Contactless Rotor Ground Fault Detection Method for Brushless Synchronous Machines Based on an AC/DC Rotating Current Sensor

Brushless synchronous machines (BSMs) are replacing conventional synchronous machines with static excitation in generation facilities due to the absence of sparking and lower maintenance. However, this excitation system makes measuring electric parameters in the rotor challenging. It is highly difficult to detect ground faults, which are the most common type of electrical fault in electric machines. In this paper, a ground fault detection method for BSMs is proposed. It is based on an inductive AC/DC rotating current sensor installed in the shaft. In the case of a ground fault in the rotating parts of the BSM, a fault current will flow through the rotor’s sensor, inducing voltage in its stator. By analyzing the frequency components of the induced voltage, the detection of a ground fault in the rotating elements is possible. The ground faults detection method proposed covers the whole rotor and discerns between DC and AC sides. This method does not need any additional power source, slip ring, or brush, which is an important advantage in comparison with the existing methods. To corroborate the detection method, experimental tests have been performed using a prototype of this sensor connected to laboratory synchronous machines, achieving satisfactory results.This research was funded by Universidad Politécnica de Madrid under grant number RP2304330031

Decentralizing Science: Towards an Interoperable Open Peer Review Ecosystem using Blockchain

Science publication and its Peer Review system strongly rely on a few major industry players controlling most journals (e.g. Elsevier), databases (e.g. Scopus) and metrics (e.g. JCR Impact Factor), while keeping most articles behind paywalls. Critics to such system include concerns about fairness, quality, performance, cost, unpaid labor, transparency, and accuracy of the evaluation process. The Open Access movement has tried to provide free access to the published research articles, but most of the aforementioned issues remain. In such context, decentralized technologies such as blockchain offer an opportunity to experiment with new models for science production and dissemination relying on a decentralized infrastructure, aiming to tackle multiple of the current system shortcomings. This paper makes a proposal for an interoperable decentralized system for an open peer review ecosystem, relying on emerging distributed technologies such as blockchain and IPFS. Such system, named ``Decentralized Science'' (DecSci), aims to enable a decentralized reviewer reputation system, which relies on an Open Access by-design infrastructure, together with transparent governance processes. Two prototypes have been implemented: a proof-of-concept prototype to validate DecSci's technological feasibility, and a Minimum Viable Product (MVP) prototype co-designed with journal editors. In addition, three evaluations have been carried out: an exploratory survey to assess interest on the issues tackled, a set of interviews to confirm the main problems for editors, and another set of interviews to validate the MVP prototype. Additionally, the paper discusses the multiple interoperability challenges such proposal faces, including an architecture to tackle them. This work finishes with a review of some of the open challenges that this ambitious proposal may face

A Decentralized Publication System for Open Science using Blockchain and IPFS

Science publication and peer review raises concerns about fairness, quality, per-formance, cost or accuracy. The Open Access movements has been unable to fulfill all itspromises, and middlemen publishers can still impose policies and concentrate profits. Thispaper, using emerging distributed technologies such as Blockchain and IPFS, proposes adecentralized publication system for open science. It provides transparent governance, adistributed reviewer reputation system, and open access by-design. The paper concludesreviewing the open challenges of such approach

El impacto de la estandarización de la I+D+i en el acceso y generación de nuevos mercados

Postprint (published version

Selecting patterns and features for between- and within- crop-row weed mapping using UAV-imagery

This paper approaches the problem of weed mapping for precision agriculture, using imagery provided by Unmanned Aerial Vehicles (UAVs) from sun ower and maize crops. Precision agriculture referred to weed control is mainly based on the design of early post-emergence site-speci c control treatments according to weed coverage, where one of the most important challenges is the spectral similarity of crop and weed pixels in early growth stages. Our work tackles this problem in the context of object-based image analysis (OBIA) by means of supervised machine learning methods combined with pattern and feature selection techniques, devising a strategy for alleviating the user intervention in the system while not compromising the accuracy. This work rstly proposes a method for choosing a set of training patterns via clustering techniques so as to consider a representative set of the whole eld data spectrum for the classi cation method. Furthermore, a feature selection method is used to obtain the best discriminating features from a set of several statistics and measures of di erent nature. Results from this research show that the proposed method for pattern selection is suitable and leads to the construction of robust sets of data. The exploitation of di erent statistical, spatial and texture metrics represents a new avenue with huge potential for between and within crop-row weed mapping via UAV-imagery and shows good synergy when complemented with OBIA. Finally, there are some measures (specially those linked to vegetation indexes) that are of great in uence for weed mapping in both sun ower and maize crop

Weed mapping in early-season sunflower fields using images from an unmanned aerial vehicle (UAV)

Revista oficial de la Asociación Española de Teledetección[EN] Weed mapping in early season requires of very high spatial resolution images (pixels <5 cm). Currently only Unmanned Aerial Vehicles (UAV) can take such images. The aim of this work was to evaluate the optimal flight altitude for mapping weeds in an early season sunflower field using a low-cost camera that took images in the visible spectrum at several flight altitudes (40, 60, 80 and 100 m). The object based image analysis procedure used for weed mapping was divided in two main phases: 1) crop-row identification, and 2) crop, weed and bare soil classification. The algorithm identified the crop rows with 100% accuracy at every flight altitude (phase 1) and it detected weed-free zones with 100% accuracy in the images captured at 40 and 60 m flight altitude. In weed-infested zones, the classification algorithm obtained the best results in the images captured at low altitude (40 m), reporting 71% of correctly classified sampling frames (phase 2). Most of errors committed (incorrectly classified frames) were produced by non-detection of weeds (negative false). Subsequent studies would consist in a multi-temporal study aiming to detect weeds are at a more advance growth stage. It could reduce the percentage of negative false in the classification.[ES] La discriminación de malas hierbas en fase temprana con técnicas de teledetección requiere imágenes re-motas de muy elevada resolución espacial (píxeles <5 cm). Actualmente, sólo los vehículos aéreos no tripulados (UAV) pueden generar este tipo de imágenes. El objetivo de este trabajo fue evaluar imágenes UAV tomadas con una cámara visible a diferentes alturas de vuelo (40, 60, 80 y 100 m) y cuantificar la influencia de la resolución espacial en la discrimi-nación de malas hierbas en fase temprana en un cultivo de girasol. Se aplicó un algoritmo de clasificación de imágenes basado en objetos, el cual se divide en dos fases principales: 1) detección de líneas de cultivo y 2) clasificación de cultivo, malas hierbas y suelo desnudo. El algoritmo resultó 100% eficaz en la detección de las líneas de cultivo en todos los ca-sos (fase 1), así como en la detección de zonas libres de mala hierba en las imágenes tomadas a 40 y 60 m de altura. En las zonas con presencia de malas hierbas, los mejores resultados se obtuvieron en las imágenes tomadas a baja altura (40 m), con un 71% de marcos de muestreo clasificados correctamente (fase 2). La mayoría de los fallos de clasificación cometidos en todas las imágenes fueron falsos negativos, es decir, malas hierbas no detectadas debido a su pequeño tamaño en el momento de la captura de las imágenes. Por tanto, el siguiente paso sería desarrollar un estudio multi-temporal para estudiar la detección de las malas hierbas en estados fenológicos más avanzados. Esto podría facilitar su discriminación en las imágenes y, por tanto, disminuir el porcentaje de falsos negativos en las clasificacionesEste trabajo fue financiado por el proyecto Recupera 2020 (Ministerio de Economía y Competitividad y Fondos FEDER de la Unión Europea). La investigación de Jorge Torres Sánchez fue financiada por el programa FPI (CSIC y fondos FEDER).Peña, J.; Torres-Sánchez, J.; Serrano-Pérez, A.; López-Granados, F. (2014). Detección de malas hierbas en girasol en fase temprana mediante imágenes tomadas con un vehículo aéreo no tripulado (UAV). Revista de Teledetección. (42):39-48. doi:10.4995/raet.2014.3148SWORD39484

Finishing lambs using an integral feed under a restricted-feeding program in an intensive production system in Northern Mexico

The objective of this study was to compare the productive performance of finishing lambs using an integral diet under a restricted-feeding program. Ten Dorper lambs were assigned to two homogenous groups according to live weight and age under a complete randomised block design. Group 1 was fed a traditional diet commonly used by the producer and group 2 was fed an integral feed restricted to 75% of dry matter requirement of lambs. The evaluated variables were: dry matter intake, initial and final live weight, daily weight gain, feed efficiency and body growth expressed in height, body length, thoracic diameter, cane length and cane width. A partial cost analysis was carried out to evaluate the economic viability. Lambs fed with the integral feed had better feed efficiency, higher dry matter intake, daily weight gain, height, body length and thoracic diameter when compared with the lambs fed the traditional diet. The use of an integral feed under a restricted-feeding program reduced the cost of finishing lambs by 2.46 dollars per head and finishing length by 120 days. Overall, providing an integral feed under a restricted-feeding program is a viable alternative for improving finishing lambs under intensive conditions in the Northern Mexico