Context dependent fuzzy modelling and its applications

Fuzzy rule-based systems (FRBS) use the principle of fuzzy sets and fuzzy logic to describe vague and imprecise statements and provide a facility to express the behaviours of the system with a human-understandable language. Fuzzy information, once defined by a fuzzy system, is fixed regardless of the circumstances and therefore makes it very difficult to capture the effect of context on the meaning of the fuzzy terms. While efforts have been made to integrate contextual information into the representation of fuzzy sets, it remains the case that often the context model is very restrictive and/or problem specific. The work reported in this thesis is our attempt to create a practical frame work to integrate contextual information into the representation of fuzzy sets so as to improve the interpretability as well as the accuracy of the fuzzy system. Throughout this thesis, we have looked at the capability of the proposed context dependent fuzzy sets as a stand alone as well as in combination with other methods in various application scenarios ranging from time series forecasting to complicated car racing control systems. In all of the applications, the highly competitive performance nature of our approach has proven its effectiveness and efficiency compared with existing techniques in the literature

Context dependent fuzzy modelling and its applications

Fuzzy rule-based systems (FRBS) use the principle of fuzzy sets and fuzzy logic to describe vague and imprecise statements and provide a facility to express the behaviours of the system with a human-understandable language. Fuzzy information, once defined by a fuzzy system, is fixed regardless of the circumstances and therefore makes it very difficult to capture the effect of context on the meaning of the fuzzy terms. While efforts have been made to integrate contextual information into the representation of fuzzy sets, it remains the case that often the context model is very restrictive and/or problem specific. The work reported in this thesis is our attempt to create a practical frame work to integrate contextual information into the representation of fuzzy sets so as to improve the interpretability as well as the accuracy of the fuzzy system. Throughout this thesis, we have looked at the capability of the proposed context dependent fuzzy sets as a stand alone as well as in combination with other methods in various application scenarios ranging from time series forecasting to complicated car racing control systems. In all of the applications, the highly competitive performance nature of our approach has proven its effectiveness and efficiency compared with existing techniques in the literature

Change of support problemへの新たな空間統計モデルの開発

筑波大学 (University of Tsukuba)201

Extrapolating incomplete animal population and surveillance data for use in national disease control : examples from Myanmar and New Zealand : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Veterinary Epidemiology, School of Veterinary Science at Massey University, Manawatu, New Zealand

National level databases of animal numbers, locations, and movements provide the essential foundations for disease outbreak investigations, disease control, and disease preparedness activities. These activities are particularly important for managing and mitigating the risks of high impact exotic disease outbreaks like foot-and-mouth disease (FMD) as well as other economically important endemic diseases, which can significantly impact international trade and food security. However, many countries worldwide either lack national animal databases entirely or have multiple, fragmented databases that provide an incomplete picture of animal demographics. Consequently, there has been growing interest in developing novel methods to infer missing information on animal populations from other data sources, to quantify the extent of missing information, and to understand the impacts of missing information on the predictions made from national disease simulation models. This thesis explores these issues in the context of an FMD free country (New Zealand) as well as a country with endemic FMD (Myanmar). In Chapter 3, regression models were used to predict farm-level animal populations in New Zealand based on available data on farm type and location. When the results were compared against a subset of validated animal population data, the predictions at the farm level were found to be inaccurate especially for small-scale farms that keep animals for personal consumption or as a hobby. These properties are of particular interest to animal health authorities as they have been identified as at risk for exotic disease outbreaks. In Chapter 4, the impacts of having inaccurate herd size data on the predictions made by an FMD disease spread simulation model were explored. The results were analysed using cox proportional hazard models and logistic regression models, which showed that simulations run using actual animal population data indicated different optimal control strategies for FMD than models run with imperfect data and these effects differed by the region in New Zealand where the hypothetical disease outbreak was seeded. In Chapter 5, high-resolution local survey data and low-resolution national remote sensor data were used alone and in combination to predict the location of FMD positive villages in Myanmar, which were identified by serological sampling conducted as part of a large OIE funded research project in 2016. The performance of both random forest models and logistic regression models were explored using training and testing data sets. Bovine populations and proximity to cattle markets were found to be significant risk factors for FMD seropositivity and the logistic regression models performed as well as machine learning techniques. Chapter 6 compared verbal reports of FMD outbreaks from village headman and householders against the serological test results from their villages to determine whether using public reports is a viable alternative to conducting resource intensive serological surveys for estimating FMD prevalence in Myanmar. Although village headmen proved to be a better source of FMD reports compared to householders, the verbal reports were still not as accurate as serological tests in an endemic situation where both sensitivity and specificity of observing clinical signs can be complicated by endemic stability and concurrent outbreaks of other diseases. The work in both chapters 5 and 6 was carried out using data from activities of the Livestock Breeding and Veterinary Department and the OIE and as such separate human ethics approval was not required for the surveys described. Chapter 7 addressed the issue of estimating the scale of missing data in a national database by comparing intensively collected interview information with recorded movements at the farm level for farms involved in New Zealand’s Mycoplasma bovis eradication programme. The results showed that dairy farmers often failed to record almost half of high risk movements including leased bulls, calves sent offsite for rearing, and adult cattle sent away for winter grazing. It was also estimated that approximately 60% of animals arriving at abattoirs in New Zealand have multiple movements missing from their life history in the National Animal Identification and Tracing system (NAIT) database. This missing information had a significant impact on the ability of government and industry to effectively respond to the outbreak. However, a positive finding from this study was that the rates of missing data are decreasing over time. Overall, this thesis demonstrated the importance of enhancing efforts to collect accurate and up-to- date national animal population and movement data. For New Zealand, the changes required to improve the national farm animal data landscape include improving compliance with the legislated requirements to record animal movements and modifying the existing databases to record information on the health status of animals against a unique animal identifier. A unique farm identifier is required at the national level and should be agreed upon by industry representatives, government and researchers. The combination of animal health data associated with the unique animal identifier and a single current farm identifier for all farms will result in a robust animal health and biosecurity system

Modeling Visit Potential of Geographic Locations Based on Mobility Data

Every day people interact with the environment by passing or visiting geographic locations. Information about such entity-location interactions can be used in a number of applications and its value has been recognized by companies and public institutions. However, although the necessary tracking technologies such as GPS, GSM or RFID have long found their way into everyday life, the practical usage of visit information is still limited. Besides economic and ethical reasons for the restricted usage of entity-location interactions there are also two very basic problems. First, no formal definition of entity-location interaction quantities exists. Second, at the current state of technology, no tracking technology guarantees complete observations, and the treatment of missing data in mobility applications has been neglected in trajectory data mining so far. This thesis therefore focuses on the definition and estimation of quantities about the visiting behavior between mobile entities and geographic locations from incomplete mobility data. In a first step we provide an application-independent language to evaluate entity-location interactions. Based on a uniform notation, we define a family of quantities called visit potential, which contains the most basic interaction quantities and can be extended on need. By identifying the common background of all quantities we are able to analyze relationships between different quantities and to infer consistency requirements between related parameterizations of the quantities. We demonstrate the general applicability of visit potential using two real-world applications for which we give a precise definition of the employed entity-location interaction quantities in terms of visit potential. Second, this thesis provides the first systematic analysis of methods for the handling of missing data in mobility mining. We select a set of promising methods that take different approaches to handling missing data and test their robustness with respect to different scenarios. Our analyses consider different mechanisms and intensities of missing data under artificial censoring as well as varying visit intensities. We hereby analyze not only the applicability of the selected methods but also provide a systematic approach for parameterization and testing that can also be applied to the analysis of other mobility data sets. Our experiments show that only two of the tested methods supply unbiased estimates of visit potential quantities and are applicable to the domain. In addition, both methods supply unbiased estimates only of a single quantity. Therefore, it will be a future challenge to design methods for the entire collection of visit potential quantities. The topic of this thesis is motivated by applied research at the Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS for business applications in outdoor advertisement. We will use the outdoor advertisement scenario throughout this thesis for demonstration and experimentation.Modellierung von Besuchsgrößen geographischer Orte anhand von Mobilitätsdaten Täglich interagieren Menschen mit ihrer Umgebung, indem sie sich im geografischen Raum bewegen oder gezielt geografische Orte aufsuchen. Informationen über derartige Besuche sind sehr wertvoll und können in einer Reihe von Anwendungen eingesetzt werden. Üblicherweise werden dazu die Bewegungen von Personen mit Hilfe von GPS, GSM oder RFID Technologien verfolgt. Durch eine räumliche Verschneidung der Trajektorien mit der Positionsangabe eines bestimmten Ortes können dann die Besuche extrahiert werden. Allerdings ist derzeitig die Verwendung von Besuchsinformationen in der Praxis begrenzt. Dies hat, neben ökonomischen und ethischen Gründen, vor allem zwei grundlegende Ursachen. Erstens existiert keine formelle Definition von Größen, um Besuchsinformationen einheitlich auszuwerten. Zweitens können aktuelle Technologien keine vollständige Erfassung von Bewegungsinformationen garantieren. Das bedeutet, dass die Basisdaten zur Auswertung von Besuchsinformationen grundsätzlich Lücken enthalten. Für eine fehlerfreie Auswertung der Daten müssen diese Lücken adäquat behandelt werden. Allerdings wurde dieses Thema in der bisherigen Data Mining Literatur zur Auswertung von Bewegungsdaten vernachlässigt. Daher widmet sich diese Dissertation der Definition von Größen zur Auswertung von Besuchsinformationen sowie dem Schätzen dieser Größen aus unvollständigen Bewegungsdaten. Im ersten Teil der Dissertation wird eine anwendungsunabhängige Beschreibungssprache formuliert, um Besuchsinformationen auszuwerten. Auf Basis einer einheitlichen Notation wird eine Familie von Größen namens visit potential definiert, die grundlegende Besuchsgrößen enthält und offen für Erweiterungen ist. Die gemeinsame Basis aller Besuchsgrößen erlaubt weiterhin, Beziehungen zwischen verschiedenen Größen zu analysieren sowie Konsistenzanforderungen zwischen ähnlichen Parametrisierungen der Größen abzuleiten. Abschließend zeigt die Arbeit die generelle Anwendbarkeit der definierten Besuchsgrößen in zwei realen Anwendungen, für die eine präzise Definition der eingesetzten Statistiken mit Hilfe der Besuchsgrößen gegeben wird. Der zweite Teil der Dissertation enthält die erste systematische Methodenanalyse für die Handhabung von unvollständigen Bewegungsdaten. Hierfür werden vier vielversprechende Methoden aus unterschiedlichen Bereichen zur Behandlung von fehlenden Daten ausgewählt und auf ihre Robustheit unter verschiedenen Annahmen getestet. Mit Hilfe einer künstlichen Zensur werden verschiedene Mechanismen und Grade von fehlenden Daten untersucht. Außerdem wird die Robustheit der Methoden für verschieden hohe Besuchsniveaus betrachtet. Die durchgeführten Experimente geben dabei nicht nur Auskunft über die Anwendbarkeit der getesteten Methoden, sondern stellen auch ein systematisches Vorgehen für das Testen und Parametrisieren weiterer Methoden zur Verfügung. Die Ergebnisse der Experimente belegen, dass nur zwei der vier ausgewählten Methoden für die Schätzung von Besuchsgrößen geeignet sind. Beide Methoden liefern jedoch nur für jeweils eine Besuchsgröße erwartungstreue Schätzwerte. Daher besteht eine zukünftige Herausforderung darin, Schätzmethoden für die Gesamtheit an Besuchsgrößen zu entwickeln. Diese Arbeit ist durch anwendungsorientierte Forschung am Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS im Bereich der Außenwerbung motiviert. Das Außenwerbeszenario sowie die darüber zur Verfügung gestellten Anwendungsdaten werden durchgängig zur Demonstration und für die Experimente in der Arbeit eingesetzt

Self-Calibration of Multi-Camera Systems for Vehicle Surround Sensing

Multi-camera systems are being deployed in a variety of vehicles and mobile robots today. To eliminate the need for cost and labor intensive maintenance and calibration, continuous self-calibration is highly desirable. In this book we present such an approach for self-calibration of multi-Camera systems for vehicle surround sensing. In an extensive evaluation we assess our algorithm quantitatively using real-world data

Quantifying the Impacts of Anthropogenic Emissions and Specific Infrastructures on Urban Air Quality

The interconnectivity between city infrastructure, energy and air quality is explored by evaluating the impact of environmental regulations, urban layout, and the transportation sector on air quality and energy use. Particular aspects of the research include assessing how controls have impacted aerosol acidity (which impacts health), linkages between energy, demographics, and how both airports and the use of autonomous and electric vehicles may impact on air quality. This research finds that while environmental regulations are effective in curbing pollution, as measured through decreases in fine particulate matter (PM2.5) emissions in the U.S., PM2.5 particles (aerosol) remain acidic. An implication of this is that it could be decades before changes in aerosol acidity, which is related to the toxicity and adverse health impacts of PM2.5, are seen. The research also found a strong statistical relationship between residential energy (electric and natural gas) consumption and socio-economic demographic (SED) factors for Zip Code Tabulated Areas (ZCTAs) in metropolitan Atlanta. However the electricity model exhibited high bias. Additional analyses found that electricity use is affected by the urban morphology of the roadways, with ZCTAs in high road density areas using more electricity The impacts of airports, mainly the Atlanta Hartsfield Jackson (ATL) on air quality, was examined using fine scale chemical transport modeling (CMAQ).CMAQ results are evaluated using ground-based and high resolution satellite-based observations from the TROPOspheric Monitoring Instrument (TROPOMI). TROPOMI's ability to provide consistent NO2 vertical column densities (VCDs) is assessed using the CMAQ results around two power plants. A 3D airport emission inventory from full flight operations is developed and compared against a base inventory with only surface airport operation emissions allocated to ATL. Results show that the magnitude and spatial extent of airport effects on air quality would be understated if only the base inventory is used for regulatory purposes. Lastly, we assess the efficacy of an electrified automated fleet of passenger cars on 2050 air quality in the US with a 2050 scenario where gasoline powered passenger cars emit lower levels of pollution than present day automobiles with CMAQ. We find that electric cars have advantages over future gasoline vehicles in terms of improving air quality, though the magnitude varies by species (O3, PM2.5). The overall implications of our findings is that policy, technology and urban infrastructure have a compounded effect on the efficacy of environmental regulations, air quality and energy use. Multiple factors should be considered when designing policies promoting equitable, sustainable cities.Ph.D

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity