13 research outputs found
Spatially-constrained clustering of ecological networks
Spatial ecological networks are widely used to model interactions between
georeferenced biological entities (e.g., populations or communities). The
analysis of such data often leads to a two-step approach where groups
containing similar biological entities are firstly identified and the spatial
information is used afterwards to improve the ecological interpretation. We
develop an integrative approach to retrieve groups of nodes that are
geographically close and ecologically similar. Our model-based
spatially-constrained method embeds the geographical information within a
regularization framework by adding some constraints to the maximum likelihood
estimation of parameters. A simulation study and the analysis of real data
demonstrate that our approach is able to detect complex spatial patterns that
are ecologically meaningful. The model-based framework allows us to consider
external information (e.g., geographic proximities, covariates) in the analysis
of ecological networks and appears to be an appealing alternative to consider
such data
Clustering framework to identify traffic conflicts and determine thresholds based on trajectory data
Traffic conflict indicators are essential for evaluating traffic safety and
analyzing trajectory data, especially in the absence of crash data. Previous
studies have used traffic conflict indicators to predict and identify
conflicts, including time-to-collision (TTC), proportion of stopping distance
(PSD), and deceleration rate to avoid a crash (DRAC). However, limited research
is conducted to understand how to set thresholds for these indicators while
accounting for traffic flow characteristics at different traffic states. This
paper proposes a clustering framework for determining surrogate safety measures
(SSM) thresholds and identifying traffic conflicts in different traffic states
using high-resolution trajectory data from the Citysim dataset. In this study,
unsupervised clustering is employed to identify different traffic states and
their transitions under a three-phase theory framework. The resulting clusters
can then be utilized in conjunction with surrogate safety measures (SSM) to
identify traffic conflicts and assess safety performance in each traffic state.
From different perspectives of time, space, and deceleration, we chose three
compatible conflict indicators: TTC, DRAC, and PSD, considering functional
differences and empirical correlations of different SSMs. A total of three
models were chosen by learning these indicators to identify traffic conflict
and non-conflict clusters. It is observed that Mclust outperforms the other
two. The results show that the distribution of traffic conflicts varies
significantly across traffic states. A wide moving jam (J) is found to be the
phase with largest amount of conflicts, followed by synchronized flow phase (S)
and free flow phase(F). Meanwhile, conflict risk and thresholds exhibit similar
levels across transitional states
Switching Markov Gaussian models for dynamic power system inertia estimation
Future power systems could benefit considerably from having a continuous real-time estimate of system inertia. If realized, this could provide reference inputs to proactive control and protection systems which could enhance not only system stability but also operational economics through, for example, more informed ancillary reserve planning using knowledge of prevailing system conditions and stability margins. Performing these predictions in real time is a significant challenge owing to the complex stochastic and temporal relationships between available measurements. This paper proposes a statistical model capable of estimating system inertia in real time through observed steady-state and relatively small frequency variations; it is trained to learn the features that inter-relate steady-state averaged frequency variations and system inertia, using historical system data demonstrated over two consecutive years. The proposed algorithm is formulated as Gaussian Mixture Model with temporal dependence encoded as a Markov chains. Applied within a UK power system scenario, it produces an optimized mean squared error within 0.1s2 for 95% of the daily estimation if being calibrated on a half-hourly basis and maintains robustness through measurement interruptions of up to a period of three hours
Techno-economic-environmental evaluation of aircraft propulsion electrification: Surrogate-based multi-mission optimal design approach
Driven by the sustainability initiatives in the aviation sector, the emerging technologies of aircraft propulsion electrification have been identified as the promising approach to realize sustainable and decarbonized aviation. This study proposes a surrogate-based multi-mission optimal design approach for aircraft propulsion electrification, which innovatively incorporates realistic aviation operations into the electric aircraft design, with the aim of improving the overall aircraft fuel economy over multiple flight missions and conditions in practical scenarios. The proposed optimal design approach starts with the flight route data analysis to cluster the flight operational data using gaussian mixture model, so that a concise representation of flight mission profiles can be achieved. Then, an optimal orthogonal array-based Latin hypercubes are employed to generate sampling points of design variables for electrified aircraft propulsion. The mission analysis is performed with coupled propulsion-airframe integration in order to propose energy management strategy for mission-dependent aircraft performance. Consequently, fuel economy surrogate model is established via support vector machines to obtain the optimal design points of electrified aircraft propulsion. For assessing the viability of novel propulsion technologies, techno-economic evaluation is conducted using sensitivity analysis and breakeven electricity prices under a series of environmental regulatory policy scenarios
An overview of clustering methods with guidelines for application in mental health research
Cluster analyzes have been widely used in mental health research to decompose inter-individual heterogeneity
by identifying more homogeneous subgroups of individuals. However, despite advances in new algorithms and
increasing popularity, there is little guidance on model choice, analytical framework and reporting requirements.
In this paper, we aimed to address this gap by introducing the philosophy, design, advantages/disadvantages and
implementation of major algorithms that are particularly relevant in mental health research. Extensions of basic
models, such as kernel methods, deep learning, semi-supervised clustering, and clustering ensembles are subsequently
introduced. How to choose algorithms to address common issues as well as methods for pre-clustering
data processing, clustering evaluation and validation are then discussed. Importantly, we also provide general
guidance on clustering workflow and reporting requirements. To facilitate the implementation of different algorithms,
we provide information on R functions and librarie