Integrated drive generators (IDGs) are the main source of electrical power for a number of critical systems in aircraft. Fast and accurate fault detection and isolation is a necessary component for safe and reliable o...
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Integrated drive generators (IDGs) are the main source of electrical power for a number of critical systems in aircraft. Fast and accurate fault detection and isolation is a necessary component for safe and reliable operation of the IDG and the aircraft. IDGs are complex systems, and a majority of the existing fault detection and isolation techniques are based on signal analysis and heuristic methods derived from experience. Model-based fault diagnosis techniques are hypothesized to be more general and powerful in designing detection and isolation schemes, but building sufficiently accurate models of complex IDGs is a difficult task. dq0 models have been developed for design and control of generators, but these models are not suitable for fault situations, where the generator may become unbalanced. In this paper, we present a hybrid phase-domain model for the aircraft generator that accurately represents both nominal and parametric faulty behaviors. We present the details of the hybrid modeling approach and simulation results of nominal operation and fault behaviors associated with parametric faults in the aircraft generator. The simulation results show that the developed model is capable of accurately capturing the generator dynamics under a variety of normal and faulty configurations.
One of the primary problems with computing clusters is to ensure that they maintain a reliable working state most of the time to justify economics of operation. In this paper, we introduce a model-based hierarchical r...
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One of the primary problems with computing clusters is to ensure that they maintain a reliable working state most of the time to justify economics of operation. In this paper, we introduce a model-based hierarchical reliability framework that enables periodic monitoring of vital health parameters across the cluster and provides for autonomic fault mitigation. We also discuss some of the challenges faced by autonomic reliability frameworks in cluster environments such as non-determinism in task scheduling in standard operating systems such as Linux and need for synchronized execution of monitoring sensors across the cluster. Additionally, we present a solution to these problems in the context of our framework, which utilizes a feedback controller based approach to compensate for the scheduling jitter in non real-time operating systems. Finally, we present experimental data that illustrates the effectiveness of our approach.
The MapReduce programming model allows users to easily develop distributed applications in data centers. However, many applications cannot be exactly expressed with MapReduce due to their specific characteristics. For...
The MapReduce programming model allows users to easily develop distributed applications in data centers. However, many applications cannot be exactly expressed with MapReduce due to their specific characteristics. For instance, genetic algorithms (GAs) naturally fit into an iterative style. That does not follow the two phase pattern of MapReduce. This paper presents an extension to the MapReduce model featuring a hierarchical reduction phase. This model is called MRPGA (MapReduce for parallel GAs), which can automatically parallelize GAs. We describe the design and implementation of the extended MapReduce model on a .NET-based enterprise grid system in detail. The evaluation of this model with its runtime system is presented using example applications.
Visual comprehension is the characteristic that deals with how efficiently and effectively users are able to grasp the underlying design intent along with the interactions to explore the visually represented informati...
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Visual comprehension is the characteristic that deals with how efficiently and effectively users are able to grasp the underlying design intent along with the interactions to explore the visually represented information. To assess comprehension i.e. to measure this seemingly immeasurable factor of visualization systems, we are proposing a set of criteria based on a detailed analysis of information flow from the raw data to the cognition of information in human mind. Our comprehension criteria are adapted from the pioneering work of two eminent researchers - Donald A. Nortnan and Aaron Marcus, who have investigated the issues of human perception and cognition, and visual effectiveness respectively. These proposed criteria are refined by experts' opinion in order to compose a minimal evaluation set that is then applied to a bioinformatics visualization study tool to show the efficacy of criteria in assessing comprehension in a more quantitative manner.
Decision tree classification is one of the most practical and effective methods which is used in inductive learning. Many different approaches, which are usually used for decision making and prediction, have been inve...
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ISBN:
(纸本)9781424429141
Decision tree classification is one of the most practical and effective methods which is used in inductive learning. Many different approaches, which are usually used for decision making and prediction, have been invented to construct decision tree classifiers. These approaches try to optimize parameters such as accuracy, speed of classification, size of constructed trees, learning speed, and the amount of used memory. There is a trade off between these parameters. That is to say that optimization of one may cause obstruction in the other, hence all existing approaches try to establish equilibrium. In this study, considering the effect of the whole data set on class assigning of any data, we propose a new approach to construct not perfectly accurate, but less complex trees in a short time, using small amount of memory. To achieve this purpose, a multi-step process has been used. We trace the training data set twice in any step, from the beginning to the end and vice versa, to extract the class pattern for attribute selection. Using the selected attribute, we make new branches in the tree. After making branches, the selected attribute and some records of training data set are deleted at the end of any step. This process continues alternatively in several steps for remaining data and attributes until the tree is completely constructed. In order to have an optimized tree the parameters which we use in this algorithm are optimized using genetic algorithms. In order to compare this new approach with previous ones we used some known data sets which have been used in different researches. This approach has been compared with others based on the classification accuracy and also the decision tree size. Experimental results show that it is efficient to use this approach particularly in cases of massive data sets, memory restrictions or short learning time.
Emerging deadline-driven Grid applications require a numberof computing resources to be available over a time frame, startingat a specific time in the future. To enable these applications, it is importantto predict th...
ISBN:
(纸本)9783540898931
Emerging deadline-driven Grid applications require a numberof computing resources to be available over a time frame, startingat a specific time in the future. To enable these applications, it is importantto predict the resource availability and utilise this informationduring provisioning because it affects their performance. It is impracticalto request the availability information upon the scheduling of everyjob due to communication overhead. However, existing work has notconsidered how the precision of availability information influences theprovisioning. As a result, limitations exist in developing advanced resourceprovisioning and scheduling mechanisms. This work investigateshow the precision of availability information affects resource provisioningin multiple site environments. Performance evaluation is conductedconsidering both multiple scheduling policies in resource providers andmultiple provisioning policies in brokers, while varying the precision ofavailability information. Experimental results show that it is possible toavoid requesting availability information for every Grid job scheduledthus reducing the communication overhead. They also demonstrate thatmultiple resource partition policies improve the slowdown of Grid jobs.
The Modeling in softwareengineering (MiSE) workshops are a collaboration between the ICSE and MoDELS research communities, with a focus on using models to facilitate software development.
ISBN:
(纸本)9781605580791
The Modeling in softwareengineering (MiSE) workshops are a collaboration between the ICSE and MoDELS research communities, with a focus on using models to facilitate software development.
Current Wi-Fi network infrastructure inherently lacks reliable positional knowledge of the origin of individual network packets. As a consequence, attackers are potentially able to impersonate legitimate Wi-Fi network...
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Current Wi-Fi network infrastructure inherently lacks reliable positional knowledge of the origin of individual network packets. As a consequence, attackers are potentially able to impersonate legitimate Wi-Fi network nodes, including both clients and access points, by generating network traffic with all the characteristics of legitimate traffic. To exclusively discriminate illegitimate nodes, we propose a method of using multiple and multiple-angle measurements of a Wi-Fi network nodepsilas signal strength. In this paper we present a real time unsupervised analysis method for identifying duplicate Wi-Fi network nodes which are physically separate. We demonstrate that, given a dense sensor network, data from a small set of identifiable critical sensors are much more valuable in node discrimination than those from other sensors, enabling large improvement in responding speed and discrimination accuracy.
Grid technologies are emerging as the next generation of distributed computing, allowing the aggregation of resources that are geographically distributed across different locations. The network remains an important re...
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Grid technologies are emerging as the next generation of distributed computing, allowing the aggregation of resources that are geographically distributed across different locations. The network remains an important requirement for any Grid application, as entities involved in a Grid system (such as users, services, and data) need to communicate with each other over a network. The performance of the network must therefore be considered when carrying out tasks such as scheduling, migration or monitoring of jobs. Network buffers management policies affect the network performance, as they can lead to poor latencies (if buffers become too large), but also leading to a lot of packet droppings and low utilization of links, when trying to keep a low buffer size. Therefore, network buffers management policies should be considered when simulating a real Grid system. In this paper, we introduce network buffers management policies into the GridSim simulation toolkit. Our framework allows new policies to be implemented easily, thus enabling researchers to create more realistic network models. Fields which will harness our work are scheduling, or QoS provision. We present a comprehensive description of the overall design and a use case scenario demonstrating the conditions of links varied over time.
The multiobjective Quadratic Assignment Problem (mQAP) is considered as one of the hardest optimization problems but with many real-world applications. Since it may not be possible to simply weight the importance of e...
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