2,132 research outputs found

    Technology for the Future: In-Space Technology Experiments Program, part 2

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    The purpose of the Office of Aeronautics and Space Technology (OAST) In-Space Technology Experiments Program In-STEP 1988 Workshop was to identify and prioritize technologies that are critical for future national space programs and require validation in the space environment, and review current NASA (In-Reach) and industry/ university (Out-Reach) experiments. A prioritized list of the critical technology needs was developed for the following eight disciplines: structures; environmental effects; power systems and thermal management; fluid management and propulsion systems; automation and robotics; sensors and information systems; in-space systems; and humans in space. This is part two of two parts and contains the critical technology presentations for the eight theme elements and a summary listing of critical space technology needs for each theme

    Nature-Inspired Learning Models

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    Intelligent learning mechanisms found in natural world are still unsurpassed in their learning performance and eficiency of dealing with uncertain information coming in a variety of forms, yet remain under continuous challenge from human driven artificial intelligence methods. This work intends to demonstrate how the phenomena observed in physical world can be directly used to guide artificial learning models. An inspiration for the new learning methods has been found in the mechanics of physical fields found in both micro and macro scale. Exploiting the analogies between data and particles subjected to gravity, electrostatic and gas particle fields, new algorithms have been developed and applied to classification and clustering while the properties of the field further reused in regression and visualisation of classification and classifier fusion. The paper covers extensive pictorial examples and visual interpretations of the presented techniques along with some testing over the well-known real and artificial datasets, compared when possible to the traditional methods

    Pattern recognition in medical images using neural networks

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    The proposal of this research line is the search for alternatives to the resolution of complex problems where human knowledge should be apprehended in a general fashion. In particular, the activities developed so far can be included in the area of Medical Diagnosis, even though similar applications in other fields are not discarded. In general, one of the greatest problems of medical diagnosis is the subjectivity of the specialist. The experience of the professional greatly affects the final diagnosis. This is due to the fact that the result does not depend on a systematized solution, but on the interpretation of the patient´s answer. The solution to this kind of problems can be found in the area of Adaptive Pattern Recognition, where the solution rests on the easiness with which the systems adapts to the information available, in this case coming from the patient. In this sense, neural networks are extremely useful, since they are not only capable of learning with the aid of an expert, but they can also make generalizations based on the information from the input data, thus showing relations that are a priori of a complex nature.Facultad de Informátic

    A blackboard-based system for learning to identify images from feature data

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    A blackboard-based system which learns recognition rules for objects from a set of training examples, and then identifies and locates these objects in test images, is presented. The system is designed to use data from a feature matcher developed at R.S.R.E. Malvern which finds the best matches for a set of feature patterns in an image. The feature patterns are selected to correspond to typical object parts which occur with relatively consistent spatial relationships and are sufficient to distinguish the objects to be identified from one another. The learning element of the system develops two separate sets of rules, one to identify possible object instances and the other to attach probabilities to them. The search for possible object instances is exhaustive; its scale is not great enough for pruning to be necessary. Separate probabilities are established empirically for all combinations of features which could represent object instances. As accurate probabilities cannot be obtained from a set of preselected training examples, they are updated by feedback from the recognition process. The incorporation of rule induction and feedback into the blackboard system is achieved by treating the induced rules as data to be held on a secondary blackboard. The single recognition knowledge source effectively contains empty rules which this data can be slotted into, allowing it to be used to recognise any number of objects - there is no need to develop a separate knowledge source for each object. Additional object-specific background information to aid identification can be added by the user in the form of background checks to be carried out on candidate objects. The system has been tested using synthetic data, and successfully identified combinations of geometric shapes (squares, triangles etc.). Limited tests on photographs of vehicles travelling along a main road were also performed successfully

    Fuzzy Logic Hemoglobin Sensors

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    Proposal for the Awakening Behavior Detection System Using Images and Adaptation for Fluctuation of Brightness Quantity in the Captured Image

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    Recently, accidents such that seniors fall down from the bed in care facilities or hospitals are increased. To prevent these accidents, we have developed the awakening behavior detection system using Neural Network. In this paper, it is a problem that the detection success rate of the current system using captured image in the clinical site is not enough. So, we analyze the captured image in the clinical site. From the result of the histogram analysis, it proves that the fluctuation of brightness quantity makes decrease the detection capability. Therefore, to decrease the influence of the brightness quantity, the histogram of the captured image should be equalized. Finally, we show that the histogram equalization reduces fluctuation of brightness quantity, numerically
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