3,529 research outputs found
Modularity-Based Clustering for Network-Constrained Trajectories
We present a novel clustering approach for moving object trajectories that
are constrained by an underlying road network. The approach builds a similarity
graph based on these trajectories then uses modularity-optimization hiearchical
graph clustering to regroup trajectories with similar profiles. Our
experimental study shows the superiority of the proposed approach over classic
hierarchical clustering and gives a brief insight to visualization of the
clustering results.Comment: 20-th European Symposium on Artificial Neural Networks, Computational
Intelligence and Machine Learning (ESANN 2012), Bruges : Belgium (2012
Co-Clustering Network-Constrained Trajectory Data
Recently, clustering moving object trajectories kept gaining interest from
both the data mining and machine learning communities. This problem, however,
was studied mainly and extensively in the setting where moving objects can move
freely on the euclidean space. In this paper, we study the problem of
clustering trajectories of vehicles whose movement is restricted by the
underlying road network. We model relations between these trajectories and road
segments as a bipartite graph and we try to cluster its vertices. We
demonstrate our approaches on synthetic data and show how it could be useful in
inferring knowledge about the flow dynamics and the behavior of the drivers
using the road network
Cluster-based feedback control of turbulent post-stall separated flows
We propose a novel model-free self-learning cluster-based control strategy
for general nonlinear feedback flow control technique, benchmarked for
high-fidelity simulations of post-stall separated flows over an airfoil. The
present approach partitions the flow trajectories (force measurements) into
clusters, which correspond to characteristic coarse-grained phases in a
low-dimensional feature space. A feedback control law is then sought for each
cluster state through iterative evaluation and downhill simplex search to
minimize power consumption in flight. Unsupervised clustering of the flow
trajectories for in-situ learning and optimization of coarse-grained control
laws are implemented in an automated manner as key enablers. Re-routing the
flow trajectories, the optimized control laws shift the cluster populations to
the aerodynamically favorable states. Utilizing limited number of sensor
measurements for both clustering and optimization, these feedback laws were
determined in only iterations. The objective of the present work is not
necessarily to suppress flow separation but to minimize the desired cost
function to achieve enhanced aerodynamic performance. The present control
approach is applied to the control of two and three-dimensional separated flows
over a NACA 0012 airfoil with large-eddy simulations at an angle of attack of
, Reynolds number and free-stream Mach number . The optimized control laws effectively minimize the flight power
consumption enabling the flows to reach a low-drag state. The present work aims
to address the challenges associated with adaptive feedback control design for
turbulent separated flows at moderate Reynolds number.Comment: 32 pages, 18 figure
Influence of wiring cost on the large-scale architecture of human cortical connectivity
In the past two decades some fundamental properties of cortical connectivity have been discovered: small-world structure, pronounced hierarchical and modular organisation, and strong core and rich-club structures. A common assumption when interpreting results of this kind is that the observed structural properties are present to enable the brain's function. However, the brain is also embedded into the limited space of the skull and its wiring has associated developmental and metabolic costs. These basic physical and economic aspects place separate, often conflicting, constraints on the brain's connectivity, which must be characterized in order to understand the true relationship between brain structure and function. To address this challenge, here we ask which, and to what extent, aspects of the structural organisation of the brain are conserved if we preserve specific spatial and topological properties of the brain but otherwise randomise its connectivity. We perform a comparative analysis of a connectivity map of the cortical connectome both on high- and low-resolutions utilising three different types of surrogate networks: spatially unconstrained (ârandomâ), connection length preserving (âspatialâ), and connection length optimised (âreducedâ) surrogates. We find that unconstrained randomisation markedly diminishes all investigated architectural properties of cortical connectivity. By contrast, spatial and reduced surrogates largely preserve most properties and, interestingly, often more so in the reduced surrogates. Specifically, our results suggest that the cortical network is less tightly integrated than its spatial constraints would allow, but more strongly segregated than its spatial constraints would necessitate. We additionally find that hierarchical organisation and rich-club structure of the cortical connectivity are largely preserved in spatial and reduced surrogates and hence may be partially attributable to cortical wiring constraints. In contrast, the high modularity and strong s-core of the high-resolution cortical network are significantly stronger than in the surrogates, underlining their potential functional relevance in the brain
Spatial Optimization Methods And System For Redistricting Problems
Redistricting is the process of dividing space into districts or zones while optimizing a set of spatial criteria under certain constraints. Example applications of redistricting include political redistricting, school redistricting, business service planning, and city management, among many others. Redistricting is a mission-critical component in operating governments and businesses alike. In research fields, redistricting (or region building) are also widely used, such as climate zoning, traffic zone analysis, and complex network analysis. However, as a combinatorial optimization problem, redistricting optimization remains one of the most difficult research challenges. There are currently few automated redistricting methods that have the optimization capability to produce solutions that meet practical needs. The absence of effective and efficient computational approaches for redistricting makes it extremely time-consuming and difficult for an individual person to consider multiple criteria/constraints and manually create solutions using a trial-and-error approach.
To address both the scientific and practical challenges in solving real-world redistricting problems, this research advances the methodology and application of redistricting by developing a new computational spatial optimization method and a system platform that can address a wide range of redistricting problems, in an automated and computation-assisted manner. The research has three main contributions. First, an efficient and effective spatial optimization method is developed for redistricting. The new method is based on a spatially constrained and Tabu-based heuristics, which can optimize multiple criteria under multiple constraints to construct high-quality optimization solutions. The new approach is evaluated with real-world redistricting applications and compared with existing methods. Evaluation results show that the new optimization algorithm is more efficient (being able to allow real-time user interaction), more flexible (considering multiple user-expressed criteria and constraints), and more powerful (in terms of optimization quality) than existing methods. As such, it has the potential to enable general users to perform complex redistricting tasks.
Second, a redistricting system, iRedistrict, is developed based on the newly developed spatial optimization method to provide user-friendly visual interface for defining redistricting problems, incorporating domain knowledge, configuring optimization criteria and methodology parameters, and ultimately meeting the needs of real-world applications for tackling complex redistricting tasks. It is particularly useful for users of different skill levels, including researchers, practitioners, and the general public, and thus enables public participation in challenging redistricting tasks that are of immense public interest. Performance evaluations with real-world case studies are carried out. Further computational strategies are developed and implemented to handle large datasets.
Third, the newly developed spatial optimization method is extended to solve a different spatial optimization problem, i.e., spatial community structure detection in complex networks, which is to partition networks to discover spatial communities by optimizing an objective function. Moreover, a series of new evaluations are carried out with synthetic datasets. This set of evaluations is different from the previous evaluations with case studies in that, the optimal solution is known with synthetic data and therefore it is possible to evaluate (1) whether the optimization method can discover the true pattern (global optima), and (2) how different data characteristics may affect the performance of the method. Evaluation results reveal that existing non-spatial methods are not robust in detecting spatial community structure, which may produce dramatically different outcomes for the same data with different characteristics, such as different spatial aggregations, sampling rates, or noise levels. The new optimization method with spatial constraints is significantly more stable and consistent. In addition to evaluations with synthetic datasets, a case study is also carried out to detect urban community structure with human movements, to demonstrate the application and effectiveness of the approach
Multi-scale analysis of the European airspace using network community detection
We show that the European airspace can be represented as a multi-scale
traffic network whose nodes are airports, sectors, or navigation points and
links are defined and weighted according to the traffic of flights between the
nodes. By using a unique database of the air traffic in the European airspace,
we investigate the architecture of these networks with a special emphasis on
their community structure. We propose that unsupervised network community
detection algorithms can be used to monitor the current use of the airspaces
and improve it by guiding the design of new ones. Specifically, we compare the
performance of three community detection algorithms, also by using a null model
which takes into account the spatial distance between nodes, and we discuss
their ability to find communities that could be used to define new control
units of the airspace.Comment: 22 pages, 14 figure
Network models of spatial interactions, human mobility and navigation ability
Networks provide a useful mathematical model for systems that are composed of agents, also called nodes, that interact in a pairwise fashion. Inside a spatial network, nodes are embedded in space and they typically interact much more strongly within their close surrounding, resulting in a special pattern of connections. Geographers, physicists and applied mathematicians have a set of tools at their disposal to understand these systems, and there is a close relationship between spatial networks and population-level models that were originally meant to represent human mobility, but that have been generalised to model more abstract spatial flows.
Over the last decade, there has been an effort to adapt network tools for community detection â whose aim is to describe a system in terms of its mesoscale organisation into groups or communities â to spatial networks. So far, the methods that have been proposed are based on the modularity function, a heuristic function that compares network partitions against a suitable null model. However, a mesoscale description that is gaining traction relies instead on the stochastic block model (SBM), a generative model that can be fitted to an observation using statistical inference. We propose a methodology to leverage an SBM for the case of spatial networks.
We are guided in this by an application to study a network linking customers to the stores they have shopped in, built from anonymised shopping records belonging to a large UK retailer. We also characterise customer shopping behaviours, specially from the lens of their mobility. Lastly, we study a dataset that was collected to assess spatial navigation, and we propose new metrics that are used in comparing the trajectories of healthy individuals, at-genetic-risk patients and patients diagnosed with dementia. Our hope is that this early proposal can be refined in the future by medical practitioners and used to detect early-onset Alzheimerâs Disease
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