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This thesis outlines the development of a control system for a P4-P0 Parallel-Through-The-Road Hybrid Electric Vehicle. This project was part of the EcoCAR Mobility Challenge, an Advanced Vehicle Technology Competition, sponsored by the U.S. Department of Energy, MathWorks and General Motors. The McMaster Engineering EcoCAR team is participating in its second iteration, re-engineering a 2019 Chevrolet Blazer to suit a car-sharing service located within the Greater Toronto Hamilton Area. The proposed architecture uses a 1.5L Engine together with a Belted Alternator Starter motor connected to the traditional low voltage system. The rear axle is electrified containing an Electric Machine, a power oriented Battery Pack and team-designed gear reduction as well as a clutch. The whole rear powertrain is operating at high voltage and has no connection to the traditional low voltage system. Fuel economy improvements up to 12% can be expected while maintaining stock performance targets. A vehicle simulation model was built to accompany the vehicle design process. This includes a mathematical representation of all powertrain components, the development of energy management algorithms, the design of the Hybrid Supervisory Controller structure, and validating and discussing gathered results. Furthermore, all necessary controllers were chosen and communication within them was established by designing the serial data architecture. The developed energy management algorithm is customized to utilize the strengths of all components and this specific architecture. A simple rule-based algorithm is used to operate the engine as close as possible to its most fuel efficient operation point at any time. The P4 and P0 motor are used to apply supportive torque to the engine or load the engine with a negative torque. In that way the energy can be regenerated inside the powertrain and charge sustaining operation v can be achieved. Fuel economy and performance targets are used to discuss the assumed performance of the vehicle once re-engineered. The set targets range from city and highway fuel economy to IVM – 60 mph acceleration time. Overall the developed control system suits a car-sharing service with its ability to adapt to the occurring driving situations ensuring a close to optimal operation for any known or unknown driving situation. It focuses on modularity, simplicity and functionality to allow a working implementation in future years of the EcoCAR Mobility Challenge. / Thesis / Master of Applied Science (MASc) / During the re-engineering of a Hybrid Electric Vehicle different expectations must be considered, for example set government fuel economy regulations, defined performance targets, novelty in innovation, stakeholder expectations as well as the used vehicle platform and the available components. The re-engineering process will be done according to the vehicle development process of the EcoCAR Mobility Challenge. Summarized expectations are the use of this vehicle inside a car-sharing service for the Greater Toronto Hamilton Area targeting “Millennials” while focusing on fuel economy improvements and a low cost of ownership. The research shown in this thesis is set by the requirements derived from the expectations mentioned above. One point of interest is achieving a working control system able to operate close to an optimal state to maximize fuel efficiency and ensuring stock vehicle performance targets. Therefore, the control system has to use the electrification components in an intelligent way. Defining what intelligent control of the engine and the electrification components was one of the main challenges. This thesis outlines how developing a control system for a Hybrid Electric Vehicle can be realized while ensuring that all included interests are met. The object of this research contains choosing the necessary controllers, building a sufficient vehicle simulation model, developing the energy management algorithm, validating the model performance and evaluating the gathered results.

By increasing the level of abstraction in the representation of regulatory network models, we can hope to allow modelers to create models that are beyond the threshold of what can currently be expressed reliably. As hundreds of reactions are difficult to understand, maintain, and extend, thousands of reactions become next to impossible without any automation or aid. Using the multistate-species concept we can reduce the number of reactions needed to represent certain systems and thus, lessen the cognitive load on modelers. A multistate species is an entity with a defined range for state variables, which refers to a group of different forms for a specific species. A multistate reaction involves one or more multistate species and compactly represents a group of similar single reactions. In this work, we have extended JCMB (the JigCell Model Builder) to comply with multistate species and reactions modeling and presented a proposal for enhancing SBML (the Systems Biology Markup Language) standards to support multistate models. / Master of Science

No / In this paper we propose a unified approach for integrating implicit and explicit knowledge in neurosymbolic systems as a combination of neural and neuro-fuzzy modules. In the developed hybrid system, training data set is used for building neuro-fuzzy modules, and represents implicit domain knowledge. The explicit domain knowledge on the other hand is represented by fuzzy rules, which are directly mapped into equivalent neural structures. The aim of this approach is to improve the abilities of modular neural structures, which are based on incomplete learning data sets, since the knowledge acquired from human experts is taken into account for adapting the general neural architecture. Three methods to combine the explicit and implicit knowledge modules are proposed. The techniques used to extract fuzzy rules from neural implicit knowledge modules are described. These techniques improve the structure and the behavior of the entire system. The proposed methodology has been applied in the field of air quality prediction with very encouraging results. These experiments show that the method is worth further investigation.

The ability to accurately predict an impending hard disk failure is important for reliable storage system design. The facility provided by most hard drive manufacturers, called S.M.A.R.T. (self-monitoring, analysis and reporting technology), has been shown by current research to have poor predictive value. The problem of finding alternatives to S.M.A.R.T. for predicting disk failure is an area of active research. In this work, we present a rule discovery methodology, and show that it is possible to construct decision support systems that can detect such failures using information recorded from live disks. It is desired that any such prediction methodology should have high accuracy and must have ease of interpretability. Black box models can deliver highly accurate solutions but do not provide an understanding of events which explains the decision given by it. To this end we explore rule based classifiers for predicting hard disk failures from various disk events. We show that it is possible to learn easy to understand rules from disk events. Our evaluation shows that our system can be tuned either to have a high failure detection rate (i.e., classify a bad disk as bad) or to have a low false alarm rate (i.e., not classify a good disk as bad). We also propose a modification of MLRules algorithm for classification of data with imbalanced class distributions. The existing algorithm, assuming relatively balanced class distributions and equal misclassfication costs, performs poorly in classification of such datasets. The performance can be considerably improved by introducing cost- sensitive learning to the existing framework.

ITC/USA 2015 Conference Proceedings / The Fifty-First Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2015 / Bally's Hotel & Convention Center, Las Vegas, NV / This paper describes a method in which users realize the benefits of a standards-based method for capturing and evaluating verification and validation (V&V) rules within and across metadata instance documents. The method uses a natural language based syntax for the T&E metadata V&V rule set in order to abstract the highly technical rule languages to a domain-specific syntax. As a result, the domain expert can easily specify, validate and manage the specification and validation of the rules themselves. Our approach is very flexible in that under the hood, the method automatically translates rules to a host of target rule languages. We validated our method in a multi-vendor scenario involving Metadata Description Language (MDL) and Instrumentation Hardware Abstraction Language (IHAL) instance documents, user constraints, and domain constraints. The rules are captured in natural language, and used to perform V&V within a single metadata instance document and across multiple metadata instance documents.

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In this work we developed rule-based algorithms for multiple-instance learning and one-class learning problems, namely, the mi-DS and OneClass-DS algorithms. Multiple-Instance Learning (MIL) is a variation of classical supervised learning where there is a need to classify bags (collection) of instances instead of single instances. The bag is labeled positive if at least one of its instances is positive, otherwise it is negative. One-class learning problem is also known as outlier or novelty detection problem. One-class classifiers are trained on data describing only one class and are used in situations where data from other classes are not available, and also for highly unbalanced data sets. Extensive comparisons and statistical testing of the two algorithms show that they generate models that perform on par with other state-of-the-art algorithms.

Problem: While global IT competition is becoming increasingly severe, various business organizations and companies, in order to maximize the profit and gain market competitiveness, are in urgent need of high-performance workflow systems to improve efficiency. However, the workflow systems that are currently used are embedded with fixed business rules that cannot be easily adjusted by users, resulting in the inability of users to make adjustments to the business rules, so as to satisfy changed requirements and deal with high cost of business management and low efficiency. Therefore, it is highly desirable for users of workflow systems, especially retail supply chain companies, to employ a new type of systems that can be easily adjusted by end users themselves when required.Solution: The rule-based workflow system architecture for the management of retail supply chain business process is recommended. In such architecture, the business rules can be separated from the system logic and managed by users via a friendly interface. The rule-based workflow systems can greatly enhance the system efficiency and lower maintenance cost, as compared with the traditional workflow system or other similar information systems. And the efficiency of retail supply chain business process management can be greatly enhanced by employing rule-based workflow systems.Methods: Two main research problems and four sub-research problems, which serve as the guidance to conduct related research work, have been identified. The research work has been divided into the theoretical part and the empirical part. In the theoretical part, the theory of rule base establishment and rule-based workflow system architecture are discussed. In the empirical part, data analysis as well as prototype design are conducted by employing both quantitative and qualitative methods of data collection. Attempts are also made to verify the theories suggested in the theoretical part by means of empirical research. Based on both theoretical and empirical research, attempts are made to find solutions to the research questions. In general, this thesis arms at providing references for the future research related to rule-based workflow system management in retail supply chain management. The thesis also aims to provide references for the practical use of rule-based systems in the retail supply chain field with such issues as system development and maintenance, especially for the system of complex and changeable business processes. Most importantly, some solutions are offered to the challenges of retail supply chain management. / Program: Magisterutbildning i informatik