14,871 research outputs found

    Building fault detection data to aid diagnostic algorithm creation and performance testing.

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    It is estimated that approximately 4-5% of national energy consumption can be saved through corrections to existing commercial building controls infrastructure and resulting improvements to efficiency. Correspondingly, automated fault detection and diagnostics (FDD) algorithms are designed to identify the presence of operational faults and their root causes. A diversity of techniques is used for FDD spanning physical models, black box, and rule-based approaches. A persistent challenge has been the lack of common datasets and test methods to benchmark their performance accuracy. This article presents a first of its kind public dataset with ground-truth data on the presence and absence of building faults. This dataset spans a range of seasons and operational conditions and encompasses multiple building system types. It contains information on fault severity, as well as data points reflective of the measurements in building control systems that FDD algorithms typically have access to. The data were created using simulation models as well as experimental test facilities, and will be expanded over time

    Innovative Hybridisation of Genetic Algorithms and Neural Networks in Detecting Marker Genes for Leukaemia Cancer

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    Methods for extracting marker genes that trigger the growth of cancerous cells from a high level of complexity microarrays are of much interest from the computing community. Through the identified genes, the pathology of cancerous cells can be revealed and early precaution can be taken to prevent further proliferation of cancerous cells. In this paper, we propose an innovative hybridised gene identification framework based on genetic algorithms and neural networks to identify marker genes for leukaemia disease. Our approach confirms that high classification accuracy does not ensure the optimal set of genes have been identified and our model delivers a more promising set of genes even with a lower classification accurac

    Atrial Fibrillation and Pacing Algorithms

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    Pacing prevention algorithms have been introduced in order to maximize the benefits of atrial pacing in atrial fibrillation prevention. It has been demonstrated that algorithms actually keep overdrive atrial pacing, reduce atrial premature contractions, and prevent short-long atrial cycle phenomenon, with good patient tolerance. However, clinical studies showed inconsistent benefits on clinical endpoints such as atrial fibrillation burden. Factors which may be responsible for neutral results include an already high atrial pacing percentage in conventional DDDR, non-optimal atrial pacing site and deleterious effects of high percentages of apical ventricular pacing. Atrial antitachycardia pacing (ATP) therapies are effective in treating spontaneous atrial tachyarrhythmias, mainly when delivered early after arrhythmia onset and/or on slower tachycardias. Effective ATP therapies may reduce atrial fibrillation burden, but conflicting evidence does exist as regards this issue, probably because current clinical studies may be underpowered to detect such an efficacy. Wide application of atrial ATP may reduce the need for hospitalizations and electrical cardioversions and favorably impact on quality of life. Consistent monitoring of atrial and ventricular rhythm as well as that of ATP effectiveness may be extremely useful for optimizing device programming and pharmacological therapy

    A Reliable and Cost-Efficient Auto-Scaling System for Web Applications Using Heterogeneous Spot Instances

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    Cloud providers sell their idle capacity on markets through an auction-like mechanism to increase their return on investment. The instances sold in this way are called spot instances. In spite that spot instances are usually 90% cheaper than on-demand instances, they can be terminated by provider when their bidding prices are lower than market prices. Thus, they are largely used to provision fault-tolerant applications only. In this paper, we explore how to utilize spot instances to provision web applications, which are usually considered availability-critical. The idea is to take advantage of differences in price among various types of spot instances to reach both high availability and significant cost saving. We first propose a fault-tolerant model for web applications provisioned by spot instances. Based on that, we devise novel auto-scaling polices for hourly billed cloud markets. We implemented the proposed model and policies both on a simulation testbed for repeatable validation and Amazon EC2. The experiments on the simulation testbed and the real platform against the benchmarks show that the proposed approach can greatly reduce resource cost and still achieve satisfactory Quality of Service (QoS) in terms of response time and availability

    Self-organization and clustering algorithms

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    Kohonen's feature maps approach to clustering is often likened to the k or c-means clustering algorithms. Here, the author identifies some similarities and differences between the hard and fuzzy c-Means (HCM/FCM) or ISODATA algorithms and Kohonen's self-organizing approach. The author concludes that some differences are significant, but at the same time there may be some important unknown relationships between the two methodologies. Several avenues of research are proposed
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