Enhancement of power system transient stability using superconducting fault current limiters

Transient stability investigations consist of studying the rotor oscillations of generators (electro-mechanic oscillations, 0.1-2 Hz) after the occurrence of a fault of large amplitude, e.g. short circuit. The goal is to indicate if the generators are capable to stay synchronous after a fault has occurred. The fault duration is one of the most important factors to be determined. In fact, the shorter the fault, the more the maintaining of synchronisation can be guaranteed. Now in case of a fault, a fault current limiter has an extremely fast current transition in comparison to electro-mechanic time constants. This implies a quasi-instantaneous elimination of the fault through a limitation of the current and consequently a better ability to maintain the synchronisation of the system. We recall that in a classic system, the elimination of a fault, by opening a circuit breaker, is carried out in two or three cycles in the best case. We have here studied a simple, radial electric network configuration with a machine and an infinite network. The study covers simulations of a fault that can occur in a network and the consequences of the recovery time of the fault current limiter

Parametrised Preisach Modelling of Hysteresis in High Temperature Superconductors

We present a parametrised Preisach-type model that describes the hysteresis exhibited by the high temperature superconductors (HTSC); hysteresis is the main cause for losses in the subcritical domain. The parametrised model, in combination with electrical measurements, is independent of geometry, number of filaments and other physical measures, and is identified by a novel method that uses electrical lock-in (loss) measurement technique, which greatly enhances the signal to noise ratio. Identification results from measurements on Bi-2223 multi-filamentary tapes are presented. We have further derived exact models for the hysteretic losses in strip and elliptic geometry strips, where the energy losses were calculated by Norris. The paper contains analysis of the Preisach Model, of its losses and of the suggested parametrisation

Bi(2223) Ag sheathed tape Ic and exponent n characterisation and modelling under DC applied magnetic field

We use a dual channel digital lock-in to perform electrical measurement of AC losses at power frequencies. A DC magnetic field between 2 and 400 mT is applied with a varying angle from parallel to perpendicular to the tape surface, thus having a complete view of the loss behavior under DC applied field. Furthermore, the same experimental layout is used to acquire time series of current and voltage across the sample. Using a triangular input current, we measure and average the voltage, which then is fitted to a power law (I/Ic)^n. The measurements are repeated for the mentioned magnetic field and angle domain to give the dependencies of Ic and n with magnetic field and angle. For device modeling purposes, we can then express a phenomenological law giving Ic and n as a function of the applied magnetic field's intensity and direction

MRI in multiple myeloma : a pictorial review of diagnostic and post-treatment findings

Magnetic resonance imaging (MRI) is increasingly being used in the diagnostic work-up of patients with multiple myeloma. Since 2014, MRI findings are included in the new diagnostic criteria proposed by the International Myeloma Working Group. Patients with smouldering myeloma presenting with more than one unequivocal focal lesion in the bone marrow on MRI are considered having symptomatic myeloma requiring treatment, regardless of the presence of lytic bone lesions. However, bone marrow evaluation with MRI offers more than only morphological information regarding the detection of focal lesions in patients with MM. The overall performance of MRI is enhanced by applying dynamic contrast-enhanced MRI and diffusion weighted imaging sequences, providing additional functional information on bone marrow vascularization and cellularity. This pictorial review provides an overview of the most important imaging findings in patients with monoclonal gammopathy of undetermined significance, smouldering myeloma and multiple myeloma, by performing a 'total' MRI investigation with implications for the diagnosis, staging and response assessment. Main message aEuro cent Conventional MRI diagnoses multiple myeloma by assessing the infiltration pattern. aEuro cent Dynamic contrast-enhanced MRI diagnoses multiple myeloma by assessing vascularization and perfusion. aEuro cent Diffusion weighted imaging evaluates bone marrow composition and cellularity in multiple myeloma. aEuro cent Combined morphological and functional MRI provides optimal bone marrow assessment for staging. aEuro cent Combined morphological and functional MRI is of considerable value in treatment follow-up

From spinal central pattern generators to cortical network: integrated BCI for walking rehabilitation

Success in locomotor rehabilitation programs can be improved with the use of brain-computer interfaces (BCIs). Although a wealth of research has demonstrated that locomotion is largely controlled by spinal mechanisms, the brain is of utmost importance in monitoring locomotor patterns and therefore contains information regarding central pattern generation functioning. In addition, there is also a tight coordination between the upper and lower limbs, which can also be useful in controlling locomotion. The current paper critically investigates different approaches that are applicable to this field: the use of electroencephalogram (EEG), upper limb electromyogram (EMG), or a hybrid of the two neurophysiological signals to control assistive exoskeletons used in locomotion based on programmable central pattern generators (PCPGs) or dynamic recurrent neural networks (DRNNs). Plantar surface tactile stimulation devices combined with virtual reality may provide the sensation of walking while in a supine position for use of training brain signals generated during locomotion. These methods may exploit mechanisms of brain plasticity and assist in the neurorehabilitation of gait in a variety of clinical conditions, including stroke, spinal trauma, multiple sclerosis, and cerebral palsy

Constant regulation for stable CD8 T-cell functional avidity and its possible implications for cancer immunotherapy.

The functional avidity (FA) of cytotoxic CD8 T cells impacts strongly on their functional capabilities and correlates with protection from infection and cancer. FA depends on TCR affinity, downstream signaling strength, and TCR affinity-independent parameters of the immune synapse, such as costimulatory and inhibitory receptors. The functional impact of coreceptors on FA remains to be fully elucidated. Despite its importance, FA is infrequently assessed and incompletely understood. There is currently no consensus as to whether FA can be enhanced by optimized vaccine dose or boosting schedule. Recent findings suggest that FA is remarkably stable in vivo, possibly due to continued signaling modulation of critical receptors in the immune synapse. In this review, we provide an overview of the current knowledge and hypothesize that in vivo, codominant T cells constantly "equalize" their FA for similar function. We present a new model of constant FA regulation, and discuss practical implications for T-cell-based cancer immunotherapy

Optimized parameter search for large datasets of the regularization parameter and feature selection for ridge regression

In this paper we propose mathematical optimizations to select the optimal regularization parameter for ridge regression using cross-validation. The resulting algorithm is suited for large datasets and the computational cost does not depend on the size of the training set. We extend this algorithm to forward or backward feature selection in which the optimal regularization parameter is selected for each possible feature set. These feature selection algorithms yield solutions with a sparse weight matrix using a quadratic cost on the norm of the weights. A naive approach to optimizing the ridge regression parameter has a computational complexity of the order with the number of applied regularization parameters, the number of folds in the validation set, the number of input features and the number of data samples in the training set. Our implementation has a computational complexity of the order . This computational cost is smaller than that of regression without regularization for large datasets and is independent of the number of applied regularization parameters and the size of the training set. Combined with a feature selection algorithm the algorithm is of complexity and for forward and backward feature selection respectively, with the number of selected features and the number of removed features. This is an order faster than and for the naive implementation, with for large datasets. To show the performance and reduction in computational cost, we apply this technique to train recurrent neural networks using the reservoir computing approach, windowed ridge regression, least-squares support vector machines (LS-SVMs) in primal space using the fixed-size LS-SVM approximation and extreme learning machines