scholarly journals An Optimal Feature Parameter Set Based on Gated Recurrent Unit Recurrent Neural Networks for Speech Segment Detection

2020 ◽  
Vol 10 (4) ◽  
pp. 1273 ◽  
Author(s):  
Özlem BATUR DİNLER ◽  
Nizamettin AYDIN
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Feature Vector ◽  
Processing Parameters ◽  
Speech Segmentation ◽  
Hybrid Features ◽  
Speech Segment ◽  
Experimental Findings ◽  
Gated Recurrent Unit ◽  
Optimal Feature

Speech segment detection based on gated recurrent unit (GRU) recurrent neural networks for the Kurdish language was investigated in the present study. The novelties of the current research are the utilization of a GRU in Kurdish speech segment detection, creation of a unique database from the Kurdish language, and optimization of processing parameters for Kurdish speech segmentation. This study is the first attempt to find the optimal feature parameters of the model and to form a large Kurdish vocabulary dataset for a speech segment detection based on consonant, vowel, and silence (C/V/S) discrimination. For this purpose, four window sizes and three window types with three hybrid feature vector techniques were used to describe the phoneme boundaries. Identification of the phoneme boundaries using a GRU recurrent neural network was performed with six different classification algorithms for the C/V/S discrimination. We have demonstrated that the GRU model has achieved outstanding speech segmentation performance for characterizing Kurdish acoustic signals. The experimental findings of the present study show the significance of the segment detection of speech signals by effectively utilizing hybrid features, window sizes, window types, and classification models for Kurdish speech.

scholarly journals Attention-based hierarchical recurrent neural networks for MOOC forum posts analysis

2020 ◽  
Author(s):  
Nicola Capuano ◽  
Santi Caballé ◽  
Jordi Conesa ◽  
Antonio Greco
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Text Categorization ◽  
Online Courses ◽  
Recurrent Network ◽  
Subject Area ◽  
Conversational Agents ◽  
Collaborative Tools ◽  
Experimental Findings ◽  
Productive Talk

AbstractMassive open online courses (MOOCs) allow students and instructors to discuss through messages posted on a forum. However, the instructors should limit their interaction to the most critical tasks during MOOC delivery so, teacher-led scaffolding activities, such as forum-based support, can be very limited, even impossible in such environments. In addition, students who try to clarify the concepts through such collaborative tools could not receive useful answers, and the lack of interactivity may cause a permanent abandonment of the course. The purpose of this paper is to report the experimental findings obtained evaluating the performance of a text categorization tool capable of detecting the intent, the subject area, the domain topics, the sentiment polarity, and the level of confusion and urgency of a forum post, so that the result may be exploited by instructors to carefully plan their interventions. The proposed approach is based on the application of attention-based hierarchical recurrent neural networks, in which both a recurrent network for word encoding and an attention mechanism for word aggregation at sentence and document levels are used before classification. The integration of the developed classifier inside an existing tool for conversational agents, based on the academically productive talk framework, is also presented as well as the accuracy of the proposed method in the classification of forum posts.

scholarly journals An Autoencoder Gated Recurrent Unit for Remaining Useful Life Prediction

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1155
Author(s):  
Yi-Wei Lu ◽  
Chia-Yu Hsu ◽  
Kuang-Chieh Huang
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
Development Trend ◽  
Production Equipment ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Smart Manufacturing ◽  
Useful Life ◽  
Gated Recurrent Unit

With the development of smart manufacturing, in order to detect abnormal conditions of the equipment, a large number of sensors have been used to record the variables associated with production equipment. This study focuses on the prediction of Remaining Useful Life (RUL). RUL prediction is part of predictive maintenance, which uses the development trend of the machine to predict when the machine will malfunction. High accuracy of RUL prediction not only reduces the consumption of manpower and materials, but also reduces the need for future maintenance. This study focuses on detecting faults as early as possible, before the machine needs to be replaced or repaired, to ensure the reliability of the system. It is difficult to extract meaningful features from sensor data directly. This study proposes a model based on an Autoencoder Gated Recurrent Unit (AE-GRU), in which the Autoencoder (AE) extracts the important features from the raw data and the Gated Recurrent Unit (GRU) selects the information from the sequences to forecast RUL. To evaluate the performance of the proposed AE-GRU model, an aircraft turbofan engine degradation simulation dataset provided by NASA was used and a comparison made of different recurrent neural networks. The results demonstrate that the AE-GRU is better than other recurrent neural networks, such as Long Short-Term Memory (LSTM) and GRU.

State of health estimation for lithium-ion batteries using recurrent neural networks with gated recurrent unit

2021 ◽  
Vol 50 (2) ◽  
pp. 20200339-20200339
Author(s):  
张少宇 Shaoyu Zhang ◽  
伍春晖 Chunhui Wu ◽  
熊文渊 Wenyuan Xiong
Keyword(s):  
Neural Networks ◽  
Lithium Ion Batteries ◽  
Recurrent Neural Networks ◽  
Lithium Ion ◽  
State Of Health ◽  
Gated Recurrent Unit

scholarly journals Convolutional Recurrent Neural Networks for Observation-Centered Plant Identification

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Xuanxin Liu ◽  
Fu Xu ◽  
Yu Sun ◽  
Haiyan Zhang ◽  
Zhibo Chen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Back Propagation ◽  
Identification Accuracy ◽  
Plant Identification ◽  
Classification Rate ◽  
Computational Overhead ◽  
End To End ◽  
Gated Recurrent Unit

Traditional image-centered methods of plant identification could be confused due to various views, uneven illuminations, and growth cycles. To tolerate the significant intraclass variances, the convolutional recurrent neural networks (C-RNNs) are proposed for observation-centered plant identification to mimic human behaviors. The C-RNN model is composed of two components: the convolutional neural network (CNN) backbone is used as a feature extractor for images, and the recurrent neural network (RNN) units are built to synthesize multiview features from each image for final prediction. Extensive experiments are conducted to explore the best combination of CNN and RNN. All models are trained end-to-end with 1 to 3 plant images of the same observation by truncated back propagation through time. The experiments demonstrate that the combination of MobileNet and Gated Recurrent Unit (GRU) is the best trade-off of classification accuracy and computational overhead on the Flavia dataset. On the holdout test set, the mean 10-fold accuracy with 1, 2, and 3 input leaves reached 99.53%, 100.00%, and 100.00%, respectively. On the BJFU100 dataset, the C-RNN model achieves the classification rate of 99.65% by two-stage end-to-end training. The observation-centered method based on the C-RNNs shows potential to further improve plant identification accuracy.

scholarly journals TRAINING RECURRENT NEURAL NETWORKS FOR PARTICULATE MATTER CONCENTRATION PREDICTION

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 1575-1582
Author(s):  
C. J. Masinde ◽  
J. Gitahi ◽  
M. Hahn
Keyword(s):  
Neural Networks ◽  
Particulate Matter ◽  
Recurrent Neural Networks ◽  
Urban Areas ◽  
Gradient Descent ◽  
Stochastic Gradient ◽  
Stochastic Gradient Descent ◽  
Particulate Matter Concentration ◽  
Matter Concentration ◽  
Gated Recurrent Unit

Abstract. A high level of particulate matter in the atmosphere has an adverse long-term effect on human health. It has been associated with increased pulmonary tract and lung infections. It is more common in urban areas, especially megacities due to the confluence of industries and motorized machinery. Considering that most of the world’s population lives in urban areas, there is a need to monitor air pollution arising from particulate matter in order to ensure clean and safe air in cities in accordance with goal 11 of the Sustainable Development Goals. One way of doing this is through the use of Recurrent Neural Networks (RNN), which are suited for time varying data. Particulate matter concentration recorded by a network of low-cost sensors in Stuttgart is trained on three of the most popular RNN variants: Standard LSTM, Peephole LSTM and Gated Recurrent Unit. Two optimizers are used, Stochastic Gradient descent and Adam. Training is done on a single sensor and the optimum weights transferred and used in the prediction of other sensor values. This study concludes that Gated Recurrent Unit with Stochastic Gradient Descent is the most effective of the three variants in predicting particulate matter PM2.5 concentrations. In addition to this, weight transfer between sensors is not affected by temperature, wind direction, wind speed and geographic distance between sensors but rather by atmospheric pressure and the similarity of recorded Particulate matter levels.

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