Relative entropy rate based model selection for linear hybrid system filters of uncertain nonlinear systems

Hybrid system representations have been exploited in a number of challenging modelling situations, including situations where the original nonlinear dynamics are too complex (or too imprecisely known) to be directly filtered. Unfortunately, the question of how to best design suitable hybrid system models has not yet been fully addressed, particularly in the situations involving model uncertainty. This paper proposes a novel joint state-measurement relative entropy rate based approach for design of hybrid system filters in the presence of (parameterised) model uncertainty. We also present a design approach suitable for suboptimal hybrid system filters. The benefits of our proposed approaches are illustrated through design examples and simulation studies.

Cross-entropy optimization for scaling factors of a fuzzy controller : a see-and-avoid approach for unmanned aerial systems

The Cross-Entropy (CE) is an efficient method for the estimation of rare-event probabilities and combinatorial optimization. This work presents a novel approach of the CE for optimization of a Soft-Computing controller. A Fuzzy controller was designed to command an unmanned aerial system (UAS) for avoiding collision task. The only sensor used to accomplish this task was a forward camera. The CE is used to reach a near-optimal controller by modifying the scaling factors of the controller inputs. The optimization was realized using the ROS-Gazebo simulation system. In order to evaluate the optimization a big amount of tests were carried out with a real quadcopter.

HMM relative entropy rate concepts for vision-based aircraft manoeuvre detection

Machine vision is emerging as a viable sensing approach for mid-air collision avoidance (particularly for small to medium aircraft such as unmanned aerial vehicles). In this paper, using relative entropy rate concepts, we propose and investigate a new change detection approach that uses hidden Markov model filters to sequentially detect aircraft manoeuvres from morphologically processed image sequences. Experiments using simulated and airborne image sequences illustrate the performance of our proposed algorithm in comparison to other sequential change detection approaches applied to this application.

The combination of empirical mode decomposition and minimum entropy deconvolution for roller bearing diagnostics in non-stationary operation

Diagnostics is based on the characterization of mechanical system condition and allows early detection of a possible fault. Signal processing is an approach widely used in diagnostics, since it allows directly characterizing the state of the system. Several types of advanced signal processing techniques have been proposed in the last decades and added to more conventional ones. Seldom, these techniques are able to consider non-stationary operations. Diagnostics of roller bearings is not an exception of this framework. In this paper, a new vibration signal processing tool, able to perform roller bearing diagnostics in whatever working condition and noise level, is developed on the basis of two data-adaptive techniques as Empirical Mode Decomposition (EMD), Minimum Entropy Deconvolution (MED), coupled by means of the mathematics related to the Hilbert transform. The effectiveness of the new signal processing tool is proven by means of experimental data measured in a test-rig that employs high power industrial size components.

Topological design and modulation strategy for buck-boost three-level inverters

To date, designed topologies for DC-AC inversion with both voltage-buck and boost capabilities are mainly focused on two-level circuitries with extensions to three-level possibilities left nearly unexplored. Contributing to this area of research, this paper presents the design of a number of viable buck-boost three-level inverters that can also support bidirectional power conversion. The proposed front-end circuitry is developed from the Cuk-derived buck-boost two-level inverter, and by using the "alternative phase opposition disposition" (APOD) modulation scheme, the buck-boost three-level inverters can perform distinct five-level line voltage and three-level phase voltage switching by simply controlling the active switches located in the designed voltage boost section of the circuits. As a cost saving option, one active switch can further be removed from the voltage-boost section of the circuits by simply re-routing the gating commands of the remaining switches without influencing the ac output voltage amplitude. To verify the validity of the proposed inverters, Matlab/PLECS simulations were performed before a laboratory prototype was implemented for experimental testing.

Topological design and modulation strategy for buck-boost three-level inverters

To date, designed topologies for DC-AC inversion with both voltage buck and boost capabilities are mainly focused on two-level circuitries with extensions to three-level possibilities left nearly unexplored. Contributing to this area of research, this paper presents the design of a number of viable buck-boost three-level inverters that can also support bidirectional power conversion. The proposed front-end circuitry is developed from the Cuk-derived buck-boost two-level inverter, and by using the ldquoalternative phase opposition dispositionrdquo modulation scheme, the buck-boost three-level inverters can perform distinct five-level line voltage and three-level phase voltage switching by simply controlling the active switches located in the designed voltage boost section of the circuits. As a cost saving option, one active switch can further be removed from the voltage boost section of the circuits by simply rerouting the gating commands of the remaining switches without influencing the AC output voltage amplitude. To verify the validity of the proposed inverters, MATLAB/PLECS simulations were performed before a laboratory prototype was implemented for experimental testing.

Lower partial moment and information entropy-based dynamic electricity purchasing strategy

In the electricity market environment, load-serving entities (LSEs) will inevitably face risks in purchasing electricity because there are a plethora of uncertainties involved. To maximize profits and minimize risks, LSEs need to develop an optimal strategy to reasonably allocate the purchased electricity amount in different electricity markets such as the spot market, bilateral contract market, and options market. Because risks originate from uncertainties, an approach is presented to address the risk evaluation problem by the combined use of the lower partial moment and information entropy (LPME). The lower partial moment is used to measure the amount and probability of the loss, whereas the information entropy is used to represent the uncertainty of the loss. Electricity purchasing is a repeated procedure; therefore, the model presented represents a dynamic strategy. Under the chance-constrained programming framework, the developed optimization model minimizes the risk of the electricity purchasing portfolio in different markets because the actual profit of the LSE concerned is not less than the specified target under a required confidence level. Then, the particle swarm optimization (PSO) algorithm is employed to solve the optimization model. Finally, a sample example is used to illustrate the basic features of the developed model and method.

An adaptive appearance-based map for long-term topological localization of mobile robots

"This work considers a mobile service robot which uses an appearance-based representation of its workplace as a map, where the current view and the map are used to estimate the current position in the environment. Due to the nature of real-world environments such as houses and offices, where the appearance keeps changing, the internal representation may become out of date after some time. To solve this problem the robot needs to be able to adapt its internal representation continually to the changes in the environment. This paper presents a method for creating an adaptive map for long-term appearance-based localization of a mobile robot using long-term and short-term memory concepts, with omni-directional vision as the external sensor."--publisher website

Topological localization using optical flow descriptors

We propose a topological localization method based on optical flow information. We analyse the statistical characteristics of the optical flow signal and demonstrate that the flow vectors can be used to identify and describe key locations in the environment. The key locations (nodes) correspond to significant scene changes and depth discontinuities. Since optical flow vectors contain position, magnitude and angle information, for each node, we extract low and high order statistical moments of the vectors and use them as descriptors for that node. Once a database of nodes and their corresponding optical flow features is created, the robot can perform topological localization by using the Mahalanobis distance between the current frame and the database. This is supported by field trials, which illustrate the repeatability of the proposed method for detecting and describing key locations in indoor and outdoor environments in challenging and diverse lighting conditions.

On the effect of topology on cellular automata rule spaces

This thesis presents an empirical study of the effects of topology on cellular automata rule spaces. The classical definition of a cellular automaton is restricted to that of a regular lattice, often with periodic boundary conditions. This definition is extended to allow for arbitrary topologies. The dynamics of cellular automata within the triangular tessellation were analysed when transformed to 2-manifolds of topological genus 0, genus 1 and genus 2. Cellular automata dynamics were analysed from a statistical mechanics perspective. The sample sizes required to obtain accurate entropy calculations were determined by an entropy error analysis which observed the error in the computed entropy against increasing sample sizes. Each cellular automata rule space was sampled repeatedly and the selected cellular automata were simulated over many thousands of trials for each topology. This resulted in an entropy distribution for each rule space. The computed entropy distributions are indicative of the cellular automata dynamical class distribution. Through the comparison of these dynamical class distributions using the E-statistic, it was identified that such topological changes cause these distributions to alter. This is a significant result which implies that both global structure and local dynamics play a important role in defining long term behaviour of cellular automata.

The space of make: A topological account of the studio

Approaches to art-practice-as-research tend to draw a distinction between the processes of creative practice and scholarly reflection. According to this template, the two sites of activity – studio/desk, work/writing, body/mind – form the ‘correlative’ entity known as research. Creative research is said to be produced by the navigation of world and thought: spaces that exist in a continual state of tension with one another. Either we have the studio tethered to brute reality while the desk floats free as a site for the fluid cross-pollination of texts and concepts. Or alternatively, the studio is characterized by the amorphous, intuitive play of forms and ideas, while the desk represents its cartography, mapping and fixing its various fluidities. In either case, the research status of art practice is figured as a fundamentally riven space. However, the nascent philosophy of Speculative Realism proposes a different ontology – one in which the space of human activity comprises its own reality, independent of human perception. The challenge it poses to traditional metaphysics is to rethink the world as if it were a real space. When applied to practice-led research, this reconceptualization challenges the creative researcher to consider creative research as a contiguous space – a topology where thinking and making are not dichotomous points but inflections in an amorphous and dynamic field. Instead of being subject to the vertical tension between earth and air, a topology of practice emphasizes its encapsulated, undulating reality – an agentive ‘object’ formed according to properties of connectedness, movement and differentiation. Taking the central ideas of Quentin Meillassoux and Graham Harman as a point of departure, this paper will provide a speculative account of the interplay of spatialities that characterise the author’s studio practice. In so doing, the paper will model the innovative methodological potential produced by the analysis of topological dimensions of the studio and the way they can be said to move beyond the ‘geo-critical’ divide.

Matter and movement: Expression as topological continuity or a return to Butades' wall

This paper aims to address the ways in which drawing can be understood as the becoming-expressive of materials, site, and body, over time. The discussion pivots around a series of studies that replace linear or causal relationships – in history, drawing and expression – with topological movement. My approach is largely through a speculative case study. In a rereading of the familiar Butades myth, I examine how a shadow tracing can variously be taken as the first mimetic art with its origins in the urge to “capture”, and, antithetically, as the originary expressive folding of matter, site and body. The paper is divided into five sections. The first presents the Butades myth, identifying the representational problem that lies at the roots of its traditional telling. The next three sections outline a series of topologies that facilitate a discussion of the Butades myth from historical, disciplinary, and expressive perspectives. The final section aims to show the relevance of this discussion to a contemporary drawing practice, using my own drawing research as a case study. The field of inquiry is that of representational critique. The fold, an image associated with a topological geometry, replaces the relational or signifying disjuncture of representational structures, and suggests a becoming- expressive of subject and object, form and matter.

Hierarchical topological network analysis of anatomical human brain connectivity and differences related to sex and kinship

Modern non-invasive brain imaging technologies, such as diffusion weighted magnetic resonance imaging (DWI), enable the mapping of neural fiber tracts in the white matter, providing a basis to reconstruct a detailed map of brain structural connectivity networks. Brain connectivity networks differ from random networks in their topology, which can be measured using small worldness, modularity, and high-degree nodes (hubs). Still, little is known about how individual differences in structural brain network properties relate to age, sex, or genetic differences. Recently, some groups have reported brain network biomarkers that enable differentiation among individuals, pairs of individuals, and groups of individuals. In addition to studying new topological features, here we provide a unifying general method to investigate topological brain networks and connectivity differences between individuals, pairs of individuals, and groups of individuals at several levels of the data hierarchy, while appropriately controlling false discovery rate (FDR) errors. We apply our new method to a large dataset of high quality brain connectivity networks obtained from High Angular Resolution Diffusion Imaging (HARDI) tractography in 303 young adult twins, siblings, and unrelated people. Our proposed approach can accurately classify brain connectivity networks based on sex (93% accuracy) and kinship (88.5% accuracy). We find statistically significant differences associated with sex and kinship both in the brain connectivity networks and in derived topological metrics, such as the clustering coefficient and the communicability matrix.