ROCOV scheme for Fault Detection and Location in HVDC sytems

A reliable DC fault protection system is essential for the development of HVDC grids. Therefore, this paper deals with the voltage derivative ROCOV scheme to locate and detect DC faults. The algorithm is able to differentiate internal and external faults considerably fast. The proposed algorithm is analyzed in a HVDC grid with different fault case scenarios. Finally, the ROCOV protection thresholds are discussed.The authors thank the support from the Spanish Ministry of Economy, Industry and Competitiveness (project ENE2016-79145-R AEI/FEDER, UE) and GISEL research group IT1083-16), as well as from the University of the Basque Country UPV/EHU (research group funding PPG17/23)

The academic learning viewed from the perspective of John Biggs' «3P model»

El objetivo de este trabajo es presentar la contrastación de un modelo teórico de aprendizaje en el cual se postula que, de acuerdo con el Modelo 3P (Biggs, 1987a, 1993a), las variables de presagio inciden sobre las de producto, mediadas por las de proceso. El modelo ha sido analizado en base a las respuestas dadas por 561 estudiantes portugueses de Educación Secundaria, a un conjunto de instrumentos de medida (Inventario de Estilos de Pensamiento IEP, Batería de Pruebas de Razonamiento Diferencial BPRD, Cuestionario de Estrategias de Autorregulación del Aprendizaje, cuestiones para evaluar las concepciones de aprendizaje, metas escolares y un problema evaluado a partir de la taxonomía SOLO) y el rendimiento final del curso en diferentes áreas académicas. Los resultados confirman las hipótesis formuladas en el modelo postulado en esta investigación y son discutidas algunas consecuencias para la práctica educativa y para el desarrollo de los alumnos.The academic learning viewed from the perspective of John Biggs’ «3P model». The authors tested a theoretical model in which is postulated that, in accordance with the Pattern 3P (Biggs, 1987a, 1993a) the presage variables, impact on those of product, mediated by those of process. The model was contrasted in a group of 561 portuguese students of Secondary Education, using the following instruments: Inventory of Thinking Styles IEP, Battery of Differential Reasoning Tests BPRD, Questionnaire of Self-regulation learning strategies, questions to evaluate the learning conceptions, school goals and a problem evaluated with the SOLO taxonomy. Academic Achievement was measured by the grades obtained by the students in different areas at the end of academic year. The results confirm the hypotheses formulated in the pattern postulated in this investigation. Consequences for the educational practice and the promoting of students learning are discussed

Generalized multiscale RBF networks and the DCT for breast cancer detection

The use of the multiscale generalized radial basis function (MSRBF) neural networks for image feature extraction and medical image analysis and classification is proposed for the first time in this work. The MSRBF networks hold a simple and flexible architecture that has been successfully used in forecasting and model structure detection of input-output nonlinear systems. In this work instead, MSRBF networks are part of an integrated computer-aided diagnosis (CAD) framework for breast cancer detection, which holds three stages: an input-output model is obtained from the image, followed by a high-level image feature extraction from the model and a classification module aimed at predicting breast cancer. In the first stage, the image data is rendered into a multiple-input-single-output (MISO) system. In order to improve the characterisation, the nonlinear autoregressive with exogenous inputs (NARX) model is introduced to rearrange the available input-output data in a nonlinear way. The forward regression orthogonal least squares (FROLS) algorithm is then used to take advantage of the previous arrangement by solving the system as a model structure detection problem and finding the output layer weights of the NARX-MSRBF network. In the second stage, once the network model is available, the feature extraction takes place by stimulating the input to produce output signals to be compressed by the discrete cosine transform (DCT). In the third stage, we leverage the extracted features by using a clustering algorithm for classification to integrate a CAD system for breast cancer detection. To test the method performance, three different and well-known public image repositories were used: the mini-MIAS and the MMSD for mammography, and the BreaKHis for histopathology images. A comparison exercise was also made between different database partitions to understand the mammogram breast density effect in the performance since there are few remarks in the literature on this factor. Classification results show that the new CAD method reached an accuracy of 93.5% in mini-Mammo graphic image analysis society (mini-MIAS), 93.99% in digital database for screening mammography (DDSM) and 86.7% in the BreaKHis. We found that the MSRBF networks are able to build tailored and precise image models and, combined with the DCT, to extract high-quality features from both black and white and coloured images

Characterisation of retrotransposon insertion polymorphisms in whole genome sequencing data from individuals with amyotrophic lateral sclerosis

The genetics of an individual is a crucial factor in understanding the risk of developing the neurodegenerative disease amyotrophic lateral sclerosis (ALS). There is still a large proportion of the heritability of ALS, particularly in sporadic cases, to be understood. Among others, active transposable elements drive inter-individual variability, and in humans long interspersed element 1 (LINE1, L1), Alu and SINE-VNTR-Alu (SVA) retrotransposons are a source of polymorphic insertions in the population. We undertook a pilot study to characterise the landscape of non-reference retrotransposon insertion polymorphisms (non-ref RIPs) in 15 control and 15 ALS individuals’ whole genomes from Project MinE, an international project to identify potential genetic causes of ALS. The combination of two bioinformatics tools (mobile element locator tool (MELT) and TEBreak) identified on average 1250 Alu, 232 L1 and 77 SVA non-ref RIPs per genome across the 30 analysed. Further PCR validation of individual polymorphic retrotransposon insertions showed a similar level of accuracy for MELT and TEBreak. Our preliminary study did not identify a specific RIP or a significant difference in the total number of non-ref RIPs in ALS compared to control genomes. The use of multiple bioinformatic tools improved the accuracy of non-ref RIP detection and our study highlights the potential importance of studying these elements further in ALS

Are congenital malformations more frequent in fetuses with intrahepatic persistent right umbilical vein? A comparative study

Objective Persistent right umbilical vein (PRUV) is a vascular anomaly where the right umbilical vein remains as the only conduit that returns oxygenated blood to the fetus. It has classically been described as associated with numerous defects. We distinguish the intrahepatic variant (better prognosis) and the extrahepatic variant (associated with worse prognosis). The objective of this study was to compare rates of congenital malformations in fetuses with intrahepatic PRUV (I-PRUV) versus singleton pregnancies without risk factors. Materials and Methods A multicenter, crossover design, comparative study was performed between 2003 and 2013 on fetuses diagnosed with I-PRUV (n = 56), and singleton pregnancies without congenital malformation risk factors (n = 4050). Results Fifty-six cases of I-PRUV were diagnosed (incidence 1:770). A statistically significant association between I-PRUV and the presence of congenital malformations (odds ratio 4.321; 95% confidence interval 2.15–8.69) was found. This positive association was only observed with genitourinary malformations (odds ratio 3.038; 95% confidence interval 1.08–8.56). Conclusion Our rate of malformations associated with I-PRUV (17.9%) is similar to previously published rates. I-PRUV has shown a significant increase in the rate of associated malformations, although this association has only been found to be statistically significant in the genitourinary system. Noteworthy is the fact that this comparative study has not pointed to a significant increase in the congenital heart malformation rate. Diagnosis of isolated I-PRUV does not carry a worse prognosis

IN-SERVICE INSPECTION OF AERONAUTICS PARTS PRODUCED BY ADDITIVE LAYER MANUFACTURING (ALM) - in the framework of Bionic Aircraft project (GA nº 690689)

Bionic Aircraft is a project founded under the H2020 Framework Program and it is a result of a need to reduce emissions due to the impact of the growth of the aviation industry. The introduction of Additive Laser Manufacturing (ALM) to produce some metal aircraft parts is considered as an opportunity to address this issue. This technology allows to produce ultra-lightweight and highly complex parts (so-called “bionic parts”). One of the actions to consider in the project is the development of new NDT strategies to inspect, in-service, parts produced by ALM made of Al-based alloys. This need arises because, ALM processes for these alloys are at low maturity level (TRL2) and hence, no proven and certified NDT methods are yet developed. Moreover, in-service inspection of aeronautic bionic parts involves challenges like the uncertainty of the inner inspection of a layered material, the lack of accessibility (the part is attached to the aircraft fuselage), and the expected defects under in-service conditions, something still under study. The objective of this work is to assess the inspection, in-service, of this kind of parts, by selecting and customizing the most suitable NDT methods, according to the type and maximum tolerable damage sizes estimated by a fatigue life prediction evaluation.H2020, 690689, Bionic Aircraf

A Multilayer Interval Type-2 Fuzzy Extreme Learning Machine for the recognition of walking activities and gait events using wearable sensors

In this paper, a novel Multilayer Interval Type-2 Fuzzy Extreme Learning Machine (ML-IT2-FELM) for the recognition of walking activities and Gait events is presented. The ML-IT2-FELM uses a hierarchical learning scheme that consists of multiple layers of IT2 Fuzzy Autoencoders (FAEs), followed by a final classification layer based on an IT2-FELM architecture. The core building block in the ML-IT2-FELM is the IT2-FELM, which is a generalised model of the Interval Type-2 Radial Basis Function Neural Network (IT2-RBFNN) and that is functionally equivalent to a class of simplified IT2 Fuzzy Logic Systems (FLSs). Each FAE in the ML-IT2-FELM employs an output layer with a direct-defuzzification process based on the Nie-Tan algorithm, while the IT2-FELM classifier includes a Karnik-Mendel type-reduction method (KM). Real data was collected using three inertial measurements units attached to the thigh, shank and foot of twelve healthy participants. The validation of the ML-IT2-FELM method is performed with two different experiments. The first experiment involves the recognition of three different walking activities: Level-Ground Walking (LGW), Ramp Ascent (RA) and Ramp Descent (RD). The second experiment consists of the recognition of stance and swing phases during the gait cycle. In addition, to compare the efficiency of the ML-IT2-FELM with other ML fuzzy methodologies, a kernel-based ML-IT2-FELM that is inspired by kernel learning and called KML-IT2-FELM is also implemented. The results from the recognition of walking activities and gait events achieved an average accuracy of 99.98% and 99.84% with a decision time of 290.4ms and 105ms, respectively, by the ML-IT2-FELM, while the KML-IT2-FELM achieved an average accuracy of 99.98% and 99.93% with a decision time of 191.9ms and 94ms. The experiments demonstrate that the ML-IT2-FELM is not only an effective Fuzzy Logic-based approach in the presence of sensor noise, but also a fast extreme learning machine for the recognition of different walking activities

Spectra of heavy-light and heavy-heavy mesons containing charm quarks, including higher spin states for Nf=2+1N_f=2+ 1

We study the spectra of heavy-light and heavy-heavy mesons containing charm quarks, including higher spin states. We use two sets of $N_f = 2 + 1$ gauge configurations, one set from QCDSF using the SLiNC action, and the other configurations from the Budapest-Marseille-Wuppertal collaboration, using the HEX smeared clover action. To extract information about the excited states, we choose a suitable basis of operators to implement the variational method.Comment: 7 pages, 5 figures, Talk presented at the XXIX International Symposium on Lattice Field Theory, Lattice2011, July 11-16, 2011, The Village at Squaw Valley, California, US