2,145 research outputs found
Automated and model-free bridge damage indicators with simultaneous multi-parameter modal anomaly detection
This paper pursues a simultaneous modal parameter anomaly detection paradigm to structural damage identification inferred from vibration-based structural health monitoring (SHM) sensors, e.g., accelerometers. System Realization Using Information Matrix (SRIM) method is performed in short duration sweeping time windows for identification of state matrices, and then, modal parameters with enhanced automation. Stable modal poles collected from stability diagrams are clustered and fed into the Gaussian distribution-based anomaly detection platform. Different anomaly thresholds are examined both on frequency and damping ratio terms taking two testbed bridge structures as application means, and simplistic Boolean Operators are performed to merge univariate anomalies. The first bridge is a reinforced concrete bridge subjected to incremental damage through a series of seismic shake table experiments conducted at the University of Nevada, Reno. The second bridge is a steel arch structure at Columbia University Morningside Campus, which reflects no damage throughout the measurements, unlike the first one. Two large-scale implementations indicate the realistic performance of automated modal analysis and anomaly recognition with minimal human intervention in terms of parameter extraction and learning supervision. Anomaly detection performance, presented in this paper, shows variation according to the designated thresholds, and hence, the information retrieval metrics being considered. The methodology is well-fitted to SHM problems which require sole data-driven, scalable, and fully autonomous perspectives
Artificial immune systems based committee machine for classification application
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A new adaptive learning Artificial Immune System (AIS) based committee machine is developed in this thesis. The new proposed approach efficiently tackles the general problem of clustering high-dimensional data. In addition, it helps on deriving useful decision and results related to other application domains such classification and prediction. Artificial Immune System (AIS) is a branch of computational intelligence field inspired by the biological immune system, and has gained increasing interest among researchers in the development of immune-based models and techniques to solve diverse complex computational or engineering problems. This work presents some applications of AIS techniques to health problems, and a thorough survey of existing AIS models and algorithms. The main focus of this research is devoted to building an ensemble model integrating different AIS techniques (i.e. Artificial Immune Networks, Clonal Selection, and Negative Selection) for classification applications to achieve better classification results. A new AIS-based ensemble architecture with adaptive learning features is proposed by integrating different learning and adaptation techniques to overcome individual limitations and to achieve synergetic effects through the combination of these techniques. Various techniques related to the design and enhancements of the new adaptive learning architecture are studied, including a neuro-fuzzy based detector and an optimizer using particle swarm optimization method to achieve enhanced classification performance. An evaluation study was conducted to show the performance of the new proposed adaptive learning ensemble and to compare it to alternative combining techniques. Several experiments are presented using different medical datasets for the classification problem and findings and outcomes are discussed. The new adaptive learning architecture improves the accuracy of the ensemble. Moreover, there is an improvement over the existing aggregation techniques. The outcomes, assumptions and limitations of the proposed methods with its implications for further research in this area draw this research to its conclusion
An overview on structural health monitoring: From the current state-of-the-art to new bio-inspired sensing paradigms
In the last decades, the field of structural health monitoring (SHM) has grown exponentially. Yet, several technical constraints persist, which are preventing full realization of its potential. To upgrade current state-of-the-art technologies, researchers have started to look at nature’s creations giving rise to a new field called ‘biomimetics’, which operates across the border between living and non-living systems. The highly optimised and time-tested performance of biological assemblies keeps on inspiring the development of bio-inspired artificial counterparts that can potentially outperform conventional systems. After a critical appraisal on the current status of SHM, this paper presents a review of selected works related to neural, cochlea and immune-inspired algorithms implemented in the field of SHM, including a brief survey of the advancements of bio-inspired sensor technology for the purpose of SHM. In parallel to this engineering progress, a more in-depth understanding of the most suitable biological patterns to be transferred into multimodal SHM systems is fundamental to foster new scientific breakthroughs. Hence, grounded in the dissection of three selected human biological systems, a framework for new bio-inspired sensing paradigms aimed at guiding the identification of tailored attributes to transplant from nature to SHM is outlined.info:eu-repo/semantics/acceptedVersio
Undersampling and Cumulative Class Re-decision Methods to Improve Detection of Agitation in People with Dementia
Agitation is one of the most prevalent symptoms in people with dementia (PwD)
that can place themselves and the caregiver's safety at risk. Developing
objective agitation detection approaches is important to support health and
safety of PwD living in a residential setting. In a previous study, we
collected multimodal wearable sensor data from 17 participants for 600 days and
developed machine learning models for predicting agitation in one-minute
windows. However, there are significant limitations in the dataset, such as
imbalance problem and potential imprecise labels as the occurrence of agitation
is much rarer in comparison to the normal behaviours. In this paper, we first
implement different undersampling methods to eliminate the imbalance problem,
and come to the conclusion that only 20\% of normal behaviour data are adequate
to train a competitive agitation detection model. Then, we design a weighted
undersampling method to evaluate the manual labeling mechanism given the
ambiguous time interval (ATI) assumption. After that, the postprocessing method
of cumulative class re-decision (CCR) is proposed based on the historical
sequential information and continuity characteristic of agitation, improving
the decision-making performance for the potential application of agitation
detection system. The results show that a combination of undersampling and CCR
improves F1-score and other metrics to varying degrees with less training time
and data used, and inspires a way to find the potential range of optimal
threshold reference for clinical purpose.Comment: 19 pages, 8 figure
Magnetic and radar sensing for multimodal remote health monitoring
With the increased life expectancy and rise in health conditions related to aging, there is a need for new technologies that can routinely monitor vulnerable people, identify their daily pattern of activities and any anomaly or critical events such as falls. This paper aims to evaluate magnetic and radar sensors as suitable technologies for remote health monitoring purpose, both individually and fusing their information. After experiments and collecting data from 20 volunteers, numerical features has been extracted in both time and frequency domains. In order to analyse and verify the validation of fusion method for different classifiers, a Support Vector Machine with a quadratic kernel, and an Artificial Neural Network with one and multiple hidden layers have been implemented. Furthermore, for both classifiers, feature selection has been performed to obtain salient features. Using this technique along with fusion, both classifiers can detect 10 different activities with an accuracy rate of approximately 96%. In cases where the user is unknown to the classifier, an accuracy of approximately 92% is maintained
Rare Life Event Detection via Mobile Sensing Using Multi-Task Learning
Rare life events significantly impact mental health, and their detection in
behavioral studies is a crucial step towards health-based interventions. We
envision that mobile sensing data can be used to detect these anomalies.
However, the human-centered nature of the problem, combined with the
infrequency and uniqueness of these events makes it challenging for
unsupervised machine learning methods. In this paper, we first investigate
granger-causality between life events and human behavior using sensing data.
Next, we propose a multi-task framework with an unsupervised autoencoder to
capture irregular behavior, and an auxiliary sequence predictor that identifies
transitions in workplace performance to contextualize events. We perform
experiments using data from a mobile sensing study comprising N=126 information
workers from multiple industries, spanning 10106 days with 198 rare events
(<2%). Through personalized inference, we detect the exact day of a rare event
with an F1 of 0.34, demonstrating that our method outperforms several
baselines. Finally, we discuss the implications of our work from the context of
real-world deployment.Comment: 15 pages, 4 figures, CHIL 2023 (Accepted
MTAD: Tools and Benchmarks for Multivariate Time Series Anomaly Detection
Key Performance Indicators (KPIs) are essential time-series metrics for
ensuring the reliability and stability of many software systems. They
faithfully record runtime states to facilitate the understanding of anomalous
system behaviors and provide informative clues for engineers to pinpoint the
root causes. The unprecedented scale and complexity of modern software systems,
however, make the volume of KPIs explode. Consequently, many traditional
methods of KPI anomaly detection become impractical, which serves as a catalyst
for the fast development of machine learning-based solutions in both academia
and industry. However, there is currently a lack of rigorous comparison among
these KPI anomaly detection methods, and re-implementation demands a
non-trivial effort. Moreover, we observe that different works adopt independent
evaluation processes with different metrics. Some of them may not fully reveal
the capability of a model and some are creating an illusion of progress. To
better understand the characteristics of different KPI anomaly detectors and
address the evaluation issue, in this paper, we provide a comprehensive review
and evaluation of twelve state-of-the-art methods, and propose a novel metric
called salience. Particularly, the selected methods include five traditional
machine learning-based methods and seven deep learning-based methods. These
methods are evaluated with five multivariate KPI datasets that are publicly
available. A unified toolkit with easy-to-use interfaces is also released. We
report the benchmark results in terms of accuracy, salience, efficiency, and
delay, which are of practical importance for industrial deployment. We believe
our work can contribute as a basis for future academic research and industrial
application.Comment: The code and datasets are available at https://github.com/OpsPAI/MTA
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