Fuzzy ART Choice Functions

Adaptive Resonance Theory (ART) models are real-time neural networks for category learning, pattern recognition, and prediction. Unsupervised fuzzy ART and supervised fuzzy ARTMAP networks synthesize fuzzy logic and ART by exploiting the formal similarity between tile computations of fuzzy subsethood and the dynamics of ART category choice, search, and learning. Fuzzy ART self-organizes stable recognition categories in response to arbitrary sequences of analog or binary input patterns. It generalizes the binary ART 1 model, replacing the set-theoretic intersection (∩) with the fuzzy intersection(∧), or component-wise minimum. A normalization procedure called complement coding leads to a symmetric theory in which the fuzzy intersection and the fuzzy union (∨), or component-wise maximum, play complementary roles. A geometric interpretation of fuzzy ART represents each category as a box that increases in size as weights decrease. This paper analyzes fuzzy ART models that employ various choice functions for category selection. One such function minimizes total weight change during learning. Benchmark simulations compare peformance of fuzzy ARTMAP systems that use different choice functions.

3-D Object Recognition by the ART-EMAP Evidence Accumulation Network

ART-EMAP synthesizes adaptive resonance theory (AHT) and spatial and temporal evidence integration for dynamic predictive mapping (EMAP). The network extends the capabilities of fuzzy ARTMAP in four incremental stages. Stage I introduces distributed pattern representation at a view category field. Stage 2 adds a decision criterion to the mapping between view and object categories, delaying identification of ambiguous objects when faced with a low confidence prediction. Stage 3 augments the system with a field where evidence accumulates in medium-term memory (MTM). Stage 4 adds an unsupervised learning process to fine-tune performance after the limited initial period of supervised network training. Simulations of the four ART-EMAP stages demonstrate performance on a difficult 3-D object recognition problem.

The Art of Seeing and Painting

The human urge to represent the three-dimensional world using two-dimensional pictorial representations dates back at least to Paleolithic times. Artists from ancient to modern times have struggled to understand how a few contours or color patches on a flat surface can induce mental representations of a three-dimensional scene. This article summarizes some of the recent breakthroughs in scientifically understanding how the brain sees that shed light on these struggles. These breakthroughs illustrate how various artists have intuitively understand paradoxical properties about how the brain sees, and have used that understanding to create great art. These paradoxical properties arise from how the brain forms the units of conscious visual perception; namely, representations of three-dimensional boundaries and surfaces. Boundaries and surfaces are computed in parallel cortical processing streams that obey computationally complementary properties. These streams interact at multiple levels to overcome their complementary weaknesses and to transform their complementary properties into consistent percepts. The article describes how properties of complementary consistency have guided the creation of many great works of art.

Fuzzy ART: An Adaptive Resonance Algorithm for Rapid, Stable Classification of Analog Patterns

The Fuzzy ART system introduced herein incorporates computations from fuzzy set theory into ART 1. For example, the intersection (n) operator used in ART 1 learning is replaced by the MIN operator (A) of fuzzy set theory. Fuzzy ART reduces to ART 1 in response to binary input vectors, but can also learn stable categories in response to analog input vectors. In particular, the MIN operator reduces to the intersection operator in the binary case. Learning is stable because all adaptive weights can only decrease in time. A preprocessing step, called complement coding, uses on-cell and off-cell responses to prevent category proliferation. Complement coding normalizes input vectors while preserving the amplitudes of individual feature activations.

ART 2-A: An Adaptive Resonance Algorithm for Rapid Category Learning and Recognition

This article introduces ART 2-A, an efficient algorithm that emulates the self-organizing pattern recognition and hypothesis testing properties of the ART 2 neural network architecture, but at a speed two to three orders of magnitude faster. Analysis and simulations show how the ART 2-A systems correspond to ART 2 dynamics at both the fast-learn limit and at intermediate learning rates. Intermediate learning rates permit fast commitment of category nodes but slow recoding, analogous to properties of word frequency effects, encoding specificity effects, and episodic memory. Better noise tolerance is hereby achieved without a loss of learning stability. The ART 2 and ART 2-A systems are contrasted with the leader algorithm. The speed of ART 2-A makes practical the use of ART 2 modules in large-scale neural computation.

Fuzzy ART: Fast Stable Learning and Categorization of Analog Patterns by an Adaptive Resonance System

A Fuzzy ART model capable of rapid stable learning of recognition categories in response to arbitrary sequences of analog or binary input patterns is described. Fuzzy ART incorporates computations from fuzzy set theory into the ART 1 neural network, which learns to categorize only binary input patterns. The generalization to learning both analog and binary input patterns is achieved by replacing appearances of the intersection operator (n) in AHT 1 by the MIN operator (Λ) of fuzzy set theory. The MIN operator reduces to the intersection operator in the binary case. Category proliferation is prevented by normalizing input vectors at a preprocessing stage. A normalization procedure called complement coding leads to a symmetric theory in which the MIN operator (Λ) and the MAX operator (v) of fuzzy set theory play complementary roles. Complement coding uses on-cells and off-cells to represent the input pattern, and preserves individual feature amplitudes while normalizing the total on-cell/off-cell vector. Learning is stable because all adaptive weights can only decrease in time. Decreasing weights correspond to increasing sizes of category "boxes". Smaller vigilance values lead to larger category boxes. Learning stops when the input space is covered by boxes. With fast learning and a finite input set of arbitrary size and composition, learning stabilizes after just one presentation of each input pattern. A fast-commit slow-recode option combines fast learning with a forgetting rule that buffers system memory against noise. Using this option, rare events can be rapidly learned, yet previously learned memories are not rapidly erased in response to statistically unreliable input fluctuations.

A Neural Network Realization of Fuzzy ART

A neural network realization of the fuzzy Adaptive Resonance Theory (ART) algorithm is described. Fuzzy ART is capable of rapid stable learning of recognition categories in response to arbitrary sequences of analog or binary input patterns. Fuzzy ART incorporates computations from fuzzy set theory into the ART 1 neural network, which learns to categorize only binary input patterns, thus enabling the network to learn both analog and binary input patterns. In the neural network realization of fuzzy ART, signal transduction obeys a path capacity rule. Category choice is determined by a combination of bottom-up signals and learned category biases. Top-down signals impose upper bounds on feature node activations.

Adaptive Resonance Associative Map: A Hierarchical ART System for Fast Stable Associative Learning

This paper introduces a new class of predictive ART architectures, called Adaptive Resonance Associative Map (ARAM) which performs rapid, yet stable heteroassociative learning in real time environment. ARAM can be visualized as two ART modules sharing a single recognition code layer. The unit for recruiting a recognition code is a pattern pair. Code stabilization is ensured by restricting coding to states where resonances are reached in both modules. Simulation results have shown that ARAM is capable of self-stabilizing association of arbitrary pattern pairs of arbitrary complexity appearing in arbitrary sequence by fast learning in real time environment. Due to the symmetrical network structure, associative recall can be performed in both directions.

Conversion as transformative experience: a sociological study of identity formation and transformation processes

This thesis contributes to the understanding of the processes involved in the formation and transformation of identities. It achieves this goal by establishing the critical importance of ‘background’ and ‘liminality’ in the shaping of identity. Drawing mainly from the work of cultural anthropology and philosophical hermeneutics a theoretical framework is constructed from which transformative experiences can be analysed. The particular experience at the heart of this study is the phenomenon of conversion and the dynamics involved in the construction of that process. Establishing the axial age as the horizon from which the process of conversion emerged will be the main theme of the first part of the study. Identifying the ‘birth’ of conversion allows a deeper understanding of the historical dynamics that make up the process. From these fundamental dynamics a theoretical framework is constructed in order to analyse the conversion process. Applying this theoretical framework to a number of case-studies will be the central focus of this study. The transformative experiences of Saint Augustine, the fourteenth century nun Margaret Ebner, the communist revolutionary Karl Marx and the literary figure of Arthur Koestler will provide the material onto which the theoretical framework can be applied. A synthesis of the Judaic religious and the Greek philosophical traditions will be the main findings for the shaping of Augustine’s conversion experience. The dissolution of political order coupled with the institutionalisation of the conversion process will illuminate the mystical experiences of Margaret Ebner at a time when empathetic conversion reached its fullest expression. The final case-studies examine two modern ‘conversions’ that seem to have an ideological rather than a religious basis to them. On closer examination it will be found that the German tradition of Biblical Criticism played a most influential role in the ‘conversion’ of Marx and mythology the best medium to understand the experiences of Koestler. The main ideas emerging from this study highlight the fluidity of identity and the important role of ‘background’ in its transformation. The theoretical framework, as constructed for this study, is found to be a useful methodological tool that can offer insights into experiences, such as conversion, that otherwise would remain hidden from our enquiries.

Resonant power conversion topologies for inductive charging of electrical vehicle batteries

This thesis is concerned with inductive charging of electric vehicle batteries. Rectified power form the 50/60 Hz utility feeds a dc-ac converter which delivers high-frequency ac power to the electric vehicle inductive coupling inlet. The inlet configuration has been defined by the Society of Automotive Engineers in Recommended Practice J-1773. This thesis studies converter topologies related to the series resonant converter. When coupled to the vehicle inlet, the frequency-controlled series-resonant converter results in a capacitively-filtered series-parallel LCLC (SP-LCLC) resonant converter topology with zero voltage switching and many other desirable features. A novel time-domain transformation analysis, termed Modal Analysis, is developed, using a state variable transformation, to analyze and characterize this multi-resonant fourth-orderconverter. Next, Fundamental Mode Approximation (FMA) Analysis, based on a voltage-source model of the load, and its novel extension, Rectifier-Compensated FMA (RCFMA) Analysis, are developed and applied to the SP-LCLC converter. The RCFMA Analysis is a simpler and more intuitive analysis than the Modal Analysis, and provides a relatively accurate closed-form solution for the converter behavior. Phase control of the SP-LCLC converter is investigated as a control option. FMA and RCFMA Analyses are used for detailed characterization. The analyses identify areas of operation, which are also validated experimentally, where it is advantageous to phase control the converter. A novel hybrid control scheme is proposed which integrates frequency and phase control and achieves reduced operating frequency range and improved partial-load efficiency. The phase-controlled SP-LCLC converter can also be configured with a parallel load and is an excellent option for the application. The resulting topology implements soft-switching over the entire load range and has high full-load and partial-load efficiencies. RCFMA Analysis is used to analyze and characterize the new converter topology, and good correlation is shown with experimental results. Finally, a novel single-stage power-factor-corrected ac-dc converter is introduced, which uses the current-source characteristic of the SP-LCLC topology to provide power factor correction over a wide output power range from zero to full load. This converter exhibits all the advantageous characteristics of its dc-dc counterpart, with a reduced parts count and cost. Simulation and experimental results verify the operation of the new converter.

High-efficiency photovoltaics through mechanically stacked integration of solar cells based on the InP lattice constant

Solar Energy is a clean and abundant energy source that can help reduce reliance on fossil fuels around which questions still persist about their contribution to climate and long-term availability. Monolithic triple-junction solar cells are currently the state of the art photovoltaic devices with champion cell efficiencies exceeding 40%, but their ultimate efficiency is restricted by the current-matching constraint of series-connected cells. The objective of this thesis was to investigate the use of solar cells with lattice constants equal to InP in order to reduce the constraint of current matching in multi-junction solar cells. This was addressed by two approaches: Firstly, the formation of mechanically stacked solar cells (MSSC) was investigated through the addition of separate connections to individual cells that make up a multi-junction device. An electrical and optical modelling approach identified separately connected InGaAs bottom cells stacked under dual-junction GaAs based top cells as a route to high efficiency. An InGaAs solar cell was fabricated on an InP substrate with a measured 1-Sun conversion efficiency of 9.3%. A comparative study of adhesives found benzocyclobutene to be the most suitable for bonding component cells in a mechanically stacked configuration owing to its higher thermal conductivity and refractive index when compared to other candidate adhesives. A flip-chip process was developed to bond single-junction GaAs and InGaAs cells with a measured 4-terminal MSSC efficiency of 25.2% under 1-Sun conditions. Additionally, a novel InAlAs solar cell was identified, which can be used to provide an alternative to the well established GaAs solar cell. As wide bandgap InAlAs solar cells have not been extensively investigated for use in photovoltaics, single-junction cells were fabricated and their properties relevant to PV operation analysed. Minority carrier diffusion lengths in the micrometre range were extracted, confirming InAlAs as a suitable material for use in III-V solar cells, and a 1-Sun conversion efficiency of 6.6% measured for cells with 800 nm thick absorber layers. Given the cost and small diameter of commercially available InP wafers, InGaAs and InAlAs solar cells were fabricated on alternative substrates, namely GaAs. As a first demonstration the lattice constant of a GaAs substrate was graded to InP using an InxGa1-xAs metamorphic buffer layer onto which cells were grown. This was the first demonstration of an InAlAs solar cell on an alternative substrate and an initial step towards fabricating these cells on Si. The results presented offer a route to developing multi-junction solar cell devices based on the InP lattice parameter, thus extending the range of available bandgaps for high efficiency cells.

Design, fabrication and characterisation of components for microfluidic enzymatic biofuel cells

The concept of a biofuel cell takes inspiration from the natural capability of biological systems to catalyse the conversion of organic matter with a subsequent release of electrical energy. Enzymatic biofuel cells are intended to mimic the processes occurring in nature in a more controlled and efficient manner. Traditional fuel cells rely on the use of toxic catalysts and are often not easily miniaturizable making them unsuitable as implantable power sources. Biofuel cells however use highly selective protein catalysts and renewable fuels. As energy consumption becomes a global issue, they emerge as important tools for energy generation. The microfluidic platforms developed are intended to maximize the amount of electrical energy extracted from renewable fuels which are naturally abundant in the environment and in biological fluids. Combining microfabrication processes, chemical modification and biological surface patterning these devices are promising candidates for micro-power sources for future life science and electronic applications. This thesis considered four main aspects of a biofuel cell research. Firstly, concept of a miniature compartmentalized enzymatic biofuel cell utilizing simple fuels and operating in static conditions is verified and proves the feasibility of enzyme catalysis in energy conversion processes. Secondly, electrode and microfluidic channel study was performed through theoretical investigations of the flow and catalytic reactions which also improved understanding of the enzyme kinetics in the cell. Next, microfluidic devices were fabricated from cost-effective and disposable polymer materials, using the state-of-the-art micro-processing technologies. Integration of the individual components is difficult and multiple techniques to overcome these problems have been investigated. Electrochemical characterization of gold electrodes modified with Nanoporous Gold Structures is also performed. Finally, two strategies for enzyme patterning and encapsulation are discussed. Several protein catalysts have been effectively immobilized on the surface of commercial and microfabricated electrodes by electrochemically assisted deposition in sol-gel and poly-(o-phenylenediamine) polymer matrices and characterised with confirmed catalytic activity.

The architecture of influence: Paul Rudolph's Art and Architecture Building at Yale

Focussing on Paul Rudolph’s Art & Architecture Building at Yale, this thesis demonstrates how the building synthesises the architect’s attitude to architectural education, urbanism and materiality. It tracks the evolution of the building from its origins – which bear a relationship to Rudolph’s pedagogical ideas – to later moments when its occupants and others reacted to it in a series of ways that could never have been foreseen. The A&A became the epicentre of the university’s counter culture movement before it was ravaged by a fire of undetermined origins. Arguably, it represents the last of its kind in American architecture, a turning point at the threshold of postmodernism. Using an archive that was only made available to researchers in 2009, this is the first study to draw extensively on the research files of the late architectural writer and educator, C. Ray Smith. Smith’s 1981 manuscript about the A&A entitled “The Biography of a Building,” was never published. The associated research files and transcripts of discussions with some thirty interviewees, including Rudolph, provide a previously unavailable wealth of information. Following Smith’s methodology, meetings were recorded with those involved in the A&A including, where possible, some of Smith’s original interviewees. When placed within other significant contexts – the physicality of the building itself as well as the literature which surrounds it – these previously untold accounts provide new perspectives and details, which deepen the understanding of the building and its place within architectural discourse. Issues revealed include the importance of the influence of Louis Kahn’s Yale Art Gallery and Yale’s Collegiate Gothic Campus on the building’s design. Following a tumultuous first fifty years, the A&A remains an integral part of the architectural education of Yale students and, furthermore, constitutes an important didactic tool for all students of architecture.

Art markets

SCOPUS: ch.b

Léo Strauss: art d'écrire, politique, philosophie

L'ouvrage examine la pensée de Léo Strauss (1899-1973) et étudie à partir d'elle les stratégies d'exposition et de dissimulation de la philosophie. Les études qu'il réunit mesurent la portée de l'hypothèse d'un "art d'écrire oublié" et examinent la fécondité et les limites de la conception straussienne de l'écriture philosophique.