Optimal Estimation Method Research Articles

Tuning-Free Bayesian Estimation Algorithms for Faulty Sensor Signals in State-Space

Sensors provide insights into the industrial processes, while misleading sensor outputs may result in inappropriate decisions or even catastrophic accidents. In this article, the Bayesian estimation algorithms are developed to estimate unforeseen signals in sensor outputs without tuning. The optimal Bayesian estimation method is first derived by incorporating a Gaussian distribution specifying potential unmodeled dynamics into the measurement equation. Since its performance depends on tuning parameters, an iterative Bayesian estimation algorithm is developed using the variational inference technique. Specifically, an inverse Wishart distribution is introduced to describe the predicted covariance of abnormal signals. We then estimate it together with the other independent Gaussian distributions to conditionally approximate the joint posterior distribution, by which the effects of tuning parameters can be replaced adaptively. Testing the proposed algorithms through a simulated electromechanical brake model and a real experimental system shows that the proposed algorithm can satisfactorily estimate additive sensor faults online and services as a sensor monitor that simultaneously provides the locations and magnitudes of faulty signals without tuning.

Fast estimation method of optimal modal gain for adaptive optical system

In an adaptive optical system, the optimal modal control method refers to applying proportional integral control of different bandwidths to the wavefront aberrations after modal decomposition to achieve better closed-loop results than the unified bandwidth modal control. The optimal modal gain usually needs to be obtained by ergodic solution based on the transfer function model of the adaptive optical system, the measured disturbance power spectral density, and the noise power spectral density, which usually takes a long time. Owing to the time-varying statistical characteristics of atmospheric turbulence, it is difficult to ensure the timeliness of the optimal modal gain. Therefore, we propose a method of fast estimating optimal modal gain based on quadratic polynomial fitting. In the method, it is only necessary to choose three reasonable gain coefficients and calculate their corresponding closed-loop residual errors respectively in order to estimate the optimal gain of single mode. The simulated slope data used in this work are cited from Lijiang 1.8 m adaptive telescope system, which consists of a 241-unit deformable secondary mirror and a Shaker-Hartmann wavefront sensor with 192 sub-apertures, with the first 135-order modes corrected by modal method. Our experiment is to test directly on-line on this system. The results show that under the same atmospheric environment, the proposed method can accurately estimate the optimal modal gain in a very short time and effectively suppress the high-order wavefront aberration. At the same time, owing to the reduced time complexity of the algorithm, the improved optimal modal gain estimation method takes only 0.33 s. Comparatively, it will take 7.08 s to obtain the optimal modal gain coefficient by using the parameter traversal method. Therefore the time spent on obtaining the optimal modal gain is shortened by about 95.3%, which is easier to meet the real-time requirements of the telescope, and beneficial to the adaptive optics system with more high-order modes. For the future adaptive optics system with more than one-thousand units, the proposed method can update the optimal gain to the second level, while the traversal method can only reach the minute level.

Fast satellite selection algorithm for GNSS multi-system based on Sherman–Morrison formula

Efficient use of global navigation satellite system (GNSS) observations improves when applying rational satellite selection algorithms. By combining the Sherman–Morrison formula and singular value decomposition, a smaller-GDOP (geometric dilution of precision)-value method is proven for an increasing number of visible satellites. By combining this smaller-GDOP-value method with the maximum-volume-tetrahedron method, a new rapid satellite selection algorithm based on the Sherman–Morrison formula for GNSS multi-systems is proposed. The basic idea of the algorithm is as follows: First, the maximum-volume-tetrahedron method is used to obtain four initial visible satellites. Then, the other visible satellites are selected by using the smaller-GDOP-value method to reduce the GDOP value and improve the accuracy of the overall algorithm. When the number of included satellites reaches a certain value, the rate of GDOP decrease tends to approach zero. Considering the algorithm precision and the computation efficiency, reasonable thresholds and end of calculation condition equation are given, which can make the proposed algorithm autonomous. The reasonable thresholds and the end of calculation parameters are suggested by means of experiments. Under the thresholds and the end of calculation parameters, the algorithm has an adaptive functionality. Furthermore, the GDOP values of the algorithm are less than 2, indicating that this algorithm can meet one of the requirements of high-precision navigation. Moreover, compared with the computation complexity values of the optimal GDOP estimation method, which includes all visible satellites, the values of the new algorithm are about half, indicating that this algorithm has a rapid performance. These findings verify that the proposed satellite selection algorithm based on the Sherman–Morrison formula provides autonomous functionality, high-performance computing, and high-accuracy results.

Robust air-light estimation for a single hazy image using haze-lines in Plüker coordinates.

Image dehazing is a valid way to reduce the negative impacts of bad weather, such as fog and haze, on outdoor optical imaging. The effect of the image dehazing heavily relies on the air-light estimation. Most existing methods exploit the prior information, which is generally obtained by simply assessing the brightest pixels' intensity in a hazy image, to estimate the air-light. However, bright objects, such as lamps, lead to the failure of these methods. So, a novel method for robust and optimal air-light estimation using haze-lines described in Plüker coordinates is proposed. First, the exact analytic solution of the air-light, i.e., the intersection of haze-lines, is derived via analyzing the geometric relationship between haze-lines in three-dimensional RGB space using their Plüker coordinates. Then, a novel air-light estimation algorithm using haze-lines in Plüker coordinates is elaborated. Finally, extensive experiments are performed to validate the proposed algorithm. Experimental results reveal that the proposed algorithm can more robustly and accurately estimate the air-light.

Parameter estimation of Weibull distribution for the number of days between drug administration and early onset adverse event

ABSTRACT The Weibull distribution is applied to the number of days between the start date of drug administration and the date of occurrence of an adverse event. The tendency of occurrence of adverse events can be clarified by estimating the two- or three-parameter Weibull distribution, using the data regarding the number of days. Our purpose is to estimate the parameters of the Weibull distribution with high accuracy, even in low-reported adverse events, such as new drugs, polypharmacy and small clinical trials. Furthermore, the two-sample Kolmogorov – Smirnov test (two-sided) is used to examine whether the tendency of occurrence of adverse events is different for two Weibull distributions estimated from two drugs with similar efficacy. We used discrete data derived from FDA Adverse Event Reporting System (FAERS), as the FAERS data are presented in years, months and days without hours and minutes. Because this study focuses on early onset adverse events, data may be contained 0 days. The discreteness of the data and the fact that it may include zero make this distribution different from the general Weibull distribution, which is defined for continuous data greater than zero. We search for the optimal parameter estimation method for the Weibull distribution under these two conditions, and verify its effectiveness using Monte Carlo simulations and FAERS data. Because the results obtained from FAERS data may differ depending on data handling, we describe the of data handling technique and the sample code that can reproduce the results.

Calculus of variations for estimation in ODE–PDE landslide-like models with discrete-time asynchronous measurements

Motivated by some landslide models, and related estimation challenges, this paper presents an optimal estimation method for state and parameter in a special class of so-called ODE–PDE system based on the adjoint method for discrete-time asynchronous measurements. This system is described by a pair of coupled Ordinary Differential Equation (ODE) and Partial Differential Equation (PDE), with a mixed boundary condition for the PDE. The coupling appears both in the ODE and in the Neuman boundary condition of the PDE. For this system, initial conditions or state variables and some empirical parameters are assumed to be unknown and need to be estimated. The Lagrangian multiplier method is used to connect the dynamics of the system and the cost function defined as the least square error between the simulation values and the available measurements. The adjoint state method is applied to the objective functional to get the adjoint system and the gradients with respect to parameters and initial state. The cost functional is optimised, employing the steepest descent method to estimate parameters and initial state. This general approach is illustrated by two application examples corresponding to two different landslide models that validate the presented optimal estimation approach. The first one is about state and parameter estimation in an extended sliding-consolidation landslide model, and the second one is in the viscoplastic sliding-consolidation landslide model.

Novel Special Orthogonal Group Optimization for Coarse Alignment Method of SINS on a Rocking Base

In order to solve the coarse alignment problem of the strapdown inertial navigation system on a rocking base, a fast coarse alignment method using the Special Orthogonal Group optimization has been proposed in this paper. In this method, based on the alignment idea of tracing gravitational apparent motion in inertial frame, the model of coarse alignment on a rocking base has been established using the Special Orthogonal Group directly. A new attitude error function has been proposed on the basis of the cosines between the measurement vector and predictive vector to describe the error between the estimated attitude and the true one. In order to directly reflect the change in the attitude error in the new innovation term and enable the attitude error to converge to zero as fast as possible, the gradient of the new attitude error function has been selected as the new innovation term to compensate for the attitude in the estimation process. Finally, the stability of the proposed optimization estimation method has been proved by employing the Lyapunov stability theory. Simulation and experiment results show that the method presented in this paper exhibits good performance in terms of alignment accuracy and time and can be applied to coarse alignment under a rocking base under different environments.

Retrieval and validation of long-term aerosol optical depth from AVHRR data over China

ABSTRACT Advanced Very High Resolution Radiometer (AVHRR) onboard National Oceanic and Atmospheric Administration (NOAA) satellites can provide over 40 years of global remote sensing observations, which can be used to retrieve long-term aerosol optical depth (AOD). This is of great significance to the study of global climate change. In this paper, we proposed an algorithm to jointly calculate AOD and land surface properties from AVHRR observations. With assumptions that AOD doesn’t vary in adjacent space and earth surface property doesn’t vary in two days, the algorithm considered non-Lambertian surface reflection based on the shape of bidirectional reflectance distribution function (BRDF shape) and obtained AOD and surface property by optimal estimation (OE) method. The algorithm has been applied to NOAA-7, 9, 11, 14, 16, 18, and 19 satellites and AVHRR-retrieved AOD with 5 × 10 km over China (15°–60° N, 70°–140°E) has been obtained from 1982 to 2016. Comparisons of AVHRR-retrieved AOD against AErosol RObotic NETwork (AERONET) (in and around China) and China Aerosol Remote Sensing Network (CARSNET) AOD show good consistency with 62.62% points within the uncertainty of Δτ = ± (0.05 + 0.25τ) and root-mean-square error (RMSE) of 0.26. Further comparison of the monthly mean AOD of multiple AOD datasets in the ‘Beijing’, ‘Dalanzadgad’, ‘NCU_Taiwan’ and ‘Kanpur’ stations shows that the results of the algorithm are stable. The yearly averaged AOD data also has similar agreements with MERRA-2 (The Modern-Era Retrospective analysis for Research and Applications, Version 2) and AVHRRDB data (AVHRR ‘Deep Blue’ aerosol data set). The multi-year mean correlation coefficient is 0.70 and 0.61 and the percentages within the uncertainty are 80.01% and 67.29% compared with MERRA-2 AOD and AVHRRDB AOD respectively.

Ozone profiles without blind area retrieved from MAX-DOAS measurements and comprehensive validation with multi-platform observations

Tropospheric ozone (O3) profiles, especially within the boundary layer, are essential for studying the vertical, temporal, spatial variations, as well as the formation sensitivity and regional transport of O3. However, it is rare to find continuous tropospheric O3 profiles with high temporal and spatial resolutions without blind areas using current remote sensing technologies, with issues such as low near-surface sensitivity or systematic blind areas from satellite and LiDAR observations, respectively, being encountered. In this study, multi-source data including stratospheric O3 profiles from external datasets and local monthly dependent a priori profiles were fused in the retrieval algorithm, then vertical O3 profiles from the near-surface to the free troposphere were retrieved from multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements. With the aim of deriving a set of best practice recommendations for applying a profile inversion algorithm to long-term observations, we comprehensively investigated the influence of various settings on profile retrieval, with emphasis on the selection of a Fraunhofer reference spectrum and appropriate a priori profiles in the upper troposphere. These recommendations are essential for applying the algorithm to real long-term measurements. In this study, tropospheric O3 profiles were retrieved for operational MAX-DOAS observations in Beijing, and the results, especially for the boundary layer, were evaluated in detail with respect to well-established independent O3 datasets, including one-year ozonesonde profiles and tower-based in-situ measurements at different altitudes. A good level of agreement was found for both near-surface and elevated-altitude results, and the MAX-DOAS O3 profiles were able to reproduce the vertical distributions measured by ozonesonde. However, MAX-DOAS measurements are less sensitive to the upper troposphere and converge to the a priori profile, and the smoothing effect induced by the optimal estimation method makes it difficult to reproduce steep vertical gradients in real atmosphere.

A Method for Optimal Estimation of Shoreline in Cliff Zones Based on Point Cloud Segmentation and Centroid Calculation

The integrity of shoreline is disrupted by cliffs, posing obstacles to marine surveying and chart mapping, particularly in research related to the cliff section of the sea area. The present study proposes a method to solve this problem. In the proposed method, we first extract the boundary of the cliff to segment the point cloud according to a designed rule, then calculate the centroid coordinates of each point cloud block, followed by the coordinates of side points on both sides of the boundary of each block from the centroid, and finally create a side point cloud projected to the water surface corrected for elevation. The corrected point cloud is considered a point cloud dataset of the innermost shoreline. Combined with a point cloud projected from the boundary of the cliff section to the water surface, we developed a calculation method for the optimal shoreline position. Our experiment proved that the method could effectively extract the shoreline of the study area. Moreover, compared with that of the commonly used shoreline extraction method, the absolute error of the isoline tracking method was very large, up to several meters. However, the proposed method achieved smaller standard deviation and variance (0.1254 and 0.0157, respectively) than the isoline tracking method (0.9837 and 0.9677, respectively).

Synergistic Retrieval of Temperature and Humidity Profiles from Space-Based and Ground-Based Infrared Sounders Using an Optimal Estimation Method

The atmospheric temperature and humidity profiles of the troposphere are generally measured by radiosondes and satellites, which are essential for analyzing and predicting weather. Nevertheless, the insufficient observation frequencies and low detection accuracy of the boundary layer restricts the description of atmospheric state changes by the temperature and humidity profiles. Therefore, this work focus on retrieving the temperature and humidity profiles using observations of the FengYun-4 (FY-4) Geostationary Interferometric Infrared Sounder (GIIRS) combined with ground-based infrared spectral observations from the Atmospheric Emitted Radiance Interferometer (AERI), which are more accurate than space-based individual retrieval results and have a wider effective retrieval range than ground-based individual retrieval results. Based on the synergistic observations, which are made by matching the space-based and ground-based data with those of different spatial and temporal resolutions, a synergistic retrieval process is proposed to obtain the temperature and humidity profiles at a high frequency under clear-sky conditions based on the optimal estimation method. In this research, using the line-by-line radiative transfer model (LBLRTM) as the forward model for observing simulations, a retrieval experiment was carried out in Qingdao, China, where an AERI is situated. Taking radiosonde data as a reference for comparing the retrieval results of the temperature and humidity profiles of the troposphere, the root-mean-square error (RMSE) of the synergistic retrieval algorithm below 400 hPa is within 2 K for temperature and within 12% for relative humidity. Compared with the GIIRS individual retrieval, the RMSE of temperature and relative humidity for the synergistic method is reduced by 0.13–1.5 K and 2.7–4.4% at 500 hPa, and 0.13–2.1 K and 2.5–7.2% at 900 hPa. Moreover, the forecast index (FI) calculated from the retrieval results shows reasonable consistency with the FIs calculated from the ERA5 reanalysis and from radiosonde data. The synergistic retrieval results have higher temporal resolution than space-based retrieval results and can reflect the changes in the atmospheric state more accurately. Overall, the results demonstrated the promising potential of the synergistic retrieval of temperature and humidity profiles at high accuracy and high temporal resolution under clear-sky conditions from FY-4/GIIRS and AERI.

Analysis of Longitudinal Binomial Data with Positive Association between the Number of Successes and the Number of Failures: An Application to Stock Instability Study

Numerous methods have been developed for longitudinal binomial data in the literature. These traditional methods are reasonable for longitudinal binomial data with a negative association between the number of successes and the number of failures over time; however, a positive association may occur between the number of successes and the number of failures over time in some behaviour, economic, disease aggregation and toxicological studies as the numbers of trials are often random. In this paper, we propose a joint Poisson mixed modelling approach to longitudinal binomial data with a positive association between longitudinal counts of successes and longitudinal counts of failures. This approach can accommodate both a random and zero number of trials. It can also accommodate overdispersion and zero inflation in the number of successes and the number of failures. An optimal estimation method for our model has been developed using the orthodox best linear unbiased predictors. Our approach not only provides robust inference against misspecified random effects distributions, but also consolidates the subject-specific and population-averaged inferences. The usefulness of our approach is illustrated with an analysis of quarterly bivariate count data of stock daily limit-ups and limit-downs.

Retrieval of tropospheric ozone profiles using ground-based MAX-DOAS

In recent years, surface ozone concentrations have increased in many cities in China. Ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) is a powerful technique for retrieving the profiles of tropospheric trace gases, such as NO2, SO2, and HCHO. However, since the difficulties in deducting the effects of stratospheric ozone, there are few studies on the retrieval of tropospheric ozone profiles using MAX-DOAS measurements. Here, we developed an accurate inversion method to retrieve tropospheric ozone concentrations during the PRIDE-GBA Campaign, wherein the ozone differential slant column densities (DSCDs) were retrieved in QDOAS software using the “time-interpolated zenith spectrum” as the reference spectrum. The tropospheric DSCDs (DSCDstrop) were then calculated by subtracting the simulated stratospheric DSCDs (DSCDsstr, simulated from the SCIATRAN model) from the DSCDs. Tropospheric ozone profiles were retrieved from the DSCDstrop using the optimal estimation method (OEM). The results showed that high values of tropospheric ozone were mainly distributed below 1 km, which is consistent with lidar measurements. In addition, the observed surface ozone concentrations were highly correlated with the in-situ measurements, with correlation coefficients (R) of 0.75 and 0.81, respectively. Combined with the retrieved NO2 and HCHO profiles using the MAX-DOAS measurements, we found that the planetary boundary layer ozone pollution of HeShan Observatory during the PRIDE-GBA Campaign are controlled by the NOx-limited regime. The results of this study indicate that the MAX-DOAS technique has the potential to retrieve tropospheric ozone profiles with high temporal and spatial resolution.

Estimation of Potato Chlorophyll Content from UAV Multispectral Images with Stacking Ensemble Algorithm

Rapid and accurate crop chlorophyll content estimation is crucial for guiding field management and improving crop yields. This study explored the potential for potato chlorophyll content estimation based on unmanned aerial vehicle (UAV) multispectral imagery. To search the optimal estimation method, three parts of research were conducted as following. First, a combination of support vector machines (SVM) and a gaussian mixture model (GMM) thresholding method was proposed to estimate fractional vegetation cover (FVC) during the potato growing period, and the proposed method produced efficient estimates of FVC; among all the selected vegetation indices (VIs), the soil adjusted vegetation index (SAVI) had the highest accuracy. Second, the recursive feature elimination (RFE) algorithm was utilized to screen the VIs and texture features derived from multispectral images: three Vis, including modified simple ratio (MSR), ratio vegetation index (RVI) and normalized difference vegetation index (NDVI); three texture features, including correlation in the NIR band (corr-NIR), correlation in the red-edge band (corr-Red-edge) and homogeneity in the NIR band (hom-NIR), showed higher contribution to chlorophyll content estimation. Finally, a stacking model was constructed with K-Nearest Neighbor (KNN), a light gradient boosting machine (light-GBM), SVM algorithm as the base model and linear fitting as the metamodel, and four machine learning algorithms (SVM, KNN, light-GBM and stacking) were used to build the chlorophyll content estimation model suitable for different growing seasons. The results were: (1) The performance of the estimation model could be improved based on both VIs and texture features over using single-type features, and the stacking algorithm yielded the highest estimation accuracy with an R2 value of 0.694 and an RMSE value of 0.553; (2) When FVC was added, the estimation model accuracy was further improved, and the stacking algorithm also produced the highest estimation accuracy with R2 value of 0.739, RMSE value of 0.511 (3) When comparing modeling algorithms, stacking algorithms had greater advantages in the estimation chlorophyll content with potato plants than using single machine learning algorithms. This study indicates that taking into account the combination of VIs reflecting spectral characteristics, texture features reflecting spatial information and the FVC reflecting canopy structure properties can accomplish higher chlorophyll content estimation accuracy, and the stacking algorithm can integrate the advantages of a single machine learning model, with great potential for estimation of potato chlorophyll content.

A Gaussian process-driven worst realistic imperfection method for cylindrical shells by limited data

In this study, we propose a Gaussian process-driven worst imperfection method to generate simulated geometric imperfections and find the lower limit of buckling capability. First, we utilize the nested stochastic Kriging to estimate the global trend and envelopes of the geometric imperfections. Second, the expansion optimal linear estimation and direct weighting construction method are used to expand limited data, which allow the simulated geometric imperfections to have the same characteristics as the initial data. Third, two magnitude-based constraints are provided, which can make the simulated geometric imperfections more consistent with the realistic data and can represent more design space. Finally, for the cylindrical shells, the data pre-processing method is further provided in this paper. Two numerical examples with assumed geometric imperfections are used to validate the proposed method, and the minimum buckling capacity of a cylindrical shell with 8 realistic geometric imperfections data is estimated. The results show that the proposed method can achieve the best performance in terms of accuracy, efficiency, and robustness.

Efficient simulation of multivariate non-stationary ground motions based on a virtual continuous process and EOLE

Simulation of multivariate non-stationary ground motions (MNSGM) is of paramount importance to seismic reliability evaluation of large-span structures, e.g., bridge and dome structures. In this paper, the expansion optimal linear estimation (EOLE) method is extended to the simulation of MNSGM with high efficiency. A virtual continuous process is proposed to connect the multivariate ground motions to formulate a 1D−1V process. In that regard, the main work of this study is to obtain the expanded covariance/correlation matrix for the virtual 1D−1V process including the auto- and cross-correlation information, which will be determined by using inverse Fourier transform from the corresponding auto- and cross-evolutionary power spectrum density (EPSD) functions. Then, the EOLE method is applied to generate the 1D−1V virtual continuous process by decomposing the expanded covariance/correlation matrix. Finally, the MNSGM can be easily obtained by splitting the 1D−1V virtual continuous process correspondingly. The most attractive feature of the proposed method is that it only needs to decompose the covariance/correlation matrix once, which effectively circumvents the computational challenge of classic spectral representation method (SRM) for simulating MNSGM. Besides, two types of modulation functions for non-stationarity, i.e., the time- and time–frequency-modulations are considered. Two examples, involving the simulation of two types of MNSGM are presented to verify the effectiveness of this approach.

Reducing Weather Influences on an 89 GHz Sea Ice Concentration Algorithm in the Arctic Using Retrievals From an Optimal Estimation Method

AbstractSea ice concentration (SIC) derived from 89 GHz data has up to four times finer spatial resolution compared to that from the widely used 19 and 37 GHz data. But it has lower accuracy due to the enhanced weather influences from water vapor, cloud liquid water (CLW), wind, and surface temperature. Here we improve a high‐resolution SIC algorithm, called the ASI algorithm, based on the difference between vertical and horizontal polarization 89 GHz data, by correcting the observed data for these weather influences through a radiative transfer model and geophysical data retrieved by an optimal estimation method. The improved algorithm denoted ASI3, is developed for the Arctic based on the weather‐corrected brightness temperatures and newly identified open water (80 K) and sea ice (14 K) tie‐points. The most important component of this correction is the inclusion of CLW, the largest weather influence contributor. ASI3 results are evaluated over pure surface sites of 0% and 100% SICs under various weather conditions, showing a much lower average standard deviation (1.1%) than ASI (16.2%). ASI3 reduces weather patterns over pack ice resulting in more homogeneous retrievals but biased toward lower values. Comparison to Landsat imagery under clear‐sky conditions shows that ASI3 results in better agreement with the Landsat SIC than ASI. The number of cases where real sea ice is falsely identified as open water is reduced by ASI3 due to its relaxed open‐water mask and wider water/ice dynamic range.

Neural networks for self-adjusting mutation rate estimation when the recombination rate is unknown.

Estimating the mutation rate, or equivalently effective population size, is a common task in population genetics. If recombination is low or high, optimal linear estimation methods are known and well understood. For intermediate recombination rates, the calculation of optimal estimators is more challenging. As an alternative to model-based estimation, neural networks and other machine learning tools could help to develop good estimators in these involved scenarios. However, if no benchmark is available it is difficult to assess how well suited these tools are for different applications in population genetics. Here we investigate feedforward neural networks for the estimation of the mutation rate based on the site frequency spectrum and compare their performance with model-based estimators. For this we use the model-based estimators introduced by Fu, Futschik et al., and Watterson that minimize the variance or mean squared error for no and free recombination. We find that neural networks reproduce these estimators if provided with the appropriate features and training sets. Remarkably, using the model-based estimators to adjust the weights of the training data, only one hidden layer is necessary to obtain a single estimator that performs almost as well as model-based estimators for low and high recombination rates, and at the same time provides a superior estimation method for intermediate recombination rates. We apply the method to simulated data based on the human chromosome 2 recombination map, highlighting its robustness in a realistic setting where local recombination rates vary and/or are unknown.

Optimal Charging Infrastructure Portfolio for Minimizing Grid Impact of Plug-In Electric Vehicles

Charging infrastructure (CI) is crucial for the widespread adoption of plug-in electric vehicles (PEVs). The optimal portfolio of CI is beneficial for PEV owners, power systems, and policymakers. Therefore, an optimal CI portfolio estimation method is proposed to minimize the grid impacts of PEVs. All possible CI types (residential, workplace, public, and commercial) along with the three commonly used charging levels are considered and seven PEV charging profiles are estimated. Then, an optimization model is developed to determine the optimal portfolio of CI. An index is proposed to compare the performance of the proposed method with other CI portfolios. Various cases are simulated to analyze the performance under diverse conditions such as the presence of renewables and specific policy scenarios. The proposed method can be used by policymakers for making new policies, in new PEV markets, and to incentivize the use of specific CI types, in mature/developing markets.

Joint state and fault estimation for nonlinear complex networks with mixed time-delays and uncertain inner coupling: non-fragile recursive method

In this paper, the non-fragile joint state and fault estimation problem is investigated for a class of nonlinear time-varying complex networks (NTVCNs) with uncertain inner coupling and mixed time-delays. Compared with the constant inner coupling strength in the existing literature, the inner coupling strength is permitted to vary within certain intervals. A new non-fragile model is adopted to describe the parameter perturbations of the estimator gain matrix which is described by zero-mean multiplicative noises. The attention of this paper is focussed on the design of a locally optimal estimation method, which can estimate both the state and the fault at the same time. Then, by reasonably designing the estimator gain matrix, the minimized upper bound of the state estimation error covariance matrix (SEECM) can be obtained. In addition, the boundedness analysis is taken into account, and a sufficient condition is provided to ensure the boundedness of the upper bound of the SEECM by using the mathematical induction. Lastly, a simulation example is provided to testify the feasibility of the joint state and fault estimation scheme.