10,233 research outputs found
Self-tuning routine alarm analysis of vibration signals in steam turbine generators
This paper presents a self-tuning framework for knowledge-based diagnosis of routine alarms in steam turbine generators. The techniques provide a novel basis for initialising and updating time series feature extraction parameters used in the automated decision support of vibration events due to operational transients. The data-driven nature of the algorithms allows for machine specific characteristics of individual turbines to be learned and reasoned about. The paper provides a case study illustrating the routine alarm paradigm and the applicability of systems using such techniques
Learning Concise Models from Long Execution Traces
Abstract models of system-level behaviour have applications in design
exploration, analysis, testing and verification. We describe a new algorithm
for automatically extracting useful models, as automata, from execution traces
of a HW/SW system driven by software exercising a use-case of interest. Our
algorithm leverages modern program synthesis techniques to generate predicates
on automaton edges, succinctly describing system behaviour. It employs trace
segmentation to tackle complexity for long traces. We learn concise models
capturing transaction-level, system-wide behaviour--experimentally
demonstrating the approach using traces from a variety of sources, including
the x86 QEMU virtual platform and the Real-Time Linux kernel
The Semantic Grid: A future e-Science infrastructure
e-Science offers a promising vision of how computer and communication technology can support and enhance the scientific process. It does this by enabling scientists to generate, analyse, share and discuss their insights, experiments and results in an effective manner. The underlying computer infrastructure that provides these facilities is commonly referred to as the Grid. At this time, there are a number of grid applications being developed and there is a whole raft of computer technologies that provide fragments of the necessary functionality. However there is currently a major gap between these endeavours and the vision of e-Science in which there is a high degree of easy-to-use and seamless automation and in which there are flexible collaborations and computations on a global scale. To bridge this practiceâaspiration divide, this paper presents a research agenda whose aim is to move from the current state of the art in e-Science infrastructure, to the future infrastructure that is needed to support the full richness of the e-Science vision. Here the future e-Science research infrastructure is termed the Semantic Grid (Semantic Grid to Grid is meant to connote a similar relationship to the one that exists between the Semantic Web and the Web). In particular, we present a conceptual architecture for the Semantic Grid. This architecture adopts a service-oriented perspective in which distinct stakeholders in the scientific process, represented as software agents, provide services to one another, under various service level agreements, in various forms of marketplace. We then focus predominantly on the issues concerned with the way that knowledge is acquired and used in such environments since we believe this is the key differentiator between current grid endeavours and those envisioned for the Semantic Grid
Big Data and the Internet of Things
Advances in sensing and computing capabilities are making it possible to
embed increasing computing power in small devices. This has enabled the sensing
devices not just to passively capture data at very high resolution but also to
take sophisticated actions in response. Combined with advances in
communication, this is resulting in an ecosystem of highly interconnected
devices referred to as the Internet of Things - IoT. In conjunction, the
advances in machine learning have allowed building models on this ever
increasing amounts of data. Consequently, devices all the way from heavy assets
such as aircraft engines to wearables such as health monitors can all now not
only generate massive amounts of data but can draw back on aggregate analytics
to "improve" their performance over time. Big data analytics has been
identified as a key enabler for the IoT. In this chapter, we discuss various
avenues of the IoT where big data analytics either is already making a
significant impact or is on the cusp of doing so. We also discuss social
implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski
(eds.) Big Data Analysis: New algorithms for a new society, Springer Series
on Studies in Big Data, to appea
The Relationship between Fuzzy Reasoning and Its Temporal Characteristics for Knowledge Management
The knowledge management systems based on artificial reasoning (KMAR) tries to provide computers the capabilities of performing various intelligent tasks for which their human users resort to their knowledge and collective intelligence. There is a need for incorporating aspects of time and imprecision into knowledge management systems, considering appropriate semantic foundations. The aim of this paper is to present the FRTES, a real-time fuzzy expert system, embedded in a knowledge management system. Our expert system is a special possibilistic expert system, developed in order to focus on fuzzy knowledge.Knowledge Management, Artificial Reasoning, predictability
Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection
Selective weeding is one of the key challenges in the field of agriculture
robotics. To accomplish this task, a farm robot should be able to accurately
detect plants and to distinguish them between crop and weeds. Most of the
promising state-of-the-art approaches make use of appearance-based models
trained on large annotated datasets. Unfortunately, creating large agricultural
datasets with pixel-level annotations is an extremely time consuming task,
actually penalizing the usage of data-driven techniques. In this paper, we face
this problem by proposing a novel and effective approach that aims to
dramatically minimize the human intervention needed to train the detection and
classification algorithms. The idea is to procedurally generate large synthetic
training datasets randomizing the key features of the target environment (i.e.,
crop and weed species, type of soil, light conditions). More specifically, by
tuning these model parameters, and exploiting a few real-world textures, it is
possible to render a large amount of realistic views of an artificial
agricultural scenario with no effort. The generated data can be directly used
to train the model or to supplement real-world images. We validate the proposed
methodology by using as testbed a modern deep learning based image segmentation
architecture. We compare the classification results obtained using both real
and synthetic images as training data. The reported results confirm the
effectiveness and the potentiality of our approach.Comment: To appear in IEEE/RSJ IROS 201
Applications of aerospace technology in the electric power industry
An overview of the electric power industry, selected NASA contributions to progress in the industry, linkages affecting the transfer and diffusion of technology, and, finally, a perspective on technology transfer issues are presented
Spray automated balancing of rotors: Methods and materials
The work described consists of two parts. In the first part, a survey is performed to assess the state of the art in rotor balancing technology as it applies to Army gas turbine engines and associated power transmission hardware. The second part evaluates thermal spray processes for balancing weight addition in an automated balancing procedure. The industry survey reveals that: (1) computerized balancing equipment is valuable to reduce errors, improve balance quality, and provide documentation; (2) slow-speed balancing is used exclusively, with no forseeable need for production high-speed balancing; (3) automated procedures are desired; and (4) thermal spray balancing is viewed with cautious optimism whereas laser balancing is viewed with concern for flight propulsion hardware. The FARE method (Fuel/Air Repetitive Explosion) was selected for experimental evaluation of bond strength and fatigue strength. Material combinations tested were tungsten carbide on stainless steel (17-4), Inconel 718 on Inconel 718, and Triballoy 800 on Inconel 718. Bond strengths were entirely adequate for use in balancing. Material combinations have been identified for use in hot and cold sections of an engine, with fatigue strengths equivalent to those for hand-ground materials
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