2 research outputs found
Estimaci贸n de la madurez 贸sea en canales de bovino por fluorescencia diferencial
At present, carcass grading is dependent to a great degree on age of the animal. However, in M茅xico standards are vague on this matter, because the only requirement is stating if the animal is younger or older than 30 mo. This vagueness is the source of a need of developing objective systems for determining physiological maturity. Therefore, the purpose of the present study was estimating with great accuracy ossification of the cartilage in the 12th rib, as an indicator of bone ossification in bovine carcasses, by means of an electronic pattern obtained through artificial vision. In the model developed to this end, better use of fluorescence generated by the cartilage is made, in comparison to both bone and fat, and as a result the cartilage area stands out clearly, and by developing image processing algorithms for analyzing it, ossification percentage can be estimated with accuracy, and consequently, carcass physiological age. The artificial vision system developed in the present study allows evaluating bone maturity in a carcass in less than one second and data obtained is used for establishing carcass electronic grading in less than five seconds.Actualmente, el an谩lisis de clasificaci贸n de calidad de la canal depende en gran medida de la edad del animal, sin embargo, el patr贸n en M茅xico es demasiado impreciso, pues s贸lo requiere identificar si 茅ste es menor o mayor a 30 meses. Esta falta de precisi贸n genera la necesidad de sistemas objetivos para medir la madurez fisiol贸gica. El prop贸sito de esta investigaci贸n fue estimar de manera precisa el porcentaje de osificaci贸n del cart铆lago en la 12ava costilla, como un indicador de madurez 贸sea en canales de bovino, utilizando un esquema electr贸nico por visi贸n artificial. En el esquema desarrollado se aprovecha la mayor fluorescencia que genera el cart铆lago, en comparaci贸n con la del hueso y la grasa, sobresaliendo claramente la regi贸n del cart铆lago, desarrollando algoritmos de procesamiento de im谩genes para analizar esta regi贸n, y estimar de manera precisa el porcentaje de osificaci贸n, y por tanto la edad fisiol贸gica de la canal. El sistema de visi贸n artificial desarrollado permiti贸 evaluar la madurez 贸sea de una canal en menos de un segundo, y la informaci贸n obtenida fue utilizada para establecer la clasificaci贸n electr贸nica de la canal en menos de cinco segundos
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Interpretable Models for Information Extraction
There is an abundance of information being generated constantly, most of it encoded as unstructured text. The information expressed this way, although publicly available, is not directly usable by computer systems because it is not organized according to a data model that could inform us how different data nuggets relate to each other. Information extraction provides a way of scanning unstructured text and extracting structured knowledge suitable for querying and manipulation. Most information extraction research focuses on machine learning approaches that can be considered black boxes when deployed in information extraction systems. We propose a declarative language designed for the information extraction task. It allows the use of syntactic patterns alongside token-based surface patterns that incorporate shallow linguistic features. It captures complex constructs such as nested structures, and complex regular expressions over syntactic patterns for event arguments. We implement a novel information extraction runtime system designed for the compilation and execution of the proposed language. The runtime system has novel features for better declarative support, while preserving practicality. It supports features required for handling natural language, like the preservation of ambiguity and the efficient use of contextual information. It has a modular architecture that allows it to be extended with new functionality, which, together with the language design, provides a powerful framework for the development and research of new ideas for declarative information extraction. We use our language and runtime system to build a biomedical information extraction system. This system is capable of recognizing biological entities (e.g., genes, proteins, protein families, simple chemicals), events over entities (e.g., biochemical reactions), and nested events that take other events as arguments (e.g., catalysis). Additionally, it captures complex natural language phenomena like coreference and hedging. Finally, we propose a rule learning procedure to extract rules from statistical systems trained for information extraction. Rule learning combines the advantages of machine learning with the interpretability of our models. This enables us to train information extraction systems using annotated data that can then be extended and modified by human experts, and in this way accelerate the deployment of new systems that can still be extended or modified by human experts