Consistent and Asymptotically Efficient Localization from Range-Difference Measurements

We consider signal source localization from range-difference measurements. First, we give some readily-checked conditions on measurement noises and sensor deployment to guarantee the asymptotic identifiability of the model and show the consistency and asymptotic normality of the maximum likelihood (ML) estimator. Then, we devise an estimator that owns the same asymptotic property as the ML one. Specifically, we prove that the negative log-likelihood function converges to a function, which has a unique minimum and positive definite Hessian at the true source's position. Hence, it is promising to execute local iterations, e.g., the Gauss-Newton (GN) algorithm, following a consistent estimate. The main issue involved is obtaining a preliminary consistent estimate. To this aim, we construct a linear least-squares problem via algebraic operation and constraint relaxation and obtain a closed-form solution. We then focus on deriving and eliminating the bias of the linear least-squares estimator, which yields an asymptotically unbiased (thus consistent) estimate. Noting that the bias is a function of the noise variance, we further devise a consistent noise variance estimator that involves $3$-order polynomial rooting. Based on the preliminary consistent location estimate, a one-step GN iteration suffices to achieve the same asymptotic property as the ML estimator. Simulation results demonstrate the superiority of our proposed algorithm in the large sample case

Robust prior-based single image super resolution under multiple Gaussian degradations

Although SISR (Single Image Super Resolution) problem can be effectively solved by deep learning based methods, the training phase often considers single degradation type such as bicubic interpolation or Gaussian blur with fixed variance. These priori hypotheses often fail and lead to reconstruction error in real scenario. In this paper, we propose an end-to-end CNN model RPSRMD to handle SR problem in multiple Gaussian degradations by extracting and using as side information a shared image prior that is consistent in different Gaussian degradations. The shared image prior is generated by an AED network RPGen with a rationally designed loss function that contains two parts: consistency loss and validity loss. These losses supervise the training of AED to guarantee that the image priors of one image with different Gaussian blurs to be very similar. Afterwards we carefully designed a SR network, which is termed as PResNet (Prior based Residual Network) in this paper, to efficiently use the image priors and generate high quality and robust SR images when unknown Gaussian blur is presented. When we applied variant Gaussian blurs to the low resolution images, the experiments prove that our proposed RPSRMD, which includes RPGen and PResNet as two core components, is superior to many state-of-the-art SR methods that were designed and trained to handle multi-degradation

Disentangling superconducting and magnetic orders in NaFe_1-xNi_xAs using muon spin rotation

Muon spin rotation and relaxation studies have been performed on a "111" family of iron-based superconductors NaFe_1-xNi_xAs. Static magnetic order was characterized by obtaining the temperature and doping dependences of the local ordered magnetic moment size and the volume fraction of the magnetically ordered regions. For x = 0 and 0.4 %, a transition to a nearly-homogeneous long range magnetically ordered state is observed, while for higher x than 0.4 % magnetic order becomes more disordered and is completely suppressed for x = 1.5 %. The magnetic volume fraction continuously decreases with increasing x. The combination of magnetic and superconducting volumes implies that a spatially-overlapping coexistence of magnetism and superconductivity spans a large region of the T-x phase diagram for NaFe_1-xNi_xAs . A strong reduction of both the ordered moment size and the volume fraction is observed below the superconducting T_C for x = 0.6, 1.0, and 1.3 %, in contrast to other iron pnictides in which one of these two parameters exhibits a reduction below TC, but not both. The suppression of magnetic order is further enhanced with increased Ni doping, leading to a reentrant non-magnetic state below T_C for x = 1.3 %. The reentrant behavior indicates an interplay between antiferromagnetism and superconductivity involving competition for the same electrons. These observations are consistent with the sign-changing s-wave superconducting state, which is expected to appear on the verge of microscopic coexistence and phase separation with magnetism. We also present a universal linear relationship between the local ordered moment size and the antiferromagnetic ordering temperature TN across a variety of iron-based superconductors. We argue that this linear relationship is consistent with an itinerant-electron approach, in which Fermi surface nesting drives antiferromagnetic ordering.Comment: 20 pages, 14 figures, Correspondence should be addressed to Prof. Yasutomo Uemura: [email protected]

Research of long-span bridge and traffic system subjected to winds: A system and multi-hazard perspective

Wind effects on long-span bridges and moving vehicles have drawn considerable attention during the past years. Effectively considering the dynamic interactions between wind, vehicles and bridges and rationally assessing the bridge performance becomes essential to this type of critical infrastructure system. The impact of strong winds on long-span bridge transportation systems has received a lot of attention during the past decades. Meanwhile, low to moderate winds may serve as a type of important service load acting on the long-span bridge transportation system, along with other extreme or hazardous loads. In addition to the structural integrity, it is also important to appropriately assess the vehicle performance, such as safety and comforting issues, under wind so that the associated risks can be identified and mitigated. This paper summarizes some recent advances on the research of wind effects on long-span bridge and traffic systems with a focus on the efforts from a system and multi-hazard perspective

Corona Onset Characteristics of Bundle Conductors in UHV AC Power Lines at 2200 m Altitude

The corona onset characteristic of bundle conductors is an important limiting factor for the design of UHV AC power lines in high-altitude areas. An experimental study on the corona characteristics of 8 × LGJ630, 6 × LGJ720, 8 × LGJ720 and 10 × LGJ720 bundle conductors commonly used in UHV power lines under dry and wet conductor conditions, as well as artificial moderate and heavy rain conditions, was conducted in Ping’an County, Xining City (elevation 2200 m). By using the tangent line method, the corona onset voltages and onset electric field of four types of conductors at high altitudes are obtained for the first time. In addition, the calculation model of corona onset voltage considering the outer strands’ effect on the electric field and the geometric factor in the corona cage in high altitude areas is established. The comparison of the calculation results and experimental results under dry conditions verifies the model’s correctness. Based on the results, an optimal selection scheme for high altitudes is proposed. The roughness coefficient was also calculated and analysed: the roughness coefficient of bundled conductors was between 0.59 and 0.77, and the roughness coefficient of the wet conductor was between the dry and rainy conditions. Both the experimental data and the calculation model can provide a reference for conductor selection for UHV AC power lines for use in high-altitude areas

A Dynamic Model for Vulnerability Assessment of Regional Water Resources in Arid Areas: A Case Study of Bayingolin, China

Water scarcity is a common problem in many countries, especially those located in arid zones. The vulnerability of water resources due to climate change is an imperative research focus in the field of water resources management. In this study, a System Dynamics (SD) model was developed to simulate the water supply-and-demand process in Bayingolin, a prefecture in China, and to evaluate water resources vulnerability currently as well as in the future. The model was calibrated and validated using historical data. Three alternative scenarios were designed by changing parameters to test the vulnerability of water resources: i) increase the Wastewater Treatment Rate by 50 %; ii) decrease the Irrigation Water Demand per Hectare by 20 %; iii) increase Total Water Supply by 5 %. Results show that the baseline vulnerability of study region is high. The agricultural irrigation is the largest water use, and the water demand structure will change in future. Decreasing the irrigation water demand is the most suitable intervention to relatively reduce the vulnerability. Results also demonstrated that SD is a suitable method to explore management options for a complex water supply and demand system