Near knowledge : inductive learning systems in law

Induction is an interesting model of legal reasoning, since it provides a method of capturing initial states of legal principles and rules, and adjusting these principles and rules over time as the law changes. In this article I explain how Artificial Intelligence-based inductive learning algorithms work, and show how they have been used in law to model legal domains. I identify some problems with implementations undertaken in law to date, and create a taxonomy of appropriate cases to use in legal inductive inferencing systems. I suggest that inductive learning algorithms have potential in modeling law, but that the artificial intelligence implementations to date are problematic. I argue that induction should be further investigated, since it has the potential to be an extremely useful mechanism for understanding legal domains.

Deductive, inductive and analogical reasoning in legal decision support systems

In this paper we provide an overview of a number of fundamental reasoning formalisms in artificial intelligence which can and have been used in modelling legal reasoning. We describe deduction, induction and analogical reasoning formalisms, and show how they can be used separately to model legal reasoning. We argue that these formalisms can be used together to model legal reasoning more accurately, and describe a number of attempts to integrate the approaches.

Semantic search as inference : applications in health informatics

This thesis developed new search engine models that elicit the meaning behind the words found in documents and queries, rather than simply matching keywords. These new models were applied to searching medical records: an area where search is particularly challenging yet can have significant benefits to our society.

A SiC-based matrix converter topology for inductive power transfer system

Typical inductive power transfer (IPT) systems employ two power conversion stages to generate a high-frequency primary current from low-frequency utility supply. This paper proposes a matrix-converter-based IPT system, which employs high-speed SiC devices to facilitate the generation of high-frequency current through a single power conversion stage. The proposed matrix converter topology transforms a three-phase low-frequency voltage system to a high-frequency single-phase voltage, which, in turn, powers a series compensated IPT system. A comprehensive mathematical model is developed and power losses are evaluated to investigate the efficiency of the proposed converter topology. Theoretical results are presented with simulations, which are performed in MATLAB/Simulink, in comparison to a conventional two-stage converter. Experimental evident of a prototype IPT system is also presented to demonstrate the applicability of the proposed concept.

Five-level current-source inverters with buck-boost and inductive-current balancing capabilities

This paper presents new five-level current-source inverters (CSIs) with voltage/current buck-boost capability, unlike existing five-level CSIs where only voltage-boost operation is supported. The proposed inverters attain self-inductive-current-balancing per switching cycle at their dc front ends without having to include additional balancing hardware or complex control manipulation. The inverters can conveniently be controlled by using the well-established phase-shifted carrier modulation scheme with only two additional linear references and a mapping logic table needed. Existing modulators can therefore be conveniently retrofitted for controlling the presented inverters. By appropriately coordinating the inverter gating signals, their implementations can be realized by using the least number of components without degrading performance. These enhanced features of the inverters have already been verified in simulation and experimentally using a scaled-down laboratory platform.

Contact with existing adipose tissue is inductive for adipogenesis in matrigel

The effect of adipose tissue on inductive adipogenesis within Matrigel (BD Biosciences) was assessed by using a murine chamber model containing a vascular pedicle. Three-chamber configurations that varied in the access to an adipose tissue source were used, including sealed- and open-chamber groups that had no access and limited access, respectively, to the surrounding adipose tissue, and a sealed-chamber group in which adipose tissue was placed as an autograft. All groups showed neovascularization, but varied in the amount of adipogenesis seen in direct relation to their access to preexisting adipose tissue: open chambers showed strong adipogenesis, whereas the sealed chambers had little or no adipose tissue; adipogenesis was restored in the autograft chamber group that contained 2- to 5-mg fat autografts. These showed significantly more adipogenesis than the sealed chambers with no autograft (p < 0.01). Autografts with 1 mg of fat were capable of producing adipogenesis but did so less consistently than the larger autografts. These findings have important implications for adipose tissue engineering strategies and for understanding de novo production of adipose tissue.

Automatic inference of driving task demand from visual cues of emotion and attention

Sensing the mental, physical and emotional demand of a driving task is of primary importance in road safety research and for effectively designing in-vehicle information systems (IVIS). Particularly, the need of cars capable of sensing and reacting to the emotional state of the driver has been repeatedly advocated in the literature. Algorithms and sensors to identify patterns of human behavior, such as gestures, speech, eye gaze and facial expression, are becoming available by using low cost hardware: This paper presents a new system which uses surrogate measures such as facial expression (emotion) and head pose and movements (intention) to infer task difficulty in a driving situation. 11 drivers were recruited and observed in a simulated driving task that involved several pre-programmed events aimed at eliciting emotive reactions, such as being stuck behind slower vehicles, intersections and roundabouts, and potentially dangerous situations. The resulting system, combining face expressions and head pose classification, is capable of recognizing dangerous events (such as crashes and near misses) and stressful situations (e.g. intersections and way giving) that occur during the simulated drive.

Optimal Bayesian experimental design for models with intractable likelihoods using indirect inference applied to biological process models

This paper addresses the problem of determining optimal designs for biological process models with intractable likelihoods, with the goal of parameter inference. The Bayesian approach is to choose a design that maximises the mean of a utility, and the utility is a function of the posterior distribution. Therefore, its estimation requires likelihood evaluations. However, many problems in experimental design involve models with intractable likelihoods, that is, likelihoods that are neither analytic nor can be computed in a reasonable amount of time. We propose a novel solution using indirect inference (II), a well established method in the literature, and the Markov chain Monte Carlo (MCMC) algorithm of Müller et al. (2004). Indirect inference employs an auxiliary model with a tractable likelihood in conjunction with the generative model, the assumed true model of interest, which has an intractable likelihood. Our approach is to estimate a map between the parameters of the generative and auxiliary models, using simulations from the generative model. An II posterior distribution is formed to expedite utility estimation. We also present a modification to the utility that allows the Müller algorithm to sample from a substantially sharpened utility surface, with little computational effort. Unlike competing methods, the II approach can handle complex design problems for models with intractable likelihoods on a continuous design space, with possible extension to many observations. The methodology is demonstrated using two stochastic models; a simple tractable death process used to validate the approach, and a motivating stochastic model for the population evolution of macroparasites.

Controller synthesis of a bidirectional inductive power interface for electric vehicles

Bidirectional Inductive Power Transfer (IPT) systems are preferred for Vehicle-to-Grid (V2G) applications. Typically, bidirectional IPT systems consist of high order resonant networks, and therefore, the control of bidirectional IPT systems has always been a difficulty. To date several different controllers have been reported, but these have been designed using steady-state models, which invariably, are incapable of providing an accurate insight into the dynamic behaviour of the system A dynamic state-space model of a bidirectional IPT system has been reported. However, currently this model has not been used to optimise the design of controllers. Therefore, this paper proposes an optimised controller based on the dynamic model. To verify the operation of the proposed controller simulated results of the optimised controller and simulated results of another controller are compared. Results indicate that the proposed controller is capable of accurately and stably controlling the power flow in a bidirectional IPT system.

A matrix converter based Inductive Power Transfer system

Typical Inductive Power Transfer (IPT) systems employ two power conversion stages to generate a high frequency current from low frequency utility supply. This paper proposes a matrix converter based IPT system that facilitates the generation of high frequency current through a single power conversion stage. The proposed matrix converter topology transforms a 3-phase low frequency voltage system to a high frequency single phase voltage which in turn powers a series compensated IPT system. A comprehensive mathematical model is developed to investigate the behavior of the proposed IPT topology. Theoretical results are presented in comparison to simulations, which are performed in Matlab/ Simulink, to demonstrate the applicability of the proposed concept and the validity of the developed model.

Cascaded multilevel converter based bidirectional inductive power transfer (BIPT) system

A typical low power IPT system employs an H-Bridge converter with a simple control strategy to generate a high frequency current from DC power supply. This paper proposes a cascaded multilevel converter for bidirectional IPT (BIPT) systems, which is suitable for low to medium power applications as well as for situations such as PV cells where several individual DC sources are to be utilized. A novel modulation strategy is proposed for the multilevel converter with the aim of minimizing switching losses. Series - Series (SS) compensation circuit is adopted for the IPT system and a mathematical model is presented to minimize the coil losses of the system under varying output power. Theoretical results presented in comparison to the simulations to demonstrate the applicability of the proposed concept and the validity of the developed model. The experimental results show the feasibility of the proposed phase shift modulation.

An efficiency optimization scheme for bidirectional inductive power transfer systems

Unidirectional inductive power transfer (UIPT) systems allow loads to consume power while bidirectional IPT (BIPT) systems are more suitable for loads requiring two way power flow such as vehicle to grid (V2G) applications with electric vehicles (EVs). Many attempts have been made to improve the performance of BIPT systems. In a typical BIPT system, the output power is control using the pickup converter phase shift angle (PSA) while the primary converter regulates the input current. This paper proposes an optimized phase shift modulation strategy to minimize the coil losses of a series – series (SS) compensated BIPT system. In addition, a comprehensive study on the impact of power converters on the overall efficiency of the system is also presented. A closed loop controller is proposed to optimize the overall efficiency of the BIPT system. Theoretical results are presented in comparison to both simulations and measurements of a 0.5 kW prototype to show the benefits of the proposed concept. Results convincingly demonstrate the applicability of the proposed system offering high efficiency over a wide range of output power.

Characterisation of the cascode gate drive of power MOSFETs in clamped inductive switching applications

This thesis proposes a novel gate drive circuit to improve the switching performance of MOSFET power switches in power electronic converters. The proposed topology exploits the cascode configuration, allowing the minimisation of switching losses in the presence of practical circuit constraints, which enables efficiency and power density improvements. Switching characteristics of the new topology are investigated and key mechanisms that control the switching process are identified. Unique analysis tools and techniques are also developed to demonstrate the application of the cascode gate drive circuit for switching performance optimisation.