685,002 research outputs found

    Case studies of outdoor testing and analysis of building components

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    The construction and development of the PASSYS/PASLINK outdoor test cells were funded by the European Commission, with the objective of providing high-quality test environments for quantifying the performance of passive solar building components. Over the years since the original test cells were commissioned, the initial concept for outdoor testing has been extended to include other test cell types. Significant improvements have been made to the experimental procedures and analysis techniques, and a broad range of components has been tested. This paper describes representative experiments that have been conducted using these highly controlled outdoor test environments, indicates some of the related analysis, and shows the type of information that can be obtained from such tests. It demonstrates the way in which component performance can be ascertained in the realistic external environment. The case studies chosen range from building component tests within EC research projects to commercial tests, and from conventional building components to novel integrated facade systems. They also include a large range of passive and active components. Each case study summarises the test component, the purpose of the test, details of the test configuration (period of test, instrumentation, etc.), results and analysis, and associated modelling and monitoring where appropriate. The paper concludes with an appraisal of the advantages and limitations of the test cells for the various component types

    Identifying and Classifying Processes (traditional and soft factors) that Support COTS Component Selection

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    COTS-Based Systems (CBS) development focuses on building large software systems by integrating previously existing software components. CBS success depends on successful evaluation and selection of Commercial-Off-The- Shelf (COTS) software components to fit customer requirements. Literature shows that successful selection of offthe- shelf systems to fit customer requirements remains problematic. This paper presents the outcome of a study aimed at using a social-technical approach to identify and classify processes (including traditional and soft factors) that support COTS software selection. The identified factors and lessons learnt from case study assisted in elaborating and further development of Social-Technical Approach to COTS Evaluation framework (STACE)

    Developing a low-cost beer dispensing robotic system for the service industry

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    As the prices of commercially available electronic and mechanical components decrease, manufacturers such as Devantech and Revolution Education have made encoded motor controller systems and microcontrollers very accessible to engineers and designers. This has made it possible to design sophisticated robotic and mechatronic systems very rapidly and at relatively low cost. A recent project in the Autonomous Systems Lab at Middlesex University, UK was to design and build a small, automated, robotic bartender based around the 5 litre Heineken 'Draughtkeg' system, which is capable of patrolling a bar and dispensing beer when signalled to by a customer. Because the system was designed as a commercial product, design constraints focused on keeping the build cost down, and so electronic components were sourced from outside companies and interfaced with a bespoke chassis and custom mechanical parts designed and manufactured on site at the University. All the programming was conducted using the proprietary BASIC language, which is freely available from the PicAXE supplier at no cost. This paper will discuss the restrictions involved in building a robot chassis around 'off-theshelf' components, and the issues arising from making the human-machine interaction intuitive whilst only using low-cost ultrasonic sensors. Programming issues will also be discussed, such as the control of accuracy when interfacing a PicAXE microcontroller with a Devantech MD25 Motor Controller board. Public live testing of the system was conducted at the Kinetica Art Fair 2010 event in London and has since been picked up by websites such as Engadget.com and many others. Feedback on the system will be described, as well as the refinements made as a result of these test

    Neo: A Learned Query Optimizer

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    Query optimization is one of the most challenging problems in database systems. Despite the progress made over the past decades, query optimizers remain extremely complex components that require a great deal of hand-tuning for specific workloads and datasets. Motivated by this shortcoming and inspired by recent advances in applying machine learning to data management challenges, we introduce Neo (Neural Optimizer), a novel learning-based query optimizer that relies on deep neural networks to generate query executions plans. Neo bootstraps its query optimization model from existing optimizers and continues to learn from incoming queries, building upon its successes and learning from its failures. Furthermore, Neo naturally adapts to underlying data patterns and is robust to estimation errors. Experimental results demonstrate that Neo, even when bootstrapped from a simple optimizer like PostgreSQL, can learn a model that offers similar performance to state-of-the-art commercial optimizers, and in some cases even surpass them

    Modelling and optimization of direct expansion air conditioning system for commercial building energy saving

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    This paper presents a comprehensive refinement of system modeling and optimization study of air-cooled direct expansion (DX) refrigeration systems for commercial buildings to address the energy saving problem. An actual DX rooftop package of a commercial building in the hot and dry climate condition is used for experimentation and data collection. Both inputs and outputs are known and measured from the field monitoring. The optimal supply air temperature and refrigerant flow rate are calculated based on the cooling load and ambient dry-bulb temperature profiles in one typical week in the summer. Optimization is performed by using empirically-based models of the refrigeration system components for energy savings. The results are promising as approximately 9% saving of the average power consumption can be achieved subject to a predetermined comfort constraint on the ambient temperature. The proposed approach will make an attractive contribution to residential and commercial building HVAC applications in moving towards green automation
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