Análisis de las causas de fallas de operación del variador de frecuencia del molino en la Sociedad Minera “El Brocal” para mejorar su confiabilidad

La introducción de variadores de frecuencia (VFD) para el accionamiento y control del motor fue impulsada por el deseo de aumentar la eficiencia de motor del molino de la Planta Concentradora de Huaraucaca de la Sociedad Minera “El Brocal”, sin embargo, una vez instalado el sistema y en funcionamiento se tuvo fallas por falta de conocimiento de operación y mantenimiento, entonces hubo la necesidad de analizar las causas que originan tales problemas y dar las soluciones del cada uno de los casos. Para dar solución a este problema al inicio se ha recurrido a la investigación exploratoria por ser un tema algo desconocido y poco estudiado y, luego a la investigación descriptiva para caracterizar los fenómenos que se presentan en el funcionamiento del variador de frecuencia cuando se producen las fallas para analizarlos y corregirlos con el fin de mejorar la confiabilidad del VFD. Entre las principales causas que ha podido encontrar es el polvo del mineral y la humedad que se aparece en los distintos equipos, como en los ventiladores del variador lo cual hace que no funcionen adecuadamente y aumente la temperatura produciéndose paradas de éste. Al ser el variador de frecuencia un sistema electrónico según normas debe tener una resistencia del sistema de puesta menor o igual a 5Ω, pero se descubrió que mayor y esto también causaba paradas en el variador. Otra causa de falla descubierto después del análisis fue que el valor del aislamiento de conductor de conexión entre el variador y el motor era menor a lo que indican las normas, lo cual al haber fugas de corriente a tierra terminaba por parar al variador. También se pudo detectar que la configuración del variador no era la más adecuada y este generaba sobrecorrientes. Al conocer estas causas se dio solución y con esto se mejoró la confiabilidad del sistema de molienda.Tesi

Split Gröbner Bases for Satisfiability Modulo Finite Fields

Satisfiability modulo finite fields enables automated verification for cryptosystems. Unfortunately, previous solvers scale poorly for even some simple systems of field equations, in part because they build a full Gröbner basis (GB) for the system. We propose a new solver that uses multiple, simpler GBs instead of one full GB. Our solver, implemented within the cvc5 SMT solver, admits specialized propagation algorithms, e.g., for understanding bitsums. Experiments show that it solves important bitsum-heavy determinism benchmarks far faster than prior solvers, without introducing much overhead for other benchmarks

Practical Security Analysis of Zero-Knowledge Proof Circuits

As privacy-sensitive applications based on zero-knowledge proofs (ZKPs) gain increasing traction, there is a pressing need to detect vulnerabilities in ZKP circuits. This paper studies common vulnerabilities in Circom (the most popular domain-specific language for ZKP circuits) and describes a static analysis framework for detecting these vulnerabilities. Our technique operates over an abstraction called the circuit dependence graph (CDG) that captures key properties of the circuit and allows expressing semantic vulnerability patterns as queries over the CDG abstraction. We have implemented 9 different detectors using this framework and perform an experimental evaluation on over 258 circuits from popular Circom projects on Github. According to our evaluation, these detectors can identify vulnerabilities, including previously unknown ones, with high precision and recall

Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks

Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection. In this study, two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The models were extensively validated on a multicenter dataset with nearly 1000 patients, from 12 hospitals in Europe and the United States. The best performance achieved was a 61% Dice score, and the best classification performance was about 80% balanced accuracy, with a demonstrated ability to generalize across hospitals. In addition, the segmentation performance of the best models was on par with human expert raters. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection

Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks

Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection. In this study, two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The models were extensively validated on a multicenter dataset with nearly 1000 patients, from 12 hospitals in Europe and the United States. The best performance achieved was a 61\% Dice score, and the best classification performance was about 80\% balanced accuracy, with a demonstrated ability to generalize across hospitals. In addition, the segmentation performance of the best models was on par with human expert raters. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection.Comment: 13 pages, 4 figures, 4 table

Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks

Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection. In this study, two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The models were extensively validated on a multicenter dataset with nearly 1000 patients, from 12 hospitals in Europe and the United States. The best performance achieved was a 61% Dice score, and the best classification performance was about 80% balanced accuracy, with a demonstrated ability to generalize across hospitals. In addition, the segmentation performance of the best models was on par with human expert raters. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection