State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Exploratory study to explore the role of ICT in the process of knowledge management in an Indian business environment

In the 21st century and the emergence of a digital economy, knowledge and the knowledge base economy are rapidly growing. To effectively be able to understand the processes involved in the creating, managing and sharing of knowledge management in the business environment is critical to the success of an organization. This study builds on the previous research of the authors on the enablers of knowledge management by identifying the relationship between the enablers of knowledge management and the role played by information communication technologies (ICT) and ICT infrastructure in a business setting. This paper provides the findings of a survey collected from the four major Indian cities (Chennai, Coimbatore, Madurai and Villupuram) regarding their views and opinions about the enablers of knowledge management in business setting. A total of 80 organizations participated in the study with 100 participants in each city. The results show that ICT and ICT infrastructure can play a critical role in the creating, managing and sharing of knowledge in an Indian business environment

CloudMon: a resource-efficient IaaS cloud monitoring system based on networked intrusion detection system virtual appliances

The networked intrusion detection system virtual appliance (NIDS-VA), also known as virtualized NIDS, plays an important role in the protection and safeguard of IaaS cloud environments. However, it is nontrivial to guarantee both of the performance of NIDS-VA and the resource efficiency of cloud applications because both are sharing computing resources in the same cloud environment. To overcome this challenge and trade-off, we propose a novel system, named CloudMon, which enables dynamic resource provision and live placement for NIDS-VAs in IaaS cloud environments. CloudMon provides two techniques to maintain high resource efficiency of IaaS cloud environments without degrading the performance of NIDS-VAs and other virtual machines (VMs). The first technique is a virtual machine monitor based resource provision mechanism, which can minimize the resource usage of a NIDS-VA with given performance guarantee. It uses a fuzzy model to characterize the complex relationship between performance and resource demands of a NIDS-VA and develops an online fuzzy controller to adaptively control the resource allocation for NIDS-VAs under varying network traffic. The second one is a global resource scheduling approach for optimizing the resource efficiency of the entire cloud environments. It leverages VM migration to dynamically place NIDS-VAs and VMs. An online VM mapping algorithm is designed to maximize the resource utilization of the entire cloud environment. Our virtual machine monitor based resource provision mechanism has been evaluated by conducting comprehensive experiments based on Xen hypervisor and Snort NIDS in a real cloud environment. The results show that the proposed mechanism can allocate resources for a NIDS-VA on demand while still satisfying its performance requirements. We also verify the effectiveness of our global resource scheduling approach by comparing it with two classic vector packing algorithms, and the results show that our approach improved the resource utilization of cloud environments and reduced the number of in-use NIDS-VAs and physical hosts.The authors gratefully acknowledge the anonymous reviewers for their helpful suggestions and insightful comments to improve the quality of the paper. The work reported in this paper has been partially supported by National Nature Science Foundation of China (No. 61202424, 61272165, 91118008), China 863 program (No. 2011AA01A202), Natural Science Foundation of Jiangsu Province of China (BK20130528) and China 973 Fundamental R&D Program (2011CB302600)

Study of challenges in technology development and market penetration of hybrid electric vehicles in Canada

Growing concerns of the economic and environmental impact of petroleum combustion by on-road transportation have accelerated the development of alternative fuel vehicles; of these, the hybrid electric vehicle (HEV) is currently the most commercially successful technology. It integrates an electric drivetrain to the internal combustion engine for optimized engine operation giving significantly higher fuel efficiency and lower emissions. However, despite their well recognized benefits, Canadian consumers have shown reluctance in adapting HEVs so far. This thesis discusses the immediate need for Canada to adopt more efficient and eco-friendly transportation systems and analyzes the cost effectiveness and tailpipe emissions of HEVs that offer a suitable alternative. The factors inhibiting market acceptance of hybrids are have been reviewed and a set of comprehensive policy guidelines and measures have been proposed to provide financial incentives, enforce emission regulations and support technology development of hybrid vehicles. As part of the highlighted target, challenges in key areas of HEV technology have been discussed and one such challenge is addressed by proposing a more robust electric motor drive for vehicle traction

Data-Driven Shape Analysis and Processing

Data-driven methods play an increasingly important role in discovering geometric, structural, and semantic relationships between 3D shapes in collections, and applying this analysis to support intelligent modeling, editing, and visualization of geometric data. In contrast to traditional approaches, a key feature of data-driven approaches is that they aggregate information from a collection of shapes to improve the analysis and processing of individual shapes. In addition, they are able to learn models that reason about properties and relationships of shapes without relying on hard-coded rules or explicitly programmed instructions. We provide an overview of the main concepts and components of these techniques, and discuss their application to shape classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis, through reviewing the literature and relating the existing works with both qualitative and numerical comparisons. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing.Comment: 10 pages, 19 figure

PROJEKTOWANIE WIELOFUNKCYJNEGO SYMULATORA DO SZKOLENIA PERSONELU MASZYNOWNI

International requirements for improving energy efficiency and environmental protection and the necessary goals for their implementation in the marine industry are an actual problem. To integrate state-of-the-industry technologies and marine specialists education, the training complex is proposed. It is based on the platform of a hardware-software complex with the ability to integrate training equipment, simulators and software. That makes such a training complex multitask, universal, and flexible in achieving a variety of tasks and goals. The complex also implements high-quality education and training of marine specialists, conducting research after processing working out the results of engineering modelling of structural, thermal power, hydraulic, electrical, electronic, multi-physical and other solutions. The need to use the training complex allows us to form the necessary competence of the engine team personnel, develop methods and criteria for assessing competence, evaluate and demonstrate practical skills.Międzynarodowe wymogi dotyczące poprawy efektywności energetycznej i ochrony środowiska oraz cele niezbędne do ich wdrożenia w przemyśle morskim stanowią aktualny problem. W celu zintegrowania najnowocześniejszych technologii i kształcenia specjalistów z branży morskiej proponuje się utworzenie kompleksu szkoleniowego. Jest on oparty na platformie kompleksu sprzętowo-programowego z możliwością integracji sprzętu szkoleniowego, symulatorów i oprogramowania. To sprawia, że taki kompleks jest wielozadaniowy, uniwersalny i elastyczny w realizacji różnorodnych zadań i celów. Ponadto kompleks realizuje wysokiej jakości kształcenie i szkolenie specjalistów morskich, prowadząc badania po opracowaniu wyników modelowania inżynierskiego rozwiązań konstrukcyjnych, cieplnych, hydraulicznych, elektrycznych, elektronicznych, i innych. Wykorzystanie kompleksu szkoleniowego pozwala na kształtowanie niezbędnych kompetencji personelu zespołu inżynierskiego, opracowanie metod i kryteriów oceny kompetencji, ocenę i wykazanie umiejętności praktycznych