Geometric Algebra Model of Distributed Representations

Formalism based on GA is an alternative to distributed representation models developed so far --- Smolensky's tensor product, Holographic Reduced Representations (HRR) and Binary Spatter Code (BSC). Convolutions are replaced by geometric products, interpretable in terms of geometry which seems to be the most natural language for visualization of higher concepts. This paper recalls the main ideas behind the GA model and investigates recognition test results using both inner product and a clipped version of matrix representation. The influence of accidental blade equality on recognition is also studied. Finally, the efficiency of the GA model is compared to that of previously developed models.Comment: 30 pages, 19 figure

Powertrain modelling for engine stop-start dynamics and control of micro/mild hybrid construction machines

Engine stop-start control is considered as the key technology for micro/mild hybridisation of vehicles and machines. To utilize this concept, especially for construction machines, the engine is desired to be started in such a way that the operator discomfort can be minimized. To address this issue, this paper aims to develop a simple powertrain modelling approach for engine stop-start dynamic analysis and an advanced engine start control scheme newly applicable for micro/mild hybrid construction machines. First, a powertrain model of a generic construction machine is mathematically developed in a general form which allows to investigate the transient responses of the system during the engine cranking process. Second, a simple parameterisation procedure with a minimum set of data required to characterise the dynamic model is presented. Third, a model- based adaptive controller is designed for the starter to crank the engine quickly and smoothly without the need of fuel injection while the critical problems of machine noise, vibration and harshness can be eliminated. Finally, the advantages and effectiveness of the proposed modelling and control approaches have been validated through numerical simulations. The results imply that with the limited data set for training, the developed model works better than a high fidelity model built in AMESim while the adaptive controller can guarantee the desired cranking performance

Powertrain modelling and engine start control of construction machines

This paper aims to develop an engine start control approach for a micro/mild hybrid machine for a capable of cranking the engine without injection. First, the powertrain is physically modelled using a co-simulation platform. Second, experiment data of the traditional machine is acquired to optimize the model. Third, a model-based adaptive controller is designed for the starter to crank the engine quickly and smoothly to minimize the operator discomfort. The effectiveness of the proposed approach is validated through numerical simulations with the established model

Challenges of micro/mild hybridisation for construction machinery and applicability in UK

In recent years, micro/mild hybridisation (MMH) is known as a feasible solution for powertrain development with high fuel efficiency, less energy use and emission and, especially, low cost and simple installation. This paper focuses on the challenges of MMH for construction machines and then, pays attention to its applicability to UK construction machinery. First, hybrid electric configurations are briefly reviewed; and technological challenges towards MMH in construction sector are clearly stated. Second, the current development of construction machinery in UK is analysed to point out the potential for MMH implementation. Thousands of machines manufactured in UK have been sampled for the further study. Third, a methodology for big data capturing, compression and mining is provided for a capable of managing and analysing effectively performances of various construction machine types. By using this method, 96% of data memory can be reduced to store the huge machine data without lacking the necessary information. Forth, an advanced decision tool is built using a fuzzy cognitive map based on the big data mining and knowledge from experts to enables users to define a target machine for MMH utilization. The numerical study with this tool on the sampled machines has been done and finally realized that one class of heavy excavators is the most suitable to apply MMH technology

The Ncoa7 locus regulates V-ATPase formation and function, neurodevelopment and behaviour

Members of the Tre2/Bub2/Cdc16 (TBC), lysin motif (LysM), domain catalytic (TLDc) protein family are associated with multiple neurodevelopmental disorders, although their exact roles in disease remain unclear. For example, nuclear receptor coactivator 7 (NCOA7) has been associated with autism, although almost nothing is known regarding the mode-of-action of this TLDc protein in the nervous system. Here we investigated the molecular function of NCOA7 in neurons and generated a novel mouse model to determine the consequences of deleting this locus in vivo. We show that NCOA7 interacts with the cytoplasmic domain of the vacuolar (V)-ATPase in the brain and demonstrate that this protein is required for normal assembly and activity of this critical proton pump. Neurons lacking Ncoa7 exhibit altered development alongside defective lysosomal formation and function; accordingly, Ncoa7 deletion animals exhibited abnormal neuronal patterning defects and a reduced expression of lysosomal markers. Furthermore, behavioural assessment revealed anxiety and social defects in mice lacking Ncoa7. In summary, we demonstrate that NCOA7 is an important V-ATPase regulatory protein in the brain, modulating lysosomal function, neuronal connectivity and behaviour; thus our study reveals a molecular mechanism controlling endolysosomal homeostasis that is essential for neurodevelopment

Model-based visual self-localization using geometry and graphs

Abstrac

Caracterización de las reflexiones de estudiantes del máster en formación del profesorado de matemáticas de educación secundaria obligatoria y bachillerato sobre el recuerdo de su experiencia escolar en matemáticas de secundaria

Este estudio es un primer paso para analizar la identidad profesional de estudiantes para profesor de matemáticas de secundaria. A pesar de tratarse de una muestra limitada y en un contexto concreto, los resultados reflejan la situación en la que se encuentran los futuros profesores de matemáticas de secundaria cuando están realizando un máster profesionalizante, lo que puede ser importante para el diseño y desarrollo de dicho máster a la vez que abre perspectivas de futuro en diferentes sentidos

Uses for humanised mouse models in precision medicine for neurodegenerative disease

Neurodegenerative disease encompasses a wide range of disorders afflicting the central and peripheral nervous systems and is a major unmet biomedical need of our time. There are very limited treatments, and no cures, for most of these diseases, including Alzheimer’s Disease, Parkinson's Disease, Huntington Disease, and Motor Neuron Diseases. Mouse and other animal models provide hope by analysing them to understand pathogenic mechanisms, to identify drug targets, and to develop gene therapies and stem cell therapies. However, despite many decades of research, virtually no new treatments have reached the clinic. Increasingly, it is apparent that human heterogeneity within clinically defined neurodegenerative disorders, and between patients with the same genetic mutations, significantly impacts disease presentation and, potentially, therapeutic efficacy. Therefore, stratifying patients according to genetics, lifestyle, disease presentation, ethnicity, and other parameters may hold the key to bringing effective therapies from the bench to the clinic. Here, we discuss genetic and cellular humanised mouse models, and how they help in defining the genetic and environmental parameters associated with neurodegenerative disease, and so help in developing effective precision medicine strategies for future healthcare