212 research outputs found

    Learning and recognition by a dynamical system with a plastic velocity field

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    Learning is a mechanism intrinsic to all sentient biological systems. Despite the diverse range of paradigms that exist, it appears that an artificial system has yet to be developed that can emulate learning with a comparable degree of accuracy or efficiency to the human brain. With the development of new approaches comes the opportunity to reduce this disparity in performance. A model presented by Janson and Marsden [arXiv:1107.0674 (2011)] (Memory foam model) redefines the critical features that an intelligent system should demonstrate. Rather than focussing on the topological constraints of the rigid neuron structure, the emphasis is placed on the on-line, unsupervised, classification, retention and recognition of stimuli. In contrast to traditional AI approaches, the system s memory is not plagued by spurious attractors or the curse of dimensionality. The ability to continuously learn, whilst simultaneously recognising aspects of a stimuli ensures that this model more closely embodies the operations occurring in the brain than many other AI approaches. Here we consider the pertinent deficiencies of classical artificial learning models before introducing and developing this memory foam self-shaping system. As this model is relatively new, its limitations are not yet apparent. These must be established by testing the model in various complex environments. Here we consider its ability to learn and recognize the RGB colours composing cartoons as observed via a web-camera. The self-shaping vector field of the system is shown to adjust its composition to reflect the distribution of three-dimensional inputs. The model builds a memory of its experiences and is shown to recognize unfamiliar colours by locating the most appropriate class with which to associate a stimuli. In addition, we discuss a method to map a three-dimensional RGB input onto a line spectrum of colours. The corresponding reduction of the models dimensions is shown to dramatically improve computational speed, however, the model is then restricted to a much smaller set of representable colours. This models prototype offers a gradient description of recognition, it is evident that a more complex, non-linear alternative may be used to better characterize the classes of the system. It is postulated that non-linear attractors may be utilized to convey the concept of hierarchy that relates the different classes of the system. We relate the dynamics of the van der Pol oscillator to this plastic self-shaping system, first demonstrating the recognition of stimuli with limit cycle trajectories. The location and frequency of each cycle is dependent on the topology of the systems energy potential. For a one-dimensional stimuli the dynamics are restricted to the cycle, the extension of the model to an N dimensional stimuli is approached via the coupling of N oscillators. Here we study systems of up to three mutually coupled oscillators and relate limit cycles, fixed points and quasi-periodic orbits to the recognition of stimuli

    The design of digital-adaptive controllers for VTOL aircraft

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    Design procedures for VTOL automatic control systems have been developed and are presented. Using linear-optimal estimation and control techniques as a starting point, digital-adaptive control laws have been designed for the VALT Research Aircraft, a tandem-rotor helicopter which is equipped for fully automatic flight in terminal area operations. These control laws are designed to interface with velocity-command and attitude-command guidance logic, which could be used in short-haul VTOL operations. Developments reported here include new algorithms for designing non-zero-set-point digital regulators, design procedures for rate-limited systems, and algorithms for dynamic control trim setting

    Pre-encounter predictions of DART impact ejecta behavior and observability

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    We overview various efforts within the DART Investigation Team’s Ejecta Working Group to predict the characteristics, quantity, dynamical behavior, and observability of DART impact ejecta. We discuss various methodologies for simulation of the impact/cratering process with their advantages and drawbacks in relation to initializing ejecta for subsequent dynamical propagation through and away from the Didymos system. We discuss the most relevant forces acting on ejecta once decoupled from Dimorphos’s surface and highlight various software packages we have developed and used to dynamically simulate ejecta under the action of those forces. With some additional software packages, we explore the influence of additional perturbing effects, such as interparticle collisions within true N-body codes and nonspherical and rotating particles’ interplay with solar radiation pressure. We find that early-timescale and close-proximity ejecta evolution is highly sensitive to some of these effects (e.g., collisions) while relatively insensitive to other factors. We present a methodology for turning the time-evolving size- and spatially discretized number density field output from ejecta simulations into synthetic images for multiple platforms/cameras over wide-ranging vantage points and timescales. We present such simulated images and apply preliminary analyses to them for nominal and off-nominal cases bracketing realistic total mass of ejecta and ejecta cumulative size–frequency distribution slope. Our analyses foreshadow the information content we may be able to extract from the actual images taken during and after the DART encounter by both LICIACube and Earth-vicinity telescopes.ANII: FCE_1_2019_1_15645

    Anharmonic infrared spectra from short QM/MM simulations.

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    Study of the best linear approximation of nonlinear systems with arbitrary inputs

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    System identification is the art of modelling of a process (physical, biological, etc.) or to predict its behaviour or output when the environment condition or parameter changes. One is modelling the input-output relationship of a system, for example, linking temperature of a greenhouse (output) to the sunlight intensity (input), power of a car engine (output) with fuel injection rate (input). In linear systems, changing an input parameter will result in a proportional increase in the system output. This is not the case in a nonlinear system. Linear system identification has been extensively studied, more so than nonlinear system identification. Since most systems are nonlinear to some extent, there is significant interest in this topic as industrial processes become more and more complex. In a linear dynamical system, knowing the impulse response function of a system will allow one to predict the output given any input. For nonlinear systems this is not the case. If advanced theory is not available, it is possible to approximate a nonlinear system by a linear one. One tool is the Best Linear Approximation (Bla), which is an impulse response function of a linear system that minimises the output differences between its nonlinear counterparts for a given class of input. The Bla is often the starting point for modelling a nonlinear system. There is extensive literature on the Bla obtained from input signals with a Gaussian probability density function (p.d.f.), but there has been very little for other kinds of inputs. A Bla estimated from Gaussian inputs is useful in decoupling the linear dynamics from the nonlinearity, and in initialisation of parameterised models. As Gaussian inputs are not always practical to be introduced as excitations, it is important to investigate the dependence of the Bla on the amplitude distribution in more detail. This thesis studies the behaviour of the Bla with regards to other types of signals, and in particular, binary sequences where a signal takes only two levels. Such an input is valuable in many practical situations, for example where the input actuator is a switch or a valve and hence can only be turned either on or off. While it is known in the literature that the Bla depends on the amplitude distribution of the input, as far as the author is aware, there is a lack of comprehensive theoretical study on this topic. In this thesis, the Blas of discrete-time time-invariant nonlinear systems are studied theoretically for white inputs with an arbitrary amplitude distribution, including Gaussian and binary sequences. In doing so, the thesis offers answers to fundamental questions of interest to system engineers, for example: 1) How the amplitude distribution of the input and the system dynamics affect the Bla? 2) How does one quantify the difference between the Bla obtained from a Gaussian input and that obtained from an arbitrary input? 3) Is the difference (if any) negligible? 4) What can be done in terms of experiment design to minimise such difference? To answer these questions, the theoretical expressions for the Bla have been developed for both Wiener-Hammerstein (Wh) systems and the more general Volterra systems. The theory for the Wh case has been verified by simulation and physical experiments in Chapter 3 and Chapter 6 respectively. It is shown in Chapter 3 that the difference between the Gaussian and non-Gaussian Bla’s depends on the system memory as well as the higher order moments of the non-Gaussian input. To quantify this difference, a measure called the Discrepancy Factor—a measure of relative error, was developed. It has been shown that when the system memory is short, the discrepancy can be as high as 44.4%, which is not negligible. This justifies the need for a method to decrease such discrepancy. One method is to design a random multilevel sequence for Gaussianity with respect to its higher order moments, and this is discussed in Chapter 5. When estimating the Bla even in the absence of environment and measurement noise, the nonlinearity inevitably introduces nonlinear distortions—deviations from the Bla specific to the realisation of input used. This also explains why more than one realisation of input and averaging is required to obtain a good estimate of the Bla. It is observed that with a specific class of pseudorandom binary sequence (Prbs), called the maximum length binary sequence (Mlbs or the m-sequence), the nonlinear distortions appear structured in the time domain. Chapter 4 illustrates a simple and computationally inexpensive method to take advantage this structure to obtain better estimates of the Bla—by replacing mean averaging by median averaging. Lastly, Chapters 7 and 8 document two independent benchmark studies separate from the main theoretical work of the thesis. The benchmark in Chapter 7 is concerned with the modelling of an electrical Wh system proposed in a special session of the 15th International Federation of Automatic Control (Ifac) Symposium on System Identification (Sysid) 2009 (Schoukens, Suykens & Ljung, 2009). Chapter 8 is concerned with the modelling of a ‘hyperfast’ Peltier cooling system first proposed in the U.K. Automatic Control Council (Ukacc) International Conference on Control, 2010 (Control 2010)

    Hardware-Conscious Wireless Communication System Design

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    The work at hand is a selection of topics in efficient wireless communication system design, with topics logically divided into two groups.One group can be described as hardware designs conscious of their possibilities and limitations. In other words, it is about hardware that chooses its configuration and properties depending on the performance that needs to be delivered and the influence of external factors, with the goal of keeping the energy consumption as low as possible. Design parameters that trade off power with complexity are identified for analog, mixed signal and digital circuits, and implications of these tradeoffs are analyzed in detail. An analog front end and an LDPC channel decoder that adapt their parameters to the environment (e.g. fluctuating power level due to fading) are proposed, and it is analyzed how much power/energy these environment-adaptive structures save compared to non-adaptive designs made for the worst-case scenario. Additionally, the impact of ADC bit resolution on the energy efficiency of a massive MIMO system is examined in detail, with the goal of finding bit resolutions that maximize the energy efficiency under various system setups.In another group of themes, one can recognize systems where the system architect was conscious of fundamental limitations stemming from hardware.Put in another way, in these designs there is no attempt of tweaking or tuning the hardware. On the contrary, system design is performed so as to work around an existing and unchangeable hardware limitation. As a workaround for the problematic centralized topology, a massive MIMO base station based on the daisy chain topology is proposed and a method for signal processing tailored to the daisy chain setup is designed. In another example, a large group of cooperating relays is split into several smaller groups, each cooperatively performing relaying independently of the others. As cooperation consumes resources (such as bandwidth), splitting the system into smaller, independent cooperative parts helps save resources and is again an example of a workaround for an inherent limitation.From the analyses performed in this thesis, promising observations about hardware consciousness can be made. Adapting the structure of a hardware block to the environment can bring massive savings in energy, and simple workarounds prove to perform almost as good as the inherently limited designs, but with the limitation being successfully bypassed. As a general observation, it can be concluded that hardware consciousness pays off
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