• Title/Summary/Keyword: long term neural network

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Earthquake events classification using convolutional recurrent neural network (합성곱 순환 신경망 구조를 이용한 지진 이벤트 분류 기법)

  • Ku, Bonhwa;Kim, Gwantae;Jang, Su;Ko, Hanseok
    • The Journal of the Acoustical Society of Korea
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    • v.39 no.6
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    • pp.592-599
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    • 2020
  • This paper proposes a Convolutional Recurrent Neural Net (CRNN) structure that can simultaneously reflect both static and dynamic characteristics of seismic waveforms for various earthquake events classification. Addressing various earthquake events, including not only micro-earthquakes and artificial-earthquakes but also macro-earthquakes, requires both effective feature extraction and a classifier that can discriminate seismic waveform under noisy environment. First, we extract the static characteristics of seismic waveform through an attention-based convolution layer. Then, the extracted feature-map is sequentially injected as input to a multi-input single-output Long Short-Term Memory (LSTM) network structure to extract the dynamic characteristic for various seismic event classifications. Subsequently, we perform earthquake events classification through two fully connected layers and softmax function. Representative experimental results using domestic and foreign earthquake database show that the proposed model provides an effective structure for various earthquake events classification.

Forecasting realized volatility using data normalization and recurrent neural network

  • Yoonjoo Lee;Dong Wan Shin;Ji Eun Choi
    • Communications for Statistical Applications and Methods
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    • v.31 no.1
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    • pp.105-127
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    • 2024
  • We propose recurrent neural network (RNN) methods for forecasting realized volatility (RV). The data are RVs of ten major stock price indices, four from the US, and six from the EU. Forecasts are made for relative ratio of adjacent RVs instead of the RV itself in order to avoid the out-of-scale issue. Forecasts of RV ratios distribution are first constructed from which those of RVs are computed which are shown to be better than forecasts constructed directly from RV. The apparent asymmetry of RV ratio is addressed by the Piecewise Min-max (PM) normalization. The serial dependence of the ratio data renders us to consider two architectures, long short-term memory (LSTM) and gated recurrent unit (GRU). The hyperparameters of LSTM and GRU are tuned by the nested cross validation. The RNN forecast with the PM normalization and ratio transformation is shown to outperform other forecasts by other RNN models and by benchmarking models of the AR model, the support vector machine (SVM), the deep neural network (DNN), and the convolutional neural network (CNN).

Prediction for Rolling Force in Hot-rolling Mill Using On-line learning Neural Network (On-line 학습 신경회로망을 이용한 열간 압연하중 예측)

  • Son Joon-Sik;Lee Duk-Man;Kim Ill-Soo;Choi Seung-Gap
    • Transactions of the Korean Society of Machine Tool Engineers
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    • v.14 no.1
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    • pp.52-57
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    • 2005
  • In the foe of global competition, the requirements for the continuously increasing productivity, flexibility and quality(dimensional accuracy, mechanical properties and surface properties) have imposed a mai or change on steel manufacturing industries. Indeed, one of the keys to achieve this goal is the automation of the steel-making process using AI(Artificial Intelligence) techniques. The automation of hot rolling process requires the developments of several mathematical models for simulation and quantitative description of the industrial operations involved. In this paper, an on-line training neural network for both long-term teaming and short-term teaming was developed in order to improve the prediction of rolling force in hot rolling mill. This analysis shows that the predicted rolling force is very closed to the actual rolling force, and the thickness error of the strip is considerably reduced.

Prediction for Rolling Force in Hot-rolling Mill Using On-line loaming Neural Network (On-line 학습 신경회로망을 이용한 열간 압연하중 예측)

  • 손준식;이덕만;김일수;최승갑
    • Proceedings of the Korean Society of Machine Tool Engineers Conference
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    • 2003.04a
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    • pp.124-129
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    • 2003
  • In the face of global competitor the requirements flor the continuously increasing productivity, flexibility and quality(dimensional accuracy, mechanical properties and surface properties) have imposed a major change on steel manufacturing industries. Indeed, one of the keys to achieve this goal is the automation of the steel-making process using AI(Artificial Intelligence) techniques. The automation of hot rolling process requires the developments of several mathematical models fir simulation and quantitative description of the industrial operations involved. In this paper, a on-line training neural network for both long-term teaming and short-term teaming was developed in order to improve the prediction of rolling force in hot rolling mill. This analysis shows that the predicted rolling force is very closed to the actual rolling force, and the thickness error of the strip is considerably reduced.

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MALICIOUS URL RECOGNITION AND DETECTION USING ATTENTION-BASED CNN-LSTM

  • Peng, Yongfang;Tian, Shengwei;Yu, Long;Lv, Yalong;Wang, Ruijin
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.11
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    • pp.5580-5593
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    • 2019
  • A malicious Uniform Resource Locator (URL) recognition and detection method based on the combination of Attention mechanism with Convolutional Neural Network and Long Short-Term Memory Network (Attention-Based CNN-LSTM), is proposed. Firstly, the WHOIS check method is used to extract and filter features, including the URL texture information, the URL string statistical information of attributes and the WHOIS information, and the features are subsequently encoded and pre-processed followed by inputting them to the constructed Convolutional Neural Network (CNN) convolution layer to extract local features. Secondly, in accordance with the weights from the Attention mechanism, the generated local features are input into the Long-Short Term Memory (LSTM) model, and subsequently pooled to calculate the global features of the URLs. Finally, the URLs are detected and classified by the SoftMax function using global features. The results demonstrate that compared with the existing methods, the Attention-based CNN-LSTM mechanism has higher accuracy for malicious URL detection.

Shooting sound analysis using convolutional neural networks and long short-term memory (합성곱 신경망과 장단기 메모리를 이용한 사격음 분석 기법)

  • Kang, Se Hyeok;Cho, Ji Woong
    • The Journal of the Acoustical Society of Korea
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    • v.41 no.3
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    • pp.312-318
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    • 2022
  • This paper proposes a model which classifies the type of guns and information about sound source location using deep neural network. The proposed classification model is composed of convolutional neural networks (CNN) and long short-term memory (LSTM). For training and test the model, we use the Gunshot Audio Forensic Dataset generated by the project supported by the National Institute of Justice (NIJ). The acoustic signals are transformed to Mel-Spectrogram and they are provided as learning and test data for the proposed model. The model is compared with the control model consisting of convolutional neural networks only. The proposed model shows high accuracy more than 90 %.

Crime amount prediction based on 2D convolution and long short-term memory neural network

  • Dong, Qifen;Ye, Ruihui;Li, Guojun
    • ETRI Journal
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    • v.44 no.2
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    • pp.208-219
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    • 2022
  • Crime amount prediction is crucial for optimizing the police patrols' arrangement in each region of a city. First, we analyzed spatiotemporal correlations of the crime data and the relationships between crime and related auxiliary data, including points-of-interest (POI), public service complaints, and demographics. Then, we proposed a crime amount prediction model based on 2D convolution and long short-term memory neural network (2DCONV-LSTM). The proposed model captures the spatiotemporal correlations in the crime data, and the crime-related auxiliary data are used to enhance the regional spatial features. Extensive experiments on real-world datasets are conducted. Results demonstrated that capturing both temporal and spatial correlations in crime data and using auxiliary data to extract regional spatial features improve the prediction performance. In the best case scenario, the proposed model reduces the prediction error by at least 17.8% and 8.2% compared with support vector regression (SVR) and LSTM, respectively. Moreover, excessive auxiliary data reduce model performance because of the presence of redundant information.

Remaining Useful Life Prediction for Litium-Ion Batteries Using EMD-CNN-LSTM Hybrid Method (EMD-CNN-LSTM을 이용한 하이브리드 방식의 리튬 이온 배터리 잔여 수명 예측)

  • Lim, Je-Yeong;Kim, Dong-Hwan;Noh, Tae-Won;Lee, Byoung-Kuk
    • The Transactions of the Korean Institute of Power Electronics
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    • v.27 no.1
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    • pp.48-55
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    • 2022
  • This paper proposes a battery remaining useful life (RUL) prediction method using a deep learning-based EMD-CNN-LSTM hybrid method. The proposed method pre-processes capacity data by applying empirical mode decomposition (EMD) and predicts the remaining useful life using CNN-LSTM. CNN-LSTM is a hybrid method that combines convolution neural network (CNN), which analyzes spatial features, and long short term memory (LSTM), which is a deep learning technique that processes time series data analysis. The performance of the proposed remaining useful life prediction method is verified using the battery aging experiment data provided by the NASA Ames Prognostics Center of Excellence and shows higher accuracy than does the conventional method.

Automatic Evaluation of Elementary School English Writing Based on Recurrent Neural Network Language Model (순환 신경망 기반 언어 모델을 활용한 초등 영어 글쓰기 자동 평가)

  • Park, Youngki
    • Journal of The Korean Association of Information Education
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    • v.21 no.2
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    • pp.161-169
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    • 2017
  • We often use spellcheckers in order to correct the syntactic errors in our documents. However, these computer programs are not enough for elementary school students, because their sentences are not smooth even after correcting the syntactic errors in many cases. In this paper, we introduce an automated method for evaluating the smoothness of two synonymous sentences. This method uses a recurrent neural network to solve the problem of long-term dependencies and exploits subwords to cope with the rare word problem. We trained the recurrent neural network language model based on a monolingual corpus of about two million English sentences. In our experiments, the trained model successfully selected the more smooth sentences for all of nine types of test set. We expect that our approach will help in elementary school writing after being implemented as an application for smart devices.

Multivariate Congestion Prediction using Stacked LSTM Autoencoder based Bidirectional LSTM Model

  • Vijayalakshmi, B;Thanga, Ramya S;Ramar, K
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.17 no.1
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    • pp.216-238
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    • 2023
  • In intelligent transportation systems, traffic management is an important task. The accurate forecasting of traffic characteristics like flow, congestion, and density is still active research because of the non-linear nature and uncertainty of the spatiotemporal data. Inclement weather, such as rain and snow, and other special events such as holidays, accidents, and road closures have a significant impact on driving and the average speed of vehicles on the road, which lowers traffic capacity and causes congestion in a widespread manner. This work designs a model for multivariate short-term traffic congestion prediction using SLSTM_AE-BiLSTM. The proposed design consists of a Bidirectional Long Short Term Memory(BiLSTM) network to predict traffic flow value and a Convolutional Neural network (CNN) model for detecting the congestion status. This model uses spatial static temporal dynamic data. The stacked Long Short Term Memory Autoencoder (SLSTM AE) is used to encode the weather features into a reduced and more informative feature space. BiLSTM model is used to capture the features from the past and present traffic data simultaneously and also to identify the long-term dependencies. It uses the traffic data and encoded weather data to perform the traffic flow prediction. The CNN model is used to predict the recurring congestion status based on the predicted traffic flow value at a particular urban traffic network. In this work, a publicly available Caltrans PEMS dataset with traffic parameters is used. The proposed model generates the congestion prediction with an accuracy rate of 92.74% which is slightly better when compared with other deep learning models for congestion prediction.