Recent studies have focused on leveraging large-scale artificial intelligence (LAI) models to improve semantic representation and compression capabilities. However, the substantial computational demands of LAI models ...
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Floor plans can provide valuable prior information that helps enhance the accuracy of indoor positioning systems. However, existing research typically faces challenges in efficiently leveraging floor plan information ...
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In this paper we propose a protection strategy for light-trail [1-4] networks for ring topologies. A light-trail is a multi-point lightpath, such that multiple users can take part in communication along the trail, thr...
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With the increasing availability of huge quantities of manufacturing data, and the pressures of continuous process improvement and scrap reduction, engineers are beginning to use machine learning techniques along with...
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With the increasing availability of huge quantities of manufacturing data, and the pressures of continuous process improvement and scrap reduction, engineers are beginning to use machine learning techniques along with traditional statistical methods. In this paper, we discuss the application of standard machine learning techniques to analyze, classify, and predict the quality of metal etch using RIE. Three types of data were used to characterize a metal etch: in-process sensor data from the etch chamber, metrology data for critical dimension measurements before and after etch, and metal resistance measurements from probe tests. Three machine learning paradigms were applied: neural networks, induction learning, and case-based reasoning. This paper describes the techniques used, the results obtained, and the conclusions drawn.
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