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

Search Result 111, Processing Time 0.255 seconds

Data Mining based Forest Fires Prediction Models using Meteorological Data (기상 데이터를 이용한 데이터 마이닝 기반의 산불 예측 모델)

  • Kim, Sam-Keun;Ahn, Jae-Geun
    • Journal of the Korea Academia-Industrial cooperation Society
    • /
    • v.21 no.8
    • /
    • pp.521-529
    • /
    • 2020
  • Forest fires are one of the most important environmental risks that have adverse effects on many aspects of life, such as the economy, environment, and health. The early detection, quick prediction, and rapid response of forest fires can play an essential role in saving property and life from forest fire risks. For the rapid discovery of forest fires, there is a method using meteorological data obtained from local sensors installed in each area by the Meteorological Agency. Meteorological conditions (e.g., temperature, wind) influence forest fires. This study evaluated a Data Mining (DM) approach to predict the burned area of forest fires. Five DM models, e.g., Stochastic Gradient Descent (SGD), Support Vector Machines (SVM), Decision Tree (DT), Random Forests (RF), and Deep Neural Network (DNN), and four feature selection setups (using spatial, temporal, and weather attributes), were tested on recent real-world data collected from Gyeonggi-do area over the last five years. As a result of the experiment, a DNN model using only meteorological data showed the best performance. The proposed model was more effective in predicting the burned area of small forest fires, which are more frequent. This knowledge derived from the proposed prediction model is particularly useful for improving firefighting resource management.

Hybrid CTC-Attention Based End-to-End Speech Recognition Using Korean Grapheme Unit (한국어 자소 기반 Hybrid CTC-Attention End-to-End 음성 인식)

  • Park, Hosung;Lee, Donghyun;Lim, Minkyu;Kang, Yoseb;Oh, Junseok;Seo, Soonshin;Rim, Daniel;Kim, Ji-Hwan
    • Annual Conference on Human and Language Technology
    • /
    • /
    • pp.453-458
    • /
    • 2018
  • 본 논문은 한국어 자소를 인식 단위로 사용한 hybrid CTC-Attention 모델 기반 end-to-end speech recognition을 제안한다. End-to-end speech recognition은 기존에 사용된 DNN-HMM 기반 음향 모델과 N-gram 기반 언어 모델, WFST를 이용한 decoding network라는 여러 개의 모듈로 이루어진 과정을 하나의 DNN network를 통해 처리하는 방법을 말한다. 본 논문에서는 end-to-end 모델의 출력을 추정하기 위해 자소 단위의 출력구조를 사용한다. 자소 기반으로 네트워크를 구성하는 경우, 추정해야 하는 출력 파라미터의 개수가 11,172개에서 49개로 줄어들어 보다 효율적인 학습이 가능하다. 이를 구현하기 위해, end-to-end 학습에 주로 사용되는 DNN 네트워크 구조인 CTC와 Attention network 모델을 조합하여 end-to-end 모델을 구성하였다. 실험 결과, 음절 오류율 기준 10.05%의 성능을 보였다.

  • PDF

Parameter-Efficient Neural Networks Using Template Reuse (템플릿 재사용을 통한 패러미터 효율적 신경망 네트워크)

  • Kim, Daeyeon;Kang, Woochul
    • KIPS Transactions on Software and Data Engineering
    • /
    • v.9 no.5
    • /
    • pp.169-176
    • /
    • 2020
  • Recently, deep neural networks (DNNs) have brought revolutions to many mobile and embedded devices by providing human-level machine intelligence for various applications. However, high inference accuracy of such DNNs comes at high computational costs, and, hence, there have been significant efforts to reduce computational overheads of DNNs either by compressing off-the-shelf models or by designing a new small footprint DNN architecture tailored to resource constrained devices. One notable recent paradigm in designing small footprint DNN models is sharing parameters in several layers. However, in previous approaches, the parameter-sharing techniques have been applied to large deep networks, such as ResNet, that are known to have high redundancy. In this paper, we propose a parameter-sharing method for already parameter-efficient small networks such as ShuffleNetV2. In our approach, small templates are combined with small layer-specific parameters to generate weights. Our experiment results on ImageNet and CIFAR100 datasets show that our approach can reduce the size of parameters by 15%-35% of ShuffleNetV2 while achieving smaller drops in accuracies compared to previous parameter-sharing and pruning approaches. We further show that the proposed approach is efficient in terms of latency and energy consumption on modern embedded devices.

A Study on Development of a Prediction Model for Korean Music Box Office Based on Deep Learning (딥러닝을 이용한 음악흥행 예측모델 개발 연구)

  • Lee, Do-Yeon;Chang, Byeng-Hee
    • The Journal of the Korea Contents Association
    • /
    • v.20 no.8
    • /
    • pp.10-18
    • /
    • 2020
  • Among various contents industry, this study especially focused on music industry and tried to develop a prediction model for music box office using deep learning. The deep learning prediction model designed to predict music chart-in period based on 17 variables -singer power, singer influence, featuring singer power, featuring singer influence, number of participating singers, gender of participating singers, lyric writer power, composer power, arranger power, production agency power, distributing agency power, title track, LIKEs on streaming platform, comments on streaming platform, pre-promotion article, teaser-video view, first-week performance. Additionally we conducted a linear regression analysis to sort out factors, and tried to compare the prediction performance between the original DNN prediction model and the DNN model made of sorted out factors.

UI Elements Identification for Mobile Applications based on Deep Learning using Symbol Marker (심볼마커를 사용한 딥러닝 기반 모바일 응용 UI 요소 인식)

  • Park, Jisu;Jung, Jinman;Eun, Seungbae;Yun, Young-Sun
    • The Journal of The Institute of Internet, Broadcasting and Communication
    • /
    • v.20 no.3
    • /
    • pp.89-95
    • /
    • 2020
  • Recently, studies are being conducted to recognize a sketch image of a GUI (Graphical User Interface) based on a deep learning and to make it into a code implemented in an application. UI / UX designers can communicate with developers through storyboards when developing mobile applications. However, UI / UX designers can create different widgets for ambiguous widgets. In this paper, we propose an automatic UI detection method using symbol markers to improve the accuracy of DNN (Deep Neural Network) based UI identification. In order to evaluate the performance with or without the symbol markers, their accuracy is compared. In order to improve the accuracy according to of the symbol marker, the results are analyzed when the shape is a circle or a parenthesis. The use of symbol markers will reduce feedback between developer and designer, time and cost, and reduce sketch image UI false positives and improve accuracy.

OrdinalEncoder based DNN for Natural Gas Leak Prediction (천연가스 누출 예측을 위한 OrdinalEncoder 기반 DNN)

  • Khongorzul, Dashdondov;Lee, Sang-Mu;Kim, Mi-Hye
    • Journal of the Korea Convergence Society
    • /
    • v.10 no.10
    • /
    • pp.7-13
    • /
    • 2019
  • The natural gas (NG), mostly methane leaks into the air, it is a big problem for the climate. detected NG leaks under U.S. city streets and collected data. In this paper, we introduced a Deep Neural Network (DNN) classification of prediction for a level of NS leak. The proposed method is OrdinalEncoder(OE) based K-means clustering and Multilayer Perceptron(MLP) for predicting NG leak. The 15 features are the input neurons and the using backpropagation. In this paper, we propose the OE method for labeling target data using k-means clustering and compared normalization methods performance for NG leak prediction. There five normalization methods used. We have shown that our proposed OE based MLP method is accuracy 97.7%, F1-score 96.4%, which is relatively higher than the other methods. The system has implemented SPSS and Python, including its performance, is tested on real open data.

Deep Neural Network Architecture for Video - based Facial Expression Recognition (동영상 기반 감정인식을 위한 DNN 구조)

  • Lee, Min Kyu;Choi, Jun Ho;Song, Byung Cheol
    • Proceedings of the Korean Society of Broadcast Engineers Conference
    • /
    • /
    • pp.35-37
    • /
    • 2019
  • 최근 딥 러닝의 급격한 발전과 함께 얼굴표정인식 기술이 상당한 진보를 이루었다. 그러나 기존 얼굴표정인식 기법들은 제한된 환경에서 취득한 인위적인 동영상에 대해 주로 개발되었기 때문에 실제 wild 한 환경에서 취득한 동영상에 대해 강인하게 동작하지 않을 수 있다. 이런 문제를 해결하기 위해 3D CNN, 2D CNN 그리고 RNN 의 새로운 결합으로 이루어진 Deep neural network 구조를 제안한다. 제안 네트워크는 주어진 동영상으로부터 두 가지 서로 다른 CNN 을 통해서 영상 내 공간적 정보뿐만 아니라 시간적 정보를 담고 있는 특징 벡터를 추출할 수 있다. 그 다음, RNN 이 시간 도메인 학습을 수행할 뿐만 아니라 상기 네트워크들에서 추출된 특징 벡터들을 융합한다. 상기 기술들이 유기적으로 연동하는 제안된 네트워크는 대표적인 wild 한 공인 데이터세트인 AFEW 로 실험한 결과 49.6%의 정확도로 종래 기법 대비 향상된 성능을 보인다.

  • PDF

Faults detection and identification for gas turbine using DNN and LLM

  • Oliaee, Seyyed Mohammad Emad;Teshnehlab, Mohammad;Shoorehdeli, Mahdi Aliyari
    • Smart Structures and Systems
    • /
    • v.23 no.4
    • /
    • pp.393-403
    • /
    • 2019
  • Applying more features gives us better accuracy in modeling; however, increasing the inputs causes the curse of dimensions. In this paper, a new structure has been proposed for fault detecting and identifying (FDI) of high-dimensional systems. This structure consist of two structure. The first part includes Auto-Encoders (AE) as Deep Neural Networks (DNNs) to produce feature engineering process and summarize the features. The second part consists of the Local Model Networks (LMNs) with LOcally LInear MOdel Tree (LOLIMOT) algorithm to model outputs (multiple models). The fault detection is based on these multiple models. Hence the residuals generated by comparing the system output and multiple models have been used to alarm the faults. To show the effectiveness of the proposed structure, it is tested on single-shaft industrial gas turbine prototype model. Finally, a brief comparison between the simulated results and several related works is presented and the well performance of the proposed structure has been illustrated.

Anomaly Classification of Railway Point Machine Using Sound Information and DNN (소리정보와 DNN을 이용한 선로전환기의 비정상 상황 분류)

  • Noh, Byeongjoon;Lee, Jonguk;Park, Daihee;Chung, Yonghwa;Kim, Heeyoung;Yoon, SukHan
    • Proceedings of the Korea Information Processing Society Conference
    • /
    • /
    • pp.611-614
    • /
    • 2016
  • 최근 철도 산업의 비중이 증가함에 따라 열차의 안정적인 주행이 그 어느 때보다 중요한 이슈로 부각되고있다. 특히, 열차의 진로 변경을 위한 핵심 요소인 선로전환기의 결함은 열차의 사고와 직결되는 장비 중 하나로써, 그 이상 여부를 사전에 인지하여 선로전환기의 안정성을 확보하기 위한 유지보수의 지능화 시스템이 필요하다. 본 논문에서는 선로전환기의 작동 시 발생하는 소리정보를 활용하여 선로전환기의 비정상 상황을 분류하는 시스템을 제안한다. 제안하는 시스템은 먼저, 선로전환기의 상황별 소리를 수집하고, 다양한 소리정보를 추출하여 특징 벡터를 생성한다. 다음으로, 딥러닝 모델 중 하나인 DNN(Deep Neural Network)을 이용하여 선로전환기의 비정상 상황을 분류한다. 실제 선로전환기의 전환 시 발생하는 소리 데이터를 기반으로 DNN의 파라미터에 따른 다양한 실험을 수행한 결과, 약 93.10%의 정확도를 갖는 안정적인 DNN 모델을 설계하였다.

A Personal Video Event Classification Method based on Multi-Modalities by DNN-Learning (DNN 학습을 이용한 퍼스널 비디오 시퀀스의 멀티 모달 기반 이벤트 분류 방법)

  • Lee, Yu Jin;Nang, Jongho
    • Journal of KIISE
    • /
    • v.43 no.11
    • /
    • pp.1281-1297
    • /
    • 2016
  • In recent years, personal videos have seen a tremendous growth due to the substantial increase in the use of smart devices and networking services in which users create and share video content easily without many restrictions. However, taking both into account would significantly improve event detection performance because videos generally have multiple modalities and the frame data in video varies at different time points. This paper proposes an event detection method. In this method, high-level features are first extracted from multiple modalities in the videos, and the features are rearranged according to time sequence. Then the association of the modalities is learned by means of DNN to produce a personal video event detector. In our proposed method, audio and image data are first synchronized and then extracted. Then, the result is input into GoogLeNet as well as Multi-Layer Perceptron (MLP) to extract high-level features. The results are then re-arranged in time sequence, and every video is processed to extract one feature each for training by means of DNN.