Space Charge Behaviour in Oil-Paper Insulation with Different Aging Condition

Oil-paper insulation system is widely used in power transformers and cables. The dielectric properties of oilpaper insulation play an important role in the reliable operation of power equipment. Oil-paper insulation degrades under a combined stress of thermal (the most important factor), electrical, mechanical, and chemical stresses during routine operations, which has great effect on the dielectric properties of oil-paper insulation [1]. Space charge in oil-paper insulation has a close relation to its electrical performance [1]. In this paper, space charge behaviour of oil-paper insulation sample with three different ageing conditions (aged for 0, 35 and 77 days) was investigated using the pulsed electroacoustic (PEA) technique. The influence of aging on the space charge dynamics behaviour was analysed. Results show that aging has great effect on the space charge dynamics of oil-paper insulation. The homocharge injection takes place under all three aging conditions above. Positive charges tend to accumulate in the sample, and increase with the oil-paper insulation sample deterioration. The time to achieve the maximum injection charge density is 30s, 2min and 10min for oil-paper insulation sample aged for 0, 35 and 77 days, respectively. The maximum charge density injected in the sample aged for 77 days is more than two times larger than the initial sample. In addition, the charge decay speed becomes much slower with the aging time increase. There is an exponential relationship between the total charge amount and the decay time. The decay time constant ? increases with the increasing deterioration condition of the oil-paper insulation sample. The ? value may be used to reflect the aging status of oil-paper insulation

Representation of SO(3) Group by a Maximally Entangled State

A representation of the SO(3) group is mapped into a maximally entangled two qubit state according to literatures. To show the evolution of the entangled state, a model is set up on an maximally entangled electron pair, two electrons of which pass independently through a rotating magnetic field. It is found that the evolution path of the entangled state in the SO(3) sphere breaks an odd or even number of times, corresponding to the double connectedness of the SO(3) group. An odd number of breaks leads to an additional $\pi$ phase to the entangled state, but an even number of breaks does not. A scheme to trace the evolution of the entangled state is proposed by means of entangled photon pairs and Kerr medium, allowing observation of the additional $\pi$ phase.Comment: 4 pages, 3 figure

Deep Learning for Single Image Super-Resolution: A Brief Review

Single image super-resolution (SISR) is a notoriously challenging ill-posed problem, which aims to obtain a high-resolution (HR) output from one of its low-resolution (LR) versions. To solve the SISR problem, recently powerful deep learning algorithms have been employed and achieved the state-of-the-art performance. In this survey, we review representative deep learning-based SISR methods, and group them into two categories according to their major contributions to two essential aspects of SISR: the exploration of efficient neural network architectures for SISR, and the development of effective optimization objectives for deep SISR learning. For each category, a baseline is firstly established and several critical limitations of the baseline are summarized. Then representative works on overcoming these limitations are presented based on their original contents as well as our critical understandings and analyses, and relevant comparisons are conducted from a variety of perspectives. Finally we conclude this review with some vital current challenges and future trends in SISR leveraging deep learning algorithms.Comment: Accepted by IEEE Transactions on Multimedia (TMM

Observations of HONO by laser-induced fluorescence at the South Pole during ANTCI 2003

Observations of nitrous acid (HONO) by laser-induced fluorescence (LIF) at the South Pole taken during the Antarctic Troposphere Chemistry Investigation (ANTCI), which took place over the time period of Nov. 15, 2003 to Jan. 4, 2004, are presented here. The median observed mixing ratio of HONO 10 m above the snow was 5.8 pptv (mean value 6.3 pptv) with a maximum of 18.2 pptv on Nov 30th, Dec 1st, 3rd, 15th, 17th, 21st, 22nd, 25th, 27th and 28th. The measurement uncertainty is ±35%. The LIF HONO observations are compared to concurrent HONO observations performed by mist chamber/ion chromatography (MC/IC). The HONO levels reported by MC/IC are about 7.2 ± 2.3 times higher than those reported by LIF. Citation: Liao, W., A. T. Case, J. Mastromarino, D. Tan, and J. E. Dibb (2006), Observations of HONO by laser-induced fluorescence at the South Pole during ANTCI 2003, Geophys. Res. Lett., 33, L09810, doi:10.1029/2005GL025470

A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data

A great improvement to the insight on brain function that we can get from fMRI data can come from effective connectivity analysis, in which the flow of information between even remote brain regions is inferred by the parameters of a predictive dynamical model. As opposed to biologically inspired models, some techniques as Granger causality (GC) are purely data-driven and rely on statistical prediction and temporal precedence. While powerful and widely applicable, this approach could suffer from two main limitations when applied to BOLD fMRI data: confounding effect of hemodynamic response function (HRF) and conditioning to a large number of variables in presence of short time series. For task-related fMRI, neural population dynamics can be captured by modeling signal dynamics with explicit exogenous inputs; for resting-state fMRI on the other hand, the absence of explicit inputs makes this task more difficult, unless relying on some specific prior physiological hypothesis. In order to overcome these issues and to allow a more general approach, here we present a simple and novel blind-deconvolution technique for BOLD-fMRI signal. Coming to the second limitation, a fully multivariate conditioning with short and noisy data leads to computational problems due to overfitting. Furthermore, conceptual issues arise in presence of redundancy. We thus apply partial conditioning to a limited subset of variables in the framework of information theory, as recently proposed. Mixing these two improvements we compare the differences between BOLD and deconvolved BOLD level effective networks and draw some conclusions

Provision of secondary frequency regulation by coordinated dispatch of industrial loads and thermal power plants

Demand responsive industrial loads with high thermal inertia have potential to provide ancillary service for frequency regulation in the power market. To capture the benefit, this study proposes a new hierarchical framework to coordinate the demand responsive industrial loads with thermal power plants in an industrial park for secondary frequency control. In the proposed framework, demand responsive loads and generating resources are coordinated for optimal dispatch in two-time scales: (1) the regulation reserve of the industrial park is optimally scheduled in a day-ahead manner. The stochastic regulation signal is replaced by the specific extremely trajectories. Furthermore, the extremely trajectories are achieved by the day-ahead predicted regulation mileage. The resulting benefit is to transform the stochastic reserve scheduling problem into a deterministic optimization; (2) a model predictive control strategy is proposed to dispatch the industry park in real time with an objective to maximize the revenue. The proposed technology is tested using a real-world industrial electrolysis power system based upon Pennsylvania, Jersey, and Maryland (PJM) power market. Various scenarios are simulated to study the performance of the proposed approach to enable industry parks to provide ancillary service into the power market. The simulation results indicate that an industrial park with a capacity of 500 MW can provide up to 40 MW ancillary service for participation in the secondary frequency regulation. The proposed strategy is demonstrated to be capable of maintaining the economic and secure operation of the industrial park while satisfying performance requirements from the real world regulation market

An Equalized Margin Loss for Face Recognition

In this paper, we propose a new loss function, termed the equalized margin (EqM) loss, which is designed to make both intra-class scopes and inter-class margins similar over all classes, such that all the classes can be evenly distributed on the hypersphere of the feature space. The EqM loss controls both the lower limit of intra-class similarity by exploiting hard sample mining and the upper limit of inter-class similarity by assuring equalized margins. Therefore, using the EqM loss, we can not only obtain more discriminative features, but also overcome the negative impacts from the data imbalance on the inter-class margins. We also observe that the EqM loss is stable with the variation of the scale in normalized Softmax. Furthermore, by conducting extensive experiments on LFW, YTF, CFP, MegaFace and IJB-B, we are able to verify the effectiveness and superiority of the EqM loss, compared with other state-of-the- art loss functions for face recogniti