Limited Number Of Measurements Research Articles

Bistatic ISAR Imaging with a V-FM Waveform Based on a Dual-Channel-Coupled 2D-CS Algorithm.

Due to the sparsity of the space distribution of point scatterers and radar echo data, the theory of Compressed Sensing (CS) has been successfully applied in Inverse Synthetic Aperture Radar (ISAR) imaging, which can recover an unknown sparse signal from a limited number of measurements by solving a sparsity-constrained optimization problem. In this paper, since the V style modulation(V-FM) signal can mitigate the ambiguity apparent in range and velocity, the dual-channel, two-dimension, compressed-sensing (2D-CS) algorithm is proposed for Bistatic ISAR (Bi-ISAR) imaging, which directly deals with the 2D signal model for image reconstruction based on solving a nonconvex optimization problem. The coupled 2D super-resolution model of the target’s echoes is firstly established; then, the 2D-SL0 algorithm is applied in each channel with different dictionaries, and the final image is obtained by synthesizing the two channels. Experiments are used to test the robustness of the Bi-ISAR imaging framework with the two-dimensional CS method. The results show that the framework is capable accurately reconstructing the Bi-ISAR image within the conditions of low SNR and low measured data.

MMSE-Based Analytical Estimator for Uncertain Power System With Limited Number of Measurements

The expected penetration of a large number of renewable distributed energy resources (DER's) is driving next generation power systems toward uncertainties that can have a huge impact on the reliability and complexities of state estimation. Therefore, the stochastic power flow (SPF) and forecasting-aided state estimation of power systems integrating DER's are becoming a major challenge for operation of the future grid. In this paper we propose a new state estimation method referred to as 'mean squared estimator' (MSE) to deal with the uncertain nature of the power system parameters. The estimator benefits from the prior study of SPF, which involves the probability density functions (PDF's) of the system parameters. The main advantage of this estimator is based on its ability to instantaneously incorporate the dynamics of the power system. Moreover, the analytical formula of MSE expresses the mean value of the estimated parameters corrected by an additional term that takes into account the measurement of the parameters. It is shown that the proposed MSE can provide an accurate state estimation with a limited number of measurements with guaranteed convergence. MSE has been tested using IEEE 14, 30, 39 and 118 bus models for different measurement redundancies. The results have been compared to methods such as weighted least square (WLS), unscented Kalman filter (UKF) and compressive sensing-based UKF (CS-UKF). The numerical results show superior performances, especially under a limited number of measurements where WLS and UKF may lead to divergence.

Modal reconstruction of the sound field in a room at low frequencies

Capturing the spatio-temporal (or spatio-spectral) properties of the sound field in a room is valuable for its characterization. To measure the full spatio-spectral properties within the volume of the room, i.e., the frequency response functions at all points, a very large number of measurements is required. Even at low frequencies, sampling the three-dimensional space enclosed by a room is challenging. It is therefore of interest to employ sound field reconstruction methods to predict (interpolate/extrapolate) the sound field over the volume of the room, based on a limited number of measurements. The aim of this study is to develop a sound field reconstruction method that exploits the modal structure of the sound field in a room, to reconstruct the sound field at low frequencies over the entire volume of the space. The proposed methodology makes use of the fact that the spatial structure of the acoustic field in a room is inherently block-sparse and can be extracted from spatially distributed measurements. The modeshapes are represented as the superposition of a small number of plane waves, and the modal basis is used to reconstruct the sound field.

B-WIM SYSTEM USING FEWER SENSORS

Bridge Weigh-in-Motion (B-WIM) is the concept of using measured response on a bridge to calculate the static weights of passing traffic loads as they pass overhead at full highway speed. This paper describes an enhancement to the Moving Force Identification (MFI) algorithm by estimating the response of some DOFs using limited number of measurements in order to increase measurements number (Input). The pseudoinverse of the mode shape matrix has been utilized to approximately calculate the modal response using limited measured response. Then the calculated modal response has been used to estimate more DOFs that are different from the measured one. The proper orthogonal decomposition (POD) technique is employed to determine the governing modes that increase the modal response accuracy. Numerical example for quarter car model passing over simply supported bridge has been established to demonstrate the idea.

A Method for Efficient Localization of Magnetic Field Sources Excited by Execution of Instructions in a Processor

This paper proposes a method for efficient identification of instruction-dependent sources on a printed circuit board (PCB) by localizing magnetic field sources from a limited number of measurements around the PCB. We first excite the processor by generating an artificial leakage signal at a specific frequency that is directly related to processor instructions. Then, we collect all three components of the magnetic field, but only at locations around the edge of the board. Furthermore, we model these magnetic field sources and then solve a forward–backward optimization problem using the model and measured data to identify the locations of the magnetic field sources, the magnitudes of the moments, and their orientations. The localization results are first verified using simulations, then tested when noise is added to the simulation results, and finally verified against measurements on field-programmable gate array (FPGA) and internet of things (IoT) development boards. The results show that the number of strong magnetic field sources on a board depends on the instructions used to excite the board. Furthermore, the results show that the proposed localization algorithm can accurately identify those sources, regardless of the frequency at which the measurements are conducted and the instruction pairs that are executed. Finally, the proposed method can significantly reduce the number of measurement points and the time needed to identify magnetic field sources on a PCB.

ALGORITHM BASED ON ARTIFICIAL BEE COLONY FOR UNFOLDING OF NEUTRON SPECTRA OBTAINED WITH BONNER SPHERES.

Occupational neutron fields usually have energies from the thermal range to some MeV and the characterization of the spectra is essential for estimation of the radioprotection quantities. Thus, the spectrum must be unfolded based on a limited number of measurements. This study implemented an algorithm based on the bee colonies behavior, named Artificial Bee Colony (ABC), where the intelligent behavior of the bees in search of food is reproduced to perform the unfolding of neutron spectra. The experimental measurements used Bonner spheres and 6LiI (Eu) detector, with irradiations using a thermal neutron flux and three reference fields: 241Am-Be, 252Cf and 252Cf (D2O). The ABC obtained good estimation of the expected spectrum even without previous information and its results were closer to expected spectra than those obtained by the SPUNIT algorithm.

Effective Representation of River Geometry in Hydraulic Flood Forecast Models

AbstractBathymetric data are a critical input to hydraulic models. However, river depth and shape cannot be systematically observed remotely, and field data are both scarce and expensive to collect. In flood modeling, river roughness and geometry compensate for each other, with different parameter sets often being able to map model predictions equally well to the observed data, commonly known as equifinality. This study presents a numerical experiment to investigate an effective yet parsimonious representation of channel geometry that can be used for operational flood forecasting. The LISFLOOD‐FP hydraulic model was used to simulate a hypothetical flood event in the Clarence catchment (Australia). A high‐resolution model simulation based on accurate bathymetric field data was used to benchmark coarser model simulations based on simplified river geometries. These simplified river geometries were derived from a combination of globally available empirical formulations, remote sensing data, and a limited number of measurements. Model predictive discrepancy between simulations with field data and simplified geometries allowed an assessment of the geometry impact on inundation dynamics. In this study site, the channel geometrical representation for a reliable inundation forecast could be achieved using remote sensing‐derived river width values combined with a few measurements of river depth sampled at strategic locations. Furthermore, this study showed that spatially distributed remote sensing‐derived inundation levels at the very early stages of a flood event have the potential to support the effective diagnosis of errors in model implementations.

Reference Cross Sections for Charged-particle Monitor Reactions

Evaluated cross sections of beam-monitor reactions are expected to become the de-facto standard for cross-section measurements that are performed over a very broad energy range in accelerators in order to produce particular radionuclides for industrial and medical applications. The requirements for such data need to be addressed in a timely manner, and therefore an IAEA coordinated research project was launched in December 2012 to establish or improve the nuclear data required to characterise charged-particle monitor reactions. An international team was assembled to recommend more accurate cross-section data over a wide range of targets and projectiles, undertaken in conjunction with a limited number of measurements and more extensive evaluations of the decay data of specific radionuclides. Least-square evaluations of monitor-reaction cross sections including uncertainty quantification have been undertaken for charged-particle beams of protons, deuterons, 3He- and 4He-particles. Recommended beam monitor reaction data with their uncertainties are available at the IAEA-NDS medical portal www-nds.iaea.org/medical/monitor_reactions.html.

Nitrogen and hydrogen fractionation in high-mass star-forming cores from observations of HCN and HNC

The ratio between the two stable isotopes of nitrogen, 14N and 15N, is well measured in the terrestrial atmosphere (~272), and for the pre-solar nebula (~441, deduced from the solar wind). Interestingly, some pristine solar system materials show enrichments in 15N with respect to the pre-solar nebula value. However, it is not yet clear if and how these enrichments are linked to the past chemical history because we have only a limited number of measurements in dense star-forming regions. In this respect, dense cores, which are believed to be the precursors of clusters and also contain intermediate- and high-mass stars, are important targets because the solar system was probably born within a rich stellar cluster, and such clusters are formed in high-mass star-forming regions. The number of observations in such high-mass dense cores has remained limited so far. In this work, we show the results of IRAM-30 m observations of the J = 1−0 rotational transition of the molecules HCN and HNC and their 15N-bearing counterparts towards 27 intermediate- and high-mass dense cores that are divided almost equally into three evolutionary categories: high-mass starless cores, high-mass protostellar objects, and ultra-compact Hii regions. We have also observed the DNC(2–1) rotational transition in order to search for a relation between the isotopic ratios D/H and 14N/15N. We derive average 14N/15N ratios of 359 ± 16 in HCN and of 438 ± 21 in HNC, with a dispersion of about 150–200. We find no trend of the 14N/15N ratio with evolutionary stage. This result agrees with what has been found for N2H+ and its isotopologues in the same sources, although the 14N/15N ratios from N2H+ show a higher dispersion than in HCN/HNC, and on average, their uncertainties are larger as well. Moreover, we have found no correlation between D/H and 14N/15N in HNC. These findings indicate that (1) the chemical evolution does not seem to play a role in the fractionation of nitrogen, and that (2) the fractionation of hydrogen and nitrogen in these objects is not related.

Distributed fault detection in large-scale systems using hybrid extended information filter

A fault monitoring system plays an important role to ensure and improve the reliability of an industrial plant to operate safely and efficiently. For large-scale systems, their high-dimensional nature impels the designer to develop a lower-order algorithm to overcome the practical limitations. To reduce the order of computations and the amount of communication, the system should be decomposed into low-dimensional sub-systems. For this purpose, a distributed fault detection algorithm was presented based on hybrid extended information filter. This algorithm has the advantages of two existing approaches, the hybrid extended Kalman filter and the information filter. In the proposed method, several local fault detectors were employed to monitor the local sub-system instead of a single centralized monitoring node. For each detector, a limited number of measurements were accessible based on the generated local state and residual vectors using a local filter. The local detector extracted the faults’ information content of the residual signals via CUSUM statistical hypothesis testing. Moreover, it could communicate the processed information of its neighbors if needed. The algorithm was implemented on alkylation of benzene process plant. The results obtained from the distributed algorithm showed a considerable decrease in computational burden and communication bandwidth.

Interpolation of Noisy Measurements in a Coarse Grid: Creating Waveforms for Intermediate Points on “747” Curves

In some situations, only a limited number of measurements under a limited number of conditions may be available for analysis. This paper describes an approach for creating additional interpolated measurements in which both measurement noise as well as signal have appropriate values. This is important, for example, when evaluating the behavior of advanced readback channels where quantities, such as sector failure rate (SFR), change very abruptly from 0% to 100% in response to relatively small parameter changes. This means that a smooth interpolation of SFR between coarse measurement points is little help in determining the actual boundary of good performance. If, instead, interpolated waveforms are fed into the channel, the boundary of the region of good performance can be clearly identified. This paper formulates a solution for the general problem of noisy measurement (waveform) interpolation and then applies this approach specifically to the situation, where a series of spin-stand waveforms were captured at various reader positions and with various written track squeeze positions (i.e., a set of “747s”). Using this approach, realistic waveforms can now be created and the SFR evaluated at any arbitrary intermediate reader and writer positions.

Detecting metrologically useful asymmetry and entanglement by a few local measurements

Important properties of a quantum system are not directly measurable, but they can be disclosed by how fast the system changes under controlled perturbations. In particular, asymmetry and entanglement can be verified by reconstructing the state of a quantum system. Yet, this usually requires experimental and computational resources which increase exponentially with the system size. Here we show how to detect metrologically useful asymmetry and entanglement by a limited number of measurements. This is achieved by studying how they affect the speed of evolution of a system under a unitary transformation. We show that the speed of multiqubit systems can be evaluated by measuring a set of local observables, providing exponential advantage with respect to state tomography. Indeed, the presented method requires neither the knowledge of the state and the parameter-encoding Hamiltonian nor global measurements performed on all the constituent subsystems. We implement the detection scheme in an all-optical experiment.

Modeling annual variations in high-frequency scattering from sea ice

As sea ice evolves naturally through its different growth and decay structures, so too does it manifest important changes in physical properties affecting its acoustic response. Variations in the porosity, permeability, and roughness of the ice lead to changes in scattered and reflected acoustic energy as well as the degree of attenuation. Knowledge of the long-term variations of high-frequency under-ice scattering over annual formation and melting cycles is essentially non-existent as only a limited number of measurements have been performed. Owing to this lack of data, fundamental questions remain as to the dominant mechanisms influencing the under-ice scattering process at any given time. In this work, we will show modeling results for high-frequency acoustic scattering from different types of sea ice as it forms and melts, highlighting the relative influence of various ice parameters on mean scattered levels and the dependence of these levels on frequency and grazing angle. Results yield insight into controlling factors and lead to recommendations for planned long-term measurements from moored instruments.

Multitouch ultrasonic touchscreen

Touchscreen sensors are widely used in many devices such as smart phones, tablets, laptops, etc. We present the design, analysis, and implementation of an ultrasonic touchscreen system that utilizes interaction of transient Lamb waves with objects in contact with the screen. The governing principle revolves around the propagation and reverberation of guided elastic waves in a bounded space, in which the localization of touch points can be challenging. Reverberant fields in enclosures can potentially carry useful information, however, in an incoherent way. Incoherency comes from consecutive reflections of the wave energy several times in the domain, ultimately leading to mixing of the wave energy in a seemingly random way. However, spreading of the wave energy can lead to multiple interrogations of each point in the enclosure. Hence, temporal information buries information about substructural changes making it feasible to conduct only a few spatial measurements. We present a learning localization algorithm capable of localizing multiple simultaneous touch points (up to 12 touches on a tablet) and with a very limited number of measurements (one or two). This in turn can significantly reduce the manufacturing cost. We also present an algorithm to improve on robustness to environmental and thermal noise.

Mode separation in frequency–wavenumber domain through compressed sensing of far-field Lamb waves

This method based on Lamb waves shows great potential for long-range damage detection. Mode superposition resulting from multi-modal and dispersive characteristics makes signal interpretation and damage feature extraction difficult. Mode separation in the frequency–wavenumber (f–k) domain using a 1D sparse sensing array is a promising solution. However, due to the lack of prior knowledge about damage location, this method based on 1D linear measurement, for the mode extraction of arbitrary reflections caused by defects that are not in line with the sensor array, is restricted. In this paper, an improved compressed sensing method under the far-field assumption is established, which is beneficial to the reconstruction of reflections in the f–k domain. Hence, multiple components consisting of structure and damage features could be recovered via a limited number of measurements. Subsequently, a mode sweeping process based on theoretical dispersion curves has been designed for mode characterization and direction of arrival estimation. Moreover, 2D f–k filtering and inverse transforms are applied to the reconstructed f–k distribution in order to extract the purified mode of interest. As a result, overlapping waveforms can be separated and the direction of defects can be estimated. A uniform linear sensor array consisting of 16 laser excitations is finally employed for experimental investigations and the results demonstrate the efficiency of the proposed method.

Parameterizing surface wind speed over complex topography

AbstractSubgrid parameterizations are used in coarse‐scale meteorological and land surface models to account for the impact of unresolved topography on wind speed. While various parameterizations have been suggested, these were generally validated on a limited number of measurements in specific geographical areas. We used high‐resolution wind fields to investigate which terrain parameters most affect near‐surface wind speed over complex topography under neutral conditions. Wind fields were simulated using the Advanced Regional Prediction System (ARPS) on Gaussian random fields as model topographies to cover a wide range of terrain characteristics. We computed coarse‐scale wind speed, i.e., a spatial average over the large grid cell accounting for influence of unresolved topography, using a previously suggested subgrid parameterization for the sky view factor. We only require correlation length of subgrid topographic features and mean square slope in the coarse grid cell. Computed coarse‐scale wind speed compared well with domain‐averaged ARPS wind speed. To further statistically downscale coarse‐scale wind speed, we use local, fine‐scale topographic parameters, namely, the Laplacian of terrain elevations and mean square slope. Both parameters showed large correlations with fine‐scale ARPS wind speed. Comparing downscaled numerical weather prediction wind speed with measurements from a large number of stations throughout Switzerland resulted in overall improved correlations and distribution statistics. Since we used a large number of model topographies to derive the subgrid parameterization and the downscaling framework, both are not scale dependent nor bound to a specific geographic region. Both can readily be implemented since they are based on easy to derive terrain parameters.

IAEA coordinated research project on nuclear data for charged-particle monitor reactions and medical isotope production

An IAEA coordinated research project was launched in December 2012 to establish and improve the nuclear data required to characterise charged-particle monitor reactions and extend data for medical radionuclide production. An international team was assembled to undertake work addressing the requirements for more accurate cross-section data over a wide range of targets and projectiles, undertaken in conjunction with a limited number of measurements and more extensive evaluations of the decay data of specific radionuclides. These studies are nearing completion, and are briefly described below.

Lamb Wave Multitouch Ultrasonic Touchscreen

Touchscreen sensors are widely used in many devices such as smart phones, tablets, and laptops with diverse applications. We present the design, analysis, and implementation of an ultrasonic touchscreen system that utilizes the interaction of transient Lamb waves with objects in contact with the screen. It attempts to improve on the existing ultrasound technologies, with the potential of addressing some of the weaknesses of the dominant technologies, such as the capacitive or resistive ones. Compared with the existing ultrasonic and acoustic modalities, among other advantages, it provides the capability of detecting several simultaneous touch points and also a more robust performance. The localization algorithm, given the hardware design, can detect several touch points with a very limited number of measurements (one or two). This in turn can significantly reduce the manufacturing cost.

Voltage stability monitoring of power systems using reduced network and artificial neural network

This paper presents network reduction based methodologies to monitor voltage stability of power systems using limited number of measurements. In a multi-area power system, artificial neural networks (ANNs) are used to estimate the loading margin of the overall system, based on measurements from the internal area only. Information regarding the important measurements from the external areas is considered in measurement transformation through the network reduction process, to enhance the estimation accuracy of the ANNs. A Z-score based bad or missing data processing algorithm is implemented to make the methodologies robust. To account for changing operating conditions, adaptive training of the ANNs is also suggested. The proposed methods are successfully implemented on IEEE 14-bus and 118-bus test systems.

Sequential multi-nuclide emission rate estimation method based on gamma dose rate measurement for nuclear emergency management

In case of a nuclear accident, the source term is typically not known but extremely important for the assessment of the consequences to the affected population. Therefore the assessment of the potential source term is of uppermost importance for emergency response. A fully sequential method, derived from a regularized weighted least square problem, is proposed to reconstruct the emission and composition of a multiple-nuclide release using gamma dose rate measurement. The a priori nuclide ratios are incorporated into the background error covariance (BEC) matrix, which is dynamically augmented and sequentially updated. The negative estimations in the mathematical algorithm are suppressed by utilizing artificial zero-observations (with large uncertainties) to simultaneously update the state vector and BEC. The method is evaluated by twin experiments based on the JRodos system. The results indicate that the new method successfully reconstructs the emission and its uncertainties. Accurate a priori ratio accelerates the analysis process, which obtains satisfactory results with only limited number of measurements, otherwise it needs more measurements to generate reasonable estimations. The suppression of negative estimation effectively improves the performance, especially for the situation with poor a priori information, where it is more prone to the generation of negative values.