Non-contact vision-based deformation monitoring on bridge structures

Information on deformation is an important metric for bridge condition and performance assessment, e.g. identifying abnormal events, calibrating bridge models and estimating load carrying capacities, etc. However, accurate measurement of bridge deformation, especially for long-span bridges remains as a challenging task. The major aim of this research is to develop practical and cost-effective techniques for accurate deformation monitoring on bridge structures. Vision-based systems are taken as the study focus due to a few reasons: low cost, easy installation, desired sample rates, remote and distributed sensing, etc. This research proposes an custom-developed vision-based system for bridge deformation monitoring. The system supports either consumer-grade or professional cameras and incorporates four advanced video tracking methods to adapt to different test situations. The sensing accuracy is firstly quantified in laboratory conditions. The working performance in field testing is evaluated on one short-span and one long-span bridge examples considering several influential factors i.e. long-range sensing, low-contrast target patterns, pattern changes and lighting changes. Through case studies, some suggestions about tracking method selection are summarised for field testing. Possible limitations of vision-based systems are illustrated as well. To overcome observed limitations of vision-based systems, this research further proposes a mixed system combining cameras with accelerometers for accurate deformation measurement. To integrate displacement with acceleration data autonomously, a novel data fusion method based on Kalman filter and maximum likelihood estimation is proposed. Through field test validation, the method is effective for improving displacement accuracy and widening frequency bandwidth. The mixed system based on data fusion is implemented on field testing of a railway bridge considering undesired test conditions (e.g. low-contrast target patterns and camera shake). Analysis results indicate that the system offers higher accuracy than using a camera alone and is viable for bridge influence line estimation. With considerable accuracy and resolution in time and frequency domains, the potential of vision-based measurement for vibration monitoring is investigated. The proposed vision-based system is applied on a cable-stayed footbridge for deck deformation and cable vibration measurement under pedestrian loading. Analysis results indicate that the measured data enables accurate estimation of modal frequencies and could be used to investigate variations of modal frequencies under varying pedestrian loads. The vision-based system in this application is used for multi-point vibration measurement and provides results comparable to those obtained using an array of accelerometers

How much data is enough to track tourists? The tradeoff between data granularity and storage costs

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceIn the increasingly technology-dependent world, data is one of the key strategic resources for organizations. Often, the challenge that many decision-makers face is to determine which data and how much to collect, and what needs to be kept in their data storage. The challenge is to preserve enough information to inform decisions but doing so without overly high costs of storage and data processing cost. In this thesis, this challenge is studied in the context of a collection of mobile signaling data for studying tourists’ behavioral patterns. Given the number of mobile phones in use, and frequency of their interaction with network infrastructure and location reporting, mobile data sets represent a rich source of information for mobility studies. The objective of this research is to analyze to what extent can individual trajectories be reconstructed if only a fraction of the original location data is preserved, providing insights about the tradeoff between the volume of data available and the accuracy of reconstructed paths. To achieve this, a signaling data of 277,093 anonymized foreign travelers is sampled with different sampling rates, and the full trajectories are reconstructed, using the last seen, linear, and cubic interpolations completion methods. The results of the comparison are discussed from the perspective of data management and implications on the research, especially the results of research with lower time-density mobile phone data

Review of machine-vision based methodologies for displacement measurement in civil structures

This is the author accepted manuscript. The final version is available from Springer Verlag via the DOI in this record.Vision-based systems are promising tools for displacement measurement in civil structures, possessing advantages over traditional displacement sensors in instrumentation cost, installation efforts and measurement capacity in terms of frequency range and spatial resolution. Approximately one hundred papers to date have appeared on this subject, investigating topics like: system development and improvement, the viability on field applications and the potential for structural condition assessment. The main contribution of this paper is to present a literature review of vision-based displacement measurement, from the perspectives of methodologies and applications. Video processing procedures in this paper are summarised as a three-component framework, camera calibration, target tracking and structural displacement calculation. Methods for each component are presented in principle, with discussions about the relative advantages and limitations. Applications in the two most active fields: bridge deformation and cable vibration measurement are examined followed by a summary of field challenges observed in monitoring tests. Important gaps requiring further investigation are presented e.g. robust tracking methods, non-contact sensing and measurement accuracy evaluation in field conditions

Travel Time Prediction Based on Raw GPS Data

Aja planeerimine on muutunud aina olulisemaks üha kiireneva elutempoga ühiskonnas. Üheks oluliseks komponendiks aja planeerimisel on võimalikult täpselt hinnata, kui palju aega kulub ühest kohast teise liikumiseks. Käesolev magistritöö on valminud koostöös Boltiga, mis on üks suurimaid sõidujagamisteenust pakkuvaid ettevõtteid. Sõiduaja ennustamine tooreste GPS andmete põhjal nõuab suures koguses andmete eeltöötlemist, kasutades seejuures väliseid andmekogusid, et siduda tooreid GPS andmeid ümbritseva keskkonnaga. Käesolevas töös käsitletakse kõiki vajaminevaid eeltöötlemise samme, millest moodustub terviklik meetod sõiduaja ennustamiseks töötlemata GPS andmete põhjal. Meetodi efektiivsuse valideerimiseks on seda võrreldud kahe laialdaselt kasutu-ses oleva meetodiga.With the ever growing pace of our everyday lives, time planning has gained a lot of im-portance. One of the key factors for time planning is to estimate the duration of moving from one place to another. Therefore, travel time prediction has become essential part of any logistics based business. This thesis is conducted in collaboration with Bolt, which is one of the leading ride hailing companies. This thesis is describing route based travel time prediction algorithm based on raw GPS data. The goal is to analyze each of the pre-processing steps and to develop a coherent method to predict arrival time based on GPS input data supplied by Bolt. Furthermore, route based method described in this thesis is validated by comparing it to two well-known and established methods

A non-contact vision-based system for multi-point displacement monitoring in a cable-stayed footbridge

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this record.Vision-based monitoring receives increased attention for measuring displacements of civil infrastructure such as towers and bridges. Currently, most field applications rely on artificial targets for video processing convenience, leading to high installation effort and focus on only single-point displacement measurement e.g. at mid-span of a bridge. This study proposes a low-cost and non-contact vision-based system for multi-point displacement measurement based on a consumer-grade camera for video acquisition and a custom-developed package for video processing. The system has been validated on a cable-stayed footbridge for deck deformation and cable vibration measurement under pedestrian loading. The analysis results indicate that the system provides valuable information about bridge deformation of the order of a few cm induced, in this application, by pedestrian passing. The measured data enables accurate estimation of modal frequencies of either the bridge deck or the bridge cables and could be used to investigate variations of modal frequencies under varying pedestrian loads

Applying Augmented Reality to Outdoors Industrial Use

Augmented Reality (AR) is currently gaining popularity in multiple different fields. However, the technology for AR still requires development in both hardware and software when considering industrial use. In order to create immersive AR applications, more accurate pose estimation techniques to define virtual camera location are required. The algorithms for pose estimation often require a lot of processing power, which makes robust pose estimation a difficult task when using mobile devices or designated AR tools. The difficulties are even larger in outdoor scenarios where the environment can vary a lot and is often unprepared for AR. This thesis aims to research different possibilities for creating AR applications for outdoor environments. Both hardware and software solutions are considered, but the focus is more on software. The majority of the thesis focuses on different visual pose estimation and tracking techniques for natural features. During the thesis, multiple different solutions were tested for outdoor AR. One commercial AR SDK was tested, and three different custom software solutions were developed for an Android tablet. The custom software solutions were an algorithm for combining data from magnetometer and a gyroscope, a natural feature tracker and a tracker based on panorama images. The tracker based on panorama images was implemented based on an existing scientific publication, and the presented tracker was further developed by integrating it to Unity 3D and adding a possibility for augmenting content. This thesis concludes that AR is very close to becoming a usable tool for professional use. The commercial solutions currently available are not yet ready for creating tools for professional use, but especially for different visualization tasks some custom solutions are capable of achieving a required robustness. The panorama tracker implemented in this thesis seems like a promising tool for robust pose estimation in unprepared outdoor environments.Lisätyn todellisuuden suosio on tällä hetkellä kasvamassa usealla eri alalla. Saatavilla olevat ohjelmistot sekä laitteet eivät vielä riitä lisätyn todellisuuden soveltamiseen ammattimaisessa käytössä. Erityisesti posen estimointi vaatii tarkempia menetelmiä, jotta immersiivisten lisätyn todellisuuden sovellusten kehittäminen olisi mahdollista. Posen estimointiin (laitteen asennon- sekä paikan arviointiin) käytetyt algoritmit ovat usein monimutkaisia, joten ne vaativat merkittävästi laskentatehoa. Laskentatehon vaatimukset ovat usein haasteellisia varsinkin mobiililaitteita sekä lisätyn todellisuuden laitteita käytettäessä. Lisäongelmia tuottaa myös ulkotilat, jossa ympäristö voi muuttua usein ja ympäristöä ei ole valmisteltu lisätyn todellisuuden sovelluksille. Diplomityön tarkoituksena on tutkia mahdollisuuksia lisätyn todellisuuden sovellusten kehittämiseen ulkotiloihin. Sekä laitteisto- että ohjelmistopohjaisia ratkaisuja käsitellään. Ohjelmistopohjaisia ratkaisuja käsitellään työssä laitteistopohjaisia ratkaisuja laajemmin. Suurin osa diplomityöstä keskittyy erilaisiin visuaalisiin posen estimointi tekniikoihin, jotka perustuvat kuvasta tunnistettujen luonnollisten piirteiden seurantaan. Työn aikana testattiin useita ratkaisuja ulkotiloihin soveltuvaan lisättyyn todellisuuteen. Yhtä kaupallista työkalua testattiin, jonka lisäksi toteutettiin kolme omaa sovellusta Android tableteille. Työn aikana kehitetyt sovellukset olivat yksinkertainen algoritmi gyroskoopin ja magnetometrin datan yhdistämiseen, luonnollisen piirteiden seuranta-algoritmi sekä panoraamakuvaan perustuva seuranta-algoritmi. Panoraamakuvaan perustuva seuranta-algoritmi on toteuteutettu toisen tieteellisen julkaisun pohjalta, ja algoritmia jatkokehitettiin integroimalla se Unity 3D:hen. Unity 3D-integrointi mahdollisti myös sisällön esittämisen lisätyn todellisuuden avulla. Työn lopputuloksena todetaan, että lisätyn todellisuuden teknologia on lähellä pistettä, jossa lisätyn todellisuuden työkaluja voitaisiin käyttää ammattimaisessa käytössä. Tällä hetkellä saatavilla olevat kaupalliset työkalut eivät vielä pääse ammattikäytön vaatimalle tasolle, mutta erityisesti visualisointitehtäviin soveltuvia ei-kaupallisia ratkaisuja on jo olemassa. Lisäksi työn aikana toteutetun panoraamakuviin perustuvan seuranta-algoritmin todetaan olevan lupaava työkalu posen estimointiin ulkotiloissa.Siirretty Doriast

Link Prediction via Generalized Coupled Tensor Factorisation

This study deals with the missing link prediction problem: the problem of predicting the existence of missing connections between entities of interest. We address link prediction using coupled analysis of relational datasets represented as heterogeneous data, i.e., datasets in the form of matrices and higher-order tensors. We propose to use an approach based on probabilistic interpretation of tensor factorisation models, i.e., Generalised Coupled Tensor Factorisation, which can simultaneously fit a large class of tensor models to higher-order tensors/matrices with com- mon latent factors using different loss functions. Numerical experiments demonstrate that joint analysis of data from multiple sources via coupled factorisation improves the link prediction performance and the selection of right loss function and tensor model is crucial for accurately predicting missing links