14,398 research outputs found
Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications
The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version
A LightGBM-Based EEG Analysis Method for Driver Mental States Classification
Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals and families. Recently, electroencephalography-
(EEG-) based physiological and brain activities for fatigue detection have been increasingly investigated.
However, how to find an effective method or model to timely and efficiently detect the mental states of drivers still remains a
challenge. In this paper, we combine common spatial pattern (CSP) and propose a light-weighted classifier, LightFD, which is
based on gradient boosting framework for EEG mental states identification. ,e comparable results with traditional classifiers,
such as support vector machine (SVM), convolutional neural network (CNN), gated recurrent unit (GRU), and large margin
nearest neighbor (LMNN), show that the proposed model could achieve better classification performance, as well as the decision
efficiency. Furthermore, we also test and validate that LightFD has better transfer learning performance in EEG classification of
driver mental states. In summary, our proposed LightFD classifier has better performance in real-time EEG mental state
prediction, and it is expected to have broad application prospects in practical brain-computer interaction (BCI)
Proceedings of Abstracts Engineering and Computer Science Research Conference 2019
© 2019 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Note: Keynote: Fluorescence visualisation to evaluate effectiveness of personal protective equipment for infection control is © 2019 Crown copyright and so is licensed under the Open Government Licence v3.0. Under this licence users are permitted to copy, publish, distribute and transmit the Information; adapt the Information; exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application. Where you do any of the above you must acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/This book is the record of abstracts submitted and accepted for presentation at the Inaugural Engineering and Computer Science Research Conference held 17th April 2019 at the University of Hertfordshire, Hatfield, UK. This conference is a local event aiming at bringing together the research students, staff and eminent external guests to celebrate Engineering and Computer Science Research at the University of Hertfordshire. The ECS Research Conference aims to showcase the broad landscape of research taking place in the School of Engineering and Computer Science. The 2019 conference was articulated around three topical cross-disciplinary themes: Make and Preserve the Future; Connect the People and Cities; and Protect and Care
Sonification of Network Traffic Flow for Monitoring and Situational Awareness
Maintaining situational awareness of what is happening within a network is
challenging, not least because the behaviour happens within computers and
communications networks, but also because data traffic speeds and volumes are
beyond human ability to process. Visualisation is widely used to present
information about the dynamics of network traffic dynamics. Although it
provides operators with an overall view and specific information about
particular traffic or attacks on the network, it often fails to represent the
events in an understandable way. Visualisations require visual attention and so
are not well suited to continuous monitoring scenarios in which network
administrators must carry out other tasks. Situational awareness is critical
and essential for decision-making in the domain of computer network monitoring
where it is vital to be able to identify and recognize network environment
behaviours.Here we present SoNSTAR (Sonification of Networks for SiTuational
AwaReness), a real-time sonification system to be used in the monitoring of
computer networks to support the situational awareness of network
administrators. SoNSTAR provides an auditory representation of all the TCP/IP
protocol traffic within a network based on the different traffic flows between
between network hosts. SoNSTAR raises situational awareness levels for computer
network defence by allowing operators to achieve better understanding and
performance while imposing less workload compared to visual techniques. SoNSTAR
identifies the features of network traffic flows by inspecting the status flags
of TCP/IP packet headers and mapping traffic events to recorded sounds to
generate a soundscape representing the real-time status of the network traffic
environment. Listening to the soundscape allows the administrator to recognise
anomalous behaviour quickly and without having to continuously watch a computer
screen.Comment: 17 pages, 7 figures plus supplemental material in Github repositor
Multimodal Polynomial Fusion for Detecting Driver Distraction
Distracted driving is deadly, claiming 3,477 lives in the U.S. in 2015 alone.
Although there has been a considerable amount of research on modeling the
distracted behavior of drivers under various conditions, accurate automatic
detection using multiple modalities and especially the contribution of using
the speech modality to improve accuracy has received little attention. This
paper introduces a new multimodal dataset for distracted driving behavior and
discusses automatic distraction detection using features from three modalities:
facial expression, speech and car signals. Detailed multimodal feature analysis
shows that adding more modalities monotonically increases the predictive
accuracy of the model. Finally, a simple and effective multimodal fusion
technique using a polynomial fusion layer shows superior distraction detection
results compared to the baseline SVM and neural network models.Comment: INTERSPEECH 201
Intelligent Biosignal Analysis Methods
This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others
Systematic Review of Experimental Paradigms and Deep Neural Networks for Electroencephalography-Based Cognitive Workload Detection
This article summarizes a systematic review of the electroencephalography
(EEG)-based cognitive workload (CWL) estimation. The focus of the article is
twofold: identify the disparate experimental paradigms used for reliably
eliciting discreet and quantifiable levels of cognitive load and the specific
nature and representational structure of the commonly used input formulations
in deep neural networks (DNNs) used for signal classification. The analysis
revealed a number of studies using EEG signals in its native representation of
a two-dimensional matrix for offline classification of CWL. However, only a few
studies adopted an online or pseudo-online classification strategy for
real-time CWL estimation. Further, only a couple of interpretable DNNs and a
single generative model were employed for cognitive load detection till date
during this review. More often than not, researchers were using DNNs as
black-box type models. In conclusion, DNNs prove to be valuable tools for
classifying EEG signals, primarily due to the substantial modeling power
provided by the depth of their network architecture. It is further suggested
that interpretable and explainable DNN models must be employed for cognitive
workload estimation since existing methods are limited in the face of the
non-stationary nature of the signal.Comment: 10 Pages, 4 figure
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