13,909 research outputs found
Probabilistic latent semantic analysis as a potential method for integrating spatial data concepts
In this paper we explore the use of Probabilistic Latent Semantic Analysis (PLSA) as a method for quantifying semantic differences between land cover classes. The results are promising, revealing âhiddenâ or not easily discernible data concepts. PLSA provides a âbottom upâ approach to interoperability problems for users in the face of âtop downâ solutions provided by formal ontologies. We note the potential for a meta-problem of how to interpret the concepts and the need for further research to reconcile the top-down and bottom-up approaches
Comparing Rough Set Theory with Multiple Regression Analysis as Automated Valuation Methodologies
This paper focuses on the problem of applying rough set theory to mass appraisal. This methodology was first introduced by a Polish mathematician, and has been applied recently as an automated valuation methodology by the author. The method allows the appraiser to estimate a property without defining econometric modeling, although it does not give any quantitative estimation of marginal prices. In a previous paper by the author, data were organized into classes prior to the valuation process, allowing for the if-then, or right âruleâ for each property class to be defined. In that work, the relationship between property and class of valued was said to be dichotomic.mass appraisal; property valuation; rough set theory; valued tolerance relation
Historical collaborative geocoding
The latest developments in digital have provided large data sets that can
increasingly easily be accessed and used. These data sets often contain
indirect localisation information, such as historical addresses. Historical
geocoding is the process of transforming the indirect localisation information
to direct localisation that can be placed on a map, which enables spatial
analysis and cross-referencing. Many efficient geocoders exist for current
addresses, but they do not deal with the temporal aspect and are based on a
strict hierarchy (..., city, street, house number) that is hard or impossible
to use with historical data. Indeed historical data are full of uncertainties
(temporal aspect, semantic aspect, spatial precision, confidence in historical
source, ...) that can not be resolved, as there is no way to go back in time to
check. We propose an open source, open data, extensible solution for geocoding
that is based on the building of gazetteers composed of geohistorical objects
extracted from historical topographical maps. Once the gazetteers are
available, geocoding an historical address is a matter of finding the
geohistorical object in the gazetteers that is the best match to the historical
address. The matching criteriae are customisable and include several dimensions
(fuzzy semantic, fuzzy temporal, scale, spatial precision ...). As the goal is
to facilitate historical work, we also propose web-based user interfaces that
help geocode (one address or batch mode) and display over current or historical
topographical maps, so that they can be checked and collaboratively edited. The
system is tested on Paris city for the 19-20th centuries, shows high returns
rate and is fast enough to be used interactively.Comment: WORKING PAPE
Towards development of fuzzy spatial datacubes : fundamental concepts with example for multidimensional coastal erosion risk assessment and representation
Les systĂšmes actuels de base de donnĂ©es gĂ©odĂ©cisionnels (GeoBI) ne tiennent gĂ©nĂ©ralement pas compte de l'incertitude liĂ©e Ă l'imprĂ©cision et le flou des objets; ils supposent que les objets ont une sĂ©mantique, une gĂ©omĂ©trie et une temporalitĂ© bien dĂ©finies et prĂ©cises. Un exemple de cela est la reprĂ©sentation des zones Ă risque par des polygones avec des limites bien dĂ©finies. Ces polygones sont crĂ©Ă©s en utilisant des agrĂ©gations d'un ensemble d'unitĂ©s spatiales dĂ©finies sur soit des intĂ©rĂȘts des organismes responsables ou les divisions de recensement national. MalgrĂ© la variation spatio-temporelle des multiples critĂšres impliquĂ©s dans lâanalyse du risque, chaque polygone a une valeur unique de risque attribuĂ© de façon homogĂšne sur l'Ă©tendue du territoire. En rĂ©alitĂ©, la valeur du risque change progressivement d'un polygone Ă l'autre. Le passage d'une zone Ă l'autre n'est donc pas bien reprĂ©sentĂ© avec les modĂšles dâobjets bien dĂ©finis (crisp). Cette thĂšse propose des concepts fondamentaux pour le dĂ©veloppement d'une approche combinant le paradigme GeoBI et le concept flou de considĂ©rer la prĂ©sence de lâincertitude spatiale dans la reprĂ©sentation des zones Ă risque. En fin de compte, nous supposons cela devrait amĂ©liorer lâanalyse du risque. Pour ce faire, un cadre conceptuel est dĂ©veloppĂ© pour crĂ©er un model conceptuel dâune base de donnĂ©e multidimensionnelle avec une application pour lâanalyse du risque dâĂ©rosion cĂŽtier. Ensuite, une approche de la reprĂ©sentation des risques fondĂ©e sur la logique floue est dĂ©veloppĂ©e pour traiter l'incertitude spatiale inhĂ©rente liĂ©e Ă l'imprĂ©cision et le flou des objets. Pour cela, les fonctions d'appartenance floues sont dĂ©finies en basant sur lâindice de vulnĂ©rabilitĂ© qui est un composant important du risque. Au lieu de dĂ©terminer les limites bien dĂ©finies entre les zones Ă risque, l'approche proposĂ©e permet une transition en douceur d'une zone Ă une autre. Les valeurs d'appartenance de plusieurs indicateurs sont ensuite agrĂ©gĂ©es basĂ©es sur la formule des risques et les rĂšgles SI-ALORS de la logique floue pour reprĂ©senter les zones Ă risque. Ensuite, les Ă©lĂ©ments clĂ©s d'un cube de donnĂ©es spatiales floues sont formalisĂ©s en combinant la thĂ©orie des ensembles flous et le paradigme de GeoBI. En plus, certains opĂ©rateurs d'agrĂ©gation spatiale floue sont prĂ©sentĂ©s. En rĂ©sumĂ©, la principale contribution de cette thĂšse se rĂ©fĂšre de la combinaison de la thĂ©orie des ensembles flous et le paradigme de GeoBI. Cela permet lâextraction de connaissances plus comprĂ©hensibles et appropriĂ©es avec le raisonnement humain Ă partir de donnĂ©es spatiales et non-spatiales. Pour ce faire, un cadre conceptuel a Ă©tĂ© proposĂ© sur la base de paradigme GĂ©oBI afin de dĂ©velopper un cube de donnĂ©es spatiale floue dans le system de Spatial Online Analytical Processing (SOLAP) pour Ă©valuer le risque de l'Ă©rosion cĂŽtiĂšre. Cela nĂ©cessite d'abord d'Ă©laborer un cadre pour concevoir le modĂšle conceptuel basĂ© sur les paramĂštres de risque, d'autre part, de mettre en Ćuvre lâobjet spatial flou dans une base de donnĂ©es spatiales multidimensionnelle, puis l'agrĂ©gation des objets spatiaux flous pour envisager Ă la reprĂ©sentation multi-Ă©chelle des zones Ă risque. Pour valider l'approche proposĂ©e, elle est appliquĂ©e Ă la rĂ©gion Perce (Est du QuĂ©bec, Canada) comme une Ă©tude de cas.Current Geospatial Business Intelligence (GeoBI) systems typically do not take into account the uncertainty related to vagueness and fuzziness of objects; they assume that the objects have well-defined and exact semantics, geometry, and temporality. Representation of fuzzy zones by polygons with well-defined boundaries is an example of such approximation. This thesis uses an application in Coastal Erosion Risk Analysis (CERA) to illustrate the problems. CERA polygons are created using aggregations of a set of spatial units defined by either the stakeholdersâ interests or national census divisions. Despite spatiotemporal variation of the multiple criteria involved in estimating the extent of coastal erosion risk, each polygon typically has a unique value of risk attributed homogeneously across its spatial extent. In reality, risk value changes gradually within polygons and when going from one polygon to another. Therefore, the transition from one zone to another is not properly represented with crisp object models. The main objective of the present thesis is to develop a new approach combining GeoBI paradigm and fuzzy concept to consider the presence of the spatial uncertainty in the representation of risk zones. Ultimately, we assume this should improve coastal erosion risk assessment. To do so, a comprehensive GeoBI-based conceptual framework is developed with an application for Coastal Erosion Risk Assessment (CERA). Then, a fuzzy-based risk representation approach is developed to handle the inherent spatial uncertainty related to vagueness and fuzziness of objects. Fuzzy membership functions are defined by an expert-based vulnerability index. Instead of determining well-defined boundaries between risk zones, the proposed approach permits a smooth transition from one zone to another. The membership values of multiple indicators (e.g. slop and elevation of region under study, infrastructures, houses, hydrology network and so on) are then aggregated based on risk formula and Fuzzy IF-THEN rules to represent risk zones. Also, the key elements of a fuzzy spatial datacube are formally defined by combining fuzzy set theory and GeoBI paradigm. In this regard, some operators of fuzzy spatial aggregation are also formally defined. The main contribution of this study is combining fuzzy set theory and GeoBI. This makes spatial knowledge discovery more understandable with human reasoning and perception. Hence, an analytical conceptual framework was proposed based on GeoBI paradigm to develop a fuzzy spatial datacube within Spatial Online Analytical Processing (SOLAP) to assess coastal erosion risk. This necessitates developing a framework to design a conceptual model based on risk parameters, implementing fuzzy spatial objects in a spatial multi-dimensional database, and aggregating fuzzy spatial objects to deal with multi-scale representation of risk zones. To validate the proposed approach, it is applied to Perce region (Eastern Quebec, Canada) as a case study
Technology Incubators as Nodes in Knowledge Networks
It is widely accepted that new knowledge underpinned innovation and growth influences economic activities. Economic agents rely not only on their own knowledge but also knowledge from others, whether it be codified and âtransferred via ICTâ or in tacit form. Moreover, it has long been argued that the acquisition of latter type of knowledge is influenced by geographic proximity. Based on this argument, it follows that the part firmsâ supply of knowledge depends on how close, in terms of physical distance, to other firms, suppliers, customers, and research institutions, they are located. They are all can be categorize as a pool of knowledge that important for the firmsâ growth and innovation capacity. Today, we witness many initiatives from policy makers around the world to compete in an increasingly technology- driven global economy through the establishing of technology incubators. Technology incubators can be conceived as organizations and/or facilities to enhance high-technology firm establishment and survival. Mostly they are located near the university or research center. There are many success stories on the contribution of incubators to the regional growth. At the same time, technology incubators have been widely criticized in the academic literature when judged in terms of regional innovation and knowledge development. The critics include the relying on an outdated, linear, model of innovation, which assumes that knowledge can be transferred directly from university to firms. However, innovation is now widely recognized as a complex non-linear process involving feedback loops and the creation of synergies through a diverse range of knowledge networks. Therefore, our understanding about knowledge spillover processes connected with incubator is yet poor. Very little is known about the mechanisms of knowledge exchange and spillover initiated by incubator and their role in supporting the growth of the firm. In this study we draw on the current body of literature, mainly agglomeration theories, and use the concepts of tacit knowledge and context to understand how knowledge spillovers actually take place. Our objective is to build a conceptual framework that describes how technology incubators operate as a mediator of knowledge for their tenants. In addition, based on empirical data of high-technology start-ups at TU Delft (The Netherlands), this study tests the proposition that not only geographic proximity to the university, but also that relations with other firms, particularly customers and suppliers matters. We also consider the function of ICT in shaping the new role of technology incubators in providing knowledge support. By explicitly analyzing the knowledge spillovers and mediation role offered by technology incubators, we seek to open up the âblack boxâ of the incubation process as a source of learning and gaining knowledge resources. We conclude the paper with a few recommendations for policymaking and further research.
Adaptive Neuro-Fuzzy Inference System for Dynamic Load Balancing in 3GPP LTE
ANFIS is applicable in modeling of key parameters when investigating the performance and functionality of wireless networks. The need to save both capital and operational expenditure in the management of wireless networks cannot be over-emphasized. Automation of network operations is a veritable means of achieving the necessary reduction in CAPEX and OPEX. To this end, next generations networks such WiMAX and 3GPP LTE and LTE-Advanced provide support for self-optimization, self-configuration and self-healing to minimize human-to-system interaction and hence reap the attendant benefits of automation. One of the most important optimization tasks is load balancing as it affects network operation right from planning through the lifespan of the network. Several methods for load balancing have been proposed. While some of them have a very buoyant theoretical basis, they are not practically implementable at the current state of technology. Furthermore, most of the techniques proposed employ iterative algorithm, which in itself is not computationally efficient. This paper proposes the use of soft computing, precisely adaptive neuro-fuzzy inference system for dynamic QoS-aware load balancing in 3GPP LTE. Three key performance indicators (i.e. number of satisfied user, virtual load and fairness distribution index) are used to adjust hysteresis task of load balancing
SciTech News Volume 71, No. 1 (2017)
Columns and Reports From the Editor 3
Division News Science-Technology Division 5 Chemistry Division 8 Engineering Division Aerospace Section of the Engineering Division 9 Architecture, Building Engineering, Construction and Design Section of the Engineering Division 11
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Investigation on soft computing techniques for airport environment evaluation systems
Spatial and temporal information exist widely in engineering fields, especially
in airport environmental management systems. Airport environment is influenced
by many different factors and uncertainty is a significant part of the
system. Decision support considering this kind of spatial and temporal information
and uncertainty is crucial for airport environment related engineering
planning and operation. Geographical information systems and computer aided
design are two powerful tools in supporting spatial and temporal information
systems. However, the present geographical information systems and computer
aided design software are still too general in considering the special features in
airport environment, especially for uncertainty. In this thesis, a series of parameters
and methods for neural network-based knowledge discovery and training
improvement are put forward, such as the relative strength of effect, dynamic
state space search strategy and compound architecture. [Continues.
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