Hybrid Estimation Research Articles

Paracrocinitomix mangrovi gen. nov., sp. nov., isolated from a mangrove sediment: proposal of two new families, Phaeocystidibacteraceae fam. nov. and Owenweeksiaceae fam. nov., and emended description of the family Schleiferiaceae.

A Gram-stain-negative and short rod-shaped bacterial strain designated GM2-3-6-6T was obtained from a mangrove sediment. Cells were light yellow-pigmented, catalase-positive and oxidase-positive. Carotenoid pigment was produced. Phylogeny of the 16S rRNA gene showed that strain GM2-3-6-6T was affiliated to the family Crocinitomicaceae, sharing maximum sequence similarities with Crocinitomix algicola 0182T, C. catalasitica IFO 15977T, and Putridiphycobacter roseus SM1701T of 93.8%, 93.6%, and 92.5%, respectively. The average nucleotide identity values, digital DNA-DNA hybridization estimates and average amino acid identity values between strain GM2-3-6-6T and the three close relatives were 68.6-68.8%, 18.5-19.2%, and 59.0-62.3%, respectively. The complete circular genome of strain GM2-3-6-6T was 4,365,762bp in length with a DNA G + C content of 35.0%. The respiratory quinone was MK-7. The major polar lipids consisted of phosphatidylethanolamine, two unidentified phospholipids, one unidentified aminoglycolipid, one unidentified aminolipid and four other unidentified lipids. The major fatty acids were iso-C15:0, iso-C15:1 G, summed feature 3 (C16:1ω7c and/or C16:1ω6c), and iso-C17:0 3-OH. Based on genomic, phenotypic, and chemotaxonomic characterizations, strain GM2-3-6-6T represents a novel species of a novel genus, for which the name Paracrocinitomix mangrovi gen. nov., sp. nov. is proposed. The type strain is GM2-3-6-6T (= MCCC 1K04831T = KCTC 82931T). Additionally, phylogenomic analysis of the type strains of the family Schleiferiaceae and family Cryomorphaceae related members including uncultivated bacteria, was performed using the Genome Taxonomic Database toolkit (GTDB-Tk). Based on 16S rRNA gene phylogeny and genomic features, two novel families, Phaeocystidibacteraceae fam. nov. and Owenweeksiaceae fam. nov. are proposed. An emended description of the family Schleiferiaceae is also proposed.

Development of a Hybrid Adaptive Neuro-fuzzy Inference System with Coulomb-Counting State-of-Charge Estimator for Lithium–Sulphur Battery

This study presents the development of an improved state of charge (SOC) estimation technique for lithium–sulphur (Li–S) batteries. This is a promising technology with advantages in comparison with the existing lithium-ion (Li-ion) batteries such as lower production cost and higher energy density. In this study, a state-of-the-art Li–S prototype cell is subjected to experimental tests, which are carried out to replicate real-life duty cycles. A system identification technique is then used on the experimental test results to parameterize an equivalent circuit model for the Li–S cell. The identification results demonstrate unique features of the cell’s voltage-SOC and ohmic resistance-SOC curves, in which a large flat region is observed in the middle SOC range. Due to this, voltage and resistance parameters are not sufficient to accurately estimate SOC under various initial conditions. To solve this problem, a forgetting factor recursive least squares (FFRLS) identification technique is used, yielding four parameters which are then used to train an adaptive neuro-fuzzy inference system (ANFIS). The Sugeno-type fuzzy system features four inputs and one output (SOC), totalling 375 rules. Each of the inputs features Gaussian-type membership functions while the output is of a linear type. This network is then combined with the coulomb-counting method to obtain a hybrid estimator that can accurately estimate SOC for a Li–S cell under various conditions with a maximum error of 1.64%, which outperforms the existing methods of Li–S battery SOC estimation.

State Estimation Using SCADA and PMU Measurements for Networks Containing Classic HVDC Links

• Hybrid state estimation (HSE) algorithm based on SCADA and PMUs measurements for AC power systems with embedded classic high voltage direct current (HVDC) links. • AC and DC measurements are modeled independently as functions of AC and DC states, respectively. • AC and DC measurements (states) are processed (estimated) simultaneously. • Coupling of AC and DC states is expressed by a set of three nonlinear equality constraints. This paper proposes a hybrid state estimation (HSE) algorithm which employs conventional measurements received from supervisory control and data acquisition (SCADA) system and synchrophasors measured from phasor measurement units (PMUs) for AC power systems with embedded classic high voltage direct current (HVDC) links. DC measurements obtained from the HVDC link are assumed to be sampled at the same rate as AC measurements. Zero injections and AC/DC coupling equations are modeled as equality constraints. By applying the nonlinear weighted least squares (WLS) algorithm with equality constraints, the HSE calculates both AC and DC states simultaneously. The effectiveness of the proposed algorithm is verified by simulations on the IEEE standard 14 and 30 bus test systems. The test results reveal the benefits of the proposed approach in terms of estimation accuracy and convergence speed.

Resilience-Based Surrogate Robustness Measure and Optimization Method for Robust Job-Shop Scheduling

This paper addresses the robust job-shop scheduling problems (RJSSP) with stochastic deteriorating processing times by considering the resilience of the production schedule. To deal with the disturbances caused by the processing time variations, the expected deviation between the realized makespan and the initial makespan is adopted to measure the robustness of a schedule. A surrogate model for robust scheduling is proposed, which can optimize both the schedule performance and robustness of RJSSP. Specifically, the computational burden of simulation is considered a deficiency for robustness evaluation under the disturbance of stochastic processing times. Therefore, a resilience-based surrogate robustness measure (SRM-R) is provided for the robustness estimation in the surrogate model. The proposed SRM-R considers the production resilience and can utilize the available information on stochastic deteriorating processing times and slack times in the schedule structure by analyzing the disturbance propagation of the correlated operations in the schedule. Finally, a multi-objective hybrid estimation of distribution algorithm is employed to obtain the Pareto optimal solutions of RJSSP. The simulation experiment results show that the presented SRM-R is effective and can provide the Pareto solutions with a lower computational burden. Furthermore, an RJSSP case derived from the manufacturing environment demonstrates that the proposed approach can generate satisfactory robust solutions with significantly improved computational efficiency.

Sulfurimonas marina sp. nov., an obligately chemolithoautotrophic, sulphur-oxidizing bacterium isolated from a deep-sea sediment sample from the South China Sea.

A novel marine bacterium, designated strain B2T, was isolated from a deep-sea sediment sample collected from the South China Sea. Cells were observed to be Gram-stain negative, motile and rod shaped with a single polar flagellum. B2T could grow at 10-45 °C (optimum, 35 °C), pH 4.5-9.0 (optimum, pH 7.0) and in the presence of 1.0-8.0 % (w/v) NaCl (optimum, 3.0%). The isolate grew chemolithoautotrophically with sulphide, elemental sulphur and thiosulphate as electron donors, carbon dioxide as the sole carbon source, and molecular oxygen as the sole electron acceptor. Molecular hydrogen did not support growth. The predominant fatty acids of B2T were C16 : 1ω7c, C16 : 0 and C18 : 1ω7c. The results of phylogenetic analysis based on 16S rRNA gene sequence indicated that B2T represented a member of the genus Sulfurimonas, with the highest similarity to the 16S rRNA gene sequences of Sulfurimonas indica NW8NT (95.9 %), Sulfurimonas crateris SN118T (95.7 %), Sulfurimonas xiamenensis 1-1NT (95.6 %) and Sulfurimonas paralvinellae GO25T (95.4 %). Sequence similarities to other members of the genus Sulfurimonas were less than 95.0 %. In addition, the average nucleotide identity (ANI) value and digital DNA-DNA hybridization (dDDH) estimate between B2T and S. indica NW8NT were 73.0 and 23.7 %, respectively. The size of the complete genome of B2T is 22 61 034 bp, with a DNA G+C content of 36.0 mol %. On the basis of the phenotypic, phylogenetic and genomic data presented here, strain B2T represent a novel species of the genus Sulfurimonas, for which the name Sulfurimonas marina sp. nov. is proposed, with the type strain B2T (=MCCC 1A14515T=KCTC 15852T).

An online hybrid estimation method for core temperature of Lithium-ion battery with model noise compensation

Temperature monitoring plays an important role in developing advanced battery management systems, ensuring safety, and improving cell performance. Core temperature provides more accurate indications of battery natures than surface temperature, but it cannot be measured directly. In this paper, a novel hybrid method by fusing a model-based method and a data-driven method is proposed to estimate the battery core temperature with model noise compensation. In the model-based method, an extended Kalman filter (EKF) is developed to estimate the core temperature based on an electro-thermal coupling model. The model parameters are updated with the feedback of the estimated core temperature and state of charge. In the data-driven method, a neural network is trained to characterize the battery model noises. For model noise compensation, the noise covariances of the EKF are dynamically adjusted by minimizing the estimation errors between the EKF and the neural network with particle swarm optimization. Experiments for implementing and validating the proposed method are conducted in a wide range of ambient temperatures. Compared with three existing methods, the proposed method can improve the estimation accuracy by at least 56.8% at −15 °C and 60.9% at 5 °C.

Angle-incremental range estimation for FDA-MIMO radar via hybrid sparse learning

In this paper, a target parameter estimation problem is addressed for the recently emerging frequency diverse array multiple-input multiple-output (FDA-MIMO) radar system, utilizing sparse learning. The scene is modeled as a two dimensional (2D) angle-incremental range grid. To solve the resulting sparse problem, the recently proposed user-parameter free algorithms including block sparse learning via iterative minimization (BSLIM), iterative adaptive approach (IAA), sparse iterative covariance-based estimation (SPICE), likelihood-based estimation of sparse parameters (LIKES), and orthogonal matching pursuit (OMP) are applied which achieve excellent parameter estimation performance. However, these algorithms do not exhibit a uniform performance under different scenarios such as the number of available snapshots and the number of present targets. In this paper, we propose a hybrid estimation approach which utilizes the model order selection (MOS), and automatically selects the best algorithm for the scenario under test. The performance analyses show that the proposed hybrid algorithm presents higher accuracy in terms of root mean square error (RMSE), as compared with the discussed sparse algorithms, and converges to the Cramér-Rao lower bound (CRLB).

Longitudinal Associations of Air Pollution With Body Size and Composition in Midlife Women: The Study of Women's Health Across the Nation.

We examined longitudinal associations of air pollution exposure, including fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3), with weight, BMI, waist circumference, fat mass, lean mass, and proportion fat mass in midlife women. The study population included 1,654 White, Black, Chinese, and Japanese women from the Study of Women's Health Across the Nation, with the baseline median age of 49.6 years, followed from 2000 to 2008. Annual air pollution exposures were assigned by linking residential addresses with hybrid estimates of air pollutant concentrations at 1-km2 resolution. Body size was measured, and body composition was measured using DXA at approximately annual visits. Linear mixed effects models were used to examine the associations between air pollution and body size and composition measures and whether these associations differed by physical activity. After adjusting for potential confounders, an interquartile range increase in PM2.5 concentration (4.5 μg/m3) was associated with 4.53% (95% CI 3.85%, 5.22%) higher fat mass, 1.10% (95% CI 0.95%, 1.25%) higher proportion fat mass, and 0.39% (95% CI -0.77%, -0.01%) lower lean mass. Similar associations were also observed for NO2 and O3. Weaker associations of PM2.5 and NO2 with body composition were observed in participants who engaged in more physical activity. Our analyses provide evidence that exposure to PM2.5, NO2, and O3, is adversely associated with body composition, including higher fat mass, higher proportional fat mass, and lower lean mass, highlighting their potential contribution to obesity.

Hybrid Estimation Strategy for the State of Health of Lithium-ion Batteries with Different Application Needs

Hybrid Estimation Strategy for the State of Health of Lithium-ion Batteries with Different Application Needs

Estimation of the Mixed Layer Depth in the Indian Ocean from Surface Parameters: A Clustering-Neural Network Method.

The effective estimation of mixed-layer depth (MLD) plays a significant role in the study of ocean dynamics and global climate change. However, the methods of estimating MLD still have limitations due to the sparse resolution of the observed data. In this study, a hybrid estimation method that combines the K-means clustering algorithm and an artificial neural network (ANN) model was developed using sea-surface parameter data in the Indian Ocean as a case study. The oceanic datasets from January 2012 to December 2019 were obtained via satellite observations, Argo in situ data, and reanalysis data. These datasets were unified to the same spatial and temporal resolution (1° × 1°, monthly). Based on the processed datasets, the K-means classifier was applied to divide the Indian Ocean into four regions with different characteristics. For ANN training and testing in each region, the gridded data of 84 months were used for training, and 12-month data were used for testing. The ANN results show that the optimized NN architecture comprises five input variables, one output variable, and four hidden layers, each of which has 40 neurons. Compared with the multiple linear regression model (MLR) with a root-mean-square error (RMSE) of 5.2248 m and the HYbrid-Coordinate Ocean Model (HYCOM) with an RMSE of 4.8422 m, the RMSE of the model proposed in this study was reduced by 27% and 22%, respectively. Three typical regions with high variability in their MLDs were selected to further evaluate the performance of the ANN model. Our results showed that the model could reveal the seasonal variation trend in each of the selected regions, but the estimation accuracy showed room for improvement. Furthermore, a correlation analysis between the MLD and input variables showed that the surface temperature and salinity were the main influencing factors of the model. The results of this study suggest that the pre-clustering ANN method proposed could be used to estimate and analyze the MLD in the Indian Ocean. Moreover, this method can be further expanded to estimate other internal parameters for typical ocean regions and to provide effective technical support for ocean researchers when studying the variability of these parameters.

A Novel Frequency Estimator for Protection Applications in Active Distribution Networks

Frequency is a useful parameter for ensuring the proper functioning of the power system operation, control, and protection. In this article, a fast and accurate frequency estimator (FE) is developed using a zero crossing and a peak, occurring in the buffered data, unlike the commonly used two zero crossings. Prior to this, the signal is preprocessed using the sliding Goertzel filter (SGF) that mitigates the impact of undesirable features such as harmonics, noise, etc., from the signal. The two FEs, with and without SGF, then work in tandem to form the hybrid estimator and are named as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Z_{C}P_{k}$</tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{HFE}}$</tex-math></inline-formula> . The estimated frequency is chosen based on the minimum error computed from the two parallel estimators. The performance of the estimator is, then, validated using the simulated signals polluted with harmonics, noise, transients, etc. The article also discusses the protection-oriented applications of the proposed FE in active distribution network (ADN). Moreover, the evaluation results with the real-time field data and the IEEE-13 ADN incorporated with photovoltaic using dSPACE1104 and real-time digital simulator show its capability to estimate the real-time frequency with satisfactory output.

Hybrid estimates of marketing and commercial enterprise’s activities

Hybrid estimates of marketing and commercial enterprise’s activities

An enhanced two phase estimation of distribution algorithm for solving scheduling problem

ABSTRACT Scheduling is one critical issue both in the field of industry engineering and combinatorial optimization research. In order to solve multi-objective scheduling problem with uncertainty, this paper presents a method of enhanced hybrid Estimation of Distribution Algorithm (EDA) with Teaching and Learning-Based Optimization Algorithm (TLBO). First, in order to concentrate their respective advantages, two algorithms of EDA and TLBO are integrated to enhance the capability of both global and local search. Second, scenario-based simulation is adopted to deal with uncertainty, and an adaptive sampling strategy is involved to dynamically adjust the number of scenarios during the evolving process. Third, a problem-specific local search is designed to further improve the optimality of candidate solutions. By comparing with existing algorithms on the benchmark problems of flexible job shop scheduling problem (FJSP), it is to demonstrate that our proposal can obtain better solutions in the aspects of optimality and computational efficiency.

Error analysis for hybrid estimates of proportions using big data1

Big data, including administrative data, is seen as a new data source for official statistics especially given the increasing difficulty of getting acceptable response rates in sample surveys. It might be used directly or perhaps with the use of models to adjust for shortcomings in the big data. Hybrid estimates using complementary survey data are another technique for overcoming these shortcomings. To make decisions on how big data might be used, we need to understand the nature of the errors in the big data source. The paper describes an Error Framework for the analysis of errors in big data and hybrid estimates. The paper also describes the circumstances under which hybrid estimates will provide more accurate estimates than big data in isolation or survey data. A case study is provided to illustrate the application of hybrid estimates in practice. A potential application of hybrid estimation is also described to address the upward biases that often exist in epidemiological modelling.

How to estimate health service coverage in 58 districts of Benin with no survey data: Using hybrid estimation to fill the gaps.

The global movement to use routine information for managing health systems to achieve the Sustainable Development Goals, relies on administrative data which have inherent biases when used to estimate coverage with health services. Health policies and interventions planned with incorrect information can have detrimental impacts on communities. Statistical inferences using administrative data can be improved when they are combined with random probability survey data. Sometimes, survey data are only available for some districts. We present new methods for extending combined estimation techniques to all districts by combining additional data sources. Our study uses data from a probability survey (n = 1786) conducted during 2015 in 19 of Benin's 77 communes and administrative count data from all of them for a national immunization day (n = 2,792,803). Communes are equivalent to districts. We extend combined-data estimation from 19 to 77 communes by estimating denominators using the survey data and then building a statistical model using population estimates from different sources to estimate denominators in adjacent districts. By dividing administrative numerators by the model-estimated denominators we obtain extrapolated hybrid prevalence estimates. Framing the problem in the Bayesian paradigm guarantees estimated prevalence rates fall within the appropriate ranges and conveniently incorporates a sensitivity analysis. Our new methodology, estimated Benin's polio vaccination rates for 77 communes. We leveraged probability survey data from 19 communes to formulate estimates for the 58 communes with administrative data alone; polio vaccination coverage estimates in the 58 communes decreased to ranges consistent with those from the probability surveys (87%, standard deviation = 0.09) and more credible than the administrative estimates. Combining probability survey and administrative data can be extended beyond the districts in which both are collected to estimate coverage in an entire catchment area. These more accurate results will better inform health policy-making and intervention planning to reduce waste and improve health in communities.

A heterogeneous non-overlapping domain decomposition explicit finite volume method for a real-time hybrid process-state estimator of 3D unsteady advection-diffusion fields

A heterogeneous, nonoverlapping domain-decomposition, explicit, finite volume method (HT-NODDE-FVM) is developed and applied to a real-time, hybrid, process-state estimator of 3D unsteady, advection-diffusion concentration fields produced by stationary or moving gas sources. The hybrid Luenberger-naive estimator uses a single, pointwise concentration measurement of the field taken onboard a sensing aerial vehicle (SAV) and computes in real-time the concentration field in the entire domain, while guiding the SAV to locations that provide optimal information to the estimator. The computational domain of interest is divided into multiple non-overlapping subdomains using a structured and uniform grid. A Luenberger estimator (or observer) in the form of a 3D advection-diffusion partial differential equation (PDE) is developed for the subdomain where the SAV resides, and a naive estimator of a similar form is developed for the remaining subdomains. The transmission conditions are used on the interfaces between adjacent subdomains for data communication. The hybrid Luenberger-naive estimator is discretized in space by the HT-NODDE-FVM with Total Variation Diminishing (TVD) and the resulting semi-discrete equations are integrated by a fourth order Runge-Kutta scheme. Continuity and flux balance transmission conditions are enforced at the interfaces of adjacent subdomains. The HT-NODDE-FVM hybrid estimator is implemented in parallel using OpenMP. Verification and error analysis of the NODDE-FVM are performed with benchmark tests that include the 3D advection PDE for various initial density distributions and the 3D advection-diffusion PDE for instantaneous gas releases under constant wind speed and eddy diffusivities for a range of Peclet numbers. Additional benchmark tests provide verification and error analysis of the HT-NODDE-FVM hybrid estimator for an instantaneous release by a stationary gas source in a large domain with constant atmospheric properties. The tests examine the impact of grid resolution, sensor model, estimation gain, and numerical data, on the L1, L2, and L∞ norms of the estimation error. Parallelization efficiency analysis of the OpenMP implementation of the hybrid estimator is also presented. The hybrid estimator is applied to the simulation of gaseous plumes from stationary and moving sources in km-scale computational domains under realistic atmospheric conditions and the results show that it achieves real-time estimation of the concentration field.

Modeling stochastic service time for complex on-demand food delivery

Uncertainty is everywhere in the food delivery process, which significantly influences decision-making for complex on-demand food delivery problems, affecting delivery efficiency and customer satisfaction. Especially, the service time is an indispensable part of the delivery process impacted by various uncertain factors. Due to the simplicity and high accuracy requirement, we model the uncertain service time as a Gaussian mixture model (GMM). In detail, we transform the distribution estimation problem into a clustering problem by determining the probability of each data belonging to each component (each cluster as well). A hybrid estimation of distribution algorithm is proposed to intelligently solve the clustering problem with the criterion to optimize quality and simplicity simultaneously. First, to optimize the simplicity, problem-specific encoding and decoding methods are designed. Second, to generate initial solutions with good clustering results, a Chinese restaurant process-based initialization mechanism is presented. Third, a weighted-learning mechanism is proposed to effectively guide the update of the probability model. Fourth, a local intensification based on maximum likelihood is used to exploit better solutions. The effect of critical parameters on the performances of the proposed algorithm is investigated by the Taguchi design of the experimental method. To demonstrate the effectiveness of the proposed algorithm, we carry out extensive offline experiments on real-world historical data. Besides, we employ the GMMs obtained by our algorithm in a real-world on-demand food delivery platform, Meituan, to assist decision-making for order dispatching. The results of rigorous online A/B tests verify the practical value of introducing the uncertainty model into the real-life application.

Comparison study between hybrid Nelder-Mead particle swarm optimization and open circuit voltage—Recursive least square for the battery parameters estimation

Recently, the whole world has turned to the rechargeable battery as the primary source of electric vehicles (EVs). Meanwhile, the majority of the automobile manufacturers quantify lithium as a key material for the development of battery chemistry. Currently, Lithium-ion (Li-ion) batteries are the dominant technology for battery electric vehicles (BEVs). The real value of Lithium is that they deliver high and fast energy densities in a small volume or configuration. Thus, the description, identification and modeling of the battery, during its operation, are essential actors for the success of EVs development. However, modeling Li-ion-based energy storage devices has not yet been easy enough due to the complexity of chemical reactions in real time. In this work, the modeling of Li-ion cells is performed with dynamic equivalent electrical circuit model (ECM). This study aims to exploit the advantages of two hybrid estimation methods for the identification of Li-ion battery model parameters. The first method is hybrid optimization algorithm PSO-NM which is composed of a particle swarm optimization algorithm (PSO) and a Nelder–Mead algorithm (NM). On the other hand, the second hybrid model, namely the OCV-RLS algorithm, benefits from the advantages of direct measurement of open circuit voltage (OCV) and of a Least Square Recursive (RLS) to obtain an optimum overall performance estimate. The validation results support the reliability of these proposed hybrid methods to identify the Li-ion battery model. In addition, they constitute a reference for the comparison of the two algorithms PSO-NM and OCV-RLS to solve the problem of Li-ion battery SOC estimating.

Online State-of-Health Estimation of Lithium-Ion Battery Based on Incremental Capacity Curve and BP Neural Network

Lithium-ion batteries (LIBs) have been widely used in various fields. In order to ensure the safety of LIBs, it is necessary to accurately estimate of the state of health (SOH) of the LIBs. This paper proposes a SOH hybrid estimation method based on incremental capacity (IC) curve and back-propagation neural network (BPNN). The voltage and current data of the LIB during the constant current (CC) charging process are used to convert into IC curves. Taking into account the incompleteness of the actual charging process, this paper divides the IC curve into multiple voltage segments for SOH prediction. Corresponding BP neural network is established in multiple voltage segments. The experiment divides the LIBs into five groups to carry out the aging experiment under different discharge conditions. Aging experiment data are used to establish the non-linear relationship between the decline of SOH and the change of IC curve by BP neural network. Experimental results show that in all voltage segments, the maximum mean absolute error does not exceed 2%. The SOH estimation method proposed in this research makes it possible to embed the SOH estimation function in battery management system (BMS), and can realize high-precision SOH online estimation.

Sensorless vector control of PM synchronous motor by hybrid estimation technique considering effect of non-ideal physical attributes

Sensorless vector control of PM synchronous motor by hybrid estimation technique considering effect of non-ideal physical attributes