Uncertainty quantification of crosstalk using stochastic reduced order models

This paper introduces a novel statistical method, referred to as the stochastic reduced order model (SROM) method, to predict the variability of cable crosstalk subject to a range of parametric uncertainties. The SROM method is a new member of the family of stochastic approaches to quantify propagated uncertainty in the presence of multiple uncertainty sources. It is nonintrusive, accurate, efficient, and stable, thus could be a promising alternative to some well-established methods, such as the Stochastic Galerkin and stochastic collocation (SC) methods. In this paper, the SROM method is successfully applied to obtain the statistics of cable crosstalk subject to single and multiple uncertainty sources. The statistics of uncertain cable parameters is first accurately approximated by SROM, i.e., pairs of very few samples with known probabilities, such that the uncertain input space is well represented. Then, a deterministic solver is used to produce the samples of cable crosstalk with the corresponding probabilities, and finally the uncertainty propagated to the crosstalk is quantified with good accuracy. Compared to the conventional Monte-Carlo simulation, the statistics of crosstalk obtained by the SROM method converge much faster by orders of magnitude. Also, the computational cost of the SROM method is shown to be small and can be tuned flexibly depending on the accuracy requirement. The SC method based on tensor product sampling strategy is also implemented to validate the efficacy of the SROM method

Numerical Analysis of a Transmission Line Illuminated by a Random Plane-Wave Field Using Stochastic Reduced Order Models

A novel nonintrusive statistical approach, known as the stochastic reduced order model (SROM) method, is applied to efficiently estimate the statistical information of the terminal response (i.e., the induced current) in transmission lines excited by a random incident plane-wave field. The idea of the SROM method is conceptually simple, i.e., to represent the uncertain input space dimensioned by random variables using the SROM-based input model. This input model consists of a very small number of selected samples with assigned probabilities. Thus, only these input samples in the model need to be evaluated using the deterministic solver. The SROM-based output model can be constructed to approximate the propagated uncertainty to the real output response with elementary calculation. The efficiency and accuracy of the SROM method to obtain the statistics of the induced current are analyzed using two examples, where the complexity of the uncertain input space gradually increases. The performance of the SROM method is compared with that of the traditional Monte Carlo (MC) method. The stochastic collocation (SC) method based on sparse grid sampling strategy computed via the Smolyak algorithm is also implemented to fairly evaluate the SROM performance. The result shows that the SROM method is much more efficient than the MC method to obtain accurate statistics of the induced current, and even shows a faster convergence rate compared with that of the SC method in the examples considered. Therefore, the SROM method is a suitable approach to investigate the variability of radiated susceptibility in electromagnetic compatibility problems with a random incident wave

Worse outcome in breast cancer with higher tumor-infiltrating FOXP3+ Tregs : a systematic review and meta-analysis

Table S1. Characteristics of the included studies. (DOCX 39 kb

Inducing Causal Structure for Abstractive Text Summarization

The mainstream of data-driven abstractive summarization models tends to explore the correlations rather than the causal relationships. Among such correlations, there can be spurious ones which suffer from the language prior learned from the training corpus and therefore undermine the overall effectiveness of the learned model. To tackle this issue, we introduce a Structural Causal Model (SCM) to induce the underlying causal structure of the summarization data. We assume several latent causal factors and non-causal factors, representing the content and style of the document and summary. Theoretically, we prove that the latent factors in our SCM can be identified by fitting the observed training data under certain conditions. On the basis of this, we propose a Causality Inspired Sequence-to-Sequence model (CI-Seq2Seq) to learn the causal representations that can mimic the causal factors, guiding us to pursue causal information for summary generation. The key idea is to reformulate the Variational Auto-encoder (VAE) to fit the joint distribution of the document and summary variables from the training corpus. Experimental results on two widely used text summarization datasets demonstrate the advantages of our approach

A Wide Dynamic Range Rectifier Based on HEMT Device with a Variable Self-Bias Voltage

This brief focuses on a highly efficient rectifier based on a high-electron-mobility transistor (HEMT) with a wide dynamic range of input power. Due to the nonlinear characteristics of HEMT, the impedance mismatch at different input power levels is a major challenge in rectifier design. Herein, a variable voltage gate self-bias network is proposed. It can dynamically generate a DC voltage according to the input power level, and continuously provide the optimal bias for the HEMT, thereby improving the RF to DC conversion efficiency in a wide input power range. This design does not require any external sensing or dynamic control circuit. The power needed by the self-bias network is provided using a weak coupling structure placed at the input port, which couples a small amount of the received RF power to operate the self-bias network. It is demonstrated that the proposed rectifier can achieve a dynamic operating power range of 24 dB (from 1 to 25 dBm) for over 60% conversion efficiency, or 16 dB for over 70% conversion efficiency in the measurement

Wireless Power Transfer and Energy Harvesting Using Metamaterials and Metasurfaces

In this invited talk/paper, we review and explore the use of metamaterials and metasurfaces for wireless power transfer (WPT) and wireless energy harvesting (WEH) which are two closely related hot topics. The focus is on how to improve the energy conversion efficiency of both systems. It is shown that metamaterials and metasurfaces can indeed achieve a higher RF to DC energy conversion efficiency and operational distance by changing the electromagnetic fields between the transmitter and receiver, and/or making their reception less sensitive to incident wave angle and polarization. They can also be used as either parasitic elements or loading components to improve WEH performance

Magnetic Field Energy Harvesting Under Overhead Power Lines

Condition monitoring for overhead power lines is critical for power transmission networks to improve their reliability, detect potential problems in the early stage, and ensure the utilization of the transmitting full capacity. Energy harvesting can be an effective solution for autonomous self-powered wireless sensors. In this paper, a novel bow-tie-shaped coil is proposed, which is placed directly under overhead power lines to scavenge the magnetic field energy. Compared to the conventional method by mounting the energy harvester on the power lines, this approach provides more flexibility and space to power bigger sensors such as the weather station. As the harvesting coil cannot entirely enclose the power lines, the demagnetization factor that is closely related to the core geometry should be considered and optimized. Thus a new bow-tie-shape core is designed to produce a much lower demagnetization factor (hence more power) than that of the conventional solenoid. The selection of core material is studied and found that Mn-Zn ferrite is the most suitable core material because it greatly reduces the eddy current losses and also has high permeability. Experiment results show that the bow-tie coil could have a power density of 1.86 μW/cm 3 when placed in a magnetic flux density of 7 μT rms . This value is 15 times greater than the reported results under the same condition. If a longer bow-tie coil with more turns is placed in a magnetic flux density of 11μT rms , the produced power density is 103.5 μW/cm 3 , which is comparable to a solar panel working during a cloudy day. Thus, the proposed solution is a very efficient and attractive method for harvesting the magnetic field energy for a range of monitoring applications