Hybrid Estimation Research Articles

Genome-wide data reveal extensive gene flow during the diversification of the western rattlesnakes (Viperidae: Crotalinae: Crotalus)

Hybridization and introgression are important, but often overlooked processes when inferring phylogenies. When these processes are not accounted for and a strictly diverging phylogenetic model is applied to groups with a history of hybridization, phylogenetic inference and parameter estimation can be inaccurate. Recent developments in phylogenetic network approaches coupled with the increasing availability of genomic data allow inferences of reticulate evolutionary histories across the tree of life. The western rattlesnake species group (C. viridis species complex, C. mitchellii species complex, C. scutulutas, and C. tigris) is an iconic snake lineage that is widespread across western North America. This group is composed of several species complexes with unclear species limits, likely the result of ongoing gene flow among nascent lineages. Here I generate reduced representation genomic data and test for a history of reticulation within this group. I demonstrate that all species have undergone hybridization with at least one other lineage, suggesting introgression is widespread in this group. Topologies differ between phylogenies estimated under the multispecies coalescent and multispecies network coalescent methods, indicating that gene flow has obscured phylogenetic relationships within this group. These past introgression events are predominantly restricted to species that co-occur geographically. However, within species that have a history of introgression, this signature is detected regardless of specimen sampling across geography. Overall, my results suggest the accumulation of reproductive isolating barriers occurs slowly in rattlesnakes which likely leads to the difficulty in delimiting species, furthermore, the results of this study have implications for trait evolution in this group.

A Hybrid Estimation of Distribution Algorithm for the Quay Crane Scheduling Problem

The aim of the quay crane scheduling problem (QCSP) is to identify the best sequence of discharging and loading operations for a set of quay cranes. This problem is solved with a new hybrid estimation of distribution algorithm (EDA). The approach is proposed to tackle the drawbacks of the EDAs, i.e., the lack of diversity of solutions and poor ability of exploitation. The hybridization approach, used in this investigation, uses a distance based ranking model and the moth-flame algorithm. The distance based ranking model is in charge of modelling the solution space distribution, through an exponential function, by measuring the distance between solutions; meanwhile, the heuristic moth-flame determines who would be the offspring, with a spiral function that identifies the new locations for the new solutions. Based on the results, the proposed scheme, called QCEDA, works to enhance the performance of those other EDAs that use complex probability models. The dispersion results of the QCEDA scheme are less than the other algorithms used in the comparison section. This means that the solutions found by the QCEDA are more concentrated around the best value than other algorithms, i.e., the average of the solutions of the QCEDA converges better than other approaches to the best found value. Finally, as a conclusion, the hybrid EDAs have a better performance, or equal in effectiveness, than the so called pure EDAs.

A novel integrated method based on a machine learning model for estimating evapotranspiration in dryland

Evapotranspiration (ET) plays a vital role in the water cycle and energy cycle and serves as an important linkage between ecological and hydrological processes. Accurate estimation of ET based on data-driven methods is of great theoretical and practical significance for exploring soilevaporation,plant transpiration and the regional hydrological balance. Most existing estimation approaches were proposed based on multiple meteorological variables. This study proposed a novel hybrid estimation approach to estimate the monthly ET using only historical ET time series in the southeastern margins of the Tengger Desert, China. The approach consisted of three sections including data preprocessing, parameter optimization and estimation. The model evaluation demonstrated that the hybrid model based on the variational mode decomposition (VMD) method, grey wolf optimizer (GWO) algorithm and support vector machine (SVM) model achieved superior computational performance compared to the performance of other methods. The Nash–Sutcliffe coefficient of efficiency (NSCE) increased from 0.8588 to 0.8754 and the mean absolute percentage error (MAPE) decreased from 28.42% to 23.22% in the testing stage. Thus, we suggest that the hybrid VMD-GWO-SVM model will be the best choice for estimating ET in the absence of regional meteorological monitoring.

A Hybrid Estimation Technique Using Elliptical Radial Basis Neural Networks and Cokriging

A Hybrid Estimation Technique Using Elliptical Radial Basis Neural Networks and Cokriging

Aestuariisphingobium litorale Li et al. 2020 is a later heterotypic synonym of Chakrabartia godavariana Jani et al. 2019.

Chakrabartia godavariana PRB40T was compared with Aestuariisphingobium litorale SYSU M10002T to examine the taxonomic relationship between the two type strains. The 16S rRNA gene sequence of C. godavariana PRB40T had high similarity (99.8 %) to that of A. litorale SYSU M10002T. The results of phylogenetic analyses based on 16S rRNA gene sequences indicated that the two strains formed a tight cluster within the genus Chakrabartia. A draft genomic comparison between the two strains revealed an average nucleotide identity of 97.3 % and a digital DNA-DNA hybridization estimate of 79.5±2.9 %, strongly indicating that the two strains represented a single species. In addition, neither strain displayed any striking differences in metabolic, physiological or chemotaxonomic features. Therefore, we propose that Aestuariisphingobium litorale is a later heterotypic synonym of Chakrabartia godavariana.

Cascaded and Recursive ConvNets (CRCNN): An effective and flexible approach for image denoising

Recently, discriminative learning methods have gained substantial interest in solving inverse imaging problems due to their decent performance and fast inferencing capability. Those methods need separate models for specific noise levels, which in turn require multiple models to be trained to denoise an image. However, images exhibit spatial variant noise which limits the applicability of such methods. In addition, the discriminative learning methods introduce artifacts such as blurring, deblocking, and so forth while denoising an image. To address these issues, we propose a cascaded and recursive convolutional neural network (CRCNN) framework which can cope with spatial variant noise and blur artifacts in a single denoising framework. The CRCNN takes into account down-sampled sub-images for fast inferencing along with the noise level map. We adopt the hybrid orthogonal projection and estimation method on the convolutional layers to improve the generalization capability of the network in terms of non-uniform and spatial variant noise levels. In contrast to the existing methods, the CRCNN framework allows both denoising and deblurring of images using a single framework which preserves the fine details in a denoised image. Extensive experiments have been conducted to validate the effectiveness and flexibility of the CRCNN framework on real as well as synthetic noisy images in comparison to the state-of-the-art denoising methods. The results show that the CRCNN performs effectively on both synthetic as well as spatial variant noise-induced images, thus, proving the practicability of the framework.

A novel state of health estimation method for lithium-ion battery based on partial incremental capacity and Support vector regression

State of Health (SOH) is critical for lithium-ion batteries as it ensures the safety of batteries’ health condition and provides a basis for retirement of the batteries. In order to provide an accurate estimation of the SOH, a novel hybrid estimation method based on the partial incremental capacity and Support vector regression (SVR) is proposed in this paper. Firstly, the Savitzky-Golay method is applied to smooth the initial incremental capacity curves under the period of constant current charge. Then the key health features are extracted from the partial incremental curve theoretically and selected through correlation analysis methods. Finally, an SVR model is constructed to estimate the SOH. Several battery datasets under different cycling test conditions are used to validate the effectiveness of the proposed method. The result shows that the proposed method can provide a reliable and accurate estimation for SOH.

Physics-informed neural networks for electrode-level state estimation in lithium-ion batteries

An accurate estimation of the internal states of lithium-ion batteries is critical to improving the reliability and durability of battery systems. Data-driven methods have exhibited enormous potential for precisely capturing electric and thermal cell dynamics with a low computational cost. However, challenges remain regarding accurate and low-cost data acquisition as electrode-level states are unmeasurable with conventional sensors. This paper presents a hybrid state estimation method for lithium-ion batteries integrating physics-based and machine learning models to leverage their respective strengths. An electrochemical-thermal model is developed and experimentally verified, which is employed to generate a large quantity of data, i.e., voltage, current, temperature and internal electrochemical states, under a comprehensive operating condition matrix including various load profiles and temperatures. These data are fed to train a deep neural network to estimate the internal concentrations and potentials in the electrodes and the electrolyte at different spatial positions. The results show that the proposed approach is capable of bridging spatial, temporal and chemical complexity and achieves a maximum error of 2.93% for all the estimated states under new ambient temperatures, indicating high reliability and generalization ability with solid robustness to input noises and outperforming the one-dimensional network under both normal and noisy conditions.

Spatial analysis model for traffic accident-prone roads classification: a proposed framework

<span id="docs-internal-guid-9754d3d8-7fff-a7d2-6605-1c8f8c5a707a"><span>The classification method in the spatial analysis modeling based on the multi-criteria parameter is currently widely used to manage geographic information systems (GIS) software engineering. The accuracy of the proposed model will play an essential role in the successful software development of GIS. This is related to the nature of GIS used for mapping through spatial analysis. This paper aims to propose a framework of spatial analysis using a hybrid estimation model-based on a combination of multi-criteria decision-making (MCDM) and artificial neural networks (ANNs) (MCDM-ANNs) classification. The proposed framework is based on the comparison of existing frameworks through the concept of a literature review. The model in the proposed framework will be used for future work on the traffic accident-prone road classification through testing with a private or public spatial dataset. Model validation testing on the proposed framework uses metaheuristic optimization techniques. Policymakers can use the results of the model on the proposed framework for initial planning developing GIS software engineering through spatial analysis models.</span></span>

Hybrid Estimation of Distribution Algorithm for Solving Three-Stage Multiobjective Integrated Scheduling Problem

Aiming at reducing the total energy consumption of three stages processing-transportation-assembly in the assembly manufacturing industry, a three-stage multiobjective integrated scheduling problem with job batch transportation considering the energy consumption (3sMISP_JBTEC) is proposed, and a comprehensive energy consumption model of multistage of 3sMISP_JBTEC with an improved turn off/on strategy in the processing stage and considering speed in the transportation stage is formulated. Then, a hybrid estimation of distribution algorithm with variable neighborhood search (HEDA_VNS) is developed to solve the scheduling problem. In the HEDA_VNS, the reasonable coding/decoding rules and speed scheduling scheme (SSS) are designed. Moreover, two local search strategies are designed to further enhance the performance of HEDA_VNS. Among them, three types of neighborhood search strategies are devised in Local Search I to improve the search efficiency while retaining the structure of the original high-quality solution. A variable neighborhood hybrid operation based on the speed scheduling set is designed in Local Search II to further improve the quality of the solution while balancing the optimization goals. Finally, simulations and comparisons show the efficiency of the proposed HEDA_VNS.

A comparative study of hybrid estimation distribution algorithms in solving the facility layout problem

The Estimation Distribution Algorithm (EDA) is an evolutionary algorithm that uses probabilistic models to create candidate solutions. Previous researchers have suggested various hybrid methods to avoid the premature convergence of EDA. This research conducts a comparative study between several variations of hybridization in EDA with regards to the descriptive statistics in the objective values.This study also proposes a new hybrid approach, named Adapted EDA (AEDA), by adapting the structure of EDA by adding a lottery procedure, an elitism strategy, and a neighborhood search. The proposed AEDA, several hybridizations of EDA, and Genetic Algorithm (GA) plus Tabu Search (TS) are applied to the facility layout design in manufacture – Enhanced Facility Layout Problem (EFLP) – to analyze their solutions. The hybrid EDAs that are being compared are EDA plus GA (EDAGA), EDA plus Particle Swarm Optimization (EDAPSO), the combination of EDAPSO plus TS (EDAhybrid), and AEDA. The experimental results show that the AEDA can significantly improves the solution quality in solving all the EFLP instances compared to other algorithms.

Remaining Useful Life Assessment for Lithium-Ion Batteries Using CNN-LSTM-DNN Hybrid Method

The prediction of a Lithium-ion battery's lifetime is very important for ensuring safety and reliability. In addition, it is utilized as an early warning system to prevent the battery's failure. Recent advance in Machine Learning (ML) is an enabler for new data-driven estimation approaches. In this paper, we suggest a hybrid method, named the CNN-LSTM-DNN, which is a combination of Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), and Deep Neural Networks (DNN), for the estimation of the battery's remaining useful life (RUL) and improving prediction accuracy with acceptable execution time. A comparison against various ML estimation algorithms is carried out to show the superiority of the proposed hybrid estimation approach. For that, three statistical indicators, i.e., the MAE, R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , and RMSE, are selected to assess numerically the prediction results. Experimental validation is performed using two datasets of different lithium-ion batteries from NASA and CALCE. Thus, results reveal that hybrid methods perform better than the single ones, also the effectiveness of the suggested method in reducing the prediction error and in achieving better RUL prediction performance compared to the other methods.

Pelagibacterium limicola sp. nov., isolated from a soda alkali-saline soil.

A Gram-staining negative, motile, non-spore-forming, rod-shaped bacterium, designated NAJP-14T, was isolated from the alkali-saline soil in Heilongjiang, Northeast China. Phylogenetic analysis based on 16S rRNA gene sequencing illustrated that strain NAJP-14T was a member of the genus Pelagibacterium, and shared 94.6-96.6% sequence identities to species from the genus Pelagibacterium. Strain NAJP-14T grew at 20-45°C (optimum, 30°C), pH 7.0-10.0 (optimum, pH 8.0) and in the presence of up to 5% w/v NaCl. The menaquinone was determined to be Q (10). The major fatty acids were identified as C18:1w6c (38.7%), C16:0 (16.2%) and C19:0 cyclo w8c (13.9%). The G + C content of the genomic DNA was 61.2%. Out of the 3442 predicted genes, 3391 were protein-coding genes and 51 were ncRNA. Digital DNA-DNA hybridization (dDDH) estimation and average nucleotide identity (ANI) of the strain NAJP-14T and the type strains of related species in the same family ranged between 17.9 and 21.8% and between 61.4 and 78.7%, respectively. Based on these data, it is concluded that strain NAJP-14T possesses sufficient characteristics to differentiate it from all recognized Pelagibacterium species, and should be considered as a novel species for which the name Pelagibacterium limicola sp. nov. is proposed. The type strain is NAJP-14T(= CGMCC 1.16631T, = JCM 33746T).

Stochastic modeling of multiline orders in integrated storage‐order picking system

AbstractDue to demanding service levels in e‐commerce order fulfillment, modeling and analysis of integrated storage and order picking processes in warehouses deserve special attention. The upstream storage system can have a significant impact on the performance of the downstream order picking process. With a particular focus on multiline e‐commerce orders, we develop an analytical modeling framework for integrated analysis of upstream (shuttle‐based storage and retrieval system) and downstream (pick system) networks. To capture the consolidation delays in fulfilling multiline orders, the downstream pick system is modeled with a closed queuing network that includes synchronization nodes. The configuration of the synchronization station is adapted to model the variety of order profiles handled at the pick station. For the downstream closed queuing network, we propose a decomposition‐based solution methodology that results in good solution accuracy. The resulting semi‐open queuing network (SOQN) of the integrated system is analyzed using the matrix‐geometric method (MGM). To improve the accuracy of analytical estimates of the measures, we propose a hybrid simulation/analytical framework, where the performance measures of complex subnetworks are obtained from simulation. We also develop a detailed simulation model of the physical system for validating the analytical and hybrid estimates of the performance measures. The results from experiments indicate that the hybrid simulation/analytical approach reduces the error in the throughput time estimates to 3% from 18% obtained from the analytical model. Then, we investigate the effect of the upstream network configuration (such as the number of storage aisles) and the downstream network configuration (such as the mixed vs. dedicated picking, CONWIP control for orders, order batching) on the order throughput times. Our analysis provides a threshold on the maximum numbers of allowable orders (CONWIP control) and number of aisles beyond which the improvement in average throughput time of the integrated system is marginal. Numerical experiments with high‐order arrivals also highlight that mixed picking in the downstream network can result in significant throughput time reduction in comparison to dedicated picking.

A hybrid estimation of distribution algorithm for distributed flexible job shop scheduling with crane transportations

Distributed flexible job shop scheduling has attracted research interest due to the development of global manufacturing. However, constraints including crane transportation and energy consumption should be considered with the realistic requirements. To address this issue, first, we modeled the problem by utilizing an integer programming method, wherein the makespan and energy consumptions during the machine process and crane transportation are optimized simultaneously. Afterward, a hybrid algorithm consisting of estimation of distribution algorithm (EDA) and variable neighborhood search (VNS) was proposed to solve the problem, where an identification rule of four crane conditions was designed to make fitness calculation feasible. In EDA component, the parameters in probability matrices are set to be self-adaptive for stable convergence to obtain better output. Moreover, a probability mechanism was applied to control the activity of the EDA component. In VNS component, five problem-specific neighborhood structures including global and local strategies are employed to enhance exploitation ability. The simulation tests results confirmed that the proposed hybrid EDA-VNS algorithm can solve the considered problem with high efficiency compared with other competitive algorithms, and the proposed improving strategies are verified to have significance in better performance.

Hybrid State Estimation–A Contribution Towards Reliability Enhancement of Artificial Neural Network Estimators

Data-driven models are obtained purely from data without complex theoretical modeling and without explicit model knowledge. This results in black box models whose traceability and reliability constitute a major challenge. This contribution addressed this issue and presents a novel hybrid estimation method, which enhances the reliability of artificial neural networks. Within this method, a simple model based on physical knowledge secures a model based on an artificial neural network. An unscented Kalman filter realizes the interaction of the two individual models. Thereby, a confidence level determines which model is trusted to a greater extent or even entirely. As part of the method for adjusting this confidence level, the input variables of the artificial neural network are related to the data used in training. The more often the artificial neural network has encountered a situation in the training process, the greater the confidence level will be. Finally, the confidence level is used to set the covariances of the unscented Kalman filter. In this contribution, the method is presented using the application of roll angle estimation for passenger cars. By using the hybrid method the reliability of the estimation is increased in comparison to the artificial neural network. For this purpose, sensor malfunctions as well as a sensor failure are simulated. These disturbances are compensated by the introduced method. In addition, the hybrid state estimator increases the estimation quality compared to the individual estimators. The proposed method can be applied to any problem, where knowledge-based models are available to secure data-driven models.

Multivariable Binary MRAC with Guaranteed Transient Performance Using Non-homogeneous Robust Exact Differentiators

In this paper, we propose an extension of the binary model reference adaptive control (BMRAC) for uncertain multivariable linear plants with non-uniform arbitrary relative degree. The BMRAC is a robust adaptive strategy which has good transient properties and robustness of sliding mode control with the important advantage of having a continuous control signal free of chattering. The relative degree obstacle is circumvented using a multivariable version of a hybrid estimation scheme, named global robust exact differentiator (GRED). The hybrid estimator switches between robust exact differentiators (RED) based on higher-order sliding modes and lead filters in a way that the exact derivatives are globally obtained in finite time. To improve the robustness and the transient performance of the GRED, we propose a modification of the switching scheme replacing the conventional RED with a non-homogeneous one. Global exact output tracking is obtained with robustness and guaranteed transient performance without requiring stringent symmetry assumptions on the plant high-frequency-gain matrix.

Inference in cylindrical models having latent Markovian classes—With an application to ocean current data

Spatial direction vector data can be represented cylindrically by linear magnitudes and circular angles. We analyze such data by using a hierarchical Markov random field model with latent discrete classes and conditionally independent cylindrical data given the classes. The structure of a Potts model segments the spatial domain, and each class defines a cylindrical density that represents a specific structure. We consider two types of cylindrical distributions; the Weibull sine-skewed von Mises distribution, which is skewed in the circular part, and the generalized Pareto-type wrapped Cauchy distribution, which is heavy-tailed in the linear part. In this setting, we develop a statistically efficient block composite likelihood method for parameter estimation. The method is shown to provide much faster convergence than an expectation–maximization approach. However, the convergence is less stable for the block composite likelihood method, and we suggest a hybrid estimation approach for practical use. We apply the approach to study ocean surface currents in the Norwegian Sea. The models are able to describe the currents in terms of interpretable local regimes of two–three classes. Scoring rules are used to measure predictive performance of the two cylindrical densities. Results indicate that there is clearly skew angular components, and possibly also some heavy tails in magnitude.

On The Estimation Of The Shape Parameter Of A Symmetric Distribution

The shape parameter of a symmetric probability distribution is often more difficult to estimate accurately than the location and scale parameters. In this paper, we suggest an intuitive but innovative matching quantile estimation method for this parameter. The proposed shape parameter estimate is obtained by setting its value to a level such that the central 1/���� portion of the distribution will just cover all n observations, while the location and scale parameters are estimated using existing methods such as maximum likelihood (ML). This hybrid estimator is proved to be consistent and is illustrated by two distributions, namely Student-t and Exponential Power. Simulation studies show that the hybrid method provides reasonably accurate estimates. In the presence of extreme observations, this method provides thicker tails than the full ML method and protect inference on the location and scale parameters. This feature offered by the hybrid method is also demonstrated in the empirical study using two real data sets.

Unconstrained Face Detection of Multiple Humans Present in the Video

To capture a stable picture through the digital camera is a challenging task in computer vision. The While identifying facial features such as misalignment, a parasitic light effect and a change in object position are the errors found in image processing. These errors get aggregated with images and cumulatively create distortion in the output video, which makes facial feature recognition more complicated in the video. In this paper, a solutions for unconstrained facial detection from digital image processing has been proposed, which fulfilled two requirements; first is a reliable method to extract the facial feature of the humans from a video and second is the estimation of 3D-image of human from the motion video. To meet these requirements, we develop a hybrid estimation method that combines the feature selection and extraction of facial features of the human from the video. Here we have extended the estimation of 2D to 3D unconstrained facial feature recognition. In the results, we found that the object in images is detected and we are able to develop the 3D sketch of human from the video. Further to validate the robustness of the proposed method, we have performed comprehensive testing on the huge dataset. The output of testing shows that the proposed method would be better to identify multiple facial features.