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In this paper, we propose the Bayes estimators of unknown parameters of power function distribution in the context of double censoring. The maximum-likelihood estimators (MLEs) for the power function parameters are derived. Bayes estimators are derived using the squared error loss function, entropy loss function, and linex loss function using the Lindley approximation and the importance sampling procedures. We also introduced sample prediction estimates using Bayesian techniques. Finally, we perform a simulation study to compare all the proposed estimation methods and analyze a real-life data set for illustration purposes. [ABSTRACT FROM AUTHOR]

This paper presents the results of a study on software correction of speed measurements taken by GNSS receivers installed in cell phones and sports watches. Digital low-pass filters were used to compensate for fluctuations in measured speed and distance. Real data obtained from popular running applications for cell phones and smartwatches were used for simulations. Various measurement situations were analyzed, such as running at a constant speed or interval running. Taking a very high accuracy GNSS receiver as the reference equipment, the solution proposed in the article reduces the measurement error of the traveled distance by 70%. In the case of measuring speed in interval running, the error could be reduced by up to 80%. The low-cost implementation allows simple GNSS receivers to approach the quality of distance and speed estimation of very precise and expensive solutions. [ABSTRACT FROM AUTHOR]

We address the least-squares estimation of the drift coefficient parameter θ = (λ , A , B , ω p) of a time-inhomogeneous Ornstein–Uhlenbeck process that is observed at high frequency, in which the discretized step size ℎ satisfies h → 0 . In this paper, under the conditions n ⁢ h → ∞ and n ⁢ h 2 → 0 , we prove the consistency and the asymptotic normality of the estimators. We obtain the convergence of the parameters at rate n ⁢ h , except for ω p at n 3 ⁢ h 3 . To verify our theoretical findings, we do a simulation study. We then illustrate the use of the proposed model in fitting the energy use of light fixtures in one Belgium household and the stock exchange. [ABSTRACT FROM AUTHOR]

In this paper, a discrete one-parameter distribution called the semi-Poisson distribution is introduced, which is based on a set of non-negative integers. It is seen that this distribution captures over-dispersion and zero-inflation scenarios well. A few properties of the proposed distribution, such as moments, the probability-generating function, index of dispersion, recurrence relation for the moments, and negative moments are presented. The distribution is applied to two real-life datasets related to insurance claims and parasite counts, where it is noted to perform better than many of the existing discrete distributions based on Z + , including some of the recently introduced ones. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of the paper is to present an innovation-based new actuator/surface fault detection and isolation (FDI) method, which is sensitive to the changes in the innovation mean of the Kalman filter (KF) and the KF tuning method for the case of actuator/surface failure. Design/methodology/approach: The multiple system noise scale factors (MSNSFs) are used in this method as the monitoring statistics. MSNSFs are determined to make it possible to perform the actuator/surface FDI operations simultaneously. Findings: The introduced FDI algorithm can detect and isolate the loss of effectiveness type actuator/surface faults in real time. The proposed KF tuning method works effectively against actuator/surface fault. The actuator/surface fault detection, isolation and filter tuning are achieved by just using a simple modification over the conventional KF. Originality/value: The MSNSF-based actuator/surface fault detection, isolation and filter tuning algorithms are investigated together for the first time. The actuator/surface FDI operations are performed simultaneously. [ABSTRACT FROM AUTHOR]

Interbull's multiple across-country evaluaftion provides national breeding organizations with breeding values for internationally used bulls, which integrate performance data obtained in different breeding populations, environments, and production systems. However, breeding value-based selection decisions on domestic individuals born to foreign sires can only benefit from Interbull breeding values if they are integrated such that their information can contribute to the breeding values of all related domestic animals. For that purpose, several methods have been proposed which either model Interbull breeding values as prior information in a Bayesian approach, as additional pseudo data points, or as correlated traits, where these methods also differ in their software and parameterization requirements. Further, the complexity of integration also depends on the traits and genetic evaluation models. Especially random regression models require attention because of the dimensionality discrepancy between the number of Interbull breeding values and the number of modeled genetic effects. This paper presents the results from integrating 16,063 Interbull breeding values into the domestic single-step random regression test-day model for milk, fat, and protein yield for Australian Red dairy cattle breeds. Interbull breeding values were modeled as pseudo data points with data point-specific residual variances derived within animal across traits, ignoring relationships between integrated animals. Results suggest that the integration was successful with regard to alignment of Interbull breeding values with their domestic equivalent as well as with regard to the individual and population-wide increase in reliabilities. Depending on the relationship structure between integration candidates, further work is required to account for those relationships in a computationally feasible manner. Other traits with separate parity effects nationally could use a similar approach, even if not modeled with a test-day model. [ABSTRACT FROM AUTHOR]

Although the surface energy budget is essential to determine Earth's climate, site measurements of various radiative components are still too scarce to properly characterize their spatial and temporal variations. This has led to the development of a growing number of surface radiation products, mainly including remotely sensed data, model reanalysis data, and simulations using General Circulation Models (GCMs). This collection of papers introduces new techniques, including the use of machine learning methods for radiation estimation, and evaluates and compares various radiation products, as well as their spatio-temporal variations. These studies show large discrepancies among various products across nearly all radiative parameters in either accuracy or spatio-temporal variations. However, remotely sensed radiation products perform relatively better than others. Despite this, there is an urgent need for further efforts to address these discrepancies and improve the accuracy of these estimates. Even though the major radiative parameters including downward shortwave radiation, net longwave radiation, and albedo, from most products show insignificant long-term variation trends on a global scale, only specific regions, such as the Yunnan-Kweichow Plateau (YKP) and regions with permafrost (i.e. Qinghai-Tibet Plateau and Arctic) and glaciers (i.e. Altai Mountains) exhibit remarkable trends. [ABSTRACT FROM AUTHOR]

Agricultural biomass is one of the most important renewable energy sources. As a byproduct of corn, soybean and sunflower production, large amounts of biomass are produced that can be used as an energy source through conversion. In order to assess the quality and the possibility of the use of biomass, its composition and calorific value must be determined. The use of nonlinear models allows for an easier estimation of the energy properties of biomass concerning certain input and output parameters. In this paper, RFR (Random Forest Regression) and SVM (Support Vector Machine) models were developed to determine their capabilities in estimating the HHV (higher heating value) of biomass based on input parameters of ultimate analysis. The developed models showed good performance in terms of HHV estimation, confirmed by the coefficient of determination for the RFR (R2 = 0.79) and SVM (R2 = 0.93) models. The developed models have shown promising results in accurately predicting the HHV of biomass from various sources. The use of these algorithms for biomass energy prediction has the potential for further development. [ABSTRACT FROM AUTHOR]

Sparse unmixing is a semisupervised unmixing method based on the linear mixture model, in which the spectral library is known a prior, and has received considerable attention recently. It has been confirmed that the spatial information in hyperspectral images plays a crucial role in improving the performance of sparse unmixing algorithms. However, the spatial information extracted or captured in most unmixing algorithms is inaccurate and robust enough, which leads to artificial block noise or outliers in the estimated abundance maps, especially as the noise level increases. To address these problems and more efficiently utilize the spatial information, this paper proposes a graph learning and denoising-based weighted sparse mixing (GLDWSU) algorithm, which includes three stages in the unmixing procedure: graph learning, denoising and unmixing. In the first stage, the graph Laplacian matrix is adaptively learned to capture the spatial structure of HSI with the relative total variation (RTV) regularization. In the next stage, HSI is denoised with the learned Laplacian matrix by using Laplacian smoothing. In the final stage, the denoised HSI is unmixed with the reweighted-norm regularization based on the alternating direction method of multipliers (ADMM) framework. The experimental results on both simulated and real data sets show that the proposed GLDWSU algorithm can more accurately capture and utilize the spatial structure information of HSI with a low computational cost and outperforms all the compared methods. [ABSTRACT FROM AUTHOR]

Accurate state-of-charge (SOC) estimation is essential for maximizing the lifetime of battery-powered wireless sensor networks (WSNs). Lightweight estimation methods are widely used in WSNs due to their low measurement and computation requirements. However, accuracy of existing lightweight methods is not high, and their adaptability to different batteries and working conditions is relatively poor. This paper proposes a lightweight SOC estimation method, which applies Peukert's Law to estimate the effective capacity of the battery and then calculates the SOC by subtracting the cumulative current consumption from the estimated capacity. In order to evaluate the proposed method comprehensively, different primary batteries and working conditions (constant current, constant resistance, and emulated duty-cycle loads) are employed. Experimental results show that the proposed method is superior to existing methods for different batteries and working conditions, which mainly benefits from the ability of Peukert's Law to better model the rate-capacity effect of the batteries. [ABSTRACT FROM AUTHOR]

In this paper, we propose the Bayes estimators of unknown parameters of power function distribution in the context of double censoring. The maximum-likelihood estimators (MLEs) for the power function parameters are derived. Bayes estimators are derived using the squared error loss function, entropy loss function, and linex loss function using the Lindley approximation and the importance sampling procedures. We also introduced sample prediction estimates using Bayesian techniques. Finally, we perform a simulation study to compare all the proposed estimation methods and analyze a real-life data set for illustration purposes. [ABSTRACT FROM AUTHOR]

This paper aims to present a robust fault diagnosis structure-based observers for actuator faults in the pitch part system of the wind turbine benchmark. In this work, two linear estimators have been proposed and investigated: the Kalman filter and the Luenberger estimator for observing the output states of the pitch system in order to generate the appropriate residual between the measured positions of blades and the estimated values. An inference step as a decision block is employed to decide the existence of faults in the process, and to classify the detected faults using a predetermined threshold defined by upper and lower limits. All actuator faults in the pitch system of the horizontal wind turbine benchmark are studied and investigated. The obtained simulation results show the ability of the proposed diagnosis system to determine effectively the occurred faults in the pitch system. Estimation of the output variables is effectively realized in both situations: without and with the occurrence of faults in the studied process. A comparison between the two used observers is demonstrated. [ABSTRACT FROM AUTHOR]

Modeling of solar photovoltaic cell is an essential requirement in the computations involved in solar photovoltaic power systems. Some metaheuristic algorithms are used for determining the cell parameters in the literature, however, more investigation is required with reference to varying solar irradiation and temperature to improve the accuracy of the models. Hence, this paper proposes firefly algorithm for identification of the cell parameters accurate enough to construct the cell characteristics under varying solar irradiation and temperature conditions. Experimental results obtained at standard irradiation and temperature of 1000 W/m2, 25°C, and at other irradiation levels such as 80 0 W/m2 and 600 W/m2, temperature levels such as 40°C and 50°C were presented along with simulated values. The value of series resistance, shunt resistance and diode ideality factor for temperatures from 20°C to 60°C and irradiation of 400 W/m2 to 1000 W/m2 are computed using this proposed method. A comparison of the proposed method with other researchers at irradiation of 1000, 800, and 600 W/m2 and 25°C was provided. The results of implementation show that there is a good agreement between computed values and data sheet values. The proposed method will definitely be useful for large scale solar photovoltaic designers, researchers, simulators. [ABSTRACT FROM AUTHOR]

Distributed or decentralized estimation of covariance, and distributed principal component analysis have been introduced and studied in the signal processing community in recent years, and applications in array processing have been indicated in some detail. Inspired by these, this paper provides a detailed development of several distributed algorithms for array processing. New distributed algorithms are proposed for DOA estimation methods like root-MUSIC, total least squares-ESPRIT, and FOCUSS. Other contributions include distributed design of the Capon beamformer from data, and distributed implementation of the spatial smoothing method for coherent sources. A distributed implementation of a recently proposed beamspace method called the convolutional beamspace (CBS) is also proposed. The proposed algorithms are fully distributed – an average consensus (AC) is used to avoid the need for a fusion center. The algorithms are based on a recently reported finite-time version of AC which converges to the exact solution in a finite number of iterations. Numerical examples are given throughout the paper to show the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Multiple signal classification (MUSIC) has been widely applied in multiple-input multiple-output (MIMO) receivers for direction-of-arrival (DOA) estimation. To reduce the cost of radio frequency (RF) chains at millimeter-wave bands, hybrid analog-digital structure has been adopted in massive MIMO transceivers. In this situation, received signals at the antennas are unavailable to the digital receiver, and as a consequence, the spatial covariance matrix (SCM), which is essential in MUSIC algorithm, cannot be obtained using traditional sample average approach. Based on our previous work, we propose a novel algorithm for SCM reconstruction in hybrid massive MIMO systems with multiple RF chains in this paper. By switching the analog beamformers to a group of predetermined DOAs, SCM can be reconstructed through the solutions of a set of linear equations. Furthermore, a low-complexity algorithm, as well as a careful selection of the predetermined DOAs, will be also presented in this paper. Simulation results show that the proposed algorithms can reconstruct the SCM accurately so that MUSIC algorithm can be well used in hybrid massive MIMO systems with multiple RF chains. [ABSTRACT FROM AUTHOR]

Image classification needs to consider the existence of image degradations in practice. Although degraded images have various levels of degradation, the degradation levels are usually unknown. This paper proposes a convolutional neural network to classify degraded images by using a restoration network and an ensemble learning. The proposed network can automatically infer ensemble weights by using estimated degradation levels of degraded images and features of restored images, where the degradation levels are estimated internally. The proposed network is mainly discussed with JPEG distortion, while degradations of both Gaussian noise and blurring are also examined. We demonstrate that the proposed network can classify degraded images over various levels of degradation. This paper also reveals how the image-quality of training data for a classification network affects the classification performance of degraded images. [ABSTRACT FROM AUTHOR]

Synchrophasor estimators are nowadays evaluated with the Total Vector Error (TVE) using the synchrophasor representations of the few benchmark signals. This synchrophasor dependence prevents its application to power signals of real events. A new method to obtain the synchrophasor of real signals is proposed in this paper. A finite impulse response (FIR) filter, designed with the nonic O-spline is proposed to obtain phasor estimates asymptotically close to those of an ideal bandpass filter. The phasor estimation accuracy of one or several Phasor Measurement Units (PMUs) can be then assessed using the standard. In addition, it is possible to design two FIR differentiators to obtain frequency and ROCOF estimates close enough to those of ideal differentiator filters, and largely compliant with the standard. This new set of filters opens the way to apply the synchrophasor standard to assess estimates of PMUs of different brands when they process the same signals of a power system event. In this paper, the erratic phasor and frequency estimates produced by a SEL-351 A PMU from a real distributed generation system are assessed to corroborate that the synchrophasor standard can be opened to this new application based on real signals from the field, previously considered as impossible. [ABSTRACT FROM AUTHOR]

With low voltage (LV) distribution networks increasingly being re-purposed beyond their original design specifications to accommodate low carbon technologies, the ability to accurately calculate their actual spare capacity is critical. Traditionally, within the Great Britain (GB) power system, there has been limited monitoring of LV distribution networks, making this difficult. This paper proposes a method for estimating spare capacity of unmonitored LV networks using demand data from customer Smart Meters. In particular, the proposed method infers existing LV network capacity, as well as losses, across scenarios where only a limited number of customers have Smart Meters installed. Typical daily load profiles across customers with Smart Meters are learned using a Dirichlet sampled Gaussian mixture model (GMM). Learned profiles are then applied to all unmetered customers to estimate network parameters. Method accuracy is assessed by comparing estimations with simulated, fully observed, LV network models. The method is also compared to benchmark models for establishing unobserved demand profiles. Overall, results in the paper show that the proposed method outperforms benchmark models in terms of accurately assessing substation headroom, particularly in scenarios where only 10–50% of customers have Smart Meters installed. [ABSTRACT FROM AUTHOR]

In this paper, we point-out a major error in the proofs of the main results of Bahadır Yüzbaşı, Yasin Asar & S. Ejaz Ahmed [(2022). Liu-type shrinkage estimations in linear models, Statistics, 56:2, 396–420]. In particular, the proofs of their Theorems 3.4–3.5 are based on their Lemma 3.2 which is incorrect. [ABSTRACT FROM AUTHOR]

Many autonomous vehicles and advanced driver-assistance systems are equipped with front-facing cameras that detect and track objects using deep-learning-based algorithms. However, the localization capability of monocular cameras is often overlooked. In this paper, a novel method for estimating the pixel-wise error in a detected object's location versus its ground truth is proposed. As the object moves away from the camera, the pixel errors are shown to be normally distributed with unique spreads along the image's vertical axis (y-pixel). The pixel error appears to be smaller as objects get farther away, while at the same distance range, objects have similar error distribution across the camera's horizontal view. The horizontal axis (x-pixel) error appears to be smaller while the distance moves further away. However, the x-pixel location along a constant y-pixel row has no impact on the error distribution. The estimated x and y-pixel error distributions can in turn be used to form a spatial error distribution for finding the location of a detected object within a certain confidence interval. The spatial errors are then projected onto the world coordinate system using a camera transformation matrix to give a more realistic sense of what this error means. The results show that location estimation using monocular cameras generates an elliptical error distribution around the object with a larger error in the y-pixel direction compared to the x-pixel direction. This error distribution can be important to fuse information from multiple range-detecting sensors as well as multi-vehicle and multi-object tracking. The uncertainty characterization for position measurement, as demonstrated in this paper is an essential element of tracking and, is sensor and algorithm dependent. [ABSTRACT FROM AUTHOR]

This paper studies large scatter matrix estimation for heavy tailed distributions. The contributions of this paper are twofold. First, we propose and advocate to use a new distribution family, the pair-elliptical, for modeling the high dimensional data. The pair-elliptical is more flexible and easier to check the goodness of fit compared to the elliptical. Secondly, built on the pair-elliptical family, we advocate using quantile-based statistics for estimating the scatter matrix. For this, we provide a family of quantile-based statistics. They outperform the existing ones for better balancing the efficiency and robustness. In particular, we show that the propose estimators have comparable performance to the moment-based counterparts under the Gaussian assumption. The method is also tuning-free compared to Catoni’s M-estimator for covariance matrix estimation. We further apply the method to conduct a variety of statistical methods. The corresponding theoretical properties as well as numerical performances are provided. [ABSTRACT FROM AUTHOR]

Scene flow represents the 3D motion of every point in the dynamic environments. Like the optical flow that represents the motion of pixels in 2D images, 3D motion representation of scene flow benefits many applications, such as autonomous driving and service robot. This paper studies the problem of scene flow estimation from two consecutive 3D point clouds. In this paper, a novel hierarchical neural network with double attention is proposed for learning the correlation of point features in adjacent frames and refining scene flow from coarse to fine layer by layer. The proposed network has a new more-for-less hierarchical architecture. The more-for-less means that the number of input points is greater than the number of output points for scene flow estimation, which brings more input information and balances the precision and resource consumption. In this hierarchical architecture, scene flow of different levels is generated and supervised respectively. A novel attentive embedding module is introduced to aggregate the features of adjacent points using a double attention method in a patch-to-patch manner. The proper layers for flow embedding and flow supervision are carefully considered in our network designment. Experiments show that the proposed network outperforms the state-of-the-art performance of 3D scene flow estimation on the FlyingThings3D and KITTI Scene Flow 2015 datasets. We also apply the proposed network to the realistic LiDAR odometry task, which is a key problem in autonomous driving. The experiment results demonstrate that our proposed network can outperform the ICP-based method and shows good practical application ability. The source codes will be released on https://github.com/IRMVLab/HALFlow. [ABSTRACT FROM AUTHOR]

Interactive image restoration aims to generate restored images by adjusting a controlling coefficient which determines the restoration level. Previous works are restricted in modulating image with a single coefficient. However, real images always contain multiple types of degradation, which cannot be well determined by one coefficient. To make a step forward, this paper presents a new problem setup, called multi-dimension (MD) modulation, which aims at modulating output effects across multiple degradation types and levels. Compared with the previous single-dimension (SD) modulation, the MD setup to handle multiple degradations adaptively and relief data unbalancing problem in different degradation types. We also propose a deep architecture - CResMD with newly introduced controllable residual connections for multi-dimension modulation. Specifically, we add a controlling variable on the conventional residual connection to allow a weighted summation of input and residual. The values of these weights are generated by another condition network. We further propose a new data sampling strategy based on beta distribution together with a simple loss reweighting approach to balance different degradation types and levels. With corrupted image and degradation information as inputs, the network can output the corresponding restored image. By tweaking the condition vector, users can control the output effects in MD space at test time. Moreover, we also provide an estimation network to predict the condition vector, thus the base network could directly output the restored image without modulation from users. Extensive experiments demonstrate that the proposed CResMD achieves excellent performance on both SD and MD modulation tasks. [ABSTRACT FROM AUTHOR]

Adversarial attacks have been extensively studied in recent years since they can identify the vulnerability of deep learning models before deployed. In this paper, we consider the black-box adversarial setting, where the adversary needs to craft adversarial examples without access to the gradients of a target model. Previous methods attempted to approximate the true gradient either by using the transfer gradient of a surrogate white-box model or based on the feedback of model queries. However, the existing methods inevitably suffer from low attack success rates or poor query efficiency since it is difficult to estimate the gradient in a high-dimensional input space with limited information. To address these problems and improve black-box attacks, we propose two prior-guided random gradient-free (PRGF) algorithms based on biased sampling and gradient averaging, respectively. Our methods can take the advantage of a transfer-based prior given by the gradient of a surrogate model and the query information simultaneously. Through theoretical analyses, the transfer-based prior is appropriately integrated with model queries by an optimal coefficient in each method. Extensive experiments demonstrate that, in comparison with the alternative state-of-the-arts, both of our methods require much fewer queries to attack black-box models with higher success rates. [ABSTRACT FROM AUTHOR]

The group testing procedure divides sampled units into several groups, and then obtains an overall test result for each group. It is used to identify specific individuals who have a given attribute, or to estimate the overall prevalence of the attribute in the population. We here investigate how group retesting can improve precision of estimation and its cost-efficiency, which are important considerations for investigators. Retesting uses two or more group stages, with repeat testing of the original samples at each stage. Previous authors have proposed a procedure with two stages having equal group sizes, and where the number of groups tested at the second stage is based on the number of positive groups in the first stage. In this paper, our main focus is on estimating the prevalence p of affected individuals in a population, and identifying cost-efficient experimental designs, when using two-stage testing with unequal group sizes at each stage. We use analytical solutions for the precision of estimation, together with simulations to evaluate various experimental designs. We consider the value of retesting at the second stage, and determine when using only one stage of testing might be sufficiently precise. [ABSTRACT FROM AUTHOR]

Interactive image restoration aims to generate restored images by adjusting a controlling coefficient which determines the restoration level. Previous works are restricted in modulating image with a single coefficient. However, real images always contain multiple types of degradation, which cannot be well determined by one coefficient. To make a step forward, this paper presents a new problem setup, called multi-dimension (MD) modulation, which aims at modulating output effects across multiple degradation types and levels. Compared with the previous single-dimension (SD) modulation, the MD setup to handle multiple degradations adaptively and relief data unbalancing problem in different degradation types. We also propose a deep architecture - CResMD with newly introduced controllable residual connections for multi-dimension modulation. Specifically, we add a controlling variable on the conventional residual connection to allow a weighted summation of input and residual. The values of these weights are generated by another condition network. We further propose a new data sampling strategy based on beta distribution together with a simple loss reweighting approach to balance different degradation types and levels. With corrupted image and degradation information as inputs, the network can output the corresponding restored image. By tweaking the condition vector, users can control the output effects in MD space at test time. Moreover, we also provide an estimation network to predict the condition vector, thus the base network could directly output the restored image without modulation from users. Extensive experiments demonstrate that the proposed CResMD achieves excellent performance on both SD and MD modulation tasks. [ABSTRACT FROM AUTHOR]

Adversarial attacks have been extensively studied in recent years since they can identify the vulnerability of deep learning models before deployed. In this paper, we consider the black-box adversarial setting, where the adversary needs to craft adversarial examples without access to the gradients of a target model. Previous methods attempted to approximate the true gradient either by using the transfer gradient of a surrogate white-box model or based on the feedback of model queries. However, the existing methods inevitably suffer from low attack success rates or poor query efficiency since it is difficult to estimate the gradient in a high-dimensional input space with limited information. To address these problems and improve black-box attacks, we propose two prior-guided random gradient-free (PRGF) algorithms based on biased sampling and gradient averaging, respectively. Our methods can take the advantage of a transfer-based prior given by the gradient of a surrogate model and the query information simultaneously. Through theoretical analyses, the transfer-based prior is appropriately integrated with model queries by an optimal coefficient in each method. Extensive experiments demonstrate that, in comparison with the alternative state-of-the-arts, both of our methods require much fewer queries to attack black-box models with higher success rates. [ABSTRACT FROM AUTHOR]

Inflated distributions are applied in various fields, including insurance, traffic networks and survival analyses. First, they are defined by a baseline discrete distribution, and then, extra masses are added to some points of interest, called inflated points, to achieve more flexible models for data analyses. The baseline distribution is arbitrary and application dependent. Here, the rich family of power series distributions is considered as the baseline, which includes various common discrete distributions such as Poisson, negative binomial, multinomial and logarithmic series distributions. This paper deals with an extension of previous works in two directions. The former is an extension of the univariate inflated distributions to multivariate ones, and the latter is the generalization of the inflated points from the zero single point to the set k=0,1,…. Under this setting, various inflated distributions in the literature fall into the proposed family of distributions. These extensions make the proposed model flexible and practically useful in data analyses. To do this, the problem of estimating parameters with various approaches as well as hypotheses testing is studied in detail. Multivariate‐generalized linear models with inflated multivariate discrete responses are also discussed. To assess the performance of the proposed family of inflated distributions, simulation studies are conducted, and a real data set on an Australian health survey study is also analysed. [ABSTRACT FROM AUTHOR]

Fluorescence lifetime imaging is a valuable technique for probing characteristics of wide ranging samples and sensing of the molecular environment. However, the desire to measure faster and reduce effects such as photo bleaching in optical photon-count measurements for lifetime estimation lead to inevitable effects of convolution with the instrument response functions and noise, causing a degradation of the lifetime accuracy and precision. To tackle the problem, this paper presents a robust and computationally efficient framework for recovering fluorophore sample decay from the histogram of photon-count arrivals modelled as a decaying single-exponential function. In the proposed approach, the temporal histogram data is first decomposed into multiple bins via an adaptive multi-bin signal representation. Then, at each level of the multi-resolution temporal space, decay information including both the amplitude and the lifetime of a single-exponential function is rapidly decoded based on a novel statistical estimator. Ultimately, a game-theoretic model consisting of two players in an “amplitude-lifetime” game is constructed to be able to robustly recover optimal fluorescence decay signal from a set of fused multi-bin estimates. In addition to theoretical demonstrations, the efficiency of the proposed framework is experimentally shown on both synthesised and real data in different imaging circumstances. On a challenging low photon-count regime, our approach achieves about 28% improvement in bias than the best competing method. On real images, the proposed method processes data on average around 63 times faster than the gold standard least squares fit. Implementation codes are available to researchers. [ABSTRACT FROM AUTHOR]

Multilevel converters (MLCs) are pioneers in the field of electrical energy conversion. From the classification of MLCs, modular multilevel converter (MMC) is top-rated due to its attractive structure and performance. The modular nature and no need for isolated DC resources are two significant advantages of this converter. However, the need for many sensors to control its performance is a considerable drawback. In addition, the internal circulating current in the MMC causes losses, decreases efficiency, and increases the voltage ripple of capacitors. This paper proposes combining a new capacitor voltage sorting method and an open-loop controller based on arms energy estimation to control the MMC using two voltage sensors in the arms and one current sensor at the output. The proposed method ensures continuous voltage balancing of the capacitors and removes multiple voltage sensors from sub-modules (SMs). The simulation and experimental results verify the accuracy and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The use of synchronized measurement technology leads to a more reliable and secure operation of the power system. Phasor calculation is needed for all buses where a phasor measurement unit is installed, and fast and precise estimation is necessary for accurate monitoring, protection and control. The Prony algorithm is one promising method due to its capability to estimate phasor adaptively with changing frequency. The algorithm projects the signal on $L$ exponentially damped sine functions, where $L$ is the order of the Prony. However, the order under different conditions should be adapted automatically to reduce computation time. To adaptively specify the order, the empirical mode decomposition (EMD) method is proposed in this paper to be combined with the Prony and named EMD-Prony. The EMD decomposes a signal into finite single oscillatory modes, representing the number of modes in the signal. EMD is also used as a pre-processing step to filter noise from an input signal of Prony. Therefore, EMD is proposed here as an assistant of Prony in a phasor estimation. Finally, the proposed method is tested on the benchmark signals proposed in the IEEE standard, signals obtained from a simulated power system, and measured data from a real-world power system. [ABSTRACT FROM AUTHOR]

In this paper, we study the estimation of a rank-one spiked tensor in the presence of heavy tailed noise. Our results highlight some of the fundamental similarities and differences in the tradeoff between statistical and computational efficiencies under heavy tailed and Gaussian noise. In particular, we show that, for $p$ th order tensors, the tradeoff manifests in an identical fashion as the Gaussian case when the noise has finite $4(p-1)$ th moment. The difference in signal strength requirements, with or without computational constraints, for us to estimate the singular vectors at the optimal rate, interestingly, narrows for noise with heavier tails and vanishes when the noise only has finite fourth moment. Moreover, if the noise has less than fourth moment, tensor SVD, perhaps the most natural approach, is suboptimal even though it is computationally intractable. Our analysis exploits a close connection between estimating the rank-one spikes and the spectral norm of a random tensor with iid entries. In particular, we show that the order of the spectral norm of a random tensor can be precisely characterized by the moment of its entries, generalizing classical results for random matrices. In addition to the theoretical guarantees, we propose estimation procedures for the heavy tailed regime, which are easy to implement and efficient to run. Numerical experiments are presented to demonstrate their practical merits. [ABSTRACT FROM AUTHOR]

Let $\mathrm {W}\in \mathbb {C}^{n\times n}$ be a single-spiked Wishart matrix in the class $\mathrm {W}\sim \mathcal {CW}_{n}(m,\mathrm {I}_{n}+ \theta \mathrm {v}\mathrm {v}^{\dagger}) $ with $m\geq n$ , where ${\mathrm {I}}_{n}$ is the $n\times n$ identity matrix, $\mathrm {v}\in \mathbb {C}^{n\times 1}$ is an arbitrary vector with unit Euclidean norm, $\theta \geq 0$ is a non-random parameter, and $(\cdot)^{\dagger} $ represents the conjugate-transpose operator. Let u1 and ${\mathrm {u}}_{n}$ denote the eigenvectors corresponding to the smallest and the largest eigenvalues of W, respectively. This paper investigates the probability density function (p.d.f.) of the random quantity $Z_{\ell }^{(n)}=\left |{\mathrm {v}^{\dagger} \mathrm {u}_\ell }\right |^{2}\in (0,1)$ for $\ell =1,n$. In particular, we derive a finite dimensional closed-form p.d.f. for $Z_{1}^{(n)}$ which is amenable to asymptotic analysis as $m,n$ diverges with $m-n$ fixed. It turns out that, in this asymptotic regime, the scaled random variable $nZ_{1}^{(n)}$ converges in distribution to $\chi ^{2}_{2}/2(1+\theta)$ , where $\chi _{2}^{2}$ denotes a chi-squared random variable with two degrees of freedom. This reveals that u1 can be used to infer information about the spike. On the other hand, the finite dimensional p.d.f. of $Z_{n}^{(n)}$ is expressed as a double integral in which the integrand contains a determinant of a square matrix of dimension $(n-2)$. Although a simple solution to this double integral seems intractable, for special configurations of $n=2,3$ , and 4, we obtain closed-form expressions. [ABSTRACT FROM AUTHOR]

As one of the fundamental technologies for scene understanding, semantic segmentation has been widely explored in the last few years. Light field cameras encode the geometric information by simultaneously recording the spatial information and angular information of light rays, which provides us with a new way to solve this issue. In this paper, we propose a high-quality and challenging urban scene dataset, containing 1074 samples composed of real-world and synthetic light field images as well as pixel-wise annotations for 14 semantic classes. To the best of our knowledge, it is the largest and the most diverse light field dataset for semantic segmentation. We further design two new semantic segmentation baselines tailored for light field and compare them with state-of-the-art RGB, video and RGB-D-based methods using the proposed dataset. The outperforming results of our baselines demonstrate the advantages of the geometric information in light field for this task. We also provide evaluations of super-resolution and depth estimation methods, showing that the proposed dataset presents new challenges and supports detailed comparisons among different methods. We expect this work inspires new research direction and stimulates scientific progress in related fields. The complete dataset is available at https://github.com/HAWKEYE-Group/UrbanLF. [ABSTRACT FROM AUTHOR]

Translation averaging is known to be more difficult than rotation averaging due to scale ambiguity, estimation sensitivity, and solution uncertainty. Existing approaches have exposed their limitations in terms of accuracy, robustness, simplicity, or efficiency. To tackle this tough problem, a simple yet effective translation averaging pipeline, termed as Incremental Translation Averaging (ITA), is proposed in this paper. It combines the advantages of high accuracy and robustness in incremental parameter estimation pipeline and the advantages of high simplicity and efficiency in global motion averaging approach. Unlike the traditional translation averaging methods which estimate all the absolute camera locations simultaneously and suffer from inaccuracy in parameter estimation and incompleteness in scene reconstruction, our ITA computes them novelly in an incremental way with higher accuracy and robustness. Thanks to the introduction of incremental parameter estimation thought into the translation averaging pipeline, 1) our ITA is robust to measurement outliers and accurate in parameter estimation; and 2) our ITA is simple and efficient because of its less dependency on complicated optimization, carefully-designed preprocessing, or additional information. Comprehensive evaluations on the 1DSfM dataset demonstrate the effectiveness of our ITA and its advantages over several state-of-the-art translation averaging approaches. [ABSTRACT FROM AUTHOR]

Deep learning has greatly promoted the development of multi-view stereo in recent years. However, how to measure the reliability of the estimated depth map for practical applications and make reasonable depth hypothesis sampling for the cost volume building in the coarse-to-fine architecture are still unresolved crucial problems. To this end, an Uncertainty Guided multi-view Network (UGNet) is proposed in this paper. In order to enable the network to perceive the uncertainty, an uncertainty-aware loss function is introduced, which not only can infer uncertainty implicitly in an unsupervised manner but also can reduce the bad impact of high uncertainty regions and the erroneous labels in the training set during training. Moreover, an uncertainty-based depth hypothesis sampling strategy is further proposed to adaptively determine the depth search range of each pixel for finer stages, which helps to generate more rational depth intervals compared with other methods and build more compact cost volumes without redundancy. Experimental results on DTU dataset, BlendedMVS dataset, Tanks and Temples dataset and ETH3D high-res benchmark show that our method achieves promising reconstruction results compared with other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Micro-grid solutions around the world rely on the operation of DC/DC power conversion systems. The most commonly used solution for these topologies is the use of a dual active bridge (DAB) converter. Increasing the efficiency and reliability of this system contributes to the improvement in the stability of the entire microgrid. This paper discussed an analytical method of energy efficiency and power loss estimation in a single phase dual active bridge (DAB) converter controlled with a single-phase shift (SPS) modulation scheme for microgrid system stability. The presented approach uses conduction and commutation losses of semiconductors and high frequency transformer. All parameters required for the calculation may be obtained from the manufacturers' datasheets or can be based on a simple measurement. The approach was validated by the comparison of the estimated energy efficiency characteristics with the measured ones for a prototype of a 5 kW single phase DAB converter equipped with silicon carbide metal-oxide semiconductor field-effect transistors (SiC MOSFET). [ABSTRACT FROM AUTHOR]

The highly fluctuated renewable generations and electric vehicles have undergone tremendous growth in recent years. Most of them are connected to the grid via power electronic devices, resulting in wide variation ranges for several key parameters in the frequency response model (FRM), such as system inertia and load damping factors. This paper proposes an automatic generation control (AGC) method considering the uncertainties of these key parameters. First, the historical power system operation data following large power disturbances are used to identify the FRM key parameters offline. Second, the offline identification results and the normal operation data right before the large power disturbance are used to train the online probability estimation model of the FRM key parameters. Third, the online estimation results of the FRM key parameters are used as the input, and the model predictive-based AGC optimization method is developed based on distributionally robust optimization (DRO) theory. Case studies conducted on the IEEE 118-bus system show that the proposed AGC method outperforms the widely utilized PI-based and PID-based control methods in terms of performance and efficiency. [ABSTRACT FROM AUTHOR]

The wide variety of inverter control settings for solar photovoltaics (PV) causes the accurate knowledge of these settings to be difficult to obtain in practice. This paper addresses the problem of determining inverter reactive power control settings from net load advanced metering infrastructure (AMI) data. The estimation is first cast as fitting parameterized control curves. We argue for an intuitive and practical approach to preprocess the AMI data, which exposes the setting to be extracted. We then develop a more general approach with a data-driven reactive power disaggregation algorithm, reframing the problem as a maximum likelihood estimation for the native load reactive power. These methods form the first approach for reconstructing reactive power control settings of solar PV inverters from net load data. The constrained curve fitting algorithm is tested on 701 loads with behind-the-meter (BTM) PV systems with identical control settings. The settings are accurately reconstructed with mean absolute percentage errors between 0.425% and 2.870%. The disaggregation-based approach is then tested on 451 loads with variable BTM PV control settings. Different configurations of this algorithm reconstruct the PV inverter reactive power timeseries with root mean squared errors between 0.173 and 0.198 kVAR. [ABSTRACT FROM AUTHOR]

Motion blur in dynamic scenes is an important yet challenging research topic. Recently, deep learning methods have achieved impressive performance for dynamic scene deblurring. However, the motion information contained in a blurry image has yet to be fully explored and accurately formulated because: (i) the ground truth of dynamic motion is difficult to obtain; (ii) the temporal ordering is destroyed during the exposure; and (iii) the motion estimation from a blurry image is highly ill-posed. By revisiting the principle of camera exposure, motion blur can be described by the relative motions of sharp content with respect to each exposed position. In this paper, we define exposure trajectories, which represent the motion information contained in a blurry image and explain the causes of motion blur. A novel motion offset estimation framework is proposed to model pixel-wise displacements of the latent sharp image at multiple timepoints. Under mild constraints, our method can recover dense, (non-)linear exposure trajectories, which significantly reduce temporal disorder and ill-posed problems. Finally, experiments demonstrate that the recovered exposure trajectories not only capture accurate and interpretable motion information from a blurry image, but also benefit motion-aware image deblurring and warping-based video extraction tasks. Codes are available on https://github.com/yjzhang96/Motion-ETR. [ABSTRACT FROM AUTHOR]

This paper presents a photometric stereo method that works with unknown natural illumination without any calibration objects or initial guess of the target shape. To solve this challenging problem, we propose the use of an equivalent directional lighting model for small surface patches consisting of slowly varying normals, and solve each patch up to an arbitrary orthogonal ambiguity. We further build the patch connections by extracting consistent surface normal pairs via spatial overlaps among patches and intensity profiles. Guided by these connections, the local ambiguities are unified to a global orthogonal one through Markov Random Field optimization and rotation averaging. After applying the integrability constraint, our solution contains only a binary ambiguity, which could be easily removed. Experiments using both synthetic and real-world datasets show our method provides even comparable results to calibrated methods. [ABSTRACT FROM AUTHOR]

Capturing the directions of light by light field cameras powers next-generation immersive multimedia applications. A critical problem in taking advantage of the rich visual information in light field images is depth estimation. Conventional light field depth estimation methods build a cost volume that measures the photo-consistency of pixels refocused to a range of depths, and the highest consistency indicates the correct depth. This strategy works well in most regions but usually generates blurry edges in the estimated depth map due to occlusions. Recent work shows that integrating occlusion models to light field depth estimation can largely reduce blurry edges. However, existing occlusion handling methods rely on complex edge-aided processing and post-refinement, and this reliance limits the resultant depth accuracy and impacts on the computational performance. In this paper, we propose a novel occlusion-aware vote cost (OAVC) which is able to accurately preserve edges in the depth map. Instead of using photo-consistency as an indicator of the correct depth, we construct a novel cost from a new perspective that counts the number of refocused pixels whose deviations from the central-view pixel are less than a small threshold, and utilizes that number to select the correct depth. The pixels from occluders are thus excluded in determining the correct depth. Without the use of any explicit occlusion handling methods, the proposed method can inherently preserve edges and produces high-quality depth estimates. Experimental results show that the proposed OAVC outperforms state-of-the-art light field depth estimation methods in terms of depth estimation accuracy and computational complexity. [ABSTRACT FROM AUTHOR]

Existing video rain removal methods mainly focus on rain streak removal and are solely trained based on the synthetic data, which neglect more complex degradation factors, e.g., rain accumulation, and the prior knowledge in real rain data. Thus, in this paper, we build a more comprehensive rain model with several degradation factors and construct a novel two-stage video rain removal method that combines the power of synthetic videos and real data. Specifically, a novel two-stage progressive network is proposed: recovery guided by a physics model, and further restoration by adversarial learning. The first stage performs an inverse recovery process guided by our proposed rain model. An initially estimated background frame is obtained based on the input rain frame. The second stage employs adversarial learning to refine the result, i.e., recovering the overall color and illumination distributions of the frame, the background details that are failed to be recovered in the first stage, and removing the artifacts generated in the first stage. Furthermore, we also introduce a more comprehensive rain model that includes degradation factors, e.g., occlusion and rain accumulation, which appear in real scenes yet ignored by existing methods. This model, which generates more realistic rain images, will train and evaluate our models better. Extensive evaluations on synthetic and real videos show the effectiveness of our method in comparisons to the state-of-the-art methods. Our datasets, results and code are available at: https://github.com/flyywh/Recurrent-Multi-Frame-Deraining. [ABSTRACT FROM AUTHOR]

Reconstructing the 3D geometry of the surgical site and detecting instruments within it are important tasks for surgical navigation systems and robotic surgery automation. Traditional approaches treat each problem in isolation and do not account for the intrinsic relationship between segmentation and stereo matching. In this paper, we present a learning-based framework that jointly estimates disparity and binary tool segmentation masks. The core component of our architecture is a shared feature encoder which allows strong interaction between the aforementioned tasks. Experimentally, we train two variants of our network with different capacities and explore different training schemes including both multi-task and single-task learning. Our results show that supervising the segmentation task improves our network’s disparity estimation accuracy. We demonstrate a domain adaptation scheme where we supervise the segmentation task with monocular data and achieve domain adaptation of the adjacent disparity task, reducing disparity End-Point-Error and depth mean absolute error by 77.73% and 61.73% respectively compared to the pre-trained baseline model. Our best overall multi-task model, trained with both disparity and segmentation data in subsequent phases, achieves 89.15% mean Intersection-over-Union in RIS and 3.18 millimetre depth mean absolute error in SCARED test sets. Our proposed multi-task architecture is real-time, able to process ($1280\times 1024$) stereo input and simultaneously estimate disparity maps and segmentation masks at 22 frames per second. The model code and pre-trained models are made available: https://github.com/dimitrisPs/msdesis [ABSTRACT FROM AUTHOR]

In this paper we consider the problem of determining optimal convergence rates of Galerkin approximations to infinite dimensional operator Riccati equations (OREs). Optimal rates are obtained for a class of abstract distributed parameter systems evolving in an infinite dimensional Hilbert space. These general results are then applied to systems modeled by partial differential equations that generate compact and analytic semigroups. The estimates apply to distributed control and observation of classical parabolic equations and to certain vibration problems with sufficiently strong damping. The ORE is formulated as an equivalent operator‐valued Bochner integral equation and the Brezzi–Rappaz–Raviart theorem is used to obtain convergence rates. First we establish smoothing property and bounds for the solutions of the infinite dimensional ORE. Then it is shown that, under sui\ assumptions on the coefficients and domain geometry, the hp‐finite element approximations of the classical solution converges on the order of Ohk+1. Furthermore, these optimal error bounds are shown to hold for the functional gains that define observer and control gain operators. We provide numerical examples that corroborate the theoretical convergence rates. [ABSTRACT FROM AUTHOR]

Conventional direction-of-arrival (DOA) estimation algorithms like MUSIC only allow localization of fewer number of sources than the number of physical sensors. In this paper, underdetermined azimuth localization (localizing more sources than the number of sensors) using arbitrary planar arrays has been proposed, using only second-order statistics of the received data. To achieve this, we utilize the difference coarray of the actual array and express the elements of the array covariance matrix as the signal received by the virtual sensors of the coarray. We explore the structure and geometry of the difference coarray of an $N$ -element planar array and show that the coarray can provide an increased degree-of-freedom (DOF) of $\mathcal {O}(N^{2})$ which enables underdetermined localization. Then, we extend the manifold separation (MS) technique to the coarray to express the coarray steering matrix in terms of a Vandermonde structured matrix by designing a signal independent coarray characteristic matrix. As the signal model of a coarray is a single snapshot model, the Vandermonde structure enables us to perform a spatial smoothing type operation to restore the rank of the coarray covariance matrix. This allows us to propose a novel subspace-based algorithm, which we call the coarrayMS-MUSIC, to perform underdetermined source localization using arbitrary planar arrays. We have also introduced the polynomial rooting version of our algorithm called the coarrayMS-rootMUSIC. Finally, we have conducted extensive numerical simulations to verify the effectiveness and usefulness of the proposed methods. [ABSTRACT FROM AUTHOR]

In this paper, a model fusion method (MFM) is proposed for online state of charge (SOC) and state of power (SOP) co-estimation of lithium-ion batteries (LIBs) in electric vehicles (EVs). Firstly, a particle swarm optimization-genetic algorithm (PSO-GA) method is cooperated with a 2-RCCPE fractional-order model (FOM) to construct battery open-circuit voltage (OCV)-SOC curve, which only relies on a part of dynamic load profile without the prior knowledge of an initial SOC. Secondly, a dual extended Kalman filter (DEKF) algorithm based on a 1-RC model is employed to identify the model parameters and estimate battery SOC with the extracted OCV-SOC curve. Furthermore, battery polarization dynamics in a SOP prediction window is analyzed from two aspects: (1) self-recovery; and (2) current excitation. They are separately simulated using 2-RCCPE FOM and 1-RC model, and then integrated through a model fusion for online SOP estimation, which enables an analytical expression of battery peak charge/discharge current in a prediction window without weakening the nonlinear characteristic of FOM. Experimental results demonstrate the improved performance of the proposed MFM for online discharge SOP estimation, where the mean absolute error and root mean square error are only 0.288 W and 0.35 W, respectively, under the urban dynamometer driving schedule profile. [ABSTRACT FROM AUTHOR]