The Impact of Frame Transformations on Power System EMT Simulation

This article investigates the impact of frame transformations on the accuracy of numerical discretization in power system transient and stability studies. As analysed, frame transformations influence the convergence of the numerical discretization. Specifically, for an explicit discretization method (e.g., forward Euler method), the stability of the original system is best preserved in the frame where the system eigenvalue is closer to the origin of the complex plane, e.g., in the stationary frame for inductors and capacitors, and in the synchronous frame for &lt;italic&gt;dq-&lt;/italic&gt;frame controllers of inverters. Simulation results are given to validate the theoretical analysis.</p

Constraints on Neutrino Velocities Revisited

With a minimally modified dispersion relation for neutrinos, we reconsider the constraints on superluminal neutrino velocities from bremsstrahlung effects in the laboratory frame. Employing both the direct calculation approach and the virtual Z-boson approach, we obtain the generic decay width and energy loss rate of a superluminal neutrino with general energy. The Cohen-Glashow's analytical results for neutrinos with a relatively low energy are confirmed in both approaches. We employ the survival probability instead of the terminal energy to assess whether a neutrino with a given energy is observable or not in the OPERA experiment. Moreover, using our general results we perform systematical analyses on the constraints arising from the Super-Kamiokande and IceCube experiments.Comment: RevTex4, 14 pages, 5 figures, minor corrections, version to appear in Phys. Rev.

Association of Lipid Levels With the Prevalence of Hypertension in Chinese Women: A Cross-Sectional Study Based on 32 Health Check Centers

Background: Dyslipidemia is strongly associated with the development of hypertension. In our previous study, it was shown that elevated TC, LDL-c, and non-HDL-c were associated with the prevalence of hypertension in Chinese men, whereas the relationship between HDL-c and hypertension shifted from no association to a positive association after adjusting for the BMI. To further accumulate epidemiological evidence in Asian women, this study aimed to investigate the relationship between lipid profile and prevalence of hypertension in Chinese adult women. Methods: This is a cross-sectional study including 54,099 Chinese women aged>20 years at 32 health screening centers in 11 cities from 2010-2016. The original data were obtained from DATADRYAD database (www.datadryad.org). Besides, the overall women were classified into non-hypertensive and hypertensive groups based on baseline blood pressure levels. Differences between the two groups were examined by Man-Whitney test or Chi-square test. Spearman’s correlation coefficient was employed to evaluate the correlation between systolic blood pressure (SBP), diastolic blood pressure (DBP) and lipid profiles. Multivariate logistic regression was performed to estimate the relationship between different lipid levels and the prevalence of hypertension. Odds ratios (ORs) and 95% confidence intervals (CIs) indicated the risk of lipid and hypertension. Bayesian model (BN) model was constructed to further assess the relationship between baseline characteristics and the prevalence of hypertension, as well as the importance of each variable for the prevalence of hypertension. Results: Compared to the non-hypertensive population, the hypertensive population was older, and had the higher body mass index (BMI), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-c), serum creatinine (Scr), fasting blood glucose (FPG), blood urea nitrogen (BUN), alanine aminotransferase (ALT), aspartate aminotransferase (AST), and non-high-density lipoprotein cholesterol (non-HDL-c), but HDL-c and the presence concerning the family history of diabetes were lower. Multivariate logistic regression analysis revealed that TC, LDL-c, and non-HDL-c showed a positive trend with hypertension risk (p for trend < 0.05) whereas TC and HDL-c were not significantly associated with hypertension prevalence. Moreover, each 1 mg/dl increase in TC, LDL, and non-HDL hypertension prevalence increased by 0.2% [1.002 (1.000-1.003)], 0.2% [1.002 (1.000- 1.004)], and 0.2% [1.002(1.001-1.004)], respectively. BN suggested that the importance of age, BMI, FPG, non-HDL-c on the prevalence of hypertension was 52.73%, 24.98%, 11.22%, and 2.34%, respectively. Conclusion: Overall, in Chinese adult women, TC, LDL-c and non-HDL-c levels were higher and HDL-c level was lower in the hypertensive population, whereas TG did not differ significantly from the non-hypertensive population. Meanwhile, TC, LDL-c, and non-HDL-c were positively associated with prevalence of hypertension, and HDL-c was negatively associated with prevalence of hypertension but became nonsignificant after full adjustment for variables. Moreover, BN model suggested that age, BMI, FPG, and non-HDL-c had a greater effect on the development of hypertension

Quantum image rain removal: second-order photon number fluctuation correlations in the time domain

Falling raindrops are usually considered purely negative factors for traditional optical imaging because they generate not only rain streaks but also rain fog, resulting in a decrease in the visual quality of images. However, this work demonstrates that the image degradation caused by falling raindrops can be eliminated by the raindrops themselves. The temporal second-order correlation properties of the photon number fluctuation introduced by falling raindrops has a remarkable attribute: the rain streak photons and rain fog photons result in the absence of a stable second-order photon number correlation, while this stable correlation exists for photons that do not interact with raindrops. This fundamental difference indicates that the noise caused by falling raindrops can be eliminated by measuring the second-order photon number fluctuation correlation in the time domain. The simulation and experimental results demonstrate that the rain removal effect of this method is even better than that of deep learning methods when the integration time of each measurement event is short. This high-efficient quantum rain removal method can be used independently or integrated into deep learning algorithms to provide front-end processing and high-quality materials for deep learning.Comment: 5 pages, 7 figure

Quantum defogging: temporal photon number fluctuation correlation in time-variant fog scattering medium

The conventional McCartney model simplifies fog as a scattering medium with space-time invariance, as the time-variant nature of fog is a pure noise for classical optical imaging. In this letter, an opposite finding to traditional idea is reported. The time parameter is incorporated into the McCartney model to account for photon number fluctuation introduced by time-variant fog. We demonstrated that the randomness of ambient photons in the time domain results in the absence of a stable correlation, while the scattering photons are the opposite. This difference can be measured by photon number fluctuation correlation when two conditions are met. A defogging image is reconstructed from the target's information carried by scattering light. Thus, the noise introduced by time-variant fog is eliminated by itself. Distinguishable images can be obtained even when the target is indistinguishable by conventional cameras, providing a prerequisite for subsequent high-level computer vision tasks.Comment: 6 pages, 9 figure

Bayesian Non-parametric Hidden Markov Model for Agile Radar Pulse Sequences Streaming Analysis

Multi-function radars (MFRs) are sophisticated types of sensors with the capabilities of complex agile inter-pulse modulation implementation and dynamic work mode scheduling. The developments in MFRs pose great challenges to modern electronic reconnaissance systems or radar warning receivers for recognition and inference of MFR work modes. To address this issue, this paper proposes an online processing framework for parameter estimation and change point detection of MFR work modes. At first, this paper designed a fully-conjugate Bayesian non-parametric hidden Markov model with a designed prior distribution (agile BNP-HMM) to represent the MFR pulse agility characteristics. The proposed model allows fully-variational Bayesian inference. Then, the proposed framework is constructed by two main parts. The first part is the agile BNP-HMM model for automatically inferring the number of HMM hidden states and emission distribution of the corresponding hidden states. An estimation error lower bound on performance is derived and the proposed algorithm is shown to be close to the bound. The second part utilizes the streaming Bayesian updating to facilitate computation, and designed an online work mode change detection framework based upon a weighted sequential probability ratio test. We demonstrate that the proposed framework is consistently highly effective and robust to baseline methods on diverse simulated data-sets.Comment: 15 pages, 10 figures, submitted to IEEE transactions on signal processin