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Jessica Reyes

Publications and source records attributed to Jessica Reyes.

2 recordsLinked to original sources

Lock-In Infrared Thermography: Phase Analysis for Rapid, Wide-Range Thermal Conductivity Measurements

We report on a phase-based lock-in thermography approach, combined with a multilayered thermal model (often employed in thermoreflectance analysis), to measure the thermal conductivity of bulk materials and layered structures. The spatial distribution of the material's thermal phase is monitored with an infrared camera, which is locked into the frequency of a modulated laser used to heat the material. This phase distribution is then fit with a thermal model, in which properties such as thermal conductivity are extracted as fit parameters. This approach enables non-contact, front-side measurements, which are insensitive to surface roughness. The technique does not strictly require the application of a transducer layer, but we highlight the practical benefits of applying a removable adhesive layer to serve as a near-surface absorber. We demonstrate the efficacy of the method by measuring materials with thermal conductivities that span over three orders of magnitude (approximately 1 W/m/K to > 2000 W/m/K).

cond-mat.mtrl-sci

Method for Customizable Automated Tagging: Addressing the Problem of Over-tagging and Under-tagging Text Documents

Using author provided tags to predict tags for a new document often results in the overgeneration of tags. In the case where the author doesn't provide any tags, our documents face the severe under-tagging issue. In this paper, we present a method to generate a universal set of tags that can be applied widely to a large document corpus. Using IBM Watson's NLU service, first, we collect keywords/phrases that we call "complex document tags" from 8,854 popular reports in the corpus. We apply LDA model over these complex document tags to generate a set of 765 unique "simple tags". In applying the tags to a corpus of documents, we run each document through the IBM Watson NLU and apply appropriate simple tags. Using only 765 simple tags, our method allows us to tag 87,397 out of 88,583 total documents in the corpus with at least one tag. About 92.1% of the total 87,397 documents are also determined to be sufficiently-tagged. In the end, we discuss the performance of our method and its limitations.

cs.IR