Estimation Of Environmental Parameters Research Articles

Performance-based design of environmental parameters for offshore wind turbine foundations

Taking into account the coupling effects of wind, wave, and current on offshore wind turbines (OWTs), a combined approach utilizing non-linear finite element analysis (FEA), back propagation neural network (BPNN), and probabilistic statistical methodology (PSM) is proposed for accurate environmental parameter estimation in OWT foundation design. The structural bearing performances of OWT foundations are precisely predicted through a combination of FEA and BPNN models, and then serve as constraints for probabilistic statistical analysis. Furthermore, for PSM, a robust multivariate joint probability distribution is constructed leveraging copula functions, which not only maximizes marginal information but also effectively captures the intricate dependencies among ocean environmental parameters. Subsequently, the proposed models, along with traditional univariate analysis, are utilized to estimate the return values of diverse load combinations. The results indicate that, for a given return period, the conditional probability model yields lower wave heights and current velocities compared to univariate analysis, while the joint probability model results in lower values for all ocean parameters, making both approaches more suitable for OWT foundation design. This coupled approach presents a more rational and cost-effective solution compared to univariate analysis, potentially resulting in decreased investment costs.

Using machine learning techniques in inverse problems of acoustical oceanography

AbstractThe goal of the work presented here is to study a novel approach for inverting acoustic signals recorded in the marine environment for the estimation of environmental parameters of the water column and/or the seabed. The proposed approach is based on signal feature extraction using a discrete wavelet packet transform, applied to the measured signal, and hidden Markov models that exploit the sequential patterns of the signals. The signal feature is thereafter used in the framework of a mixture density network, which, after training with sets of simulated signals calculated within a predefined search space, provides conditional posterior distributions of the recoverable parameters. The technique is tested with two test cases corresponding to different types of inverse problems. The first case corresponds to a simple problem of geoacoustic inversion, while the second is referred to a, rather unusual, still interesting problem of recovering the shape of a seamount using long‐range acoustic data. Both test cases are based on simulated experiments. The inversion results obtained using the proposed scheme are compared with inversion results using statistical features of the acoustic signal, which is another inversion approach well documented in the literature and is also based on the wavelet packet transform of the measured signal.

A mode separation method based on sparse Bayesian learning for explosive sources in a shallow water waveguide

This paper presents a mode separation method based on sparse Bayesian learning (SBL) for explosive sources in a shallow water waveguide. In previous work [Niu et al., JASA, 2021, 4366], the SBL dictionary was constructed by assuming a large number of horizontal wavenumbers and utilized an approximate mode-frequency dispersion relation for low frequencies. Then, modes were separated in the frequency domain by estimating the coefficients of the dictionary atoms. However, challenges inherent to explosive sources, such as bandwidth expansion and the bubble-pulse effect, result in a mismatch in the dictionary matrix built using the approximate mode-frequency dispersion relation for low frequencies, leading to unsuccessful mode separation. To address these issues, this paper builds the SBL dictionary matrix by utilizing the acoustic model (e.g., Kraken) to derive horizontal wavenumbers under various environmental hypotheses and combines it with the secondary bubble-pulse model. By estimating the coefficients of the dictionary atoms, both environmental parameter estimation and mode separation can be achieved simultaneously. Simulation and experimental data results demonstrate the validation of the proposed method.

Research on a Method of Robot Grinding Force Tracking and Compensation Based on Deep Genetic Algorithm

In the grinding process of complex-shaped cast workpieces, discrepancies between the workpiece’s contours and their corresponding three-dimensional models frequently lead to deviations in the machining trajectory, resulting in instances of under-grinding or over-grinding. Addressing this challenge, this study introduces an advanced robotic grinding force automatic tracking technique, leveraging a combination of deep neural networks and genetic algorithms. Harnessing the capability of force sensing, our method dynamically recalibrates the grinding path, epitomizing truly flexible grinding. Initially, in line with the prerequisites for force and pose tracking, an impedance control strategy was developed, integrating pose deviations with force dynamics. Subsequently, to enhance steady-state force tracking, we employed a genetic algorithm to compensate for force discrepancies caused by positional errors. This was built upon the foundational concepts of the three-dimensional model, impedance control, and environmental parameter estimation, leading to an optimized grinding trajectory. Following tracking tests, it was observed that the grinding’s normal force was consistently controlled within the bracket of 20 ± 2.5 N. To further substantiate our methodology, a specialized experimental platform was established for grinding complex-shaped castings. Optimized strategies were employed under anticipated forces of 5 N, 10 N, and 15 N for the grinding tests. The results indicated that the contact forces during the grinding process remained stable at 5 ± 1 N, 10 ± 1.5 N, and 15 ± 2 N. When juxtaposed with conventional teaching grinding methods, our approach manifested a reduction in grinding forces by 71.4%, 70%, and 75%, respectively. Post-grinding, the workpieces presented a pronounced enhancement in surface texture, exhibiting a marked increase in surface uniformity. Surface roughness metrics, originally recorded at 17.5 μm, 17.1 μm, and 18.7 μm, saw significant reductions to 1.5 μm, 1.6 μm, and 1.4 μm, respectively, indicating reductions by 76%, 73%, and 78%. Such outcomes not only meet the surface finishing standards for complex-shaped castings but also offer an efficacious strategy for robot-assisted flexible grinding.

Probe thermometry with continuous measurements

Temperature estimation plays a vital role across natural sciences. A standard approach is provided by probe thermometry, where a probe is brought into contact with the sample and examined after a certain amount of time has passed. In situations where, for example, preparation of the probe is non-trivial or total measurement time of the experiment is the main resource that must be optimized, continuously monitoring the probe may be preferred. Here, we consider a minimal model, where the probe is provided by a two-level system coupled to a thermal reservoir. Monitoring thermally activated transitions enables real-time estimation of temperature with increasing accuracy over time. Within this framework we comprehensively investigate thermometry in both bosonic and fermionic environments employing a Bayesian approach. Furthermore, we explore adaptive strategies and find a significant improvement on the precision. Additionally, we examine the impact of noise and find that adaptive strategies may suffer more than non-adaptive ones for short observation times. While our main focus is on thermometry, our results are easily extended to the estimation of other environmental parameters, such as chemical potentials and transition rates.

Adaptive robot climbing with magnetic feet in unknown slippery structure.

Firm foot contact is the top priority of climbing robots to avoid catastrophic events, especially when working at height. This study proposes a robust planning and control framework for climbing robots that provides robustness to slippage in unknown environments. The framework includes 1) a center of mass (CoM) trajectory optimization under the estimated contact condition, 2) Kalman filter–like approach for uncertain environment parameter estimation and subsequent CoM trajectory re-planing, and 3) an online weight adaptation approach for whole-body control (WBC) framework that can adjust the ground reaction force (GRF) distribution in real time. Though the friction and adhesion characteristics are often assumed to be known, the presence of several factors that lead to a reduction in adhesion may cause critical problems for climbing robots. To address this issue safely and effectively, this study suggests estimating unknown contact parameters in real time and using the evaluated contact information to optimize climbing motion. Since slippage is a crucial behavior and requires instant recovery, the computation time for motion re-planning is also critical. The proposed CoM trajectory optimization algorithm achieved state-of-art fast computation via trajectory parameterization with several reasonable assumptions and linear algebra tricks. Last, an online weight adaptation approach is presented in the study to stabilize slippery motions within the WBC framework. This can help a robot to manage the slippage at the very last control step by redistributing the desired GRF. In order to verify the effectiveness of our method, we have tested our algorithm and provided benchmarks in simulation using a magnetic-legged climbing robot Manegto.

Methods for probability distributions estimation of indoor environmental parameters and long-term IEQ assessment

ABSTRACT To overcome the difficulties in comparing enormous time series for indoor environmental parameters and make use of technological developments in previous research, this study considers environmental parameters from a new perspective, their probability density functions (PDFs). PDFs are combined with existing indoor environmental quality (IEQ) mathematical models to assess the long-term IEQ. Two methodologies were developed: probability distribution estimation of indoor environmental parameters and long-term IEQ distribution assessment based on the Monte Carlo method. The effectiveness of the developed methodologies was illustrated in a three-month IEQ assessment of an office. PDFs were obtained with specific mathematical expressions for the three-month air temperature, sound level and illuminance. The three-month distributions for thermal, visual, acoustic and overall environmental quality were presented using eight previous IEQ mathematical models. PDFs have the advantage of using only a few main parameters instead of an enormous time series to explain the behaviour and characteristics of environmental parameters. PDFs can also potentially determine the commonalities of environmental distributions for different buildings. The obtained IEQ distributions present a straightforward and comprehensive impression of the long-term IEQ, rather than a simple index.

Online adaptive parameter identification of an unmanned surface vehicle without persistency of excitation

This paper is concerned with the parameter identification of an unmanned surface vehicle (USV) with fully unknown coefficients. An online adaptive parameter identification method is proposed for the USV to identify its model parameters without the condition of persistence of excitation. Specifically, a composite adaptive update law is developed based on a truncated integral filtered regressor, such that the model parameters can be estimated online, adapting to possible parameter changes. Then, an extended state observer is used to monitor the total estimation errors caused by environmental disturbances and parameter estimation errors. A salient feature of the proposed method is that the acceleration information is not required and only linear velocities and yaw rate are used for identification. Moreover, the convergence of the estimation errors is assured under the condition of interval excitation only. The stability of the online parameter identification method is proved by Lyapunov stability analysis. Simulations and experiments are carried out to demonstrate the effectiveness of the proposed online identification method for the USV.

Detection and Estimation of Genetic and Environmental Parameters through Model Fitting of Ten Bulb Yield Contributing Traits in Onion

Two onion varieties P2 and P3 and their products F1 and F2 were evaluated in summer and winter seasons for this investigation. Estimated mean values of different traits showed variations from generation to generation in each season. Values of six-parameters viz., m ̂, [d], [h], e1, gd1, gh1 for all the characters were significant except gd1 for a number of leaves, leaf length and bulb volume and also [d] for leaf length and neck length. Overall means ‘m ̂’ had the highest magnitude than [d], [h], e1, gd1 and gh1 for all the characters. Environmental parameter ‘e1’ also exhibited higher magnitude than [d], gd1 and gh1. As the values of [d] and gd1 were found to be non-significant, 4-parameter model was considered for leaf length only. Five-parameter model was considered for neck length, number of leaves and bulb volume and for rest of the traits 6-parameter model was considered. The goodness of fit test showed that 4, 5 and 6-parameter models were not adequate except bulb length and neck length. Therefore, for the development of these two traits in consideration of genotype × environment (G × E) interaction proper design and analysis needs to be done. Due to significant 2 values for other characters the situations becoming more complex as G × E interaction model is inadequate, so for their exact genetic explanation G × E model needs to be extended to include linkage and non-allelic parameters.

Adaptive Impedance Control of a Novel Automated Umbilical System for Propellant Loading

In this paper, an automated umbilical system based on a 6-dof (degree of freedom) hydraulic parallel mechanism is proposed to automate the rocket propellant loading process. The mechanical structure, vision acquisition algorithm, and control algorithm used in the system are described in detail in the paper. To address the fluid nonlinearity problem of the hydraulic drive system, nonlinear compensation and three-state feedback control are used in the paper to enhance the performance of the hydraulic system. For the problem of force tracking during the docking process between the umbilical system and the rocket, an adaptive impedance control algorithm based on the online environmental parameter estimation is proposed in the paper, which effectively reduces the contact force during the docking process. The dynamic tracking and docking experiments indicate that this automated umbilical system features rapid reaction speed, high measurement precision, and good flexibility, which can be used to realize the auto-mating and following task for the propellant loading robot in a hazardous environment.

A multi-load joint distribution model to estimate environmental design parameters for floating structures

Floating structure designs require an environmental condition simulation and corresponding design parameter determination. Typically, the design values are defined by individually calculating the N-yr return period; however, this not only results in overestimation, but it also reduces the accuracy of the structural reliability analysis. Therefore, a new environmental design parameter estimation method which is based on the joint distribution of multiple loads and conditional joint probability models was developed. A multivariate joint model for the metoceanic parameters of the South China Sea based on 30 yrs of annual extremum hindcast data and conditional probability models with one predominant factor were established; the most likely pair of other variables were also considered because of the complexity and variability of the environmental conditions. Environmental parameters with N-yr joint return period, as well as the structural response surface, corresponding to the most unsafe conditions were determined to derive design parameters. The possible conservatism of the conventional design standard was assessed by applying this joint design criterion to a semi-submersible platform example. The results indicate the design response is much more conservative than those obtained using proposed criteria, neither multivariate nor conditional joint models. The proposed design criteria can significantly reduce construction cost.

Estimation of Environmental Effects and Genetic Parameters for Ultrasound Traits in Hanwoo

Estimation of Environmental Effects and Genetic Parameters for Ultrasound Traits in Hanwoo

Statistical models for the analysis of longitudinal traits in beef cattle under sequential selection

The comprehensive analyses of longitudinal traits under sequential selection could improve genetic parameters estimates and lead to more accurate selection decisions. The objective of this study was to evaluate statistical models for analyzing longitudinal traits under sequential selection. We used single trait (STM), multiple trait (MTM) and random regression model with linear splines polynomials (RRM) to estimate genetic parameters for body weight records of Nellore young bulls. First, we used a complete dataset (DS100) with 60,550 body weight records of 12,110 young bulls. Two additional datasets were also obtained from DS100. They were obtained with a sequential selection of 85% (DS85) and 70% (DS70) of heaviest animals. In addition, some datasets with the same number of records as DS85 and DS70 were also obtained with random sampling of 85% (RS85) and 70% (RS70) of body weight records at each age. Body weights were standardized at 330, 385, 440, 495 and 550 days of age for STM and MTM analysis. In RRM, the knots of linear splines were fitted at 250, 330, 385, 440, 495, 550 and 597 days of age. The estimates of additive genetic, residual and phenotypic variances from STM analysis of DS85 and DS70 were lower than the corresponding estimates from STM analysis of DS100. However, the estimates of genetic and environmental parameters from MTM and RRM analysis of DS100, DS85 and DS70 were similar. The reduction of dataset size with random sampling (RS85 and RS70) did not affect the estimates of genetic and environmental parameters from STM, MTM and RRM analysis. MTM and RRM are adequate for genetic evaluation of the longitudinal traits under sequential selection, but RRM presents some advantages over MTM. RRM with linear splines does not need previous adjustments of the body weights for standard ages and it also provides estimates of genetic and environmental parameters directly at the same points as the corresponding traits in MTM.

Improvements in signal processing for the estimation of environmental parameters with machine learning using a compact tetrahedral array and sources of opportunity

In previous work, we showed that we could localize sound sources using a compact tetrahedral hydrophone array in a continental shelf environment south of Block Island, Rhode Island. The tetrahedral array of phones, 0.5 m on a side, was deployed to monitor the construction and operation of the first offshore wind farm in the United States. Directions of arrival (DOAs) for a number of ships were computed using a time difference of arrival technique. Given the DOAs, ranges are estimated using supervised machine learning techniques. We extended that work to estimate a number of environmental parameters including water depth and sediment composition. Here, we report on results using new spectrogram processing techniques based on high resolution PE modeling. These results include inversions for sediment parameters with estimates of error. With this new higher resolution spectrogram processing, we also report on the impact of the sediment parameters on range estimation. We generalize the technique to generic continental shelf environments. [Work supported by the Office of Naval Research.]

Control of Adaptive Switching in the Sensing-Executing Mode Used to Mitigate Collision in Robot Force Control

Mitigating collision is a fundamental issue in contact problems, and is required to ensure the safety of a robotic cell. Research into the contact problem between robots and their environment is divided into two parts: one uses the environmental contact model and parameter estimation, the other uses the robot force control method. There are two main problems with this research method. One is that the two research levels are not effectively combined to form a complete solution for force control in practice. The other problem is that research on excessive contact force in the collision phase has not been studied in depth for force control. In this paper, a sensing-executing bionic system is proposed that combines environmental detection and robotic force control based on the way an ant functions. The bionic system clearly explains the process from environment detection to robot control, which can provide guidance when designing a new robot control system. An adaptive switching control algorithm is proposed to mitigate the collision force in the collision phase. From the simulation results, the collision force is significantly reduced due to the implementation of adaptive switching control. Finally, a new self-sensing device is designed which can be integrated into the robot control device. However, as there are no additional sensors or computational complexity in the system, the effectiveness of the circuit and superiority of the adaptive parameter update must be verified by experimentation.

Estimation of environmental parameters with machine learning using a compact tetrahedral array and sources of opportunity

In a previous paper, we showed that we could localize sound sources using a compact tetrahedral hydrophone array in a continental shelf environment south of Block Island, Rhode Island. The tetrahedral array of phones, 0.5 m on a side, was deployed to monitor the construction and operation of the first offshore wind farm in the United States. Directions of arrival (DOAs) for a number of ships were computed using a time difference of arrival technique. Given the DOAs, ranges are estimated using supervised machine learning techniques. Here, we extend this work to estimate a number of environmental parameters including water depth and sediment composition. Training sets of range-dependent ocean waveguides and sediment sound speeds were generated using a propagation model for a neural network. Data from the tetrahedral array were processed by the neural network, which provided estimates of the water depth and sediment parameters such as sound speed and density. These estimates are compared to bathymetric data and core data collected as part of the site characterization for the wind farm. [Work supported by the Office of Naval Research and the Bureau of Ocean Energy Management.]

An Intelligent Adaptive Algorithm for Environment Parameter Estimation in Smart Cities

Least mean squares (LMS) adaptive algorithms are attractive for distributed environment parameter estimation problems in a smart city due to the benefits of cooperation, adaptation, and rapid convergence. To obtain a reliable estimate of the network-wide parameter vector, local results can be further fused by intermediate agents in a distributed incremental way. In this paper, we propose an intelligent variable step size incremental LMS (VSS-ILMS) algorithm to solve the dilemma between fast convergence rate and low mean-square deviation (MSD) in conventional incremental LMS (ILMS) algorithms. The main idea behind our proposal is that the local step-size is adaptively updated by minimizing the MSD in every iteration, where Tikhonov regularization and time-averaging estimation methods are adopted. A theoretical analysis of proposed algorithm is presented in terms of mean square performance and mean step size in a closed form. Simulation results show that VSS-ILMS algorithm outperforms the constant step size ILMS algorithm and several classical variable step-size LMS algorithms. The derived theoretical results shows good agreement with those based on simulated data. For a practical consideration, the proposed algorithm is also verified by the model of target localization in sensor networks.

Nonlinear Model-Mediated Teleoperation for Surgical Applications under Time Variant Communication Delay

Robot-assisted surgery is often carried out through master-slave teleoperation. With the progress of robotic technology and development of novel surgical procedures, new challenges emerge like need for haptic feedback and time delay in teleoperation communication channels, etc. Most existing bilateral teleoperation works in literature emphasize more on maintaining system stability at the cost of degraded transparency, with time variant communication delay further worsening the situation. In this paper, we present a nonlinear Hunt-Crossley model-mediated bilateral teleoperation scheme with targeted applications in robot-assisted surgery. The system stability is guaranteed by decoupling the master and slave subsystems and the system transparency is enhanced with online environment parameter estimation using recursive least squares (RLS) technique. Simulation studies have been carried out for both constant and time varying interaction environments. The proposed teleoperation scheme is shown to be efficient in providing stable and transparent performance and robust against communication delay and environment parameter variance.

Genotype by environment interaction effects in genetic evaluation of preweaning gain for Line 1 Hereford cattle from Miles City, Montana

It has long been recognized that genotype × environment interaction potentially influences genetic evaluation of beef cattle. However, this recognition has largely been ignored in systems for national cattle evaluation. The objective of this investigation was to determine if direct and maternal genetic effects on preweaning gain would be reranked depending on an environmental gradient as determined by year effects. Data used were from the 76-yr selection experiment with the Line 1 Hereford cattle raised at Miles City, MT. The data comprised recorded phenotypes from 7,566 animals and an additional 1,862 ancestral records included in the pedigree. The presence of genotype × environment interaction was examined using reaction norms wherein year effects on preweaning gain were hypothesized to linearly influence the EBV. Estimates of heritability for direct and maternal effects, given the average environment, were 10 ± 2 and 26 ± 3%, respectively. In an environment that is characterized by the 5th (95th) percentile of the distribution of year effects, the corresponding estimates of heritability were 18 ± 3 (22 ± 3%) and 30 ± 3% (30 ± 3%), respectively. Rank correlations of direct and maternal EBV appropriate to the 5th and 95th percentiles of the year effects were 0.67 and 0.92, respectively. In the average environment, the genetic trends were 255 ± 1 g/yr for direct effects and 557 ± 3 g/yr for maternal effects. In the fifth percentile environment, the corresponding estimates of genetic trend were 271 ± 1 and 540 ± 3 g/yr, respectively, and in the 95th percentile environment, they were 236 ± 1 and 578 ± 3 g/yr, respectively. Linear genetic trends in environmental sensitivity were observed for both the direct (-8.06 × 10 ± 0.49 × 10) and maternal (8.72 × 10 ± 0.43 × 10) effects. Therefore, changing systems of national cattle evaluation to more fully account for potential genotype × environment interaction would improve the assessment of breeding stock, particularly for direct effects. Estimates of environmental sensitivity parameters could also facilitate identification of genetic limitations to production.

FW: An R Package for Finlay-Wilkinson Regression that Incorporates Genomic/Pedigree Information and Covariance Structures Between Environments.

The Finlay–Wilkinson regression (FW) is a popular method among plant breeders to describe genotype by environment interaction. The standard implementation is a two-step procedure that uses environment (sample) means as covariates in a within-line ordinary least squares (OLS) regression. This procedure can be suboptimal for at least four reasons: (1) in the first step environmental means are typically estimated without considering genetic-by-environment interactions, (2) in the second step uncertainty about the environmental means is ignored, (3) estimation is performed regarding lines and environment as fixed effects, and (4) the procedure does not incorporate genetic (either pedigree-derived or marker-derived) relationships. Su et al. proposed to address these problems using a Bayesian method that allows simultaneous estimation of environmental and genotype parameters, and allows incorporation of pedigree information. In this article we: (1) extend the model presented by Su et al. to allow integration of genomic information [e.g., single nucleotide polymorphism (SNP)] and covariance between environments, (2) present an R package (FW) that implements these methods, and (3) illustrate the use of the package using examples based on real data. The FW R package implements both the two-step OLS method and a full Bayesian approach for Finlay–Wilkinson regression with a very simple interface. Using a real wheat data set we demonstrate that the prediction accuracy of the Bayesian approach is consistently higher than the one achieved by the two-step OLS method.