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This thesis describes the design and implementation of constructing a predictive maintenance system for a hybrid vehicle to meet the requirements of the STO (Société de transport de l'Outaouais). Thousands of sensors installed on the bus allow us to observe the real-time performance of the bus while it is running. Abnormal sensor values represent adverse operating conditions and bring attention to the inevitable failures of a bus's components. Therefore, by analyzing real-time sensor streams, predictive maintenance is accomplished based on the unnatural behaviour of a hybrid bus. Currently, transport companies still employ traditional methods of maintenance planning, such as emergency maintenance and preventive maintenance. Traditional maintenance strategies require a great deal of technicians and time to inspect the buses regularly and carefully. In comparison, predictive maintenance can monitor the performance of buses based on the condition of their equipment. To collect data from the hybrid bus and share data with the Internet, IoT technology is adopted to develop predictive maintenance architecture for a fleet management system. Our team devised an IoT architecture for the fleet management system, including the perception layer, middleware layer and application layer. My work focuses on the perception layer, which is responsible for analyzing sensor values, reporting failures of a hybrid bus and connecting with cloud-servers. As one of the predictive maintenance methods, the expert system (also known as a knowledge-based expert system) is built to store expert knowledge in a specific area. The expert system presented in this thesis can store failures of hybrid buses, symptoms of which were provided to us by technicians from the STO. Such breakdowns assist the expert system in predicting the malfunctions of the bus's components based on the symptoms. Inspired by the IDEA methodology, failure symptoms can be represented by active rules with three essential components: event, condition and action. These rules can also be translated into active database features like triggers and mapped into an active database. A gateway is installed on a bus and composed of four modules: data acquisition module, active rules module, rules management module and user interface module. Within the parameters of the architecture and the gateway, this thesis analyzes the entities, relationships and operations in the dynamic system and forms a relational database to store the information related to the bus and active rules.
This book constitutes the refereed proceedings of the First International Conference on Artificial Intelligence in HCI, AI-HCI 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020, in July 2020. The conference was planned to be held in Copenhagen, Denmark, but had to change to a virtual conference mode due to the COVID-19 pandemic. The conference presents results from academic and industrial research, as well as industrial experiences, on the use of Artificial Intelligence technologies to enhance Human-Computer Interaction. From a total of 6326 submissions, a total of 1439 papers and 238 posters has been accepted for publication in the HCII 2020 proceedings. The 30 papers presented in this volume were organized in topical sections as follows: Human-Centered AI; and AI Applications in HCI.pical sections as follows: Human-Centered AI; and AI Applications in HCI.
This book constitutes selected papers from the Second International Workshop on IoT Streams for Data-Driven Predictive Maintenance, IoT Streams 2020, and First International Workshop on IoT, Edge, and Mobile for Embedded Machine Learning, ITEM 2020, co-located with ECML/PKDD 2020 and held in September 2020. Due to the COVID-19 pandemic the workshops were held online. The 21 full papers and 3 short papers presented in this volume were thoroughly reviewed and selected from 35 submissions and are organized according to the workshops and their topics: IoT Streams 2020: Stream Learning; Feature Learning; ITEM 2020: Unsupervised Machine Learning; Hardware; Methods; Quantization.
This book is both a reference for engineers and scientists and a teaching resource, featuring tutorial chapters and research papers on feature extraction. Until now there has been insufficient consideration of feature selection algorithms, no unified presentation of leading methods, and no systematic comparisons.
The authors of this text have written a comprehensive introduction to the modeling and optimization problems encountered when designing new propulsion systems for passenger cars. It is intended for persons interested in the analysis and optimization of vehicle propulsion systems. Its focus is on the control-oriented mathematical description of the physical processes and on the model-based optimization of the system structure and of the supervisory control algorithms.
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At the crossroads of artificial intelligence, manufacturing engineering, operational research and industrial engineering and management, multi-agent based production planning and control is an intelligent and industrially crucial technology with increasing importance. This book provides a complete overview of multi-agent based methods for today’s competitive manufacturing environment, including the Job Shop Manufacturing and Re-entrant Manufacturing processes. In addition to the basic control and scheduling systems, the author also highlights advance research in numerical optimization methods and wireless sensor networks and their impact on intelligent production planning and control system operation. Enables students, researchers and engineers to understand the fundamentals and theories of multi-agent based production planning and control Written by an author with more than 20 years’ experience in studying and formulating a complete theoretical system in production planning technologies Fully illustrated throughout, the methods for production planning, scheduling and controlling are presented using experiments, numerical simulations and theoretical analysis Comprehensive and concise, Multi-Agent Based Production Planning and Control is aimed at the practicing engineer and graduate student in industrial engineering, operational research, and mechanical engineering. It is also a handy guide for advanced students in artificial intelligence and computer engineering.