Distance Estimation Research Articles

Enhanced fault distance estimation for robust protection in unbalanced active distribution networks

Active Distribution Networks (ADN) have evolved significantly by integrating renewable energy sources. While these advancements have ushered in a more sustainable and efficient power system, these have also introduced challenges for traditional protection strategies. Conventional methods, often reliant on overcurrent relays, can struggle to adapt to the complexities of active distribution networks, where fault scenarios are diverse and dynamic. This paper identifies the limitations of conventional protection strategies and explores the redundancy concept applied to protection systems. To improve protection system reliability, an approach that overcomes these limitations using the advantages of distance protection is proposed here. The distance-based protection relay, traditionally employed in transmission networks, offers a promising redundancy solution for the challenges imposed by ADN. Considering the previous exposure, this paper introduces an algorithm for fault resistance and distance estimation designed to optimise the operation of distance relays in active distribution networks. The approach achieves remarkable accuracy with consistently low estimation errors for fault resistance and distance, regardless of fault type or distance. The proposed distance-based protection method is poised to revolutionise fault analysis in ADN, ultimately leading to faster fault isolation and more reliable and improved grid resilience.

Accurate Range-Free Localization Using Cuckoo Search Optimization in IoT and Wireless Sensor Networks

Precise positioning of sensors is critical for the performance of various applications in the Internet of Things and wireless sensor networks. The efficiency of these networks heavily depends on the precision of sensor node locations. Among various localization approaches, DV-Hop is highly recommended for its simplicity and robustness. However, despite its popularity, DV-Hop suffers from significant accuracy issues, primarily due to its reliance on average hop size for distance estimation. This limitation often results in substantial localization errors, compromising the overall network effectiveness. To address this gap, we developed an enhanced DV-Hop approach that integrates the cuckoo search algorithm (CS). Our solution improves the accuracy of node localization by introducing a normalized average hop size calculation and leveraging the optimization capabilities of CS. This hybrid approach refines the distance estimation process, significantly reducing the errors inherent in traditional DV-Hop. Findings from simulations reveal that the developed approach surpasses the accuracy of both the original DV-Hop and multiple other current localization methods, providing a more precise and reliable localization method for IoT and WSN applications.

Evaluation of Several Explanations of the Strong X-Ray Polarization of the Black Hole X-Ray Binary 4U 1630-47

The Imaging X-ray Polarimetry Explorer observations of the X-ray binary 4U 1630–47 in the high soft state revealed high linear polarization degrees (PDs) rising from 6% at 2 keV to 10% at 8 keV. We discuss in this Letter three different mechanisms that impact the polarization of the observed X-rays: the reflection of gravitationally lensed emission by the accretion disk, reprocessing of the emission in outflowing plasma, and electron and ion anisotropies in the accretion disk atmosphere. We conducted detailed ray-tracing studies to evaluate the impact of the reflection of strongly gravitationally lensed emission on the PDs. Although the reflected emission can produce high PDs in the high-energy tail of the thermal emission component, we do not find models that describe the PDs and are consistent with independent estimates of the source distance. We discuss the energetics of another proposed mechanism: the emission or scattering of the X-rays in mildly relativistically moving plasma outflows. We argue that these models are disfavored as they require large mechanical luminosities on the order of, or even exceeding, the Eddington luminosity. We investigated the impact of electron and ion anisotropies but find that their impact on the observed PDs are likely negligible. We conclude with a discussion of all three effects and avenues for future research.

The roles of blur and eye convergence in distance estimation in larval zebrafish

Animals use an array of visual cues to gauge distance, and their underlying neural mechanisms remain largely unknown. Zebrafish larvae execute different hunting behaviors depending on distance to the prey, providing a simple model system in which to study this process. To identify distance cues, we presented equivalent prey stimuli at increasing distance and recorded hunting behaviors. We found that the initial convergence angle was lower for more distant prey, suggesting that they are able to gauge distance to the prey via monocular cues. We investigated blur as a possible monocular cue, and found that by artificially blurring the stimulus, we were able to reduce initial convergence and strike probability. This implicates blur as a distance cue in zebrafish prey capture, adds to our knowledge of how larvae are able to visually target and accurately capture prey.

The interacting double white dwarf population with LISA: Stochastic foreground and resolved sources

We investigate the impact of tidal torques and mass transfer on the population of double white dwarfs that will be observed with LISA. Our Galactic distribution of double white dwarfs is based on the combination of a cosmological simulation and a binary population synthesis model. We used a semi-analytical model to evolve double white dwarf binaries considering ten different hypotheses for the efficiency of tidal coupling and three hypotheses for the birth spins of white dwarfs. We then estimated the stochastic foreground and the population of resolvable binaries for LISA for these different combinations. Our predicted double white dwarf binary distribution can differ substantially from the distribution expected if only gravitational waves (GWs) are considered. If white dwarfs spin slowly, then we predict an excess of systems around a few mHz, due to binaries that outspiral after the onset of mass transfer. This excess of systems leads to differences in the confusion noise, which are most pronounced for strong tidal coupling. In that case, we find a significantly higher number of resolvable binaries than in the GW-only scenario. If instead white dwarfs spin rapidly and tidal coupling is weak, then we find no excess around a few mHz, and the confusion noise due to double white dwarfs is very low. In that scenario, we also predict a subpopulation of outspiralling binaries below 0.1 mHz. Using the Fisher matrix approximation, we estimate the uncertainty on the GW-frequency derivative of resolvable systems. We find that, even for non-accreting systems, the mismodelling error due to neglecting effects other than GWs is larger than the statistical uncertainty, and thus this neglect would lead to biased estimates for mass and distance. Our results suggest that the population of double white dwarfs is likely to be different from what is expected in the standard picture, which highlights the need for flexible tools in LISA data analysis. Because our semi-analytical model hinges upon a simplistic approach to determining the stability of mass accretion, it will be important to deepen our comprehension of stability in mass-transferring double white dwarf binaries.

Joint DOA and distance estimation via spatial diversity wideband signal synthesis calibrated by distance parameter

Wideband signal synthesis is a technique designed to achieve large bandwidth detection capabilities by utilizing multiple relatively narrow bandwidth signals. Traditional single-antenna, time-diversity wideband synthesis method is inefficient, which has led to using Multiple-Input Multiple-Output (MIMO) architectures to introduce spatial diversity. However, this method introduces challenges such as frequency-phase inconsistencies in the self-mixing signals from different array elements due to differences in wave travel distances. In previous research, we studied the calibration of frequency and phase for synthesized sub-signals using DOA parameters, which required additional high-precision direction of arrival (DOA) estimation algorithms. In contrast, this paper proposes a novel spatial-diversity wideband synthesis method that utilizes distance parameter calibration for joint DOA and distance estimation (JDDE). A key advantage of this method is the relative simplicity of obtaining distance parameters. To address the issue where the accuracy of distance parameters does not meet the requirements for synthesis, we proposed a grid search synthesis method in this paper. Furthermore, we introduced a search synthesis based on optimization algorithms to reduce computational load and enhance the synthesis performance. Theoretical analysis and simulations confirm the high accuracy of our JDDE method. Under conditions of high signal-to-noise ratios, our method significantly reduces the DOA's root mean square error—approximately halving it compared to the multiple signal classification algorithm within the same wideband self-mixing framework. Additionally, both distance resolution and range accuracy exceed those achieved with pre-synthesis methods.

Estimating the Distance Between Map Points May Be Related to Forms of Representing the Map´s Scale.

The way a map scale is represented plays a key role in comprehending it. In this research, we examined the relationships between the form of representation of the map's scale (numerical scale, linear graphic scale and circular graphic scale), the user's gender, and the distance between two map objects on user estimates of the distance between two objects on the map (i.e., a 5, 10, 15 and 20-min walk). We gave 183 college students 84 maps of three types: 28 numerical scale, 28 linear graphic scale, and 28 circular graphic scale. Each map presented varied distances to be estimated. We assessed the participants' accurate hits, errors, and their hits minus errors in these distance estimations. Participants had both more hits and more errors on circular scale maps than on linear or numerical scale maps, and the distances between objects and gender also significantly related to the estimated e distance. Both the type of scale and the distance between objects influenced the number of hits minus the number of errors.

Correction to: Rapid source classification and distance estimation for compact binary mergers with PyCBC live

Correction to: Rapid source classification and distance estimation for compact binary mergers with PyCBC live

Testing kinematic distances under a realistic Galactic potential. Investigating systematic errors in the kinematic distance method arising from a non-axisymmetric potential

Obtaining reliable distance estimates to gas clouds within the Milky Way is challenging in the absence of certain tracers. The kinematic distance approach has been used as an alternative, and it is derived from the assumption of circular trajectories around the Galactic centre. Consequently, significant errors are expected in regions where gas flow deviates from purely circular motions. We aim to quantify the systematic errors that arise from the kinematic distance method in the presence of a Galactic potential that is non-axisymmetric. We investigated how these errors differ in certain regions of the Galaxy and how they relate to the underlying dynamics. We performed 2D isothermal hydrodynamical simulation of the gas disk with the moving-mesh code Arepo adding the capability of using an external potential provided by the Agama library for galactic dynamics. We introduced a new analytic potential of the Milky Way, taking elements from existing models and adjusting parameters to match recent observational constraints. In line with results of previous studies, we report significant errors in the kinematic distance estimate for gas close to the Sun along sight lines towards the Galactic centre and anti-centre and associated with the Galactic bar. Kinematic distance errors are low within the spiral arms, as gas resides close to local potential minima and the resulting line-of-sight velocity is similar to what is expected for an axisymmetric potential. Interarm regions exhibit large deviations at any given Galactic radius, and this is caused by the gas being sped up or slowed down as it travels into or out of spiral arms. In addition, we identify ‘zones of avoidance’ in the $lv$-diagram, where the kinematic distance method is particularly unreliable and should only be used with caution and we find a power-law relation between the kinematic distance error and the deviation of the projected line-of-sight velocity from circular motion.

A bio-inspired looming detection for stable landing in unmanned aerial vehicles**This research was funded by the National Natural Science Foundation ofchina, grant number 62206065, and the Bagui Scholar Program of Guangxi.

Flying insects, such as flies and bees, have evolved the capability to rely solely on visual cues for smooth and secure landings on various surfaces. In the process of carrying out tasks, micro unmanned aerial vehicles (UAVs) may encounter various emergencies, and it is necessary to land safely in complex and unpredictable ground environments, especially when altitude information is not accurately obtained, which undoubtedly poses a significant challenge. Our study draws on the remarkable response mechanism of the Lobula Giant Movement Detector to looming scenarios to develop a novel UAV landing strategy. The proposed strategy does not require distance estimation, making it particularly suitable for payload-constrained micro aerial vehicles. Through a series of experiments, this strategy has proven to effectively achieve stable and high-performance landings in unknown and complex environments using only a monocular camera. Furthermore, a novel mechanism to trigger the final landing phase has been introduced, further ensuring the safe and stable touchdown of the drone.

The structure of the stellar halo of the Andromeda galaxy explored with the NB515 for Subaru/HSC. I.: New insights on the stellar halo up to 120 kpc

Abstract We analyse the M31 halo and its substructure within a projected radius of 120 kpc using a combination of Subaru/HSC NB515 and CFHT/MegaCam g- & i-bands. We succeed in separating M31’s halo stars from foreground contamination with ∼ 90 % accuracy by using the surface gravity sensitive NB515 filter. Based on the selected M31 halo stars, we discover three new substructures, which associate with the Giant Southern Stream (GSS) based on their photometric metallicity estimates. We also produce the distance and photometric metallicity estimates for the known substructures. While these quantities for the GSS are reproduced in our study, we find that the North-Western stream shows a steeper distance gradient than found in an earlier study, suggesting that it is likely to have formed in an orbit closer to the Milky Way. For two streams in the eastern halo (Stream C and D), we identify distance gradients that had not been resolved. Finally, we investigate the global halo photometric metallicity distribution and surface brightness profile using the NB515-selected halo stars. We find that the surface brightness of the metal-poor and metal-rich halo populations, and the all population can be fitted to a power-law profile with an index of α = −1.65 ± 0.02, −2.82 ± 0.01, and −2.44 ± 0.01, respectively. In contrast to the relative smoothness of the halo profile, its photometric metallicity distribution appears to be spatially non-uniform with nonmonotonic trends with radius, suggesting that the halo population had insufficient time to dynamically homogenize the accreted populations.

Differential Positioning with Bluetooth Low Energy (BLE) Beacons for UAS Indoor Operations: Analysis and Results.

Localization of unmanned aircraft systems (UASs) in indoor scenarios and GNSS-denied environments is a difficult problem, particularly in dynamic scenarios where traditional on-board equipment (such as LiDAR, radar, sonar, camera) may fail. In the framework of autonomous UAS missions, precise feedback on real-time aircraft position is very important, and several technologies alternative to GNSS-based approaches for UAS positioning in indoor navigation have been recently explored. In this paper, we propose a low-cost IPS for UAVs, based on Bluetooth low energy (BLE) beacons, which exploits the RSSI (received signal strength indicator) for distance estimation and positioning. Distance information from measured RSSI values can be degraded by multipath, reflection, and fading that cause unpredictable variability of the RSSI and may lead to poor-quality measurements. To enhance the accuracy of the position estimation, this work applies a differential distance correction (DDC) technique, similar to differential GNSS (DGNSS) and real-time kinematic (RTK) positioning. The method uses differential information from a reference station positioned at known coordinates to correct the position of the rover station. A mathematical model was established to analyze the relation between the RSSI and the distance from Bluetooth devices (Eddystone BLE beacons) placed in the indoor operation field. The master reference station was a Raspberry Pi 4 model B, and the rover (unknown target) was an Arduino Nano 33 BLE microcontroller, which was mounted on-board a UAV. Position estimation was achieved by trilateration, and the extended Kalman filter (EKF) was applied, considering the nonlinear propriety of beacon signals to correct data from noise, drift, and bias errors. Experimental results and system performance analysis show the feasibility of this methodology, as well as the reduction of position uncertainty obtained by the DCC technique.

SFMD‐X: A New Functional Data Classifier Based on Shrinkage Functional Mahalanobis Distance

ABSTRACTIn this article, we propose a novel classification approach for functional data based on a shrinkage estimate of functional Mahalanobis distance. We first introduce a new shrinkage functional Mahalanobis distance (SFMD), by using this new distance, we transform the functional observations into a set of vector‐valued pseudo‐samples. Furthermore, we adopt some good classification algorithms designed for multivariate data to this pseudo‐samples instead of the original functional data. The new approach has advantage of highly flexible and scalable, that is, it can easily combine with any classification algorithm, such as support vector machine, tree‐based methods, and neural networks. We demonstrate the performance of the proposed functional classifier through both extensive simulation studies and two real data applications.

Variational inference for acceleration of SN Ia photometric distance estimation with BayeSN

Abstract Type Ia supernovae (SNe Ia) are standarizable candles whose observed light curves can be used to infer their distances, which can in turn be used in cosmological analyses. As the quantity of observed SNe Ia grows with current and upcoming surveys, increasingly scalable analyses are necessary to take full advantage of these new datasets for precise estimation of cosmological parameters. Bayesian inference methods enable fitting SN Ia light curves with robust uncertainty quantification, but traditional posterior sampling using Markov Chain Monte Carlo (MCMC) is computationally expensive. We present an implementation of variational inference (VI) to accelerate the fitting of SN Ia light curves using the BayeSN hierarchical Bayesian model for time-varying SN Ia spectral energy distributions (SEDs). We demonstrate and evaluate its performance on both simulated light curves and data from the Foundation Supernova Survey with two different forms of surrogate posterior – a multivariate normal and a custom multivariate zero-lower-truncated normal distribution – and compare them with the Laplace Approximation and full MCMC analysis. To validate of our variational approximation, we calculate the pareto-smoothed importance sampling (PSIS) diagnostic, and perform variational simulation-based calibration (VSBC). The VI approximation achieves similar results to MCMC but with an order-of-magnitude speedup for the inference of the photometric distance moduli. Overall, we show that VI is a promising method for scalable parameter inference that enables analysis of larger datasets for precision cosmology.

On the accuracy of distance estimates by propagation time of sound signals on the arctic shelf

As part of numerical modeling, estimates are made of the accuracy of determining the distance between underwater sources and sound receivers located at a distance of several kilometers from each other in the Kara Sea in the autumn. It is assumed that the main source of possible errors in determining the distance is the lack of accurate data on the vertical profile of the sound speed along the propagation path of acoustic signals. Data from September and November were analyzed, in the interval between which significant changes in the profile take place, when the vertical gradient of sound speed changes from negative to positive values. Characteristic values of sound speed variations were obtained by statistical processing of hydrological data taken from the World Ocean Database. The results obtained are important for analyzing the capabilities of underwater acoustic navigation.

SPADE: A spatial information assisted collision distance estimator for robotic arm

The movement of a robotic arm in the working environment requires efficient and adequate motion planning. The procedure of collision detection based on the object geometry is crucial to plan the motion trajectories, and usually requires intensive resource and considerable time. Many learning-based collision detection schemes have been developed to improve the efficiency of collision detection. However, current learning-based collision detection methods are either not accurate enough or prone to low efficiency. We propose a simple, yet highly accurate collision distance estimator, a spatial information assisted distance estimator, i.e., SPADE, in which spatial information of both robotic arms and obstacles are encoded by multiple encoders. With evaluation in both static and dynamic environments, our model shows higher prediction accuracy than multiple baselines, and higher accuracy can be corroborated by experiment with our model under the premise of equal inference efficiency. In addition, our model shows better robustness than baseline in real-world path planning.

The red giant branch tip in the SDSS, PS1, JWST, NGRST, and Euclid photometric systems

We used synthetic photometry from Gaia DR3 BP and RP spectra for a large selected sample of stars in the Large Magellanic Cloud (LMC) and Small Magellanic Cloud (SMC) to derive the magnitude of the red giant branch (RGB) tip for these two galaxies in several passbands across a range of widely used optical photometric systems, including those of space missions that have not yet started their operations. The RGB tip is estimated by fitting a well motivated model to the RGB luminosity function (LF) within a fully Bayesian framework, allowing for a proper representation of the uncertainties of all the involved parameters and their correlations. By adopting the best available distance and interstellar extinction estimates, we provide a calibration of the RGB tip as a standard candle for the following passbands: Johnson-Kron-Cousins I (mainly used for validation purposes), Hubble Space Telescope F814W, Sloan Digital Sky Survey i and z, PanSTARRS 1 y, James Webb Space Telescope F090W, Nancy Grace Roman Space Telescope Z087, and Euclid IE, with an accuracy within a few per cent, depending on the case. We used theoretical models to explore the trend of the absolute magnitude of the tip as a function of colour in the different passbands (beyond the range spanned by the LMC and SMC), as well as its dependency on age. These calibrations can be very helpful to obtain state-of-the-art RGB tip distance estimates to stellar systems in a very large range of distances directly from data in the natural photometric system of these surveys and/or missions, without recurring to photometric transformations. We have made the photometric catalogues publicly available for calibrations in additional passbands or for different approaches in the estimate of the tip, as well as for stellar populations and stellar astrophysics studies that may take advantage of large and homogeneous datasets of stars with magnitudes in 22 different passbands.

Distance and stellar parameter estimations of solar-like stars from the LAMOST spectroscopic survey

Context. The Gaia mission has opened up a new era for the precise astrometry of stars, thus revolutionizing our understanding of the Milky Way. However, beyond a few kiloparseconds from the Sun, parallax measurements become less reliable, and even within 2 kpc, there still exist stars with large uncertainties. Aims. Our aim was to determine the distance and stellar parameters of 521 424 solar-like stars from LAMOST DR9; these stars lacked precise distance measurements (uncertainties higher than 20% or even without any distance estimations) when checked with Gaia. Methods. We proposed a convolutional neural network (CNN) model to predict the absolute magnitudes, colors, and stellar parameters (Teff, log ɡ, and [Fe/H]) directly from low-resolution spectra. For spectra with signal-to-noise ratios at ɡ band (S/Ng) greater than 10, the model achieves a precision of 85 K for Teff, 0.07 dex for log ɡ, 0.06 dex for [Fe/H], 0.25 mag for MG, and 0.03 mag for (BP – RP)0. The estimated distances have a median fractional error of 4% with a standard deviation of 8%. Results. We applied the trained CNN model to 521 424 solar-like stars to derive the distance and stellar parameters. Compared with other distance estimation studies and spectroscopic surveys, the results show good consistency. Additionally, we investigated the metallicity gradients of the Milky Way from a subsample, and find a radial gradient ranging from −0.05 < Δ[Fe/H]/ΔR < 0.0 dex kpc−1 and a vertical gradient ranging from −0.26 < Δ[Fe/H]/ΔZ < −0.07 dex kpc−1. Conclusions. We conclude that our method is effective in estimating distances and stellar parameters for solar-like stars with limited astrometric data. Our measurements are reliable for Galactic structure studies and hopefully will be useful for exoplanet researches.

Distance Regression Enhanced with Temporal Information Fusion and Adversarial Training for Robot-Assisted Endomicroscopy.

Probe-based confocal laser endomicroscopy (pCLE) has a role in characterising tissue intraoperatively to guide tumour resection during surgery. To capture good quality pCLE data which is important for diagnosis, the probe-tissue contact needs to be maintained within a working range of micrometre scale. This can be achieved through micro-surgical robotic manipulation which requires the automatic estimation of the probe-tissue distance. In this paper, we propose a novel deep regression framework composed of the Deep Regression Generative Adversarial Network (DR-GAN) and a Sequence Attention (SA) module. The aim of DR-GAN is to train the network using an enhanced image-based supervision approach. It extents the standard generator by using a well-defined function for image generation, instead of a learnable decoder. Also, DR-GAN uses a novel learnable neural perceptual loss which combines for the first time spatial and frequency domain features. This effectively suppresses the adverse effects of noise in the pCLE data. To incorporate temporal information, we've designed the SA module which is a cross-attention module, enhanced with Radial Basis Function based encoding (SA-RBF). Furthermore, to train the regression framework, we designed a multi-step training mechanism. During inference, the trained network is used to generate data representations which are fused along time in the SA-RBF module to boost the regression stability. Our proposed network advances SOTA networks by addressing the challenge of excessive noise in the pCLE data and enhancing regression stability. It outperforms SOTA networks applied on the pCLE Regression dataset (PRD) in terms of accuracy, data quality and stability.

Visual Perceptual Confidence: Exploring Discrepancies Between Self-reported and Actual Distance Perception In Virtual Reality.

Virtual Reality (VR) systems are widely used, and it is essential to know if spatial perception in virtual environments (VEs) is similar to reality. Research indicates that users tend to underestimate distances in VR. Prior work suggests that actual distance judgments in VR may not always match the users self-reported preference of where they think they most accurately estimated distances. However, no explicit investigation evaluated whether user preferences match actual performance in a spatial judgment task. We used blind walking to explore potential dissimilarities between actual distance estimates and user-selected preferences of visual complexities, VE conditions, and targets. Our findings show a gap between user preferences and actual performance when visual complexities were varied, which has implications for better visual perception understanding, VR applications design, and research in spatial perception, indicating the need to calibrate and align user preferences and true spatial perception abilities in VR.