connections between seizures, which will support drug law enforcement agencies in their effort to eliminate organized MA crime.

Prediction of Traffic Congestion in Seoul by Deep Neural Network (심층인공신경망(DNN)과 다각도 상황 정보 기반의 서울시 도로 링크별 교통 혼잡도 예측)

  • Kim, Dong Hyun;Hwang, Kee Yeon;Yoon, Young
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.18 no.4
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    • pp.44-57
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    • 2019
  • Various studies have been conducted to solve traffic congestions in many metropolitan cities through accurate traffic flow prediction. Most studies are based on the assumption that past traffic patterns repeat in the future. Models based on such an assumption fall short in case irregular traffic patterns abruptly occur. Instead, the approaches such as predicting traffic pattern through big data analytics and artificial intelligence have emerged. Specifically, deep learning algorithms such as RNN have been prevalent for tackling the problems of predicting temporal traffic flow as a time series. However, these algorithms do not perform well in terms of long-term prediction. In this paper, we take into account various external factors that may affect the traffic flows. We model the correlation between the multi-dimensional context information with temporal traffic speed pattern using deep neural networks. Our model trained with the traffic data from TOPIS system by Seoul, Korea can predict traffic speed on a specific date with the accuracy reaching nearly 90%. We expect that the accuracy can be improved further by taking into account additional factors such as accidents and constructions for the prediction.

Speech enhancement based on reinforcement learning (강화학습 기반의 음성향상기법)

  • Park, Tae-Jun;Chang, Joon-Hyuk
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.335-337
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    • 2018
  • 음성향상기법은 음성에 포함된 잡음이나 잔향을 제거하는 기술로써 마이크로폰으로 입력된 음성신호는 잡음이나 잔향에 의해 왜곡되어지므로 음성인식, 음성통신 등의 음성신호처리 기술의 핵심 기술이다. 이전에는 음성신호와 잡음신호 사이의 통계적 정보를 이용하는 통계모델 기반의 음성향상기법이 주로 사용되었으나 통계 모델 기반의 음성향상기술은 정상 잡음 환경과는 달리 비정상 잡음 환경에서 성능이 크게 저하되는 문제점을 가지고 있었다. 최근 머신러닝 기법인 심화신경망 (DNN, deep neural network)이 도입되어 음성 향상 기법에서 우수한 성능을 내고 있다. 심화신경망을 이용한 음성 향상 기법은 다수의 은닉 층과 은닉 노드들을 통하여 잡음이 존재하는 음성 신호와 잡음이 존재하지 않는 깨끗한 음성 신호 사이의 비선형적인 관계를 잘 모델링하였다. 이러한 심화신경망 기반의 음성향상기법을 향상 시킬 수 있는 방법 중 하나인 강화학습을 적용하여 기존 심화신경망 대비 성능을 향상시켰다. 강화학습이란 대표적으로 구글의 알파고에 적용된 기술로써 특정 state에서 최고의 reward를 받기 위해 어떠한 policy를 통한 action을 취해서 다음 state로 나아갈지를 매우 많은 경우에 대해 학습을 통해 최적의 action을 선택할 수 있도록 학습하는 방법을 말한다. 본 논문에서는 composite measure를 기반으로 reward를 설계하여 기존 PESQ (Perceptual Evaluation of Speech Quality) 기반의 reward를 설계한 기술 대비 음성인식 성능을 높였다.

Study on Prediction of Attendance Using Machine Learning (머신러닝을 이용한 관중 수요 예측에 관한 연구)

  • Yoo, Ji-Hyun
    • Journal of IKEEE
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    • v.23 no.4
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    • pp.1243-1249
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    • 2019
  • People who gathered to enjoy a specific event or content are called audiences or spectators, and show various propensity according to the characteristics of the crowd. Although there is such a difference, in general, the number of attendance is directly related to the business aspect, which enables stable financial operation for the sale of contents through various incomes, such as the admission fee and the use of other facilities. Therefore, prediction of audience can be used as a major factor in marketing and budgeting strategies. In this study, we review several existing models for predicting the number of attendance and propose an efficient machine learning model. In addition, we studied daily attendance prediction and abnormal attendance prediction using combine DNN(Deep Neural Network) and RF(Random Forest) model.

Machine Scheduling Models Based on Reinforcement Learning for Minimizing Due Date Violation and Setup Change (납기 위반 및 셋업 최소화를 위한 강화학습 기반의 설비 일정계획 모델)

  • Yoo, Woosik;Seo, Juhyeok;Kim, Dahee;Kim, Kwanho
    • The Journal of Society for e-Business Studies
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    • v.24 no.3
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    • pp.19-33
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    • 2019
  • Recently, manufacturers have been struggling to efficiently use production equipment as their production methods become more sophisticated and complex. Typical factors hindering the efficiency of the manufacturing process include setup cost due to job change. Especially, in the process of using expensive production equipment such as semiconductor / LCD process, efficient use of equipment is very important. Balancing the tradeoff between meeting the deadline and minimizing setup cost incurred by changes of work type is crucial planning task. In this study, we developed a scheduling model to achieve the goal of minimizing the duedate and setup costs by using reinforcement learning in parallel machines with duedate and work preparation costs. The proposed model is a Deep Q-Network (DQN) scheduling model and is a reinforcement learning-based model. To validate the effectiveness of our proposed model, we compared it against the heuristic model and DNN(deep neural network) based model. It was confirmed that our proposed DQN method causes less due date violation and setup costs than the benchmark methods.

Image-based Virtual Try-On System: Full Automatic System Design and its Performance (완전 자동 이미지기반 가상의상 착용 기술)

  • Ahn, Heejune
    • Journal of the Korean Society for Computer Game
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    • v.31 no.3
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    • pp.37-45
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    • 2018
  • We present an 2D image-based Virtual Try-On (VTON) system applicable to online shopping. The proposed method and system eliminate the need for the 3-D information requirement of clothes and humans, leveraging the recent advance in DNN-based image processing and computer graphics technology. In the first step, the cloth images are parsed into clothes and parts of a human body with a semantic segmentation network. And the pose or joint information of the cloth and human images are extracted using a pose estimation network. In the second step, the segmented cloth and joint information are jointly used for matching and deforming the cloth images onto a target human image. From our full implementation and preliminary trial, many successful result with from simple shaped and posed cloths and pose images demonstrates the feasibility of VTON system. However, we also observed many failed cases due to the diverse cloth and poses. We discuss the causes of failures to identify the applicable area of the current state-of-the art technology for commercial level VTON, and long term key problems for a future VTON system.

A New CSR-DCF Tracking Algorithm based on Faster RCNN Detection Model and CSRT Tracker for Drone Data

  • Farhodov, Xurshid;Kwon, Oh-Heum;Moon, Kwang-Seok;Kwon, Oh-Jun;Lee, Suk-Hwan;Kwon, Ki-Ryong
    • Journal of Korea Multimedia Society
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    • v.22 no.12
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    • pp.1415-1429
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    • 2019
  • Nowadays object tracking process becoming one of the most challenging task in Computer Vision filed. A CSR-DCF (channel spatial reliability-discriminative correlation filter) tracking algorithm have been proposed on recent tracking benchmark that could achieve stat-of-the-art performance where channel spatial reliability concepts to DCF tracking and provide a novel learning algorithm for its efficient and seamless integration in the filter update and the tracking process with only two simple standard features, HoGs and Color names. However, there are some cases where this method cannot track properly, like overlapping, occlusions, motion blur, changing appearance, environmental variations and so on. To overcome that kind of complications a new modified version of CSR-DCF algorithm has been proposed by integrating deep learning based object detection and CSRT tracker which implemented in OpenCV library. As an object detection model, according to the comparable result of object detection methods and by reason of high efficiency and celerity of Faster RCNN (Region-based Convolutional Neural Network) has been used, and combined with CSRT tracker, which demonstrated outstanding real-time detection and tracking performance. The results indicate that the trained object detection model integration with tracking algorithm gives better outcomes rather than using tracking algorithm or filter itself.

Speech Feature Extraction based on Spikegram for Phoneme Recognition (음소 인식을 위한 스파이크그램 기반의 음성 특성 추출 기술)

  • Han, Seokhyeon;Kim, Jaewon;An, Soonho;Shin, Seonghyeon;Park, Hochong
    • Journal of Broadcast Engineering
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    • v.24 no.5
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    • pp.735-742
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    • 2019
  • In this paper, we propose a method of extracting speech features for phoneme recognition based on spikegram. The Fourier-transform-based features are widely used in phoneme recognition, but they are not extracted in a biologically plausible way and cannot have high temporal resolution due to the frame-based operation. For better phoneme recognition, therefore, it is desirable to have a new method of extracting speech features, which analyzes speech signal in high temporal resolution following the model of human auditory system. In this paper, we analyze speech signal based on a spikegram that models feature extraction and transmission in auditory system, and then propose a method of feature extraction from the spikegram for phoneme recognition. We evaluate the performance of proposed features by using a DNN-based phoneme recognizer and confirm that the proposed features provide better performance than the Fourier-transform-based features for short-length phonemes. From this result, we can verify the feasibility of new speech features extracted based on auditory model for phoneme recognition.