The introduction of building management systems in large buildings have improved the control of building services and provided energy savings. However, current building management systems are limited by the physical level of integration of the building's services and the lack of intelligence provided in the control algorithms. This thesis proposes a new approach to the design and operation of building management systems using rule-based artificial intelligence techniques. The main aim of is to manage the services in the building in a more co-ordinated and intelligent manner than is possible by conventional techniques. This approach also aims to reduce the operational cost of the building by automatically tuning the energy consumption in accordance with occupancy profile of the building. A rule-based design methodology is proposed for building management systems. The design adopts the integrated structure made possible by the introduction of a common communications network for building services. The 'intelligence' is coded in the form of rules in such a way that it is both independent of any specific building description and easy to facilitate subsequent modification and addition. This is achieved using an object-oriented approach and classifying the range of data available into defined classes. The rules are divided into two knowledge-bases which are concerned with the building's control and its facilities management respectively. A wide range of rule-based features are proposed to operate on this data structure and are classified in terms of the data classes on which they operate. The concepts presented in this thesis were evaluated using software simulations, mathematical analysis and some hardware implementation. The conclusions of this work are that a rule-based building management system could provide significant enhancements over existing systems in terms of energy savings and improvements for both the building's management staff and its occupants.

As the bandwidth of present networks gets larger than the past, the demand of Network Intrusion Detection Systems (NIDS) that function in real time becomes the major requirement for high-speed networks. If these systems are not fast enough to process all network traffic passing, some malicious security violations may take role using this drawback. In order to make that kind of applications schedulable, some concurrency mechanism is introduced to the general flowchart of their algorithm. The principal aim is to fully utilize each resource of the platform and overlap the independent parts of the applications. In the sense of this context, a generic multi-threaded infrastructure is designed and proposed. The concurrency metrics of the new system is analyzed and compared with the original ones.

Thesis (M.S.)--Worcester Polytechnic Institute. / Keywords: Intelligent Tutoring Systems; Model Tracing; Machine Learning; Artificial Intelligence; Programming by Demonstration. Includes bibliographical references.

abstract: The thesis covers the development and modeling of the supervisory hybrid controller using two different methods to achieve real-world optimization and power split of a parallel hybrid vehicle with a fixed shaft connecting the Internal Combustion Engine (ICE) and Electric Motor (EM). The first strategy uses a rule based controller to determine modes the vehicle should operate in. This approach is well suited for real-world applications. The second approach uses Sequential Quadratic Programming (SQP) approach in conjunction with an Equivalent Consumption Minimization Strategy (ECMS) strategy to keep the vehicle in the most efficient operating regions. This latter method is able to operate the vehicle in various drive cycles while maintaining the SOC with-in allowed charge sustaining (CS) limits. Further, the overall efficiency of the vehicle for all drive cycles is increased. The limitation here is the that process is computationally expensive; however, with advent of the low cost high performance hardware this method can be used for the hybrid vehicle control. / Dissertation/Thesis / Masters Thesis Engineering 2017

Orientador: Maria Gabriela Caffarena Celani / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Civil, Arquitetura e Urbanismo / Made available in DSpace on 2018-08-11T22:00:44Z (GMT). No. of bitstreams: 1 Goncalves_GiovanadeGodoi_M.pdf: 38323426 bytes, checksum: adab0ffe54b87bae0f9096ca8453b387 (MD5) Previous issue date: 2008 / Resumo: Uma nova função atribuída atualmente aos arquitetos é a de desenvolver ferramentas de projeto, conhecidas como generative design systems, que têm em comum preocupações como produtividade e variedade, objetivando a inovação formal. Um dos paradigmas utilizados para a geração de formas é a gramática da forma, que se baseia na definição de um vocabulário de formas e de regras de combinações e operações entre elas. A gramática da forma pode ser utilizada tanto para a descrição de linguagens existentes como para a criação de novas composições. Este trabalho consiste em um estudo de caso, a partir de um corpus de análise, de aplicação da gramática da forma com essas duas finalidades. O corpus de análise é composto por fachadas de construções históricas do centro da cidade de Monte Alegre do Sul. A pequena cidade, fundada no final do século XIX, foi escolhida porque manteve suas características urbanísticas originais e seus edifícios sofreram pouquíssimas intervenções ao longo do século XX. A partir desse corpus foi criada uma gramática da forma que descreve as composições dessas fachadas. Espera-se que essa gramática possa servir de base para futuras reformas nos edifícios da cidade. / Abstract: A new role entrusted to architects today is to develop design tools, known as generative design systems, which have in common concerns such as productivity and variety, aiming at formal innovation. One of the paradigms used for the generation of forms is shape grammar, which is based on the definition of a vocabulary of forms and rules of combinations and operations between them. Shape grammar can be used both for the description of existing languages and for the creation of new compositions. In this case study, a grammar with these two purposes was developed, from a corpus of analysis. The corpus was composed of façades of historic buildings from Monte Alegre do Sul downtown. The small town, founded at the end of the nineteenth century, was chosen because it maintained its original characteristics and it buildings suffered very few interventions over the XXth century. The grammar developed describes the compositions of all the façades in the group. We expected that this grammar can be used as a guide for future renovations of other buildings in the town. / Mestrado / Arquitetura e Construção / Mestre em Engenharia Civil

Abstract Ubiquitous computing transforms physical environments into smart spaces, supporting users in an unobtrusive fashion. Such support requires sensing and interpreting the situation of the user, and providing the required functionality utilizing resources available. In other words, context acquisition, context modelling, and context reasoning are required. This thesis explores rule-based context reasoning from three perspectives: to implement the functionality of ubiquitous applications, to support the creation of ubiquitous applications, and to achieve self-adaptation. First, implementing functionality with reasoning is studied by comparing an application equipped with rule-based reasoning with an application providing similar functionality with hard coded application logic. The scalability of rule-based reasoning is studied with a large-scale student assistant scenario. Reasoning with constrained resources is explored with an application that performs reasoning partially on mobile devices. Finally, distributing a reasoning component that supports smart space interaction is explored with centralized, hybrid, and distributed architectures. Second, the creation of applications with rule-based reasoning is explored. In the first study, rules support building applications from available services and resources based on the instructions that users give via physical user interfaces. The second study supports developers, by proposing middleware that dynamically selects services and data based on the rules written by application developers. Third, self-adaptation is explored with a conceptual framework that adds self-introspective monitoring and control to smart space applications. This framework is verified with simulation and theoretical studies, and an application that fuses diverse data to provide fuel-efficient driving recommendations and adapts decision-making based on the driver’s progress and feedback. The thesis’ contributions include demonstrative cases on using rule-based reasoning from different perspectives, different scales, and with different architectures. Frameworks, a middleware, simulations, and prototypes provide the concrete contribution of the thesis. Generally, the thesis contributes to understanding how rule-based reasoning can be used in ubiquitous computing. The results presented can be used as guidelines for developers of ubiquitous applications. / Tiivistelmä Jokapaikan tietotekniikka muokkaa fyysisen ympäristömme älykkääksi tilaksi, joka tukee käyttäjää häntä häiritsemättä. Tuki toteutetaan asentamalla ympäristöön käyttäjää ja ympäristöä havainnoivia laitteita, tulkitsemalla kerätyn tiedon perusteella käyttäjän tilanne ja tarjoamalla tilanteeseen sopiva toiminnallisuus käyttäen saatavilla olevia resursseja. Toisin sanoen, älykkään tilan on kyettävä tunnistamaan ja mallintamaan toimintatilanne sekä päättelemään toimintatilanteen perusteella. Tässä työssä tutkitaan sääntöpohjaista päättelyä toimintatilanteen perusteella sovellusten toiminnallisuuden toteutuksen, kehittämisen tuen sekä mukautuvuuden näkökulmista. Sovellusten toiminnallisuuden toteuttamista päättelemällä tutkitaan vertaamalla sääntöpohjaisen päättelyn avulla toteutettua toiminnallisuutta vastaavaan suoraan sovellukseen ohjelmoituun toiminnallisuuteen. Sääntöpohjaisen päättelyn skaalautuvuutta arvioidaan laajamittaisessa opiskelija-assistenttiskenaariossa. Niukkojen resurssien vaikutusta päättelyyn arvioidaan päättelemällä osittain mobiililaitteessa. Älykkään tilan vuorovaikutusta tukevan päättelykomponentin hajauttamista tutkitaan keskitetyn, hybridi- ja hajautetun arkkitehtuurin avulla. Sovelluskehityksen tukemiseksi päättelyn säännöt muodostetaan saatavilla olevista palveluista ja resursseista käyttäjän fyysisen käyttöliittymän välityksellä antamien ohjeiden mukaisesti. Toisessa tapauksessa sovelluskehitystä tuetaan väliohjelmistolla, joka valitsee palvelut ja datan dynaamisesti sovelluskehittäjien luomien sääntöjen perusteella. Mukautuvuutta tutkitaan tilan hallintaan ja itsehavainnointiin liittyvän toiminnallisuuden lisäämiseen pystyvän käsitteellisen kehyksen avulla. Kehyksen toiminta varmennetaan simulointien sekä teoreettisten tarkastelujen avulla. Toteutettu useita datalähteitä yhdistävä sovellus antaa ajoneuvon kuljettajalle polttoaineen kulutuksen vähentämiseen liittyviä suosituksia sekä mukautuu kuljettajan ajotavan kehityksen ja palautteen perusteella. Työssä on osoitettu sääntöpohjaisen päättelyn toimivuus eri näkökulmista, eri skaalautuvuuden asteilla sekä eri arkkitehtuureissa. Työn konkreettisia tuloksia ovat kehykset, väliohjelmistot, simuloinnit sekä prototyypit. Laajemmassa mittakaavassa työ edesauttaa ymmärtämään sääntöpohjaisen päättelyn soveltamista ja työn tuloksia voidaankin käyttää suosituksina sovelluskehittäjille.

L’ Answer Set Programming (ASP) est un formalisme capable de représenter des connaissances en Intelligence Artificielle à l’aide d’un programme logique au premier ordre pouvant contenir des négations par défaut. En quelques années, plusieurs solveurs performants ont été proposés pour calculer les solutions d’un programme ASP que l’on nomme answer sets.Nous nous intéressons ici plus particulièrement au solveur ASPeRiX qui instancie les règles au premier ordre à la volée durant le calcul des answer sets. Pour réaliser cela, ASPeRiX applique un chaînage avant sur les règles à partir de littéraux précédemment déterminés.L’étude de ce solveur nous amène notamment à considérer la notion de justification dans le cadre d’une approche de calcul d’ answer sets basée sur les règles. Les justifications permettent d’expliquer pourquoi certaines propriétés sont vérifiées. Parmi celles-ci, nous nous concentrons particulièrement sur les raisons d’échecs qui justifient pourquoi certaines branches de l’arbre de recherche n’aboutissent pas à un answer set.Cela nous conduit à implémenter une version d’ ASPeRiX proposant du backjumping qui évite de parcourir systématiquement toutes les branches de l’arbre de recherche grâce aux informations fournies par les raisons d’échecs. / Answer set programming (ASP) is a formalism able to represent knowledge in Artificial Intelligence thanks to a first order logic program which can contain default negations. In recent years, several efficient solvers have been proposed to compute the solutions of an ASP program called answer sets. We are particularly interested in the ASPeRiX solver that instantiates the first order rules on the fly during the computation of answer sets. It applies a forward chaining of rules from literals previously determined. The study of this solver leads us to consider the concept of justification as part of a rule-based approach for computing answer sets. Justifications enable to explain why some properties are true or false. Among them, we focus particularly on the failure reasons which justify why some branches of the search tree does not result in an answer set. This encourages us to implement a version of ASPeRiX with backjumping in order to jump to the last choice point related to the failure in the search tree thanks to information provided by the failure reasons.

Uses rule based AI (forward and backward chaining) to model legal decision making by judges. / AI to model legal decision making (c) Eric Engle

Indiana University-Purdue University Indianapolis (IUPUI) / This thesis proposes an Adaptive Rule-Based Energy Management Strategy (ARBS EMS) for a parallel hybrid electric vehicle (P-HEV). The strategy can effciently be deployed online without the need for complete knowledge of the entire duty cycle in order to optimize fuel consumption. ARBS improves upon the established Preliminary Rule-Based Strategy (PRBS) which has been adopted in commercial vehicles. When compared to PRBS, the aim of ARBS is to maintain the battery State of Charge (SOC) which ensures the availability of the battery over extended distances. The proposed strategy prevents the engine from operating in highly ineffcient regions and reduces the total equivalent fuel consumption of the vehicle. Using an HEV model developed in Simulink, both the proposed ARBS and the established PRBS strategies are compared across eight short duty cycles and one long duty cycle with urban and highway characteristics. Compared to PRBS, the results show that, on average, a 1.19% improvement in the miles per gallon equivalent (MPGe) is obtained with ARBS when the battery initial SOC is 63% for short duty cycles. However, as opposed to PRBS, ARBS has the advantage of not requiring any prior knowledge of the engine effciency maps in order to achieve optimal performance. This characteristics can help in the systematic aftermarket hybridization of heavy duty vehicles.

Flood is one of the most devastating natural disasters. It is estimated that flooding from sea level rise will cause one trillion USD to major coastal cities of the world by the year 2050. Flood not only destroys the economy, but it also creates physical and psychological sufferings for the human and destroys infrastructures. Disseminating flood warnings and evacuating people from the flood-affected areas help to save human life. Therefore, predicting flood will help government authorities to take necessary actions to evacuate humans and arrange relief for the people. This licentiate thesis focuses on four different aspects of flood prediction using wireless sensor networks (WSNs). Firstly, different WSNs, protocols related to WSN, and backhaul connectivity in the context of predicting flood were investigated. A heterogeneous WSN network for flood prediction was proposed. Secondly, data coming from sensors contain anomaly due to different types of uncertainty, which hampers the accuracy of flood prediction. Therefore, anomalous data needs to be filtered out. A novel algorithm based on belief rule base for detecting the anomaly from sensor data has been proposed in this thesis. Thirdly, predicting flood is a challenging task as it involves multi-level factors, which cannot be measured with 100% certainty. Belief rule based expert systems (BRBESs) can be considered to handle the complex problem of this nature as they address different types of uncertainty. A web based BRBES was developed for predicting flood. This system provides better usability, more computational power to handle larger numbers of rule bases and scalability by porting it into a web-based solution. To improve the accuracy of flood prediction, a learning mechanism for multi-level BRBES was proposed. Furthermore, a comparison between the proposed multi-level belief rule based learning algorithm and other machine learning techniques including Artificial Neural Networks (ANN), Support Vector Machine (SVM) based regression, and Linear Regression has been performed. In the light of the research findings of this thesis, it can be argued that flood prediction can be accomplished more accurately by integrating WSN and BRBES.