In-situ health monitoring for wind turbine blade using acoustic wireless sensor networks at low sampling rates

PhD ThesisThe development of in-situ structural health monitoring (SHM) techniques represents a challenge for offshore wind turbines (OWTs) in order to reduce the cost of the operation and maintenance (O&M) of safety-critical components and systems. This thesis propos- es an in-situ wireless SHM system based on acoustic emission (AE) techniques. The proposed wireless system of AE sensor networks is not without its own challenges amongst which are requirements of high sampling rates, limitations in the communication bandwidth, memory space, and power resources. This work is part of the HEMOW- FP7 Project, ‘The Health Monitoring of Offshore Wind Farms’. The present study investigates solutions relevant to the abovementioned challenges. Two related topics have been considered: to implement a novel in-situ wireless SHM technique for wind turbine blades (WTBs); and to develop an appropriate signal pro- cessing algorithm to detect, localise, and classify different AE events. The major contri- butions of this study can be summarised as follows: 1) investigating the possibility of employing low sampling rates lower than the Nyquist rate in the data acquisition opera- tion and content-based feature (envelope and time-frequency data analysis) for data analysis; 2) proposing techniques to overcome drawbacks associated with lowering sampling rates, such as information loss and low spatial resolution; 3) showing that the time-frequency domain is an effective domain for analysing the aliased signals, and an envelope-based wavelet transform cross-correlation algorithm, developed in the course of this study, can enhance the estimation accuracy of wireless acoustic source localisa- tion; 4) investigating the implementation of a novel in-situ wireless SHM technique with field deployment on the WTB structure, and developing a constraint model and approaches for localisation of AE sources and environmental monitoring respectively. Finally, the system has been experimentally evaluated with the consideration of the lo- calisation and classification of different AE events as well as changes of environmental conditions. The study concludes that the in-situ wireless SHM platform developed in the course of this research represents a promising technique for reliable SHM for OWTBs in which solutions for major challenges, e.g., employing low sampling rates lower than the Nyquist rate in the acquisition operation and resource constraints of WSNs in terms of communication bandwidth and memory space are presente

Luonnollisiin audiovisuaalisiin ärsykkeisiin liittyvän fMRI-aktivaation bayesilainen luokittelu harvoja ratkaisuja suosivia Laplace-prioreja käyttäen

Bayesian linear binary classification models with sparsity promoting Laplace priors were applied to discriminate fMRI patterns related to natural auditory and audiovisual speech and music stimuli. The region of interest comprised the auditory cortex and some surrounding regions related to auditory processing. Truly sparse posterior mean solutions for the classifier weights were obtained by implementing an automatic relevance determination method using expectation propagation (ARDEP). In ARDEP, the Laplace prior was decomposed into a Gaussian scale mixture, and these scales were optimised by maximising their marginal posterior density. ARDEP was also compared to two other methods, which integrated approximately over the original Laplace prior: LAEP approximated the posterior as well by expectation propagation, whereas MCMC used a Markov chain Monte Carlo simulation method implemented by Gibbs sampling. The resulting brain maps were consistent with previous studies for simpler stimuli and suggested that the proposed model is also able to reveal additional information about activation patterns related to natural audiovisual stimuli. The predictive performance of the model was significantly above chance level for all approximate inference methods. Regardless of intensive pruning of features, ARDEP was able to describe all of the most discriminative brain regions obtained by LAEP and MCMC. However, ARDEP lost the more specific shape of the regions by representing them as one or more smaller spots, removing also some relevant features.Bayesilaisia lineaarisia binääriluokittelumalleja ja harvoja ratkaisuja suosivia Laplace- prioreja sovellettiin erottelemaan luonnollisiin auditorisiin ja audiovisuaalisiin puhe- ja musiikkiärsykkeisiin liittyvää fMRI-aktivaatiota kuuloaivokuorella ja sitä ympäröivillä auditoriseen prosessointiin liittyvillä alueilla. Absoluuttisen harvoja posteriorisia odotusarvoratkaisuja luokittimien painoille saatiin expectation propagation -algoritmin avulla toteutetulla automatic relevance determination -menetelmällä (ARDEP). ARDEP-menetelmässä hyödynnettiin Laplace-priorin gaussista skaalahajotelmaa, jonka skaalaparametrit optimoitiin maksimoimalla niiden marginaalinen posterioritiheys. Menetelmää verrattiin myös kahteen muuhun menetelmään, jotka integroivat approksimatiivisesti alkuperäisen Laplace-priorin yli: LAEP approksimoi posteriorijakaumaa niin ikään expectation propagation -algoritmin avulla, kun taas MCMC käytti Gibbs -poiminnalla toteutettua Markovin ketju Monte Carlo -simulaatiomenetelmää. Tuloksena saadut aivokartat olivat linjassa aikaisempien, yksinkertaisemmilla ärsykkeillä saatujen tutkimustulosten kanssa, ja niiden perusteella bayesilaisten luokittelumallien avulla on mahdollista saada myös uudenlaista tietoa siitä, miten luonnollisia audiovisuaalisia ärsykkeitä koodataan aivoissa. Mallien ennustuskyky oli kaikilla approksimaatiomenetelmillä merkittävästi sattumanvaraista tasoa korkeampi. Piirteiden voimakkaasta karsinnasta huolimatta ARDEP pystyi kuvaamaan kaikki huomattavimmat LAEP:n ja MCMC:n erottelemat aivoalueet. ARDEP menetti kuitenkin alueiden tarkemman muodon esittämällä ne yhtenä tai useampana pienempänä alueena, poistaen myös osan merkittävistä piirteistä

End-user action-sound mapping design for mid-air music performance

How to design the relationship between a performer’s actions and an instrument’s sound response has been a consistent theme in Digital Musical Instrument (DMI) research. Previously, mapping was seen purely as an activity for DMI creators, but more recent work has exposed mapping design to DMI musicians, with many in the field introducing soware to facilitate end-user mapping, democratising this aspect of the DMI design process. This end-user mapping process provides musicians with a novel avenue for creative expression, and offers a unique opportunity to examine how practising musicians approach mapping design.Most DMIs suffer from a lack of practitioners beyond their initial designer, and there are few that are used by professional musicians over extended periods. The Mi.Mu Gloves are one of the few examples of a DMI that is used by a dedicated group of practising musicians, many of whom use the instrument in their professional practice, with a significant aspect of creative practice with the gloves being end-user mapping design. The research presented in this dissertation investigates end-user mapping practice with the Mi.Mu Gloves, and what influences glove musicians’ design decisions based on the context of their music performance practice, examining the question: How do end-users of a glove-based mid-air DMI design action–sound mapping strategies for musical performance?In the first study, the mapping practice of existing members of the Mi.Mu Glove community is examined. Glove musicians performed a mapping design task, which revealed marked differences in the mapping designs of expert and novice glove musicians, with novices designing mappings that evoked conceptual metaphors of spatial relationships between movement and music, while more experienced musicians focused on designing ergonomic mappings that minimised performer error.The second study examined the initial development period of glove mapping practice. A group of novice glove musicians were tracked in a longitudinal study. The findings supported the previous observation that novices designed mappings using established conceptual metaphors, and revealed that transparency and the audience’s ability to perceive their mappings was important to novice glove musicians. However, creative mapping was hindered by system reliability and the novices’ poorly trained posture recognition.The third study examined the mapping practice of expert glove musicians, who took part in a series of interviews. Findings from this study supported earlier observations that expert glove musicians focus on error minimisation and ergonomic, simple controls, but also revealed that the expert musicians embellished these simple controls with performative ancillary gestures to communicate aesthetic meaning. The expert musicians also suffered from system reliability, and had developed a series of gestural techniques to mitigate accidental triggering.The fourth study examined the effects of system-related error in depth. A laboratory study was used to investigate how system-related errors impacted a musician’s ability to acquire skill with the gloves, finding that a 5% rate of system error had a significant effect on skill acquisition.Learning from these findings, a series of design heuristics are presented, applicable for use in the fields of DMI design, mid-air interaction design and end-user mapping design

Simulation of sea-state sequences

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The present PhD study, in its first part, uses artificial neural networks (ANNs), an optimization technique called simulated annealing, and statistics to simulate the significant wave height (Hs) and mean zero-up-crossing period ( ) of 3-hourly sea-states of a location in the North East Pacific using a proposed distribution called hepta-parameter spline distribution for the conditional distribution of Hs or given some inputs. Two different seven- network sets of ANNs for the simulation and prediction of Hs and were trained using 20-year observed Hs’s and ’s. The preceding Hs’s and ’s were the most important inputs given to the networks, but the starting day of the simulated period was also necessary. However, the code replaced the day with the corresponding time and the season. The networks were trained by a simulated annealing algorithm and the outputs of the two sets of networks were used for calculating the parameters of the probability density function (pdf) of the proposed hepta-parameter distribution. After the calculation of the seven parameters of the pdf from the network outputs, the Hs and of the future sea-state is predicted by generating random numbers from the corresponding pdf. In another part of the thesis, vertical piles have been studied with the goal of identifying the range of sea-states suitable for the safe pile driving operation. Pile configuration including the non-linear foundation and the gap between the pile and the pile sleeve shims were modeled using the finite elements analysis facilities within ABAQUS. Dynamic analyses of the system for a sea-state characterized by Hs and and modeled as a combination of several wave components were performed. A table of safe and unsafe sea-states was generated by repeating the analysis for various sea-states. If the prediction for a particular sea-state is repeated N times of which n times prove to be safe, then it could be said that the predicted sea-state is safe with the probability of 100(n/N). The last part of the thesis deals with the Hs return values. The return value is a widely used measure of wave extremes having an important role in determining the design wave used in the design of maritime structures. In this part, Hs return value was calculated demonstrating another application of the above simulation of future 3-hourly Hs’s. The maxima method for calculating return values was applied in such a way that avoids the conventional need for unrealistic assumptions. The significant wave height return value has also been calculated using the convolution concept from a model presented by Anderson et al. (2001)

【研究分野別】シーズ集 [英語版]

[英語版

Mixture Models for Multidimensional Positive Data Clustering with Applications to Image Categorization and Retrieval

Model-based approaches have become important tools to model data and infer knowledge. Such approaches are often used for clustering and object recognition which are crucial steps in many applications, including but not limited to, recommendation systems, search engines, cyber security, surveillance and object tracking. Many of these applications have the urgent need to reduce the semantic gap of data representation between the system level and the human being understandable level. Indeed, the low level features extracted to represent a given object can be confusing to machines which cannot differentiate between very similar objects trivially distinguishable by human beings (e.g. apple vs tomato). Such a semantic gap between the system and the user perception for data, makes the modeling process hard to be designed basing on the features space only. Moreover those models should be flexible and updatable when new data are introduced to the system. Thus, apart from estimating the model parameters, the system should be somehow informed how new data should be perceived according to some criteria in order to establish model updates. In this thesis we propose a methodology for data representation using a hierarchical mixture model basing on the inverted Dirichlet and the generalized inverted Dirichlet distributions. The proposed approach allows to model a given object class by a set of components deduced by the system and grouped according to labeled training data representing the human level semantic. We propose an update strategy to the system components that takes into account adjustable metrics representing users perception. We also consider the "page zero" problem in image retrieval systems when a given user does not possess adequate tools and semantics to express what he/she is looking for, while he/she can visually identify it. We propose a statistical framework that enables users to start a search process and interact with the system in order to find their target "mental image". Finally we propose to improve our models by using a variational Bayesian inference to learn generalized inverted Dirichlet mixtures with features selection. The merit of our approaches is evaluated using extensive simulations and real life applications

Robust and Scalable Data Representation and Analysis Leveraging Isometric Transformations and Sparsity

The main focus of this doctoral thesis is to study the problem of robust and scalable data representation and analysis. The success of any machine learning and signal processing framework relies on how the data is represented and analyzed. Thus, in this work, we focus on three closely related problems: (i) supervised representation learning, (ii) unsupervised representation learning, and (iii) fault tolerant data analysis. For the first task, we put forward new theoretical results on why a certain family of neural networks can become extremely deep and how we can improve this scalability property in a mathematically sound manner. We further investigate how we can employ them to generate data representations that are robust to outliers and to retrieve representative subsets of huge datasets. For the second task, we will discuss two different methods, namely compressive sensing (CS) and nonnegative matrix factorization (NMF). We show that we can employ prior knowledge, such as slow variation in time, to introduce an unsupervised learning component to the traditional CS framework and to learn better compressed representations. Furthermore, we show that prior knowledge and sparsity constraint can be used in the context of NMF, not to find sparse hidden factors, but to enforce other structures, such as piece-wise continuity. Finally, for the third task, we investigate how a data analysis framework can become robust to faulty data and faulty data processors. We employ Bayesian inference and propose a scheme that can solve the CS recovery problem in an asynchronous parallel manner. Furthermore, we show how sparsity can be used to make an optimization problem robust to faulty data measurements. The methods investigated in this work have applications in different practical problems such as resource allocation in wireless networks, source localization, image/video classification, and search engines. A detailed discussion of these practical applications will be presented for each method

Applications of Silicon Retinas: from Neuroscience to Computer Vision

Traditional visual sensor technology is firmly rooted in the concept of sequences of image frames. The sequence of stroboscopic images in these "frame cameras" is very different compared to the information running from the retina to the visual cortex. While conventional cameras have improved in the direction of smaller pixels and higher frame rates, the basics of image acquisition have remained the same. Event-based vision sensors were originally known as "silicon retinas" but are now widely called "event cameras." They are a new type of vision sensors that take inspiration from the mechanisms developed by nature for the mammalian retina and suggest a different way of perceiving the world. As in the neural system, the sensed information is encoded in a train of spikes, or so-called events, comparable to the action potential generated in the nerve. Event-based sensors produce sparse and asynchronous output that represents in- formative changes in the scene. These sensors have advantages in terms of fast response, low latency, high dynamic range, and sparse output. All these char- acteristics are appealing for computer vision and robotic applications, increasing the interest in this kind of sensor. However, since the sensor’s output is very dif- ferent, algorithms applied for frames need to be rethought and re-adapted. This thesis focuses on several applications of event cameras in scientific scenarios. It aims to identify where they can make the difference compared to frame cam- eras. The presented applications use the Dynamic Vision Sensor (event camera developed by the Sensors Group of the Institute of Neuroinformatics, University of Zurich and ETH). To explore some applications in more extreme situations, the first chapters of the thesis focus on the characterization of several advanced versions of the standard DVS. The low light condition represents a challenging situation for every vision sensor. Taking inspiration from standard Complementary Metal Oxide Semiconductor (CMOS) technology, the DVS pixel performances in a low light scenario can be improved, increasing sensitivity and quantum efficiency, by using back-side illumination. This thesis characterizes the so-called Back Side Illumination DAVIS (BSI DAVIS) camera and shows results from its application in calcium imaging of neural activity. The BSI DAVIS has shown better performance in the low light scene due to its high Quantum Efficiency (QE) of 93% and proved to be the best type of technology for microscopy application. The BSI DAVIS allows detecting fast dynamic changes in neural fluorescent imaging using the green fluorescent calcium indicator GCaMP6f. Event camera advances have pushed the exploration of event-based cameras in computer vision tasks. Chapters of this thesis focus on two of the most active research areas in computer vision: human pose estimation and hand gesture classification. Both chapters report the datasets collected to achieve the task, fulfilling the continuous need for data for this kind of new technology. The Dynamic Vision Sensor Human Pose dataset (DHP19) is an extensive collection of 33 whole-body human actions from 17 subjects. The chapter presents the first benchmark neural network model for 3D pose estimation using DHP19. The network archives a mean error of less than 8 mm in the 3D space, which is comparable with frame-based Human Pose Estimation (HPE) methods using frames. The gesture classification chapter reports an application running on a mobile device and explores future developments in the direction of embedded portable low power devices for online processing. The sparse output from the sensor suggests using a small model with a reduced number of parameters and low power consumption. The thesis also describes pilot results from two other scientific imaging applica- tions for raindrop size measurement and laser speckle analysis presented in the appendices

Reinforcement Learning in Traffic Control for Connected Automated Vehicles

The last years, more and people are concentrating in big cities for reasons of living and working. This effect has already some negative impacts on transportation networks including congestion and inefficiency. Parallel to the centralization, the number of autonomous vehicles on roads is continuing to grow, without completely replacing human driving vehicles. The upcoming mixed autonomy traffic situations will bring more dangers in terms of safety and transportation efficiency. The traditional traffic management solutions may not be able to handle these situations. Machine learning approaches have been already proved efficient in various complex fields. In this dissertation, a sub-field of Machine Learning, the Deep Reinforcement Learning will be investigated for enabling a smooth coexistence of automated, connected, and conventional vehicles. In particular, various reinforcement learning models, with both single and multi agent approaches, will be trained and tested on controlling the traffic flow in a specific mixed autonomy traffic scenario, where a transition from autonomous to human driving mode is needed for the vehicles