Measuring and improving community resilience: a Fuzzy Logic approach

Due to the increasing frequency of natural and man-made disasters worldwide, the scientific community has paid considerable attention to the concept of resilience engineering in recent years. Authorities and decision-makers, on the other hand, have been focusing their efforts to develop strategies that can help increase community resilience to different types of extreme events. Since it is often impossible to prevent every risk, the focus is on adapting and managing risks in ways that minimize impacts to communities (e.g., humans and other systems). Several resilience strategies have been proposed in the literature to reduce disaster risk and improve community resilience. Generally, resilience assessment is challenging due to uncertainty and unavailability of data necessary for the estimation process. This paper proposes a Fuzzy Logic method for quantifying community resilience. The methodology is based on the PEOPLES framework, an indicator-based hierarchical framework that defines all aspects of the community. A fuzzy-based approach is implemented to quantify the PEOPLES indicators using descriptive knowledge instead of hard data, accounting also for the uncertainties involved in the analysis. To demonstrate the applicability of the methodology, data regarding the functionality of the city San Francisco before and after the Loma Prieta earthquake are used to obtain a resilience index of the Physical Infrastructure dimension of the PEOPLES framework. The results show that the methodology can provide good estimates of community resilience despite the uncertainty of the indicators. Hence, it serves as a decision-support tool to help decision-makers and stakeholders assess and improve the resilience of their communities

High Dimensional Data Clustering using Self-Organized Map

As the population grows and e economic development, houses could be one of basic needs of every family. Therefore, housing investment has promising value in the future. This research implements the Self-Organized Map (SOM) algorithm to cluster house data for providing several house groups based on the various features. K-means is used as the baseline of the proposed approach. SOM has higher silhouette coefficient (0.4367) compared to its comparison (0.236). Thus, this method outperforms k-means in terms of visualizing high-dimensional data cluster. It is also better in the cluster formation and regulating the data distribution

AN INVESTIGATION INTO AN EXPERT SYSTEM FOR TELECOMMUNICATION NETWORK DESIGN

Many telephone companies, especially in Eastern-Europe and the 'third world', are developing new telephone networks. In such situations the network design engineer needs computer based tools that not only supplement his own knowledge but also help him to cope with situations where not all the information necessary for the design is available. Often traditional network design tools are somewhat removed from the practical world for which they were developed. They often ignore the significant uncertain and statistical nature of the input data. They use data taken from a fixed point in time to solve a time variable problem, and the cost formulae tend to be on an average per line or port rather than the specific case. Indeed, data is often not available or just plainly unreliable. The engineer has to rely on rules of thumb honed over many years of experience in designing networks and be able to cope with missing data. The complexity of telecommunication networks and the rarity of specialists in this area often makes the network design process very difficult for a company. It is therefore an important area for the application of expert systems. Designs resulting from the use of expert systems will have a measure of uncertainty in their solution and adequate account must be made of the risk involved in implementing its design recommendations. The thesis reviews the status of expert systems as used for telecommunication network design. It further shows that such an expert system needs to reduce a large network problem into its component parts, use different modules to solve them and then combine these results to create a total solution. It shows how the various sub-division problems are integrated to solve the general network design problem. This thesis further presents details of such an expert system and the databases necessary for network design: three new algorithms are invented for traffic analysis, node locations and network design and these produce results that have close correlation with designs taken from BT Consultancy archives. It was initially supposed that an efficient combination of existing techniques for dealing with uncertainty within expert systems would suffice for the basis of the new system. It soon became apparent, however, that to allow for the differing attributes of facts, rules and data and the varying degrees of importance or rank within each area, a new and radically different method would be needed. Having investigated the existing uncertainty problem it is believed that a new more rational method has been found. The work has involved the invention of the 'Uncertainty Window' technique and its testing on various aspects of network design, including demand forecast, network dimensioning, node and link system sizing, etc. using a selection of networks that have been designed by BT Consultancy staff. From the results of the analysis, modifications to the technique have been incorporated with the aim of optimising the heuristics and procedures, so that the structure gives an accurate solution as early as possible. The essence of the process is one of associating the uncertainty windows with their relevant rules, data and facts, which results in providing the network designer with an insight into the uncertainties that have helped produce the overall system design: it indicates which sources of uncertainty and which assumptions are were critical for further investigation to improve upon the confidence of the overall design. The windowing technique works by virtue of its ability to retain the composition of the uncertainty and its associated values, assumption, etc. and allows for better solutions to be attained.BRITISH TELECOMMUNICATIONS PL

(VANET IR-CAS): Utilizing IR Techniques in Building Context Aware Systems for VANET

Most of the available context aware dissemination systems for the Vehicular Ad hoc Network (VANET) are centralized systems with low level of user privacy and preciseness. In addition, the absence of common assessment models deprives researchers from having fair evaluation of their proposed systems and unbiased comparison with other systems. Due to the importance of the commercial, safety and convenience services, three IR-CAS systems are developed to improve three applications of these services: the safety Automatic Crash Notification (ACN), the convenience Congested Road Notification (CRN) and the commercial Service Announcement (SA). The proposed systems are context aware systems that utilize the information retrieval (IR) techniques in the context aware information dissemination. The dispatched information is improved by deploying the vector space model for estimating the relevance or severity by calculating the Manhattan distance between the current situation context and the severest context vectors. The IR-CAS systems outperform current systems that use machine learning, fuzzy logic and binary models in decentralization, effectiveness by binary and non-binary measures, exploitation of vehicle processing power, dissemination of informative notifications with certainty degrees and partial rather than binary or graded notifications that are insensitive to differences in severity within grades, and protection of privacy which achieves user satisfaction. In addition, the visual-manual and speech-visual dual-mode user interface is designed to improve user safety by minimizing distraction. An evaluation model containing ACN and CRN test collections, with around 500,000 North American test cases each, is created to enable fair effectiveness comparisons among VANET context aware systems. Hence, the novelty of VANET IR-CAS systems is: First, providing scalable abstract context model with IR based processing that raises the notification relevance and precision. Second, increasing decentralization, user privacy, and safety with the least distracting user interface. Third, designing unbiased performance evaluation as a ground for distinguishing significantly effective VANET context aware systems

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Proceedings of the GIS Research UK 18th Annual Conference GISRUK 2010

This volume holds the papers from the 18th annual GIS Research UK (GISRUK). This year the conference, hosted at University College London (UCL), from Wednesday 14 to Friday 16 April 2010. The conference covered the areas of core geographic information science research as well as applications domains such as crime and health and technological developments in LBS and the geoweb. UCL’s research mission as a global university is based around a series of Grand Challenges that affect us all, and these were accommodated in GISRUK 2010. The overarching theme this year was “Global Challenges”, with specific focus on the following themes: * Crime and Place * Environmental Change * Intelligent Transport * Public Health and Epidemiology * Simulation and Modelling * London as a global city * The geoweb and neo-geography * Open GIS and Volunteered Geographic Information * Human-Computer Interaction and GIS Traditionally, GISRUK has provided a platform for early career researchers as well as those with a significant track record of achievement in the area. As such, the conference provides a welcome blend of innovative thinking and mature reflection. GISRUK is the premier academic GIS conference in the UK and we are keen to maintain its outstanding record of achievement in developing GIS in the UK and beyond