6,807 research outputs found

    A conceptual framework for developing dashboards for big mobility data

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    Dashboards are an increasingly popular form of data visualization. Large, complex, and dynamic mobility data present a number of challenges in dashboard design. The overall aim for dashboard design is to improve information communication and decision making, though big mobility data in particular require considering privacy alongside size and complexity. Taking these issues into account, a gap remains between wrangling mobility data and developing meaningful dashboard output. Therefore, there is a need for a framework that bridges this gap to support the mobility dashboard development and design process. In this paper we outline a conceptual framework for mobility data dashboards that provides guidance for the development process while considering mobility data structure, volume, complexity, varied application contexts, and privacy constraints. We illustrate the proposed framework’s components and process using example mobility dashboards with varied inputs, end-users and objectives. Overall, the framework offers a basis for developers to understand how informational displays of big mobility data are determined by end-user needs as well as the types of data selection, transformation, and display available to particular mobility datasets

    Multi-Robot Multi-Room Exploration with Geometric Cue Extraction and Spherical Decomposition

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    This work proposes an autonomous multi-robot exploration pipeline that coordinates the behaviors of robots in an indoor environment composed of multiple rooms. Contrary to simple frontier-based exploration approaches, we aim to enable robots to methodically explore and observe an unknown set of rooms in a structured building, keeping track of which rooms are already explored and sharing this information among robots to coordinate their behaviors in a distributed manner. To this end, we propose (1) a geometric cue extraction method that processes 3D map point cloud data and detects the locations of potential cues such as doors and rooms, (2) a spherical decomposition for open spaces used for target assignment. Using these two components, our pipeline effectively assigns tasks among robots, and enables a methodical exploration of rooms. We evaluate the performance of our pipeline using a team of up to 3 aerial robots, and show that our method outperforms the baseline by 36.6% in simulation and 26.4% in real-world experiments

    Optical Remote Sensing of Oil Spills by using Machine Learning Methods in the Persian Gulf: A Multi-Class Approach

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    Marine oil spills are harmful for the environment and costly for society. Coastal areas are particularly vulnerable since they provide habitats for organisms, animals and marine ecosystems. This thesis studied machine learning methods to classify thick oil in a multi-class case, using remotely sensed multi-spectral data in the Persian Gulf. The study area covers a large area between United Arab Emirates (UAE) and Iran. The dataset is extracted from 10 Sentinel-2 tiles on six spectral bands between 492 nm to 2202 nm. These images were annotated for four classes, namely thick oil, thin oil, ocean water and turbid water by using the Bonn Agreement to analyse true color composite images. A variety of machine learning methods were trained and evaluated using this dataset. Then a robustness evaluation was done by using selected machine learning methods on an independent dataset. Initially multiple machine learning methods were included; three decision trees, six K-Nearest Neighbor (KNN) models, two Artificial Neural Network (ANN) models, two Naive bayes models, and two discriminant models. Two KNN models and two ANN models were then picked for further evaluation. The results show that the fine KNN approach with two nearest neighbors had the best performance based on the computed statistical measures. However, the robustness evaluation showed that the tri-layered NN performed better. This thesis has shown that supervised machine learning with a multi-class approach can be used for oil spill monitoring using multi-spectral remote sensing data in the Persian Gulf

    Interactive visualizations of unstructured oceanographic data

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    The newly founded company Oceanbox is creating a novel oceanographic forecasting system to provide oceanography as a service. These services use mathematical models that generate large hydrodynamic data sets as unstructured triangular grids with high-resolution model areas. Oceanbox makes the model results accessible in a web application. New visualizations are needed to accommodate land-masking and large data volumes. In this thesis, we propose using a k-d tree to spatially partition unstructured triangular grids to provide the look-up times needed for interactive visualizations. A k-d tree is implemented in F# called FsKDTree. This thesis also describes the implementation of dynamic tiling map layers to visualize current barbs, scalar fields, and particle streams. The current barb layer queries data from the data server with the help of the k-d tree and displays it in the browser. Scalar fields and particle streams are implemented using WebGL, which enables the rendering of triangular grids. Stream particle visualization effects are implemented as velocity advection computed on the GPU with textures. The new visualizations are used in Oceanbox's production systems, and spatial indexing has been integrated into Oceanbox's archive retrieval system. FsKDTree improves tree creation times by up to 4x over the C# equivalent and improves search times by up to 13x compared to the .NET C# implementation. Finally, the largest model areas can be viewed with current barbs, scalar fields, and particle stream visualizations at 60 FPS, even for the largest model areas provided by the service

    Integrating materials supply in strategic mine planning of underground coal mines

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    In July 2005 the Australian Coal Industry’s Research Program (ACARP) commissioned Gary Gibson to identify constraints that would prevent development production rates from achieving full capacity. A “TOP 5” constraint was “The logistics of supply transport distribution and handling of roof support consumables is an issue at older extensive mines immediately while the achievement of higher development rates will compound this issue at most mines.” Then in 2020, Walker, Harvey, Baafi, Kiridena, and Porter were commissioned by ACARP to investigate Australian best practice and progress made since Gibson’s 2005 report. This report was titled: - “Benchmarking study in underground coal mining logistics.” It found that even though logistics continue to be recognised as a critical constraint across many operations particularly at a tactical / day to day level, no strategic thought had been given to logistics in underground coal mines, rather it was always assumed that logistics could keep up with any future planned design and productivity. This subsequently meant that without estimating the impact of any logistical constraint in a life of mine plan, the risk of overvaluing a mining operation is high. This thesis attempts to rectify this shortfall and has developed a system to strategically identify logistics bottlenecks and the impacts that mine planning parameters might have on these at any point in time throughout a life of mine plan. By identifying any logistics constraints as early as possible, the best opportunity to rectify the problem at the least expense is realised. At the very worst if a logistics constraint was unsolvable then it could be understood, planned for, and reflected in the mine’s ongoing financial valuations. The system developed in this thesis, using a suite of unique algorithms, is designed to “bolt onto” existing mine plans in the XPAC mine scheduling software package, and identify at a strategic level the number of material delivery loads required to maintain planned productivity for a mining operation. Once an event was identified the system then drills down using FlexSim discrete event simulation to a tactical level to confirm the predicted impact and understand if a solution can be transferred back as a long-term solution. Most importantly the system developed in this thesis was designed to communicate to multiple non-technical stakeholders through simple graphical outputs if there is a risk to planned production levels due to a logistics constraint

    Nonholonomic Motion Planning as Efficient as Piano Mover's

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    We present an algorithm for non-holonomic motion planning (or 'parking a car') that is as computationally efficient as a simple approach to solving the famous Piano-mover's problem, where the non-holonomic constraints are ignored. The core of the approach is a graph-discretization of the problem. The graph-discretization is provably accurate in modeling the non-holonomic constraints, and yet is nearly as small as the straightforward regular grid discretization of the Piano-mover's problem into a 3D volume of 2D position plus angular orientation. Where the Piano mover's graph has one vertex and edges to six neighbors each, we have three vertices with a total of ten edges, increasing the graph size by less than a factor of two, and this factor does not depend on spatial or angular resolution. The local edge connections are organized so that they represent globally consistent turn and straight segments. The graph can be used with Dijkstra's algorithm, A*, value iteration or any other graph algorithm. Furthermore, the graph has a structure that lends itself to processing with deterministic massive parallelism. The turn and straight curves divide the configuration space into many parallel groups. We use this to develop a customized 'kernel-style' graph processing method. It results in an N-turn planner that requires no heuristics or load balancing and is as efficient as a simple solution to the Piano mover's problem even in sequential form. In parallel form it is many times faster than the sequential processing of the graph, and can run many times a second on a consumer grade GPU while exploring a configuration space pose grid with very high spatial and angular resolution. We prove approximation quality and computational complexity and demonstrate that it is a flexible, practical, reliable, and efficient component for a production solution.Comment: 34 pages, 37 figures, 9 tables, 4 graphs, 8 insert

    2023-2024 Lynn University Academic Catalog

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    The 2023-2024 Academic Catalog initially published as a web-only document. The Department of Marketing and Communication created a PDF version, which is available for download here.https://spiral.lynn.edu/accatalogs/1052/thumbnail.jp

    Railways and the European fertility

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    We show that the spread of the railway network slowed the decline of fertilityin Europe during the late nineteenth and early twentieth centuries. We construct novel data on market access across sub-national regions in Europe and use both a panel fixed effects approach and an instrumental variables strategy that leverages variation in market access stemming from access to distant markets. We find that greater market access predicts higher fertility, with a standardized magnitude of 0.14. Consistent with an interpretation that market access increased fertility by raising incomes relative to the returns to child quality and the opportunity cost of childbearing, we show that our results are driven by locations that achieved higher levels of income per capita despite lagging in human capital and female labor force participation
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