Stress Recognition in Thermal Videos Using Bi-directional Long-Term Recurrent Convolutional Neural Networks

2021 ◽  
pp. 491-501
Author(s):  
Siyuan Yan ◽  
Abhijit Adhikary
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Stress Recognition

scholarly journals One Dimensional Convolutional Neural Networks for Seizure Onset Detection Using Long-term Scalp and Intracranial EEG

2021 ◽  
Author(s):  
Xiaoshuang Wang ◽  
Xiulin Wang ◽  
Wenya Liu ◽  
Zheng Chang ◽  
Tommi Karkkainen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Onset Detection ◽  
Seizure Onset ◽  
Intracranial Eeg ◽  
One Dimensional

scholarly journals Systematic identification of external influences in multi-year microseismic recordings using convolutional neural networks

2019 ◽  
Vol 7 (1) ◽  
pp. 171-190 ◽  
Author(s):  
Matthias Meyer ◽  
Samuel Weber ◽  
Jan Beutel ◽  
Lothar Thiele
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Error Rate ◽  
Process Analysis ◽  
External Influences ◽  
Microseismic Data ◽  
Long Term Monitoring ◽  
Systematic Identification ◽  
Term Monitoring

Abstract. Passive monitoring of ground motion can be used for geophysical process analysis and natural hazard assessment. Detecting events in microseismic signals can provide responsive insights into active geophysical processes. However, in the raw signals, microseismic events are superimposed by external influences, for example, anthropogenic or natural noise sources that distort analysis results. In order to be able to perform event-based geophysical analysis with such microseismic data records, it is imperative that negative influence factors can be systematically and efficiently identified, quantified and taken into account. Current identification methods (manual and automatic) are subject to variable quality, inconsistencies or human errors. Moreover, manual methods suffer from their inability to scale to increasing data volumes, an important property when dealing with very large data volumes as in the case of long-term monitoring. In this work, we present a systematic strategy to identify a multitude of external influence sources, characterize and quantify their impact and develop methods for automated identification in microseismic signals. We apply the strategy developed to a real-world, multi-sensor, multi-year microseismic monitoring experiment performed at the Matterhorn Hörnligrat (Switzerland). We develop and present an approach based on convolutional neural networks for microseismic data to detect external influences originating in mountaineers, a major unwanted influence, with an error rate of less than 1 %, 3 times lower than comparable algorithms. Moreover, we present an ensemble classifier for the same task, obtaining an error rate of 0.79 % and an F1 score of 0.9383 by jointly using time-lapse image and microseismic data on an annotated subset of the monitoring data. Applying these classifiers to the whole experimental dataset reveals that approximately one-fourth of events detected by an event detector without such a preprocessing step are not due to seismic activity but due to anthropogenic influences and that time periods with mountaineer activity have a 9 times higher event rate. Due to these findings, we argue that a systematic identification of external influences using a semi-automated approach and machine learning techniques as presented in this paper is a prerequisite for the qualitative and quantitative analysis of long-term monitoring experiments.

Long-Term Bowel Sound Monitoring and Segmentation by Wearable Devices and Convolutional Neural Networks

2020 ◽  
Vol 14 (5) ◽  
pp. 985-996 ◽  
Author(s):  
Kang Zhao ◽  
Hanjun Jiang ◽  
Zhihua Wang ◽  
Ping Chen ◽  
Binjie Zhu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Wearable Devices ◽  
Bowel Sound

scholarly journals Prediction for Chaotic Time Series-Based AE-CNN and Transfer Learning

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Baogui Xin ◽  
Wei Peng
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Prediction Performance ◽  
Chaotic Time Series ◽  
Experimental Results ◽  
Prediction Scheme

It has been a hot and challenging topic to predict the chaotic time series in the medium-to-long term. We combine autoencoders and convolutional neural networks (AE-CNN) to capture the intrinsic certainty of chaotic time series. We utilize the transfer learning (TL) theory to improve the prediction performance in medium-to-long term. Thus, we develop a prediction scheme for chaotic time series-based AE-CNN and TL named AE-CNN-TL. Our experimental results show that the proposed AE-CNN-TL has much better prediction performance than any one of the following: AE-CNN, ARMA, and LSTM.

scholarly journals Online Predictive Maintenance Monitoring Adopting Convolutional Neural Networks

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4711
Author(s):  
Christian Gianoglio ◽  
Edoardo Ragusa ◽  
Paolo Gastaldo ◽  
Federico Gallesi ◽  
Francesco Guastavino
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Economic Loss ◽  
Twisted Pair ◽  
Insulation Aging ◽  
Life Model ◽  
Insulation Systems ◽  
Voltage Frequency ◽  
Model Dependency

Thermal, electrical and mechanical stresses age the electrical insulation systems of high voltage (HV) apparatuses until the breakdown. The monitoring of the partial discharges (PDs) effectively assesses the insulation condition. PDs are both the symptoms and the causes of insulation aging and—in the long term—can lead to a breakdown, with a burdensome economic loss. This paper proposes the convolutional neural networks (CNNs) to investigate and analyze the aging process of enameled wires, thus predicting the life status of the insulation systems. The CNNs training does not require any kind of assumption of how the factors (e.g., voltage, frequency and temperature) contribute to the life model. The experiments confirm that the proposal obtains better estimations of the life status of twisted pair specimens concerning existing solutions, which are based on strong hypotheses about the life model dependency on the factors.

scholarly journals Convolutional Neural Networks Ensembles Through Single-Iteration Optimization

2021 ◽  
Author(s):  
Luiz Carlos Felix Ribeiro ◽  
Gustavo Henrique de Rosa ◽  
Douglas Rodrigues ◽  
João Paulo Papa
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Object Recognition ◽  
Convolutional Neural Networks ◽  
Object Segmentation ◽  
Random Search ◽  
Heuristic Optimization ◽  
Diverse Range ◽  
Computational Burden

Abstract Convolutional Neural Networks have been widely employed in a diverse range of computer vision-based applications, including image classification, object recognition, and object segmentation. Nevertheless, one weakness of such models concerns their hyperparameters' setting, being highly specific for each particular problem. One common approach is to employ meta-heuristic optimization algorithms to find suitable sets of hyperparameters at the expense of increasing the computational burden, being unfeasible under real-time scenarios. In this paper, we address this problem by creating Convolutional Neural Networks ensembles through Single-Iteration Optimization, a fast optimization composed of only one iteration that is no more effective than a random search. Essentially, the idea is to provide the same capability offered by long-term optimizations, however, without their computational loads. The results among four well-known literature datasets revealed that creating one-iteration optimized ensembles provide promising results while diminishing the time to achieve them.

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