Browsing by Author "Church, Ian"

Now showing 1 - 13 of 13
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    A Comparison of precise point positioning GPS elevations with the Northwest Atlantic Hydrodynamic Model on the Grand Banks
    (University of New Brunswick, 2011) Theriault, Justin; Clarke, John Hughes; Church, Ian
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    Analysis of EV charging station clusters using a novel representation of temporally varying structures
    (University of New Brunswick, 2021-11) Richard, René; Church, Ian; Wachowicz, Monica
    Transport electrification introduces new opportunities in supporting sustainable mobility. Fostering Electric Vehicle (EV) adoption integrates vehicle range and infrastructure deployment concerns. An understanding of EV charging patterns is crucial for optimizing charging infrastructure placement and managing costs. Clustering EV charging events has been useful for ensuring service consistency and increasing EV adoption. However, clustering presents challenges for practitioners when first selecting the appropriate hyperparameter combination for an algorithm and later when assessing the quality of clustering results. Ground truth information is usually not available for practitioners to validate the discovered patterns. As a result, it is harder to judge the effectiveness of different modelling decisions since there is no objective way to compare them. This work proposes a clustering process that allows for the creation of relative rankings of similar clustering results. The overall goal is to support users by allowing them to compare a clustering result of interest against other similar groupings over multiple temporal granularities. The efficacy of this analytical process is demonstrated with a case study using real-world EV charging event data from charging station operators in New Brunswick.
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    Assessing the accuracy of UAV photogrammetric modelling for marine structures
    (University of New Brunswick, 2018) Duffy, Stephanie; McNeill, J. Patrick; Mullin, Ellen; Church, Ian
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    Automatic processing of Arctic crowd-sourced hydrographic data while improving bathymetric accuracy and uncertainty assessment
    (University of New Brunswick, 2019) Arfeen, Khaleel; Church, Ian
    Melting sea ice has led to an increase in navigation in Canadian Arctic waters. However, these waters are sparsely surveyed and pose a risk to mariners. Recognizing this issue, the government of Canada has granted funds towards the development of a pilot program to begin collecting bathymetric data through a novel crowd-sourced approach. The project is a coalition between four Canadian partners from across the country; The University of New Brunswick’s Ocean Mapping Group is tasked with the processing of the collected data and this thesis will focus on this aspect. Through an automated approach the data has been processed with the end-product being a final depth measurement with the associated uncertainty. The software is Python based and has been broken down into several modules to complete the task at hand. Utilizing specialized hydrographic equipment, designed to be low-cost and simple to operate, participating communities in the Canadian Arctic have been given the opportunity to collect bathymetric data while traversing their local waterways. As the pilot phase of the project is done, this thesis delves into the steps taken to fulfill the processing goals. The primary motivation surrounds how the processing workflow was completed through automation while mitigating errors and achieving transparency in the uncertainty assessment in the crowd-sourced bathymetric (CSB) data. Particular emphasis is placed upon the issues of collecting valuable hydrographic data from the Arctic with analysis of different methods to process the data with efficiency in mind. These challenges include obtaining a reliable GNSS signal through post-processing, qualification of the GNSS data for vertical reference, utilizing the HYCOM hydrodynamic model to collect sound velocity profiles and the identification and quantification of uncertainty as part of the Total Propagated Uncertainty (TPU) model. Several case study type examples are given where an investigation is conducted using processed collected and/or model data. Discussions surround the results of multi-constellation vs. single-constellation GNSS in the Arctic and the effects on the qualification rate for use as vertical referencing. Similarly, work towards comparing the model used to collect SVP data with equivalent real-world data collected by the Canadian Coast Guard is discussed. Finally, uncertainty has been quantified and assessed for the collected data and the results of the uncertainty assessment are provided using CHS/IHO survey standards as a benchmark.
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    Developing a nested finite-element hydrodynamic model to predict phase and amplitude modification of the tide within narrow fjords
    Church, Ian
    A long term monitoring project to measure the inter-annual change in pro-glacial deltaic sediments has been initiated in Oliver Sound, one of a cluster of fjords that lie off Eclipse Sound at the northern tip of Baffin Island, Canada. In order to confidently identify the decimetre-level changes in seabed morphology from multibeam surveys, adequate tidal control is required. Surveying in such remote locations presents conditions, logistics and time constraints that prohibit the installation of tide gauges throughout the survey area and existing predicted tide stations are separated from the survey area by complex fjords and islands. To overcome these hurdles, a high resolution hydrodynamic model simulation has been constructed to predict the tides throughout the survey region which accounts for the changes in tidal phase and amplitude within the complex fjords. The simulation results are compared to existing lower resolution tidal models, nearby predicted tides and Globally Corrected GPS data from survey vessels working and transiting throughout the area.
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    Developing a nested finite-element hydrodynamic model to predict phase and amplitude modification of the tide within narrow fjords
    Church, Ian
    A long term monitoring project to measure the inter-annual change in pro-glacial deltaic sediments has been initiated in Oliver Sound, one of a cluster of fjords that lie off Eclipse Sound at the northern tip of Baffin Island, Canada. In order to confidently identify the decimetre-level changes in seabed morphology from multibeam surveys, adequate tidal control is required. Surveying in such remote locations presents conditions, logistics and time constraints that prohibit the installation of tide gauges throughout the survey area and existing predicted tide stations are separated from the survey area by complex fjords and islands. To overcome these hurdles, a high resolution hydrodynamic model simulation has been constructed to predict the tides throughout the survey region which accounts for the changes in tidal phase and amplitude within the complex fjords. The simulation results are compared to existing lower resolution tidal models, nearby predicted tides and Globally Corrected GPS data from survey vessels working and transiting throughout the area.
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    Improving the sub-bottom echosounder depth estimate using a multibeam DTM
    (University of New Brunswick, 2006) Church, Ian; Hughes, Clarke John
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    Link prediction with local and global consistency preservation in spatio-temporal networks
    (University of New Brunswick, 2022-11) Forouzandeh Jonaghani, Rouzbeh; Hanson, Trevor; Wachowicz, Monica; Church, Ian
    With the increasing deployment of connected positioning devices, we are witnessing the proliferation of connected data sets in the form of spatio-temporal networks such as Location-Based Social Networks (LBSNs), the Internet of Things (IoT), and smart transportation networks. Link prediction is a key research field in studying spatiotemporal networks as it improves our understanding of the underlying dynamics of the connected data sets by predicting missing or future links that represent the relations in a system. However, current research on link predictions in spatio-temporal networks has been mostly limited to friendship prediction in Location-Based Social Networks (LBSN), and even though local and global consistency have been regarded as important factors in predictive analytics, they have not yet been studied in spatio-temporal networks. One of the main research challenges is mainly related to addressing local consistency due to the substantial difference between the sense of locality in spatio-temporal networks in comparison to non-spatial networks. Moreover, incorporating the role of communities in link prediction in spatio-temporal networks specifically under the concepts of global consistency is another challenge that has not been addressed yet. These challenges have been addressed by proposing methods for carrying out link prediction with local and global consistency which are tested using data from two different shared-mobility systems namely bike-sharing and taxi systems from Chicago and New York City. Different prediction scenarios including the presence of periodic variations in the data and multi-step prediction have been considered. The comparison of the results from the proposed and baseline methods indicates that the proposed methods accurately predict the flow and other related variables (e.g., check-ins) in shared-mobility systems in different scenarios. For example, The proposed MFLOG model improves the bike-flow and check-in/out prediction error by 4.5% and 7.5% respectively, w.r.t baseline models. This can be associated with the successful design of the methods and consideration of local and global consistency in the model.
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    Modelling the estuarine circulation of the port of Saint John: Applications in hydrographic surveying
    Church, Ian
    A 3D baroclinic hydrodynamic model has been developed to investigate the estuarine circulation within the Port of Saint John, in southern New Brunswick. The model simulates the movement and interaction between fresh waters from the Saint John River and saline waters from the Bay of Fundy over four seasonal periods of river flood stages. An improved understanding of sediment dynamics in the harbour is established from the model output, which is critical for understanding the sources of sedimentation and prediction of dredge requirements. The model describes both the longitudinal and lateral estuarine flow within the harbour. This allows for improved estimates of sediment flux through the primary channels, which reveals annual variations in the relative contributions of the river and salt wedge borne sediments to harbour sedimentation rates. Integration of the near seabed flow patterns over a tidal cycle explains regions of deposition and erosion of fine grained sediments and corridors of sediment motion through examination of the residual current velocity fields. The model simulation periods coincide with a dense physical oceanographic observation campaign. The validity of the model output has been verified through statistical comparison to the physical observation data. An innovative practical application of the model output to the assessment and prediction of hydrographic multibeam echosounder depth uncertainty is also examined.
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    OceanMappingDataframe - Scalable multi-indexed dataframe for hydrography
    (University of New Brunswick, 2023-02) Gandhi Kalidasan, Vishwa Barathy; Church, Ian; Ray, Suprio
    Ocean data constitutes one of the largest geospatial datasets. Due to developments in the field of multibeam sonar, the amount of data gathered from hydrographic surveys is growing, causing the data to fall into the category of massive spatial data. Dataframe is a popular data model used to represent the data and is widely used in data science applications. Due to the lack of a suitable dataframe that can load large volumes of multibeam sonar data and support advanced analytics libraries, in this thesis, a new multi-indexed dataframe, OceanMappingDataframe, is introduced that can be used to load, store, and analyze multibeam sonar data. The multi-indexed dataframe was implemented using the MODIN dataframe library. The multi-indexed dataframe can load the hydrography files in Generic Sensor Format (GSF) or CSV file format, and save the results in partitioned Parquet files. The multi-indexed dataframe can also support advanced AI libraries such as OpenAI. This has been demonstrated by applying the Reinforcement Learning (RL) algorithm to an outlier detection problem in hydrography.
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    Open-source web GIS for Arctic seafloor mapping: Improving the interactivity of public data dissemination
    (University of New Brunswick, 2024-03) Vainionpää, Madeline; Church, Ian
    The ease-of-use of online GIS software has encouraged many organizations to publish their geospatial data to a web mapping interface. With respect to the field of ocean mapping, this has been a breakthrough towards public accessibility since this data is easy to absorb in a visual format. There is a specific need for a Canadian Arctic-focused web portal which can host a unique dataset collected by the CCGS Amundsen over several decades. This project employs open-source JavaScript to not only display the Amundsen’s dataset on a web GIS interface, but to display it alongside third-party seabed data. The end user can manipulate the datasets using two interactive toolkits. The first is a statistical analysis toolkit which compares bathymetry raster imagepyramids that are hosted using web mapping services (WMS). The second is a three-dimensional visualization tool which virtually draws bathymetric WMS information in the same scene as cross-sectional seabed subsurface data.
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    Real-time Multibeam Echosounder error detection using deep learning
    (University of New Brunswick, 2024-08) Chian Leal, Mary Oyuky; Church, Ian
    The use of Uncrewed Surface Vessels (USVs) for marine surveying is increasing due to technological advancements, but they face logistical constraints of power and space availability. The autonomous nature of these systems would benefit from real-time detection of data errors using AI to enhance surveying capabilities. However, consideration for installing new devices capable of using deep learning algorithms on an USV must account for these constraints. Image segmentation is widely used in medicine to detect brain tumours and autonomous driving, and could be applied using the U-Net architecture to predict possible errors in real-time from USV Multibeam Echosounders. Tools like the Nvidia Jetson Orin AGX can facilitate real-time processing and analysis of data, while not impacting the operational efficiency of the USV. Integrating deep learning with USV operations shows promise in effectively identifying data errors, improving the automation of marine surveying, and simplifying data analysis.
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    User-centered design for web-mapping applications: a case study with ocean mapping data for ocean modellers
    (University of New Brunswick, 2018) Ruiz, Marta Padilla; Emmanuel, Stefanakis; Church, Ian
    Technological innovations in the last few years offer a new digital medium for map making, opening a wide range of possible interactions between the user and the map interface. Nowadays, web-mapping applications are a common way to deliver geographic data through the internet; and within the ocean mapping community, there is a demand for visualizing and downloading data online for navigation, engineering, natural resources, ocean modelling or habitat mapping purposes. However, the existing web-mapping applications are simple data repositories for data download, and the user point of view and context of use is not usually considered. In this research, a User-Centered Design (UCD) approach was applied for the development of a web-mapping application, considering only one kind of ocean mapping users, ocean modellers. A work domain analysis was conducted as the first stage of the methodology, to determine the required application functionalities and content, followed by the development of a web mapping application prototype. The application was then evaluated by users, closing the loop of the UCD methodology. The results of the evaluation show a useful tool, high user satisfaction, and states a wide range of recommendations and a need for new functionalities. This research will enlighten the ocean mapping community with the data and the spatial functionalities that ocean modellers demand, putting together these two related fields. Moreover, it will serve as the foundations for future development and improvement of the web mapping application within the Ocean Mapping Group (OMG) at the University of New Brunswick (UNB).