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The renewable power has been widely used in modern cloud data centers, which also produce large electricity bills and the negative impacts on environments. However, frequent fluctuation and intermittency of renewable power often cause the challenges in terms of the stability of both electricity grid and data centers, as well as decreasing the utilization of renewable power. Existing schemes fail to alleviate the renewable power fluctuation, which is caused by the essential properties of renewable power. In order to address this problem, we propose an efficient and easy-to-use smooth renewable power-aware scheme, called Smoother, which consists of Flexible Smoothing (FS) and Active Delay (AD). First, in order to smooth the fluctuation of renewable power, FS carries out the optimized charge/discharge operation via computing the minimum variance of the renewable power that is supplied to data centers per interval. Second, AD improves the utilization of renewable power via actively adjusting the execution time of deferrable workloads. Extensive experimental results via examining the traces of real-world data centers demonstrate that Smoother significantly reduces the negative impact of renewable power fluctuations on data centers and improves the utilization of renewable power by 250.88%. We have released the source codes for public use.

This article is concerned with the problem of compensation-based output feedback control for Takagi-Sugeno fuzzy Markov jump systems subject to packet losses. The phenomenon of packet losses is assumed to randomly occur in the feedback channel, which is modeled by a Bernoulli process. Employing the single exponential smoothing method as a compensation scheme, the missing measurements are predicted to help offset the impact of packet losses on system performance. Then, an asynchronous output feedback controller is designed by the hidden Markov model. Based on the mode-dependent Lyapunov function, some novel sufficient conditions on the controller existence are derived such that the closed-loop system is stochastically stable with strict dissipativity. Besides, an algorithm for determining the optimal smoothing parameter is proposed. Finally, the validity and advantages of the design approach are manifested by some simulation results.

Process complexities are characterized by strong nonlinearities, dynamics, and uncertainties. Monitoring such a complex process requires a high-quality model describing the corresponding nonlinear dynamic behavior. The proposed model is constructed using deep neural networks (DNNs) to represent the state transition and observation generation, both of which constitute a stochastic nonlinear state-space model. A new bidirectional recurrent neural network (RNN), creating a connection of the hidden layer between a forward RNN and a backward RNN, is proposed to generate the filtering estimation and the smoothing estimation of process states which further generate observations with DNN-based process models. The smoothing estimator and the process model are first learned offline with all collected samples. Then the filtering estimator is fine-tuned by the learned smoother and process models to achieve real-time monitoring since the filter state is estimated based on the past and the current observations. Two indices are designed based on the learned model for monitoring the process anomaly. The proposed process monitoring model can deal with complex nonlinearities, process dynamics, and process uncertainties, all of which can be very challenging for the existing methods, such as kernel mapping and stacked auto-encoder. Two case studies validate that the effectiveness of the proposed method outperforms the other comparative methods by at least 10% when using the averaged fault detection rate in the industrial experimental data.

Intrinsic image decomposition is the task of mapping image to albedo and shading. Classical approaches derive methods from spatial models. The modern literature stresses evaluation, by comparing predictions to human judgements ("lighter", "same as", "darker"). The best modern intrinsic image methods train a map from image to albedo using images rendered from computer graphics models and example human judgements. This approach yields practical methods, but obtaining rendered images can be inconvenient. Furthermore, the approach cannot explain how a one could learn to recover intrinsic images without geometric, surface and illumination models, as people and animals appear to do. This paper describes a method that learns intrinsic image decomposition without seeing human annotations, rendered data, or ground truth data. Instead, the method relies on paradigms - spatial models of albedo and of shading. Rather than finding the "best" albedo and shading for an image via optimization, our approach trains a neural network on synthetic images. The synthetic images are constructed by multiplying albedos and shading fields sampled from our models. The network is subject to a novel smoothing procedure that ensures good behavior at short scales on real images. An averaging procedure ensures that reported albedo and shading are largely equivariant - different crops and scalings of an image will report the same albedo and shading at shared points. This averaging procedure controls long scale error. The standard evaluation for an intrinsic image method is a WHDR score. Our method achieves WHDR scores competitive with those of strong recent methods allowed to see training WHDR annotations, rendered data, and ground truth data. Our method produces albedo and shading maps with attractive qualitative properties - for example, albedo fields do not suppress wood grain and represent narrow grooves in surfaces well. Because our method is unsupervised, we can compute estimates of the test/train variance of WHDR scores; these are quite large, and suggest is unsafe to rely small differences in reported WHDR.

Nowadays, online spamming has already been a remarkable threat to contents security of Internet of Things (IoT). Due to constant technical progress, online spamming activities have been more and more concealed. This brings much fuzziness to spammer detection scenarios, yielding the issue of fuzzy detection of spammers. Although existing detection techniques for spammers utilized idea of deep learning, they still ignore to release power of label spaces. As real nature about a user may be usually fuzzy, but the label annotated for a user is always certain. To remedy such gap, this paper proposes a label smoothing-based fuzzy detection method for spammers (Fuz-Spam). First of all, deep representation is still utilized to deeply fuse features, which acts as the foundation of neural computing. On this basis, generative adversarial learning is introduced to transform previous label spaces into distributed forms. In addition, two groups of experiments are carried out on two real-world datasets for evaluation. The results demonstrate that the Fuz-Spam improves identification efficiency about 10% to 20% than previous ones, and that the Fuz-Spam is endowed with proper stability.

The objective of the paper was to compare values of the muzzle brake efficiency coefficient for a rifle with active or inactive automatics systems. Special laboratory stand designed for investigating the recoil process was used. The motion of the rifle was detected by the use of the laser interferometer and the optical camera. The recoil velocity time courses were determined by smoothing and differentiation of experimental position records. The results of the experiments indicated that in the case of an active automatics system two values of the recoil velocity can be used for calculation of the energetic efficiency coefficient: the maximum recoil velocity and the final recoil velocity at the end of the automatics action cycle. The values of the coefficient, calculated using these two values of the recoil velocity, distinctly differ. However, it was shown that their values indicate the same relation between the efficiency of various muzzle brakes. The value of the efficiency coefficient, determined on the basis of the final recoil velocity value, is practically the same as that determined on the basis of the final recoil velocity value for the rifle with an inactive automatics system.

The state-of-the-art quadratic program-based control Lyapunov-barrier function (QP-CLBF) is a powerful control approach to balance safety and stability in an optimal fashion. However, under this approach, modeling inaccuracies may degrade the performance of closed-loop systems and cause violation or restriction of safety-critical constraints. This paper presents an adaptive QP-CLBF approach for a class of nonlinear systems in the presence of parameter uncertainties with an unknown control coefficient. We begin by presenting a filtering-based concurrent learning (FCL) adaptive technique to guarantee simultaneous exponential convergence of system parameters and control coefficient. The proposed FCL extends and encompasses the baseline CL technique, which was developed to achieve exponential convergence of either system parameters exclusively or control coefficient while relying on the estimation of state derivatives using numerical smoothing. The proposed FCL adaptive method is then unified with a modified version of QP-CLBF to achieve exponential convergence of system parameters, control coefficient, and control Lyapunov and control barrier functions while establishing

In Euler-Lagrange models for particulate systems, the information exchange of multiphase coupling terms, i.e. drag force, heat transfer, or mass transfer, is often smoothed from particles' location to nearby computational cells when the cell and particle sizes are comparable. The diffusion-based smoothing is among the most popular approach. However, when it comes to polydisperse systems, the state-of-the-art constant diffusivity approach does not consider variation in particle sizes, resulting in an even smoothing of exchanged information across all particles with different sizes. In this paper, a particle-size dependent smoothing scheme named “non-constant diffusivity approach” is proposed. This approach distributes the exchanged information based on the local Sauter diameter d32 such that the area of influence per particle varies by size. The one-dimensional analytical solution of the non-constant diffusivity approach is first derived and two control parameters L⁎ and S are identified, where L⁎ determines the characteristic length of the diffusivity and S determines the magnitude of the diffusivity. Together they control the characteristic length scale ℓ˜c of the smoothing operation. In the one-dimensional study, it is found that the proposed smoothing method is most suitable for the dilute particulate regime. The investigation is extended to a three-dimensional dilute particle flow based on the experiment of the CRIEPI (Central Research Institute of Electric Power Industry) pulverized coal burner. It is shown that using the non-constant diffusivity smoothing, the prediction of the O2 concentration is more accurate than when using the constant diffusivity smoothing approach, which assumes a universal smoothing length for all sizes of particles. The findings suggest that when it comes to polydisperse particulate systems, the area of influence per particle on the fluid depends on the particle size. The proposed non-constant diffusivity approach can describe that with the benefits of cheap computational cost, conservation of exchanged quantities, and easy-to-implement.

5-Hydroxymethylfurfural (HMF) can be obtained from the dehydration of monosaccharides present in biomass, which helps add value to the raw material. The present work sought to study the relationship between the parameters of biomass characterization, including the content of reducing sugars, total soluble solids and the mid-infrared (MIR) and near-infrared (NIR) spectra, and the formation of HMF after synthesis. A thorough analysis was conducted in order to evaluate the residual agricultural biomass samples from sugarcane bagasse, corn stover, cassava branch, banana pseudostem and mango fruits after the drying (45 °C maximum) and ball mill grinding pre-processing procedure. HMF synthesis was performed using 0.50 mol L-1 HCl and n-butanol as extracting solvent. The analytical quantification of HMF and furfural (co-product of synthesis) was performed using high-performance liquid chromatography coupled to diode-array detector (HPLC-DAD). The highest efficiency of the synthesis of HMF was observed for the mango fruits (55.0 kg ton-1) and sugarcane bagasse biomass (47.5 kg ton-1) samples. The application of data fusion and principal component analysis (PCA) allowed us to identify the samples with greater potential for HMF synthesis.

We explore the optimal and actual responses of fiscal policy to changes in the interest rate on newlyissued public debt (the “marginal interest rate”). We set up a simple theoretical framework with a government aiming to smooth public consumption over time. The distinctive feature is that the government issues debt of different maturities. This introduces a “valuation effect” that has received little attention so far: a rise in the marginal interest rate increases the rate of discounting and, thus, lowers the value of non-maturing debt, which relaxes the budget constraint, thereby inducing a fall in the primary balance. Still, the framework predicts that the total effect of a rise in the marginal interest rate is an increase in the primary balance. Estimates for developed countries suggest that a 1 percentagepoint higher marginal interest rate leads, on average, to roughly a 1 percentage-point higher primary balance. These findings are consistent with governments smoothing the impact of changes in the marginal interest rate and exploiting the valuation effect. Finally, estimates suggest a role for the average (or “effective”) interest rate on outstanding debt. info:eu-repo/semantics/publishedVersion

Model averaging has attracted abundant attentions in the past decades as it emerges as an impressive forecasting device in econometrics, social sciences and medicine. So far most developed model averaging methods focus only on either parametric models or nonparametric models with a continuous response. In this paper, we propose a semiparametric model averaging prediction (SMAP) method for a dichotomous response. The idea is to approximate the unknown score function by a linear combination of one-dimensional marginal score functions. The weight parameters involved in the approximation are obtained by first smoothing the nonparametric marginal scores and then applying the parametric model averaging via a maximum likelihood estimation. The proposed SMAP provides greater flexibility than parametric models while being more stable than a fully nonparametric approach. Theoretical properties are investigated in two practical scenarios: (i) covariates are conditionally independent given the response; and (ii) the conditional independence assumption does not hold. In the first scenario, we show that SMAP puts weight one to the true model and hence the model averaging estimators are consistent. In the second scenario in which a “true” model may not exist, SMAP is shown to be asymptotically optimal in the sense that its Kullback–Leibler loss is asymptotically identical to that of the best – but infeasible – model averaging estimator. Empirical evidences from simulation studies and a real data analysis are presented to support and illustrate our methods.

This paper focuses on estimation and null region detection of the coefficient function for functional generalized linear models. Traditional estimating approaches can not serve as null regions detectors. To simultaneously estimate coefficient functions and detect corresponding important subregions in functional generalized linear models, an adaptive group LASSO approach with B-spline smoothing technique is developed. The convergence rate of the resulting estimator is obtained. The consistency of domain selection and limiting distribution of the proposed estimator are then established, which is not straightforward since the groups to be penalized are overlapping. These asymptotic properties are also supported by extensive simulation studies. The resulting estimator performs better than direct adaptive LASSO estimators and some existing functional generalized linear models estimators, which are obtained without considering the domain selection. A real data application reveals the effectiveness of the proposed method.

Edge-preserving image filtering is an essential task in computational photography and imaging. In this paper, we propose a simple yet effective global edge-preserving filter based on soft clustering, and we propose a novel soft clustering algorithm based on a restricted Gaussian mixture model. Given specified parameters, the soft clustering process is firstly performed on the image to derive the partition matrix, from which the affinity matrix is then constructed for filtering. The filtering output is calculated as the weighted average of the pixels in the local window, so the proposed filter could suppress the intensity shift artifacts that impede most global filters. Besides, the weights in the proposed filter are derived by clustering, which properly separates dissimilar pixels, so the proposed filter could handle the halo artifacts that haunt many local filters. Moreover, our filter provides flexible control over the amount of smoothing that is deficient in the deep learning-based filters. Besides the efficacy in smoothing, the proposed filter naturally has low computational complexity. Qualitative and quantitative results suggest that the proposed filter benefits various applications, including edge-preserving smoothing, image enhancing, flash/non-flash fusion, HDR tone mapping, and dehazing.

When handling occlusion in unsupervised stereo matching, existing methods tend to neglect the supportive role of occlusion and to perform inappropriate disparity smoothing around the occlusion. To address these problems, we propose an occlusion-aware stereo network that contains a specific module to first estimate occlusion as an additional depth cue. In the occlusion inference module, a pixel is classified with a three-category label based on whether an area is occluded by an object on the left, occluded by an object on the right, or unoccluded. After the occluders are detected, we introduce a directed disparity smoothing loss that allows valid disparity estimates to be propagated to fill the occluded region, while ambiguous matches in the occluded region do not affect other regions. Disparity and occlusion are trained alternately in an unsupervised manner with detached backpropagation to enable the directed smoothness. Experiments show that our method achieves 3-pixel threshold error rates of 6.51% and 5.69% on the KITTI 2015 and KITTI 2012 validation sets, state-of-the-art results among unsupervised learning networks at the time of submission.

Attributed networks are ubiquitous in the real world, such as social networks. Therefore, many researchers take the node attributes into consideration in the network representation learning to improve the downstream task performance. In this article, we mainly focus on an untouched ``oversmoothing'' problem in the research of the attributed network representation learning. Although the Laplacian smoothing has been applied by the state-of-the-art works to learn a more robust node representation, these works cannot adapt to the topological characteristics of different networks, thereby causing the new oversmoothing problem and reducing the performance on some networks. In contrast, we adopt a smoothing parameter that is evaluated from the topological characteristics of a specified network, such as small worldness or node convergency and, thus, can smooth the nodes' attribute and structure information adaptively and derive both robust and distinguishable node features for different networks. Moreover, we develop an integrated autoencoder to learn the node representation by reconstructing the combination of the smoothed structure and attribute information. By observation of extensive experiments, our approach can preserve the intrinsical information of networks more effectively than the state-of-the-art works on a number of benchmark datasets with very different topological characteristics.

A robust algorithm to solve the low-thrust fuel-optimal trajectory optimization problem for interplanetary spacecraft is developed in this paper. The original nonlinear optimal control problem is convexified and transformed into a parameter optimization problem using an arbitrary-order Gauss-Lobatto discretization scheme with nonlinear control interpolation. A homotopic approach that considers the energy-to-fuel smoothing path is combined with an adaptive second-order trust-region mechanism to increase the convergence rate. The overall robustness is assessed in several fuel-optimal transfers with poor initial guesses. The results show a superior performance in terms of convergence compared to standard convex programming approaches in the literature.

Gas field production forecast is an important basis for decision-making in the gas industry, how to accurately predict the dynamic production during gas field development is an important content of reservoir engineering research. Reservoir numerical simulation is the most common method for predicting oil and gas production. However, it requires a lot of data to build an accurate geological model and the models are tedious and time-consuming. At present, many scholars have used machine learning and data mining methods to predict oil and gas production, but they have not considered whether the use of increasing production measures will affect the predicted results. Thus, ARIMA-RTS optimal smooth algorithm is the first applied to establish prediction model of gas well production. According to the historical production data, the model is processed, the production differential autoregressive integral moving average (ARIMA) model in time series is established, then ARIMA model is combined with RTS (Rauch Tung Striebel) smoothing, the production prediction model is constructed. RTS smoothing algorithm is an enhanced version of Kalman filter, the measurements are the first processed by the forward filter, and then, a separate backward smoothing pass is used for obtaining the smoothing solution. The correctness of ARIMA-RTS model was verified with the actual production data. The results show that prediction based on ARIMA-RTS model can accurately reflect the production performance of gas well. This method can effectively reduce the error caused by stimulation when prediction. Use the ARIMA-RTS model and the ARIMA-Kalman model to predict production of the same gas well, and the prediction accuracy of ARIMA-RTS model is higher than that of ARIMA-Kalman model in production wells with stimulation, compared with the ARIMA-Kalman model, the mean relative error fitted by the ARIMA-RTS model is reduced by 46.3%, and the relative mean square error is reduced by 56.48%. ARIMA-RTS optimal smooth algorithm improves the prediction accuracy of gas well that use stimulation, we therefore conclude that the ARIMA-RTS optimal smooth algorithm can service us better forecasting gas well production used stimulation, as well as other fuels output.

The real-time performance and accuracy of traffic flow prediction directly affect the efficiency of traffic flow guidance systems, and traffic flow prediction is a hotspot in the field of intelligent transportation. To further improve the accuracy of short-term traffic flow prediction, a short-term traffic flow prediction model based on traffic flow time series analysis, and an improved long short-term memory network (LSTM) is proposed. First, perform time series analysis on traffic flow data and perform smoothing and standardization processing to obtain a stable time series as model input data, which can improve the accuracy of model training and eliminate the impact of a wide range of feature values. Then, an improved LSTM model based on LSTM and bidirectional LSTM networks are established. Combining the advantages of sequential data and the long-term dependence of forwarding LSTM and reverse LSTM, the bidirectional long-term memory network (BILSTM) is integrated into the prediction model. The first layer of the LSTM network learns and predicts the input time series and further learns and trains through the bidirectional LSTM network to effectively overcome the large prediction errors. Finally, the performance of the proposed method is evaluated by comparing the predicted results with actual traffic data. The model that is proposed in this paper is compared with the long short-term memory network (LSTM) model and the bidirectional long-term memory network (BILSTM) model. The results demonstrate that the proposed method outperforms both compared methods in terms of accuracy and stability.

Acquiring accurate and reliable pedestrian trajectories is essential for providing indoor location-based services. Although a foot-mounted inertial navigation system (Foot-INS) can acquire pedestrian trajectories in multistory buildings, it will inevitably encounter heading divergence because the constraint information is not always valid. Therefore, we proposed an accurate and convenient post-processing indoor pedestrian positioning system (IPPS) to acquire pedestrian trajectories in multistory buildings such as shopping malls. Based on the hypotheses that the start and end points of the pedestrian trajectories on a single floor were closed, and the horizontal position of the closing point on each floor was identical. Therefore, in the single floor of multistory buildings, we use the closing point to control the trajectory drift error caused by the Foot-INS, and use a smoothing algorithm to reasonably distribute the drift error to the entire trajectory. Heading divergence is unavoidable with the Foot-INS, result in the pedestrian trajectories acquired on different floors were rotationally offset. Because pedestrian trajectories can epitomize the building orientation and provide an opportunity to align those trajectories on multistory buildings, an algorithm was proposed to match the trajectories acquired on different floors. A hybrid simulation experiment was conducted using an accurate reference object to evaluate the positioning performance of the proposed IPPS. The effectiveness of acquiring pedestrian trajectories was also confirmed by various experimental tests in a large shopping mall. The study findings suggest that the proposed IPPS is self-contained, low-cost, and has the potential for large-scale applications.

This article presents a reliable and robust rapidly exploring random tree (R2-RRT*) algorithm to tackle challenges in mission planning of off-road autonomous ground vehicles (AGVs) under uncertain terrain environment. Two types of mobility reliability metrics, namely state mobility reliability (SMR) and mission mobility reliability (MMR), are first defined to quantify the mobility reliability of an AGV and to incorporate mobility reliability into mission planning. SMR measures the probability that a vehicle can pass through a specific location on a map of interest, whereas MMR quantifies mobility reliability of a mission path with the consideration of dependence of soil properties and slope over space. Based on the defined SMR and MMR metrics, two reliability-based robust mission planning models are developed to identify optimal paths that have robust travel time and satisfy specific reliability requirements. Moreover, a reliability-based path smoothing algorithm is developed to address the suboptimality of R2-RRT*. Results of a case study demonstrate the efficacy of the proposed models and algorithms.

Uniform traffic flow has been shown to be unstable in certain flow regimes due to collective behaviors of human drivers, resulting in the well-observed stop-and-go waves. These traffic waves can arise even in the absence of merges, bottlenecks, or lane changing, and may lead to higher vehicle fuel consumption and emissions. In this article, we aim to smooth unstable traffic flow via optimal control of autonomous vehicles (AVs) in a predominantly human-driven traffic flow. These controlled AVs act as mobile actuators in the traffic without changing the way human-driven vehicles (HVs) normally operate. We develop a dynamic model to describe mixed traffic flow in the presence of both HVs and AVs, whose dynamics follow general nonlinear car-following principles. Based on this general framework, we formulate an optimal control problem with the objective of minimizing vehicle speed perturbation, and prove the existence of optimal AV control policy. Following the necessary conditions of optimality prescribed by the well-known Pontryagin's minimum principle, we present a computational algorithm to determine the optimal AV control strategy and prove its convergence. The mathematical model is further illustrated using the intelligent driver model (IDM) and optimal velocity with relative velocity (OVRV) model for HVs and AVs, respectively. Numerical results are presented to show the effectiveness of the proposed approach on traffic smoothing, as well as the improvement on vehicle fuel economy.

This paper develops an efficient multi-agent deep reinforcement learning algorithm for cooperative controls in powergrids. Specifically, we consider the decentralized inverter-based secondary voltage control problem in distributed generators (DGs), which is first formulated as a cooperative multi-agent reinforcement learning (MARL) problem. We then propose a novel on-policy MARL algorithm, PowerNet, in which each agent (DG) learns a control policy based on (sub-)global reward but local states and encoded communication messages from its neighbors. Motivated by the fact that a local control from one agent has limited impact on agents distant from it, we exploit a novel spatial discount factor to reduce the effect from remote agents, to expedite the training process and improve scalability. Furthermore, a differentiable, learning-based communication protocol is employed to foster the collaborations among neighboring agents. In addition, to mitigate the effects of system uncertainty and random noise introduced during on-policy learning, we utilize an action smoothing factor to stabilize the policy execution. To facilitate training and evaluation, we develop PGSim, an efficient, high-fidelity powergrid simulation platform. Experimental results in two microgrid setups show that the developed PowerNet outperforms the conventional model-based controls, as well as several state-of-the-art MARL algorithms. The decentralized learning scheme and high sample efficiency also make it viable to large-scale power grids.

Voltage stability and power quality play very effective issues in power systems. This paper aims to improve the voltage stability and enhance system power quality in the AC-DC micro-grid system based on intelligent fuzzy controllers. These controllers are fuzzy-PI (FPI) and fuzzy-PID (FPID) current-controller with the existence of distribution static synchronous compensator (D-STATCOM). The capability of proposed system has been applied in two case studies that emulate abrupt fault and dynamic load changes on AC-DC hybrid micro-grid that collects different types of renewable energy sources. In addition to, the proposed fuzzy-based controllers produce the optimum dynamic response and resolve the power quality issues. Numerical simulations associated with detailed comparisons between different controllers are provided. It was found that when the studied system is subjected to a 3-phase fault, the voltage fluctuation at the D-STATCOM is reduced by 7.86% and 4.62% and the dynamic system performance is improved by 12.9% and 8.8% with using Fuzzy-PID and fuzzy-PI, respectively. Also with the dynamic load changes, the fluctuation of system voltages at the D-STATCOM is reduced by 0.982% and 0.577 % and the dynamic system performance is improved with 6.67%, 5.71% when comparing Fuzzy-PID controller and Fuzzy-PI to the uncontrolled system. The Fuzzy-PID provides a capability to enhance dynamic performance and system power quality because achieve less fluctuation and more smoothing for signals makes it is superior for voltage control for AC-DC micro-grid.

Segmentation of MRI images in the presence of artifacts such as noise and bias field effect is challenging. In this work, to handle both artifacts, we have formulated an intuitionistic fuzzy set theory based bias-corrected intuitionistic fuzzy c-means with spatial neighborhood information method for MRI image segmentation. We represent the image in terms of intuitionistic fuzzy sets using two well-known intuitionistic fuzzy generation functions, namely Sugeno's and Yager's negation functions. In this way, the proposed approach takes advantage of the intuitionistic fuzzy set theory to address the uncertainty and vagueness present in data. The spatial neighborhood information term helps to retain the fine image details in the segmentation process, which is usually lost when smoothing is used. The bias field effect is modeled as a slow varying intensity field additive in nature with mean zero in IFS framework. The proposed approach estimates the bias field and produces a segmented image simultaneously. The segmentation performance of the proposed method is evaluated in terms of the Dice score, average segmentation accuracy, and index of variation. Further, the comparison of the proposed method with other similar state-of-the-art methods on two well-known publicly available Brain MRI datasets shows the significant improvements in segmentation performance in terms of average segmentation accuracy, Dice score, and index of variation. The proposed approach achieves 93% average segmentation accuracy in the presence of severe bias field and noise artifacts on BrainWeb simulated dataset, which outperformed the state-of-the-art methods.

The results of theexperimental investigations of supercapacitors as means for smoothing the voltage deviations and fluctuations are presented.PSCAD software (Canada) simulation models for equivalent circuits of the supercapacitor and experimental setup with photovoltaic panel are considered. Technique.To obtain the charge and discharge curves of supercapacitors, an experimental setup with photovoltaic panel was developed and tested under actual operatingconditions. It includes a 10 W photovoltaic panel, 100 Fsupercapacitors with 2.7 V rated voltage, voltage converters, and 4 W incandescent lamp with 12 V rated voltage used as a load. To measure and register voltages in different nodes of theexperimental setup, a Sanwa PC 510a digital multimeter altogether with PC Link software (Japan) were used. Besides the experimental investigations with Sanwa PC 510a digital multimeter and PC Link software, simulation by using of PSCAD software was executed in order to obtain the charge curve of supercapacitors. Results.An experimental setup to monitor and analyze the electromagnetictransientsin the electrical networks with photovoltaic panels is demonstrated. So-called "single-diode model" (equivalent circuit for photovoltaic panel) and equation describing a current-voltage characteristic of the practical photovoltaic module with several connected cells are given. Analytical expressions have been derived to obtain the parameters of the equivalent circuit for photovoltaic panel which are not typically available from manufacturer's data. Scientific novelty.Efficiency of supercapacitors as means for smoothing the voltage deviations and fluctuations arising from operation of photovoltaic panels was approved by means of experimental investigations. Practical value.The presented in paper experimental setup is used in training purposes of the State Higher Education Institution «Pryazovskyi State Technical University» during laboratory works on the subject«Alternative sources of electrical energy»., Представлены результаты экспериментальных исследований суперконденсаторовкак средств сглаживания отклонений и колебаний напряжения. Рассмотрены имитационные модели эквивалентных схем замещения суперконденсатора и экспериментальной установки с солнечной панелью, разработанные в среде PSCAD (Канада). Методика.Чтобы получить кривые заряда и разряда суперконденсаторов, была разработана и испытана в реальных условиях эксплуатации экспериментальная установка с солнечной панелью. Она состоит из солнечной панели мощностью 10 Вт, суперконденсаторовс номинальным напряжением 2,7В и ёмкостью 100 Ф каждый, преобразователей напряжения и нагрузки в виде лампы накаливания мощностью 4 Вт напряжением 12В. Для измерения и регистрации напряжений в разных точках экспериментальной установки были использованы цифровой мультиметрSanwa типа PC 510a и программа PC Link (Япония). Помимо экспериментальных исследований с использованием цифрового мультиметраSanwa и программы PC Link, было выполнено имитационное моделирование в среде PSCAD с целью получения кривой заряда суперконденсаторов. Результаты. Продемонстрирована экспериментальная установка для мониторинга и анализа электромагнитных переходных процессов в электрических сетях с солнечными панелями. Приведены так называемая «однодиодная модель» (эквивалентная схема замещения солнечной панели) и уравнение, описывающее вольт-амперную характеристику реальной фотоэлектрической установки, состоящей из ряда соединённых ячеек. Получены аналитические выражения, позволяющие определить параметры эквивалентной схемы замещения солнечной панели, которые, как правило, в паспортных данных производителями не приводятся.Научная новизна.С помощью экспериментальных исследований подтверждена эффективность применения суперконденсаторов как средств сглаживания отклонений и колебаний напряжения, возникающих при работе солнечных панелей.Практическая значимость.Представленная в работе экспериментальная установка используется в учебном процессе ГВУЗ «Приазовский государственный технический университет» при проведении лабораторных работ по дисциплине «Альтернативные источники электроэнергии»., Наведено результати експериментальних досліджень суперконденсаторів як засобів згладжування відхилень та коливань напруги. Розглянуто імітаційні моделі еквівалентних схем заміщення суперконденсатору та експериментальної установки із сонячною панеллю, розроблені у середовищі PSCAD (Канада). Методика.Щоби отримати криві заряду та розряду суперконденсаторів, було розроблена та випробувана в реальних умовах експлуатації експериментальна установка із сонячною панеллю. Вона складається з сонячної панелі потужністю 10 Вт, суперконденсаторів з номінальною напругою 2,7 В та ємністю 100 Ф кожний, перетворювачів напруги та навантаження у вигляді лампи розжарювання потужністю 4 Вт напругою 12 В. Для вимірювання і реєстрації напруг в різних точках експериментальної установки були використані цифровий мультиметрSanwa типа PC510a та програма PC Link (Японія). Окрім експериментальних досліджень з використанням цифрового мультиметруSanwa та програми PC Link, було виконано імітаційне моделювання у середовищі PSCAD з метою отримання кривої заряду суперконденсаторів. Результати.Продемонстрована експериментальна установка для моніторингу і аналізу електромагнітних перехідних процесів в електричних мережах із сонячними панелями. Наведені так звана «однодіодна модель» (еквівалентна схема заміщення сонячної панелі) та рівняння, що описує вольт-амперну характеристику реальної фотоелектричної установки, яка складається з низки з'єднаних комірок. Отримані аналітичні вирази, які дозволяють визначити параметри еквівалентної схеми заміщення сонячної панелі, що, як правило, в паспортних даних виробниками не наводяться.Наукова новизна.За допомогою експериментальних досліджень підтверджена ефективність застосування суперконденсаторівяк засобів згладжування відхилень та коливань напруги, що виникають під час роботи сонячних панелей.Практична значимість.Представлена у роботі експериментальна установка використовується у навчальному процесі ДВНЗ «Приазовський державний технічний університет» під час проведення лабораторних робіт з дисципліни «Альтернативні джерела електроенергії».

Time series of optical remote sensing data are instrumental for monitoring vegetation dynamics, but are hampered by missing or noisy observations due to varying atmospheric conditions. Reconstruction methods have been proposed, most of which focus on time series of a single vegetation index. Under the assumption that relatively high vegetation index values can be considered as trustworthy, a successful approach is to adjust the smoothed value to the upper envelope of the time series. However, this assumption does not hold for surface reflectance in general. Clouds and cloud shadows result in, respectively, high and low values in the visible and near infrared part of the electromagnetic spectrum. A novel spectral Reflectance Time Series Reconstruction (RTSR) method is proposed. Smoothed values of surface reflectance values are adjusted to approach the trustworthy observations, using a vegetation index as a proxy for reliability. The Savitzky–Golay filter was used as the smoothing algorithm here, but different filters can be used as well. The RTSR was evaluated on 100 sites in Europe, with a focus on agriculture fields. Its potential was shown using different criteria, including smoothness and the ability to retain trustworthy observations in the original time series with RMSE values in the order of 0.01 to 0.03 in terms of surface reflectance.

In this paper, we extend the correspondence between Bayesian estima- tion and optimal smoothing in a Reproducing Kernel Hilbert Space (RKHS) by adding convex constraints to the problem. Through a sequence of approxi- mating Hilbertian subspaces and a discretized model, we prove that the Max- imum a posteriori (MAP) of the posterior distribution is exactly the optimal constrained smoothing function in the RKHS. This paper can be read as a generalization of the paper [15], where it is proved that the optimal smooth- ing solution is the mean of the posterior distribution. Synthetic and real data studies confirm the correspondence established in this paper.

Decision-making is often based on the analysis of complex and evolving data. Thus, having systems which allow to incorporate human knowledge and provide valuable support to the decider becomes crucial. In this work, statistical modelling and mathematical optimiza- tion paradigms merge to address the problem of estimating smooth curves which verify structural properties, both in the observed domain in which data have been gathered and outwards. We assume that the curve to be estimated is defined through a reduced-rank basis ( B -splines) and fitted via a penalized splines approach ( P-splines). To incorporate re- quirements about the sign, monotonicity and curvature in the fitting procedure, a conic programming approach is developed which, for the first time, successfully conveys out- of-range constrained prediction. In summary, the contributions of this paper are fourfold: first, a mathematical optimization formulation for the estimation of non-negative P-splines is proposed; second, previous results are generalized to the out-of-range prediction frame- work; third, these approaches are extended to other shape constraints and to multiple curves fitting; and fourth, an open source Python library is developed: cpsplines . We use simulated instances, data of the evolution of the COVID-19 pandemic and of mortality rates for different age groups to test our approaches. This research is supported by projects PID2019-104901RB-I00, PID2019-110886RB-I00 and IJC2020-045220-I (funded by MCIN/AEI/10.13039/501100011033 and the last also supported by European Union “NextGenerationEU/PRTR” funds), FQM-329, P18-FR-2369 and US-13811 (Junta de Andalucía), IND2020/TIC-17526 (Comunidad de Madrid) and Universidad Carlos III de Madrid (Read & Publish Agreement CRUE-CSIC 2022).

OBJECTIVE: Absorbance measured using wideband tympanometry (WBT) has been shown to be sensitive to changes in middle- and inner-ear mechanics, with potential to diagnose various mechanical ear pathologies. However, artifacts in absorbance due to measurement noise can obscure information related to pathologies and increase inter-measurement variability. Published reports frequently present absorbance that has undergone smoothing to minimize artifact; however, smoothing changes the true absorbance and can destroy important narrow-band characteristics such as peaks and notches at different frequencies. Because these characteristics can be unique to specific pathologies, preserving them is important for diagnostic purposes. Here, we identify the cause of artifacts in absorbance and develop a technique to mitigate artifacts while preserving the underlying WBT information. DESIGN: A newly developed Research Platform for the Interacoustics Titan device allowed us to study raw microphone recordings and corresponding absorbances obtained by WBT measurements. We investigated WBT measurements from normal hearing ears and ears with middle and inner ear pathologies for the presence of artifact and noise. Furthermore, it was used to develop an artifact mitigation procedure and to evaluate its effectiveness in mitigating artifacts without distorting the true WBT information. RESULTS: We observed various types of noise that can plague WBT measurements and that contribute to artifacts in computed absorbances, particularly intermittent low-frequency noise. We developed an artifact mitigation procedure that incorporates a high-pass filter and a Tukey window. This artifact mitigation resolved the artifacts from low-frequency noise while preserving characteristics in absorbance in both normal hearing ears and ears with pathology. Furthermore, the artifact mitigation reduced inter-measurement variability. CONCLUSIONS: Unlike smoothing algorithms used in the past, our artifact mitigation specifically removes artifacts caused by noise. It does not change frequency-response characteristics, such as narrow-band peaks and notches in absorbance at different frequencies that can be important for diagnosis. Also, by reducing inter-measurement variability, the artifact mitigation can improve the test-retest reliability of these measurements.

Modelling and forecasting mortality is a topic of crucial importance to actuaries and demographers. However, forecasts from the majority of mortality projection models are continuations of past trends seen in the data. As such, these models are unable to account for external opinions or expert judgement. In this work, we present a method for the incorporation of deterministic opinions into the smoothing and forecasting of mortality rates using constraints. Not only does our approach yield a smooth transition from the past into the future, but also, the shapes of the resulting forecasts are governed by a combination of the opinion inputs and the speed of improvements observed in the data. In addition, our approach offers the possibility to compute the amount of uncertainty around the projected mortality trends conditional on the opinion inputs, and this allows us to highlight some of the pitfalls of deterministic projection methods.

The purpose of this study was to assess the performance and limitations of low-voltage zones (LVZ) localization by optimized late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) scar imaging in patients with cardiac implantable electronic devices (CIEDs).Scar evaluation by LGE-CMR can assist ventricular tachycardia (VT) ablation, but challenges with electroanatomical maps coregistration and presence of imaging artefacts from CIED limit accuracy.A total of 10 patients underwent VT ablation and preprocedural LGE-CMR using wideband imaging. Scar was segmented from CMR pixel signal intensity maps using commercial software (ADAS-VT, Galgo Medical) with bespoke tools and compared with detailed electroanatomical maps (CARTO). Coregistration of EP and imaging-derived scar was performed using the aorta as a fiducial marker, and the impact of coregistration was determined by assessing intraobserver/interobserver variability and using computer simulations. Spatial smoothing was applied to assess correlation at different spatial resolutions and to reduce noise.Pixel signal intensity maps localized low-voltage zones (V 1.5 mV) with area under the receiver-operating characteristic curve: 0.82 (interquartile range [IQR]: 0.76-0.83), sensitivity 74% (IQR: 71%-77%), and specificity 78% (IQR: 73%-83%) and correlated with bipolar voltage (r = -0.57 [IQR: -0.68 to -0.42]) across patients. In simulations, small random shifts and rotations worsened LVZ localization in at least some cases. The use of the full aortic geometry ensured high reproducibility of LVZ localization (r0.86 for area under the receiver-operating characteristic curve). Spatial smoothing improved localization of LVZ. Results for LVZ with V 0.5 mV were similar.In patients with CIEDs, novel wideband CMR sequences and personalized coregistration strategies can localize LVZ with good accuracy and may assist VT ablation procedures.

We build up a decomposition for the flow generated by the heat equation with a real analytic memory kernel. It consists of three components: The first one is of parabolic nature; the second one gathers the hyperbolic component of the dynamics, with null velocity of propagation; the last one exhibits a finite smoothing effect. This decomposition reveals the hybrid parabolic-hyperbolic nature of the flow and clearly illustrates the significant impact of the memory term on the parabolic behavior of the system in the absence of memory terms.

Diffusion kurtosis imaging (DKI) has been shown to be valuable in a wide range of neuroscientific and clinical applications. However, reliable estimation of DKI tensors is often compromised by noise, especially for the kurtosis tensor (KT). Here, we propose a joint denoising and estimating framework that integrates multiple sources of prior information, including nonlocal structural self-similarity (NSS), local spatial smoothness (LSS), physical relevance (PR) of the DKI model, and noise characteristics of magnitude diffusion MRI (dMRI) images for improved estimation of DKI tensors. The local and nonlocal spatial smoothing constraints are complementary to each other, making the proposed framework highly effective in reducing the noise fluctuations on DKI tensors, especially KT. As an additional refinement, we propose to impose a physically relevant constraint within our joint denoising and estimation framework. We further adopt the first-moment noise-corrected fitting model (M1NCM) to remove the noncentral χ-distribution noise bias. The effectiveness of integrating multiple sources of priors into the joint framework is verified by comparing the proposed M1NCM-NSS-LSS-PR method with various versions of M1NCM-based estimators and two state-of-the-art methods. Results show that the proposed method outperformed the compared methods in simulations and in-vivo dMRI datasets of both spatially stationary and nonstationary noise distributions. The in-vivo experiments also show that the proposed M1NCM-NSS-LSS-PR method was robust to the number of diffusion directions.

Frequency diversity array multiple input multiple output (FDA-MIMO) communication system has the advantages of frequency diversity and waveform diversity, so it has attracted extensive attention, especially the joint estimation of angle and range. The existing researches mainly focus on incoherent signals, and conventional coherent signals, which can be solved by spatial smoothing based technologies. However, meter-wave MIMO radar, the target echo signal in low-elevation area has its uniqueness, and decoherence technologies such as spatial smoothing cannot be used. At present, the field of joint angle and range estimation of low-elevation target for meter-wave FDA-MIMO radar is still a gap. This letter focuses on deriving the signal model and proposes a generalized MUSIC algorithm to fill this gap. Simulation results show the effectiveness of the algorithm.

A model-based signal processing framework is proposed for pandemic trend forecasting and control, by using non-pharmaceutical interventions (NPI) at regional and country levels worldwide. The control objective is to prescribe quantifiable NPI strategies at different levels of stringency, which balance between human factors (such as new cases and death rates) and cost of intervention per region/country. Due to infrastructural disparities and differences in priorities of regions and countries, strategists are given the flexibility to weight between different NPIs and to select the desired balance between the human factor and overall NPI cost. The proposed framework is based on a \textit{finite-horizon optimal control} (FHOC) formulation of the bi-objective problem and the FHOC is numerically solved by using an ad hoc \textit{extended Kalman filtering/smoothing} framework for optimal NPI estimation and pandemic trend forecasting. The algorithm enables strategists to select the desired balance between the human factor and NPI cost with a set of weights and parameters. The parameters of the model are partially selected by epidemiological facts from COVID-19 studies, and partially trained by using machine learning techniques. The developed algorithm is applied on ground truth data from the Oxford COVID-19 Government Response Tracker project, which has categorized and quantified the regional responses to the pandemic for more than 300 countries and regions worldwide, since January 2020. The dataset was used for NPI-based prediction and prescription during the XPRIZE Pandemic Response Challenge., 11 Pages, 6 figures, 24 references

Recently, methods based on nonnegative tensor factorization (NTF), which benefits from the tensor representation of hyperspectral imagery (HSI) without any information loss, have attracted increasing attention. However, most existing methods fail to explore the internal spatial structure of data, resulting in low unmixing performance. Moreover, when the algorithm is optimized, the solution is unstable. In this article, a regularizer based on nonlocal tensor similarity is proposed, which can not only fully preserve the global information of HSI but also mine the internal information of data in the spatial domain. HSI is regarded as a 3-D tensor and is directly subjected to endmember extraction and abundance estimation. To fully explore the structural characteristics of data, we simultaneously use the local smoothing and low tensor rank prior of the data to constrain the unmixing model. First, several 4-D tensor groups can be obtained after the nonlocal similarity structure of HSI is learned. Subsequently, a low tensor rank prior is applied to each 4-D tensor, which can fully simulate the nonlocal similarity in the image. In addition, total variation (TV) is also used to explore the local spatial relationship of data, which can generate a smooth abundance map through edge preservation. The optimization is solved by the ADMM algorithm. Experiments on synthetic and real data illustrate the superiority of the proposed method.

A thunderstorm tracking algorithm is proposed to nowcast the possibility of lightning activity over an area of concern by using the total lightning data and neighborhood technique. The lightning radiation sources observed from the Beijing Lightning Network (BLNET) were used to obtain information about the thunderstorm cells, which are significantly valuable in real-time. The boundaries of thunderstorm cells were obtained through the neighborhood technique. After smoothing, these boundaries were used to track the movement of thunderstorms and then extrapolated to nowcast the lightning approaching in an area of concern. The algorithm can deliver creditable results prior to a thunderstorm arriving at the area of concern, with accuracies of 63%, 80%, and 91% for lead times of 30, 15, and 5 minutes, respectively. The real-time observations of total lightning appear to be significant for thunderstorm tracking and lightning nowcasting, as total lightning tracking could help to fill the observational gaps in radar reflectivity due to the attenuation by hills or other obstacles. The lightning data used in the algorithm performs well in tracking the active thunderstorm cells associated with lightning activities.

The impulsive noise originated from regional lightning discharges can dramatically degrade the performance of skywave over-the-horizon radar (OTHR) target detection with generally 10-20-dB increase of noise level in the Doppler domain. As a result, it is necessary to excise impulsive noise in corrupted data while preserving the useful echo signal. Traditional techniques first locate the impulsive noise in slow-time data with a high-pass finite-duration impulse response (FIR) notch filter and then restore the contaminated samples by neighboring good data. However, the effect of impulsive noise is propagated into the subsequent filtered residuals, which will mislead the impulsive noise detection. Instead of the conventional detection-interpolation methods, we propose a novel impulsive noise excision approach based on robust smoothing. The proposed method can robustly smooth the corrupted data and determine the position of impulsive noise with a preset threshold against the residuals of corrupted data subtracting the smoothed signal. The contaminated data at last are replaced by the smoothed signal. Experimental results have demonstrated the effectiveness of our proposed method.

Vehicle re-identification (Re-ID) methods often fail to achieve robust performance due to insufficient training data and domain diversities. Although state-of-the-art methods apply image-to-image translation or web data to achieve data augmentation, the construct of new datasets will not only introduce noise, but also undergo a mismatch issue with the source domain. Moreover, the label noise of cross-domain data in existing label distribution technologies cannot be alleviated. In this paper, a multi-domain joint learning with inter-domain adaptation label smoothing regularization (IALSR) is proposed using a semi-supervised learning framework. The overall framework consists of two parts. In one part, a multi-domain joint network (MJNet) is proposed to learn multiple vehicle attributes simultaneously. The output of the training model is employed to group several inter-domain subsets, which are regarded as different domains. To adapt to domain diversities, style transfer models are learned for each pair of subsets to generate free and rich data as a novel data augmentation approach. In the other part, IALSR, which preserves self-similarity and domain-transitivity, is designed to smooth the noise of style-transferred data. Upon our basis, we further introduce the web data to verify the superiority of the IALSR. The results of extensive experimental on two large-scale vehicle Re-ID datasets demonstrate that the proposed approach is superior to other state-of-the-art ones.

Synthetic aperture radar (SAR) is widely used in the field of modern remote sensing due to its high resolution for a comparatively small antenna. However, there are still some difficulties in the processing of SAR images. In particular, accurate segmentation of small targets and image corners remains an important challenge, as these can easily be lost during conventional image smoothing and denoising methods. To address this, we propose an SAR image segmentation algorithm based on constrained smoothing and hierarchical label correction (CSHLC). First, a Canny algorithm is used to extract the edges of SAR images, and the Gaussian smoothing is performed on SAR images under edge constraints to achieve noise reduction so that the edges of small and big targets are well preserved. Second, a preliminary K-means clustering is conducted on the smoothing results, and then, a Markov random field (MRF) model is used on the clustering results (``original label'' results), iteratively calculating a maximum likelihood set of pixel labels. Finally, through two label correction methods, pixel group counting comparison (PGCC) and gray similarity comparison (GSC), the labels of the MRF output are further checked and corrected to obtain final segmentation results. Compared with seven state-of-the-art algorithms, simulation results on both simulated SAR images and real SAR images show that the proposed CSHLC delivers higher accuracy while better retaining corners and small targets.

This study focused on compensating for reactive power loss and minimizing active power loss during transients caused by fast changes in wind speed. Under transient circumstances, reactive power is critical for improving the voltage profile. In this work, we discuss how to compensate for reactive power losses while reducing active power losses. The goal function of the rotor side and grid side controllers, which are related with active power losses, is minimized using a genetic algorithm approach for voltage profile improvement and power smoothing. Two constraints for rotor side and for both rotor and grid side converter designed here to improve the voltage profile during faults. Further this work carried out by MATLAB simulink and GA optimization technique used for power smoothing. The comparative results on active and reactive power smoothing presented in this paper using Type-I Mode IV FLC and Genetic Algorithm Technique under transient conditions. The main motive is to validate that the Modified Type III wind turbine system can give efficient results using adaptive techniques to control active and reactive power. This research work can be helpful for the researchers to analyze their work reference with these research findings.

This contribution presents a method for modeling and controlling a heavy-duty biturbocharged diesel engine. The modeling scheme can incorporate expert knowledge of the control relevant combustion quantities into Gaussian process models. A nonlinear model predictive controller (MPC) is used to control the engine outputs subject to the gas path dynamics and nonlinear constraints for the emissions and for the sake of engine protection. In addition, an online learning scheme based on Gaussian process regression is used to compensate for model uncertainties due to aging effects and manufacturing tolerances. A consistent model smoothing strategy is derived to preserve the given expert knowledge and to avoid abrupt reactions of the MPC due to the online learning of the models. All parts of the controller are implemented with respect to real-time feasibility and small memory footprint. Experimental results for a real-world heavy-duty engine demonstrate the performance and the online learning ability of the presented nonlinear MPC scheme that may be transferred to various diesel engine applications.

Accurate measurement of temperature and velocity fields in combustion and flow diagnostic plays an important role in analyzing the heat transfer mechanism and ensuring the safe operation of equipment. In the present work, considering the refraction effect of sound waves, temperature and velocity fields are reconstructed simultaneously based on the nonlinear acoustic tomography (NAT) by using the covariance matrix adaptation evolution strategy (CMA-ES) algorithm. In the forward problem, the fast two-point ray-tracing algorithm is introduced to track curved sound waves more efficiently. Due to the sparse measurement signals caused by the limited space and curved acoustic paths, Tikhonov regularization is introduced as the constraint of smoothing prior information to reduce the ill-posedness of the problem. The influence of velocity regularization parameter and temperature regularization parameter on simultaneously visualizing the temperature and corresponding velocity fields are investigated, respectively. Then the selection strategies of these two regularization parameters are given, which are proved to have high selection efficiency. On this basis, inhomogeneous temperature and velocity fields are reconstructed simultaneously by using the CMA-ES algorithm, simulated annealing, and genetic algorithms. By solving the inverse problem of the NAT, which is highly nonlinear and ill-posed, the feasibility and effectiveness of CMA-ES algorithm in the simultaneous reconstruction of velocity and temperature fields are confirmed. The proposed method for visualizing the temperature and velocity fields will supply key feedback for the combustion process control.

Synthetic aperture radar (SAR) image registration is still a challenging task in remote sensing. The scale-invariant feature transform (SIFT) method and its extensions are most widely used feature detectors in SAR images. However, due to the presence of nonlinear speckle noise, some wrong features are chosen which directly influence the feature matching process. In this article, for the first time, the KAZE algorithm with a modified version of speeded-up robust features (SURF) descriptor is used for SAR image registration. It uses discrete stochastic second order nonlinear partial differential equations (PDEs) to model the SAR image's edge structure. KAZE uses nonlinear diffusion filtering to build up the scale levels of the SIFT descriptor. It preserves the edges while simultaneously decreasing the speckle noise in smoothing the image. Therefore, it captures the exact location of features in down-sampling and filtering stages of SIFT. Experimental results show that our proposed method has increased the localization accuracy and distinctiveness of feature extraction through the use of KAZE detector. Our method is also able to handle challenging cases in different image sources, different view angles, different image times, complex affine mapping, presence of blurring, and noise.

Two-dimensional (2D) nuclear magnetic resonance (NMR) inversion operates with massive echo train data and is an ill-posed problem. It is very important to select a suitable inversion method for the 2D NMR data processing. In this study, we propose a fast, robust, and effective method for 2D NMR inversion that improves the computational efficiency of the inversion process by avoiding estimation of some unneeded regularization parameters. Firstly, a method that combines window averaging (WA) and singular value decomposition (SVD) is used to compress the echo train data and obtain the singular values of the kernel matrix. Subsequently, an optimum regularization parameter in a fast manner using the signal-to-noise ratio (SNR) of the echo train data and the maximum singular value of the kernel matrix are determined. Finally, we use the Butler-Reeds-Dawson (BRD) method and the selected optimum regularization parameter to invert the compressed data to achieve a fast 2D NMR inversion. The numerical simulation results indicate that the proposed method not only achieves satisfactory 2D NMR spectra rapidly from the echo train data of different SNRs but also is insensitive to the number of the final compressed data points.

In the current work, the stable node-based smoothed radial point interpolation method (SNS-RPIM) was used to investigate the dynamic responses of magneto-electro-elastic (MEE) beams under hygrothermal environment with the generalized Newmark method. Based on the generalized smoothed Galerkin weak form and the gradient smoothing technique, the basic equations for hygro-thermo-magneto-electro-elastic (HTMEE) coupling problems were derived. The stable item of SNS-RPIM made the stiffness ‘close-to-exact’ and cured the ‘over-soft’ of the model. Through the numerical examples, the effect of empirical constants, temperature variation and moisture concentration on the elastic stiffness coefficients and generalized displacement was investigated. Besides, numerical results verified the accuracy, convergence and correctness of the present method compared with the traditional finite element method when analyzing the HTMEE coupling problems. Moreover, SNS-RPIM was very stable and accurate for extremely distributed meshes, which can have an excellent application for future studies of the coupled multi-physical problems.