Design of high fidelity building energy monitoring system

Building energy meter network, based on per-appliance monitoring system, willbe an important part of the Advanced Metering Infrastructure. Two key issues exist for designing such networks. One is the network structure to be used. The other is the implementation of the network structure on a large amount of small low power devices, and the maintenance of high quality communication when the devices have electric connection with high voltage AC line. The recent advancement of low-power wireless communication makes itself the right candidate for house and building energy network. Among all kinds of wireless solutions, the low speed but highly reliable 802.15.4 radio has been chosen in this design. While many network-layer solutions have been provided on top of 802.15.4, an IPv6 based method is used in this design. 6LOWPAN is the particular protocol which adapts IP on low power personal network radio. In order to extend the network into building area without, a specific network layer routing mechanism-RPL, is included in this design. The fundamental unit of the building energy monitoring system is a smart wall plug. It is consisted of an electricity energy meter, a RF communication module and a low power CPU. The real challenge for designing such a device is its network firmware. In this design, IPv6 is implemented through Contiki operation system. Customize hardware driver and meter application program have been developed on top of the Contiki OS. Some experiments have been done, in order to prove the network ability of this system.

From Broadstone to Zackenberg

Ecological networks are typically complex constructions of species and their interactions. During the last decade, the study of networks has moved from static to dynamic analyses, and has attained a deeper insight into their internal structure, heterogeneity, and temporal and spatial resolution. Here, we review, discuss and suggest research lines in the study of the spatio-temporal heterogeneity of networks and their hierarchical nature. We use case study data from two well-characterized model systems (the food web in Broadstone Stream in England and the pollination network at Zackenberg in Greenland), which are complemented with additional information from other studies. We focus upon eight topics: temporal dynamic space-for-time substitutions linkage constraints habitat borders network modularity individual-based networks invasions of networks and super networks that integrate different network types. Few studies have explicitly examined temporal change in networks, and we present examples that span from daily to decadal change: a common pattern that we see is a stable core surrounded by a group of dynamic, peripheral species, which, in pollinator networks enter the web via preferential linkage to the most generalist species. To some extent, temporal and spatial scales are interchangeable (i.e. networks exhibit ‘ergodicity’) and we explore how space-for-time substitutions can be used in the study of networks. Network structure is commonly constrained by phenological uncoupling (a temporal phenomenon), abundance, body size and population structure. Some potential links are never observed, that is they are ‘forbidden’ (fully constrained) or ‘missing’ (a sampling effect), and their absence can be just as ecologically significant as their presence. Spatial habitat borders can add heterogeneity to network structure, but their importance has rarely been studied: we explore how habitat generalization can be related to other resource dimensions. Many networks are hierarchically structured, with modules forming the basic building blocks, which can result in self-similarity. Scaling down from networks of species reveals another, finer-grained level of individual-based organization, the ecological consequences of which have yet to be fully explored. The few studies of individual-based ecological networks that are available suggest the potential for large intraspecific variance and, in the case of food webs, strong size-structuring. However, such data are still scarce and more studies are required to link individual-level and species-level networks. Invasions by alien species can be tracked by following the topological ‘career’ of the invader as it establishes itself within a network, with potentially important implications for conservation biology. Finally, by scaling up to a higher level of organization, it is possible to combine different network types (e.g. food webs and mutualistic networks) to form super networks, and this new approach has yet to be integrated into mainstream ecological research. We conclude by listing a set of research topics that we see as emerging candidates for ecological network studies in the near future.

Mobility and Bandwidth Prediction as a Service in Virtualized LTE Systems

Recently telecommunication industry benefits from infrastructure sharing, one of the most fundamental enablers of cloud computing, leading to emergence of the Mobile Virtual Network Operator (MVNO) concept. The most momentous intents by this approach are the support of on-demand provisioning and elasticity of virtualized mobile network components, based on data traffic load. To realize it, during operation and management procedures, the virtualized services need be triggered in order to scale-up/down or scale-out/in an instance. In this paper we propose an architecture called MOBaaS (Mobility and Bandwidth Availability Prediction as a Service), comprising two algorithms in order to predict user(s) mobility and network link bandwidth availability, that can be implemented in cloud based mobile network structure and can be used as a support service by any other virtualized mobile network services. MOBaaS can provide prediction information in order to generate required triggers for on-demand deploying, provisioning, disposing of virtualized network components. This information can be used for self-adaptation procedures and optimal network function configuration during run-time operation, as well. Through the preliminary experiments with the prototype implementation on the OpenStack platform, we evaluated and confirmed the feasibility and the effectiveness of the prediction algorithms and the proposed architecture.

(Table A1) Species list of the high Antarctic Weddell Sea food web

Human-induced habitat destruction, overexploitation, introduction of alien species and climate change are causing species to go extinct at unprecedented rates, from local to global scales. There are growing concerns that these kinds of disturbances alter important functions of ecosystems. Our current understanding is that key parameters of a community (e.g. its functional diversity, species composition, and presence/absence of vulnerable species) reflect an ecological network's ability to resist or rebound from change in response to pressures and disturbances, such as species loss. If the food web structure is relatively simple, we can analyse the roles of different species interactions in determining how environmental impacts translate into species loss. However, when ecosystems harbour species-rich communities, as is the case in most natural systems, then the complex network of ecological interactions makes it a far more challenging task to perceive how species' functional roles influence the consequences of species loss. One approach to deal with such complexity is to focus on the functional traits of species in order to identify their respective roles: for instance, large species seem to be more susceptible to extinction than smaller species. Here, we introduce and analyse the marine food web from the high Antarctic Weddell Sea Shelf to illustrate the role of species traits in relation to network robustness of this complex food web. Our approach was threefold: firstly, we applied a new classification system to all species, grouping them by traits other than body size; secondly, we tested the relationship between body size and food web parameters within and across these groups and finally, we calculated food web robustness. We addressed questions regarding (i) patterns of species functional/trophic roles, (ii) relationships between species functional roles and body size and (iii) the role of species body size in terms of network robustness. Our results show that when analyzing relationships between trophic structure, body size and network structure, the diversity of predatory species types needs to be considered in future studies.

Localization and Tracking in Energy-Constrained Networks with Dynamic Connectivity

Systems used for target localization, such as goods, individuals, or animals, commonly rely on operational means to meet the final application demands. However, what would happen if some means were powered up randomly by harvesting systems? And what if those devices not randomly powered had their duty cycles restricted? Under what conditions would such an operation be tolerable in localization services? What if the references provided by nodes in a tracking problem were distorted? Moreover, there is an underlying topic common to the previous questions regarding the transfer of conceptual models to reality in field tests: what challenges are faced upon deploying a localization network that integrates energy harvesting modules? The application scenario of the system studied is a traditional herding environment of semi domesticated reindeer (Rangifer tarandus tarandus) in northern Scandinavia. In these conditions, information on approximate locations of reindeer is as important as environmental preservation. Herders also need cost-effective devices capable of operating unattended in, sometimes, extreme weather conditions. The analyses developed are worthy not only for the specific application environment presented, but also because they may serve as an approach to performance of navigation systems in absence of reasonably accurate references like the ones of the Global Positioning System (GPS). A number of energy-harvesting solutions, like thermal and radio-frequency harvesting, do not commonly provide power beyond one milliwatt. When they do, battery buffers may be needed (as it happens with solar energy) which may raise costs and make systems more dependent on environmental temperatures. In general, given our problem, a harvesting system is needed that be capable of providing energy bursts of, at least, some milliwatts. Many works on localization problems assume that devices have certain capabilities to determine unknown locations based on range-based techniques or fingerprinting which cannot be assumed in the approach considered herein. The system presented is akin to range-free techniques, but goes to the extent of considering very low node densities: most range-free techniques are, therefore, not applicable. Animal localization, in particular, uses to be supported by accurate devices such as GPS collars which deplete batteries in, maximum, a few days. Such short-life solutions are not particularly desirable in the framework considered. In tracking, the challenge may times addressed aims at attaining high precision levels from complex reliable hardware and thorough processing techniques. One of the challenges in this Thesis is the use of equipment with just part of its facilities in permanent operation, which may yield high input noise levels in the form of distorted reference points. The solution presented integrates a kinetic harvesting module in some nodes which are expected to be a majority in the network. These modules are capable of providing power bursts of some milliwatts which suffice to meet node energy demands. The usage of harvesting modules in the aforementioned conditions makes the system less dependent on environmental temperatures as no batteries are used in nodes with harvesters--it may be also an advantage in economic terms. There is a second kind of nodes. They are battery powered (without kinetic energy harvesters), and are, therefore, dependent on temperature and battery replacements. In addition, their operation is constrained by duty cycles in order to extend node lifetime and, consequently, their autonomy. There is, in turn, a third type of nodes (hotspots) which can be static or mobile. They are also battery-powered, and are used to retrieve information from the network so that it is presented to users. The system operational chain starts at the kinetic-powered nodes broadcasting their own identifier. If an identifier is received at a battery-powered node, the latter stores it for its records. Later, as the recording node meets a hotspot, its full record of detections is transferred to the hotspot. Every detection registry comprises, at least, a node identifier and the position read from its GPS module by the battery-operated node previously to detection. The characteristics of the system presented make the aforementioned operation own certain particularities which are also studied. First, identifier transmissions are random as they depend on movements at kinetic modules--reindeer movements in our application. Not every movement suffices since it must overcome a certain energy threshold. Second, identifier transmissions may not be heard unless there is a battery-powered node in the surroundings. Third, battery-powered nodes do not poll continuously their GPS module, hence localization errors rise even more. Let's recall at this point that such behavior is tight to the aforementioned power saving policies to extend node lifetime. Last, some time is elapsed between the instant an identifier random transmission is detected and the moment the user is aware of such a detection: it takes some time to find a hotspot. Tracking is posed as a problem of a single kinetically-powered target and a population of battery-operated nodes with higher densities than before in localization. Since the latter provide their approximate positions as reference locations, the study is again focused on assessing the impact of such distorted references on performance. Unlike in localization, distance-estimation capabilities based on signal parameters are assumed in this problem. Three variants of the Kalman filter family are applied in this context: the regular Kalman filter, the alpha-beta filter, and the unscented Kalman filter. The study enclosed hereafter comprises both field tests and simulations. Field tests were used mainly to assess the challenges related to power supply and operation in extreme conditions as well as to model nodes and some aspects of their operation in the application scenario. These models are the basics of the simulations developed later. The overall system performance is analyzed according to three metrics: number of detections per kinetic node, accuracy, and latency. The links between these metrics and the operational conditions are also discussed and characterized statistically. Subsequently, such statistical characterization is used to forecast performance figures given specific operational parameters. In tracking, also studied via simulations, nonlinear relationships are found between accuracy and duty cycles and cluster sizes of battery-operated nodes. The solution presented may be more complex in terms of network structure than existing solutions based on GPS collars. However, its main gain lies on taking advantage of users' error tolerance to reduce costs and become more environmentally friendly by diminishing the potential amount of batteries that can be lost. Whether it is applicable or not depends ultimately on the conditions and requirements imposed by users' needs and operational environments, which is, as it has been explained, one of the topics of this Thesis.

Guiding functional connectivity estimation by structural connectivity in MEG: an application to discrimination of mild cognitive impaired conditions

Whole brain resting state connectivity is a promising biomarker that might help to obtain an early diagnosis in many neurological diseases, such as dementia. Inferring resting-state connectivity is often based on correlations, which are sensitive to indirect connections, leading to an inaccurate representation of the real backbone of the network. The precision matrix is a better representation for whole brain connectivity, as it considers only direct connections. The network structure can be estimated using the graphical lasso (GL), which achieves sparsity through l1-regularization on the precision matrix. In this paper, we propose a structural connectivity adaptive version of the GL, where weaker anatomical connections are represented as stronger penalties on the corre- sponding functional connections. We applied beamformer source reconstruction to the resting state MEG record- ings of 81 subjects, where 29 were healthy controls, 22 were single-domain amnestic Mild Cognitive Impaired (MCI), and 30 were multiple-domain amnestic MCI. An atlas-based anatomical parcellation of 66 regions was ob- tained for each subject, and time series were assigned to each of the regions. The fiber densities between the re- gions, obtained with deterministic tractography from diffusion-weighted MRI, were used to define the anatomical connectivity. Precision matrices were obtained with the region specific time series in five different frequency bands. We compared our method with the traditional GL and a functional adaptive version of the GL, in terms of log-likelihood and classification accuracies between the three groups. We conclude that introduc- ing an anatomical prior improves the expressivity of the model and, in most cases, leads to a better classification between groups.

Polymer interaction with macroscopic carbon nanotube fibres and fabrication of nanostructured composites

El ensamblado de nanotubos de carbono (CNT) como una fibra macroscópica en la cual están orientados preferentemente paralelos entre sí y al eje de la fibra, ha dado como resultado un nuevo tipo de fibra de altas prestaciones derivadas de la explotación eficiente de las propiedades axiales de los CNTs, y que tiene un gran número de aplicaciones potenciales. Fibras continuas de CNTs se produjeron en el Instituto IMDEA Materiales mediante el proceso de hilado directo durante la reacción de síntesis por deposición química de vapores. Uno de los objetivos de esta tesis es el estudio de la estructura de estas fibras mediante técnicas del estado del arte de difracción de rayos X de sincrotrón y la elaboración de un modelo estructural de dicho material. Mediciones texturales de adsorción de gases, análisis de micrografías de electrones y dispersión de rayos X de ángulo alto y bajo (WAXS/SAXS) indican que el material tiene una estructura mesoporosa con una distribución de tamaño de poros ancha derivada del amplio rango de separaciones entre manojos de CNTs, así como una superficie específica de 170m2/g. Los valores de dimensión fractal obtenidos mediante SAXS y análisis Barrett-Joyner-Halenda (BJH) de mediciones texturales coinciden en 2.4 y 2.5, respectivamente, resaltando el carácter de red de la estructura de dichas fibras. La estructura mesoporosa y tipo hilo de las fibra de CNT es accesible a la infiltración de moléculas externas (líquidos o polímeros). En este trabajo se estudian los cambios en la estructura multiescala de las fibras de CNTs al interactuar con líquidos y polímeros. Los efectos de la densificación en la estructura de fibras secas de CNT son estudiados mediante WAXS/SAXS. El tratamiento de densificación junta los manojos de la fibra (los poros disminuyen de tamaño), resultando en un incremento de la densidad de la fibra. Sin embargo, los dominios estructurales correspondientes a la transferencia de esfuerzo mecánica y carga eléctrica en los nanotubos no son afectados durante este proceso de densificación; como consecuencia no se produce un efecto sustancial en las propiedades mecánicas y eléctricas. Mediciones de SAXS and fibra de CNT antes y después de infiltración de líquidos confirman la penetración de una gran cantidad de líquidos que llena los poros internos de la fibra pero no se intercalan entre capas de nanotubos adyacentes. La infiltración de cadenas poliméricas de bajo peso molecular tiende a expandir los manojos en la fibra e incrementar el ángulo de apertura de los poros. Los resultados de SAXS indican que la estructura interna de la fibra en términos de la organización de las capas de tubos y su orientación no es afectada cuando las muestras consisten en fibras infiltradas con polímeros de alto peso molecular. La cristalización de varios polímeros semicristalinos es acelerada por la presencia de fibras de CNTs alineados y produce el crecimiento de una capa transcristalina normal a la superficie de la fibra. Esto es observado directamente mediante microscopía óptica polarizada, y detectado mediante calorimetría DSC. Las lamelas en la capa transcristalina tienen orientación de la cadena polimérica paralela a la fibra y por lo tanto a los nanotubos, de acuerdo con los patrones de WAXS. Esta orientación preferencial se sugiere como parte de la fuerza impulsora en la nucleación. La nucleación del dominio cristalino polimérico en la superficie de los CNT no es epitaxial. Ocurre sin haber correspondencia entre las estructuras cristalinas del polímero y los nanotubos. Estas observaciones contribuyen a la compresión del fenómeno de nucleación en CNTs y otros nanocarbonos, y sientan las bases para el desarrollo de composites poliméricos de gran escala basados en fibra larga de CNTs alineados. ABSTRACT The assembly of carbon nanotubes into a macroscopic fibre material where they are preferentially aligned parallel to each other and to the fibre axis has resulted in a new class of high-performance fibres, which efficiently exploits the axial properties of the building blocks and has numerous applications. Long, continuous CNT fibres were produced in IMDEA Materials Institute by direct fibre spinning from a chemical vapour deposition reaction. These fibres have a complex hierarchical structure covering multiple length scales. One objective of this thesis is to reveal this structure by means of state-of-the-art techniques such as synchrotron X-ray diffraction, and to build a model to link the fibre structural elements. Texture and gas absorption measurements, using electron microscopy, wide angle and small angle X-ray scattering (WAXS/SAXS), and pore size distribution analysis by Barrett-Joyner-Halenda (BJH), indicate that the material has a mesoporous structure with a wide pore size distribution arising from the range of fibre bundle separation, and a high surface area _170m2/g. Fractal dimension values of 2.4_2.5 obtained from the SAXS and BJH measurements highlight the network structure of the fibre. Mesoporous and yarn-like structure of CNT fibres make them accessible to the infiltration of foreign molecules (liquid or polymer). This work studies multiscale structural changes when CNT fibres interact with liquids and polymers. The effects of densification on the structure of dry CNT fibres were measured by WAXS/SAXS. The densification treatment brings the fibre bundles closer (pores become smaller), leading to an increase in fibre density. However, structural domains made of the load and charge carrying nanotubes are not affected; consequently, it has no substantial effect on mechanical and electrical properties. SAXS measurements on the CNT fibres before and after liquid infiltration imply that most liquids are able to fill the internal pores but not to intercalate between nanotubes. Successful infiltration of low molecular weight polymer chains tends to expand the fibre bundles and increases the pore-opening angle. SAXS results indicate that the inner structure of the fibre, in terms of the nanotube layer arrangement and the fibre alignment, are not largely affected when infiltrated with polymers of relatively high molecular weight. The crystallisation of a variety of semicrystalline polymers is accelerated by the presence of aligned fibres of CNTs and results in the growth of a transcrystalline layer perpendicular to the fibre surface. This can be observed directly under polarised optical microscope, and detected by the exothermic peaks during differential scanning calorimetry. The discussion on the driving forces for the enhanced nucleation points out the preferential chain orientation of polymer lamella with the chain axis parallel to the fibre and thus to the nanotubes, which is confirmed by two-dimensional WAXS patterns. A non-epitaxial polymer crystal growth habit at the CNT-polymer interface is proposed, which is independent of lattice matching between the polymer and nanotubes. These findings contribute to the discussion on polymer nucleation on CNTs and other nanocarbons, and their implication for the development of large polymer composites based on long and aligned fibres of CNTs.

Anomalous Consistency in Mild Cognitive Impairment: A Complex Networks Approach

Increased variability in performance has been associated with the emergence of several neurological and psychiatric pathologies. However, whether and how consistency of neuronal activity may also be indicative of an underlying pathology is still poorly understood. Here we propose a novel method for evaluating consistency from non-invasive brain recordings. We evaluate the consistency of the cortical activity recorded with magnetoencephalography in a group of subjects diagnosed with Mild Cognitive Impairment (MCI), a condition sometimes prodromal of dementia, during the execution of a memory task. We use metrics coming from nonlinear dynamics to evaluate the consistency of cortical regions. A representation known as parenclitic networks is constructed, where atypical features are endowed with a network structure, the topological properties of which can be studied at various scales. Pathological conditions correspond to strongly heterogeneous networks, whereas typical or normative conditions are characterized by sparsely connected networks with homogeneous nodes. The analysis of this kind of networks allows identifying the extent to which consistency is affected in the MCI group and the focal points where MCI is especially severe. To the best of our knowledge, these results represent the first attempt at evaluating the consistency of brain functional activity using complex networks theory.

The anatomy of urban social networks and its implications in the searchability problem

The appearance of large geolocated communication datasets has recently increased our understanding of how social networks relate to their physical space. However, many recurrently reported properties, such as the spatial clustering of network communities, have not yet been systematically tested at different scales. In this work we analyze the social network structure of over 25 million phone users from three countries at three different scales: country, provinces and cities. We consistently find that this last urban scenario presents significant differences to common knowledge about social networks. First, the emergence of a giant component in the network seems to be controlled by whether or not the network spans over the entire urban border, almost independently of the population or geographic extension of the city. Second, urban communities are much less geographically clustered than expected. These two findings shed new light on the widely-studied searchability in self-organized networks. By exhaustive simulation of decentralized search strategies we conclude that urban networks are searchable not through geographical proximity as their country-wide counterparts, but through an homophily-driven community structure.

Constrained self-organizing feature map to preserve feature extraction topology

In many classification problems, it is necessary to consider the specific location of an n-dimensional space from which features have been calculated. For example, considering the location of features extracted from specific areas of a two-dimensional space, as an image, could improve the understanding of a scene for a video surveillance system. In the same way, the same features extracted from different locations could mean different actions for a 3D HCI system. In this paper, we present a self-organizing feature map able to preserve the topology of locations of an n-dimensional space in which the vector of features have been extracted. The main contribution is to implicitly preserving the topology of the original space because considering the locations of the extracted features and their topology could ease the solution to certain problems. Specifically, the paper proposes the n-dimensional constrained self-organizing map preserving the input topology (nD-SOM-PINT). Features in adjacent areas of the n-dimensional space, used to extract the feature vectors, are explicitly in adjacent areas of the nD-SOM-PINT constraining the neural network structure and learning. As a study case, the neural network has been instantiate to represent and classify features as trajectories extracted from a sequence of images into a high level of semantic understanding. Experiments have been thoroughly carried out using the CAVIAR datasets (Corridor, Frontal and Inria) taken into account the global behaviour of an individual in order to validate the ability to preserve the topology of the two-dimensional space to obtain high-performance classification for trajectory classification in contrast of non-considering the location of features. Moreover, a brief example has been included to focus on validate the nD-SOM-PINT proposal in other domain than the individual trajectory. Results confirm the high accuracy of the nD-SOM-PINT outperforming previous methods aimed to classify the same datasets.

COMUNICAÇÃO MERCADOLÓGICA EM MÍDIAS SOCIAIS ONLINE ANÁLISES PARA RELACIONAMENTOS EM REDES VIRTUAIS

Com o propósito de incrementar suas campanhas mercadológicas, muitas organizações, recorrem às ferramentas de mídias sociais hospedadas na Internet. Com isso, procuram o aumento de produtividade com adoção de sistemas automatizados de reprodução de mensagens, ou mesmo de recursos de acesso direto, inserindo mensagens de caráter persuasivo nos fóruns de discussões em comunidades online. Uma certa falta de sensibilidade para com o trato comunicacional, num meio potencialmente promissor, mas que pede uma outra interpretação, para posterior ação. Frequentemente implica em uma possibilidade de reverberação indicando ser imprescindível maior atenção na elaboração e no direcionamento desses fluxos comunicacionais, acentuadamente os de propósitos persuasivos. Nesse sentido, o presente trabalho propõe o estudo de comunidades online nas quais possamos a partir da identificação dos fatores que levem à sua formação, analisar e interpretar sua estrutura e seus fluxos comunicacionais, tais que, indiquem seus elementos agregadores. Para tal, com os preceitos metodológicos observados, objetivou-se demonstrar que, com esses componentes, as análises podem ser desenvolvidas para melhor adequação de estratégias de relacionamentos, possibilitando ações inerentes ao processo comunicacional mercadológico com essas comunidades. A metodologia ora empregada envolveu análise estrutural da rede com aplicações de softwares como UCINET, integrado com NetDraw, e dos fluxos comunicacionais, que formaram o corpora, analisado com a suíte Wordsmith Tools. Uma rede formada em comunidade hospedada na ferramenta orkut, por meio da obtenção dos conteúdos de fóruns temáticos, forneceu o corpora para as análises lexicais. Os resultados obtidos puderam caracterizar, não só a própria existência da rede social, como as potencialidades de relacionamento, a partir de interpretações de fluxos dialógicos de seus elementos agregadores, por meio de recursos visuais (grafos), estatísticos e lexicais.

The interplay between microscopic and mesoscopic structures in complex networks

Understanding a complex network's structure holds the key to understanding its function. The physics community has contributed a multitude of methods and analyses to this cross-disciplinary endeavor. Structural features exist on both the microscopic level, resulting from differences between single node properties, and the mesoscopic level resulting from properties shared by groups of nodes. Disentangling the determinants of network structure on these different scales has remained a major, and so far unsolved, challenge. Here we show how multiscale generative probabilistic exponential random graph models combined with efficient, distributive message-passing inference techniques can be used to achieve this separation of scales, leading to improved detection accuracy of latent classes as demonstrated on benchmark problems. It sheds new light on the statistical significance of motif-distributions in neural networks and improves the link-prediction accuracy as exemplified for gene-disease associations in the highly consequential Online Mendelian Inheritance in Man database. © 2011 Reichardt et al.

The impact of architecture on the performance of artificial neural networks

A number of researchers have investigated the impact of network architecture on the performance of artificial neural networks. Particular attention has been paid to the impact on the performance of the multi-layer perceptron of architectural issues, and the use of various strategies to attain an optimal network structure. However, there are still perceived limitations with the multi-layer perceptron and networks that employ a different architecture to the multi-layer perceptron have gained in popularity in recent years, particularly, networks that implement a more localised solution, where the solution in one area of the problem space does not impact, or has a minimal impact, on other areas of the space. In this study, we discuss the major architectural issues affecting the performance of a multi-layer perceptron, before moving on to examine in detail the performance of a new localised network, namely the bumptree. The work presented here examines the impact on the performance of artificial neural networks of employing alternative networks to the long established multi-layer perceptron. In particular, networks that impose a solution where the impact of each parameter in the final network architecture has a localised impact on the problem space being modelled are examined. The alternatives examined are the radial basis function and bumptree neural networks, and the impact of architectural issues on the performance of these networks is examined. Particular attention is paid to the bumptree, with new techniques for both developing the bumptree structure and employing this structure to classify patterns being examined.

Strategic innovation networks

This thesis draws on two key areas of the innovation literature, the strategic management of technology (SMOT) and innovation networks. The aim is to integrate these two areas of the management of innovation literature to develop a framework which I describe as the Strategic Innovation Network (SIN). The key proposition that the revised framework (SIN) aims to address is based on the work of Chandler (1962). Chandler's (1962) conclusion that 'structure follows strategy' is examined in relation to the interaction between corporate/technology strategy and network structure. The SIN is intended to address weaknesses in both the SMOT and network literature. The research data is based on five detailed longitudinal case studies. The organisations are defined as mid-corporate firms operating in traditional manufacturing sectors. Each organisation was chosen on the basis that it was aiming to develop its innovative capacity through product or process innovation projects. The research was carried out over an 18 month period with interviews being held regularly to develop the longitudinal aspect of the study analysis. The data for each individual case study is examined using the SIN framework. The longitudinal approach addresses the objective to provide a dynamic model of the innovation processes by mapping the changes in network structure during the course of individual projects. The network structural changes are examined in relation to each organisation's strategy and five key dynamic network stages are identified in relation to the innovation process. These network stages show the influence strategy has on the structures adopted by the five case studies.

The fatigue ageing of vulcanised rubbers

The cause of the respective rough and smooth fatigue failure surfaces of Neoprene GS : Neoprene W and Neoprene GS : natural rubber vulcanisates is investigated. The contrasting morphology of the vulcanisates is found to be the major factor determining the fatigue behaviour of the blends. Neoprene GS and Neoprene W appear to form homogeneous blends which exhibit physical properties and fatigue failure surfaces intermediate between those of the two horropolymers. Neoprene GS and natural rubber exhibit heterogeneity when blended together. The morphology of these blends is found to influence both the fatigue resistance and failure surface of the vulcanisates. Exceptional uncut and cut initiated fatigue lives are observed for blends having an interconnecting network morphology. The network structure and cross-link density of the elastomers in the blends and the addition of carbon black and antioxidant are all found to influence the fatigue resistance but not the failure mechanism of the vulcanisate.