Estimation Performance Research Articles

Neuro-cognitive Insights into Engineering Design: Exploring EEG Predictive Associations with Task Performance

Abstract This paper investigates the relationship between brain activity, measured by Electroencephalography (EEG) data, and the performance assessment result of engineering design activities involving different cognitive processes. Employing a novel signal processing pipeline, we analyzed EEG variations of 37 subjects during two design tasks that mostly leverage, respectively, convergent and divergent thinking: the Design with Morphological Table (DwMT) task and the Problem-Solving (PS) task. The EEG recordings underwent meticulous artifact removal, allowing for a comprehensive investigation into the statistical relationships between frequency bands, channels, and design outcome performance metrics. The developed models linking better design outcomes with brain (de)synchronization demonstrated remarkable accuracy, precision, and recall across performance metrics for both tasks. Notably, the EEG data in theta band measured from the frontal area at both hemispheres and a left parietal/occipital channel were essential for estimating better design performance with brain desynchronization. On the contrary, the model based on brain synchronization produces precise estimations of design performance with alpha band and channels in temporal and parietal areas. These findings highlight EEG variation as a viable proxy for design performance, paving the way for more effective performance prediction models with fewer sensors. Overall, this research contributes to the emerging field of neurocognitive design assessment and underscores the potential for EEG-based predictions in engineering design tasks.

Novel Quantitative Liver Steatosis Assessment Method With Ultrasound Harmonic Imaging.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most prevalent liver disorder in Western countries, with approximately 20%-30% of the MASLD patients progressing to severe stages. There is an urgent need for noninvasive, cost-effective, widely accessible, and precise biomarkers to evaluate liver steatosis. This study aims to assess and compare the diagnostic performance of a novel reference frequency method-based ultrasound attenuation coefficient estimation (ACE) in both fundamental (RFM-ACE-FI) and harmonic (RFM-ACE-HI) imaging for detecting and grading liver steatosis. An Institutional Review Board-approved prospective study was carried out between December 2018 and October 2022. A total number of 130 subjects were enrolled in the study. The correlation between RFM-ACE-HI values and magnetic resonance imaging proton density fat fraction (MRI-PDFF), as well as between RFM-ACE-FI values and MRI-PDFF were calculated. The diagnostic performance of RFM-ACE-FI and RFM-ACE-HI was evaluated using receiver operating characteristic (ROC) curve analysis, as compared to MRI-PDFF. The reproducibility of RFM-ACE-HI was assessed by interobserver agreement between two sonographers. A strong correlation was observed between RFM-ACE-HI and MRI-PDFF, with R = 0.88 (95% confidence interval [CI]: 0.83-0.92; P < .001), while the correlation between RFM-ACE-FI and MRI-PDFF was R = 0.65 (95% CI: 0.50-0.76; P < .001). The area under the ROC (AUROC) curve for RFM-ACE-HI in staging liver steatosis grades of S ≥ 1 and S ≥ 2 was 0.97 (95% CI: 0.91-0.99; P < .001) and 0.98 (95% CI: 0.93-1.00; P < .001), respectively, and 0.76 (95% CI: 0.65-0.85) and 0.80 (95% CI: 0.70-0.88) for RFM-ACE-FI, respectively. Great reproducibility was achieved for RFM-ACE-HI, with an interobserver agreement of R = 0.97 (95% CI: 0.94-0.99; P < .001). The novel RFM-ACE-HI method offered high liver steatosis diagnostic accuracy and reproducibility, which has important clinical implications for early disease intervention and treatment evaluation.

Performance of the Earth Explorer 11 SeaSTAR Mission Candidate for Simultaneous Retrieval of Total Surface Current and Wind Vectors

Interactions between ocean surface currents, winds and waves at the atmosphere-ocean interface are key controls of lateral and vertical exchanges of water, heat, carbon, gases and nutrients in the global Earth System. The SeaSTAR satellite mission concept proposes to better quantify and understand these important dynamic processes by measuring two-dimensional fields of total surface current and wind vectors with unparalleled spatial and temporal resolution (1 × 1 km2 or finer, 1 day) and unmatched precision over one continuous wide swath (100 km or more). This paper presents a comprehensive numerical analysis of the expected performance of the Earth Explorer 11 (EE11) SeaSTAR mission candidate in the case of idealised and realistic 2D ocean currents and wind fields. A Bayesian framework derived from satellite scatterometry is adapted and applied to SeaSTAR’s bespoke inversion scheme that simultaneously retrieves total surface current vectors (TSCV) and ocean surface vector winds (OSVW). The results confirm the excellent performance of the EE11 SeaSTAR concept, with Root Mean Square Errors (RMSE) for TSCV and OSVW at 1 × 1 km2 resolution consistently better than 0.1 m/s and 0.4 m/s, respectively. The analyses highlight some performance degradation in some relative wind directions, particularly marked at near range and low wind speeds. Retrieval uncertainties are also reported for several variations around the SeaSTAR baseline three-azimuth configuration, indicating that RMSEs improve only marginally (by ∼0.01 m/s for TSCV) when including broadside Radial Surface Velocity or broadside dual-polarisation data in the inversion. In contrast, our results underscore a) the critical need to include broadside Normalised Radar Cross Section data in the inversion; b) the rapid performance degradation when broadside incidence angles become steeper than 20° from nadir; and c) the benefits of maintaining ground squint angle separation between fore and aft lines-of-sight close to 90°. The numerical results are consistent with experimental performance estimates from airborne data and confirm that the EE11 SeaSTAR concept satisfies the requirements of the mission objectives.

Finite Element Analysis of Electrostatic Coupling in LISA Pathfinder Inertial Sensors

In the LISA Pathfinder (LPF) mission, electrostatic noise can reach the femto-Newtonian level despite the fact that the LPF’s sensors are equipped with potential shielding. Most of the existing simulation studies focus on the electrostatic edge effect and related fields, while the simulation study of the patch effect is neglected. For that reason, this paper analyzes the basic principle of electrostatic noise and constructs a simulation model for studying the coupling effects of a TM’s residual charge and stray bias voltage. The patch effect and other perturbation factors are simulated by the simulation model with finite element operation, focusing on the suppression effect of the protective ring on the edge effect, the realization of the patch effect in the simulation model, and the possible influence. The results show that electrode area and the spacing between the electrode and the TM together limit the suppression effect of the protective ring on the edge effect. The spatial and temporal variations of the patch effect significantly affect the distributed electric field between the electrodes and the TM, as well as the charge distribution density of the TM. In the worst-case scenario of LPF electrostatic input parameters, the electrostatic noise is about 1.03 × 10−15 m/s2/√Hz at 1 mHz, which is about 6% different from the expected performance estimate. Finally, considering the limitations of multiple environmental factors on the inertial sensors, the present model will be useful to explore the interactive effects of multi-field coupling and to further investigate the impact of low-energy electron charging on the performance of the inertial sensors.

Brotherhood and unity: ethnic diversity and economic performance in socialist Yugoslavia

AbstractLiterature on economic consequences of ethnic diversity suggests that ethnic diversity impedes economic growth in dictatorships, while inclusive policies may foster economic benefits from diversity. This paper investigates the impact of ethnic diversity on municipal economic growth in socialist Yugoslavia, a distinctive context due to its dictatorial regime’s active promotion of ethnic inclusion. Utilizing novel estimations of economic performance for 498 municipalities across the census years 1961, 1971, and 1981, the analysis based on seemingly unrelated regressions model reveals that higher ethnic diversity, measured through an ethnic fractionalization index is associated with slower economic growth of Yugoslav municipalities. The study is the first quantitative assessment of the economic consequences of ethnic diversity in Yugoslavia and provides nuanced insights into the effectiveness of inclusive policies, suggesting that while such policies may foster economic benefits, they might not fully counteract the economic drawbacks of ethnic diversity under dictatorship and in an economy that is not primarily driven by market forces.

Soil moisture content estimation of drip-irrigated citrus orchard based on UAV images and machine learning algorithm in Southwest China

Soil moisture content (SMC), as a pivotal component in the energy and matter exchange processes within the soil-plant-atmosphere continuum, plays a crucial role in surface water dynamics, energy fluxes, and carbon cycling within ecosystems. The development of remote sensing technology has offered new perspectives for monitoring soil moisture at regional scales. Unmanned aerial vehicles (UAV) equip with multispectral have distinct advantages for vegetation monitoring, including rapidity and cost-effectiveness, which has superior applicability and practicality. Therefore, in a 5a "Daya" late-maturing citrus orchard, the vegetation index (VI) and texture feature (TF) information of citrus canopy based on UAV multi-spectral images were extracted, and soil and plant analyzer development (SPAD) of citrus was collected. These different data sources were integrated into the framework of the random forest algorithm (RF) and genetic algorithm-optimized random forest (GA-RF) to evaluate the accuracy of surface SMC (SSMC) estimation in citrus orchard. The Biswas model was utilized to simulate the root zone SMC (RSMC). The spatiotemporal variations of SMC in citrus orchard were analyzed, and the potential of low-cost sensor-equipped drones in rapidly acquiring spatial and temporal distribution information of SMC at a large regional scale was explored. The results indicated that the GA-RF models outperformed the RF models in estimating citrus orchard SMC (with R2 ranging from 0.502 to 0.949 and RMSE ranging from 0.552 % to 3.166 % for GA-RF, compared to R2 ranging from 0.430 to 0.936 and RMSE ranging from 0.587 % to 3.449 % for the RF). The GA-RF models using VI+SPAD as inputs exhibited the best performance for SMC at depths of 5 cm, 10 cm, 20 cm and 40 cm (SMC5, SMC10, SMC20 and SMC40) across citrus growth stages (R2 ranging from 0.793 to 0.949 at 5 cm, R2 ranging from 0.702 to 0.938 at 10 cm, R2 ranging from 0.714 to 0.927 at 20 cm). In bud bust to flowering, young fruit and fruit maturation stages (stage Ⅰ, ⅠⅠ and ⅠⅤ), all models demonstrated good accuracy in estimating SMC at depth of 10 cm (R2 ranging from 0.567 to 0.908 in stage Ⅰ, with R2 ranging from 0.681 to 0.916 in stage ⅠⅠ and R2 ranging from 0.579 to 0.938 in stage ⅠⅤ). In fruit expansion stage (stage III), the models performed best in predicting SMC5 (R2 ranging from 0.698 to 0.861). The Biswas model was constructed to simulate SMC40 by utilizing the inverted SMC10 and SMC20, thereby generating spatiotemporal distribution maps of SMC at different depths in citrus orchard. The SSMC was susceptible to environmental factors, exhibiting significant spatiotemporal heterogeneity. In summary, this study illustrated that the integration of multiple data sources into GA-RF enhanced the estimation performance of SMC at different growth stages of late-maturing citrus orchard in the Southwest China. Additionally, it enabled the rapid and efficient monitoring of spatiotemporal variations in SMC, providing an effective method and practical foundation for precision irrigation and improved water use efficiency.

FusionNetV2: Explicit Enhancement of Edge Features for 6D Object Pose Estimation

FusionNet is a hybrid model that incorporates convolutional neural networks and Transformers, achieving state-of-the-art performance in 6D object pose estimation while significantly reducing the number of model parameters. Our study reveals that FusionNet has local and global attention mechanisms for enhancing deep features in two paths and the attention mechanisms play a role in implicitly enhancing features around object edges. We found that enhancing the features around object edges was the main reason for the performance improvement in 6D object pose estimation. Therefore, in this study, we attempt to enhance the features around object edges explicitly and intuitively. To this end, an edge boosting block (EBB) is introduced that replaces the attention blocks responsible for local attention in FusionNet. EBB is lightweight and can be directly applied to FusionNet with minimal modifications. EBB significantly improved the performance of FusionNet in 6D object pose estimation in experiments on the LINEMOD dataset.

Forest Canopy Height Estimation Combining Dual-Polarization PolSAR and Spaceborne LiDAR Data

Forest canopy height data are fundamental parameters of forest structure and are critical for understanding terrestrial carbon stock, global carbon cycle dynamics and forest productivity. To address the limitations of retrieving forest canopy height using conventional PolInSAR-based methods, we proposed a method to estimate forest height by combining single-temporal polarimetric synthetic aperture radar (PolSAR) images with sparse spaceborne LiDAR (forest height) measurements. The core idea of our method is that volume scattering energy variations which are linked to forest canopy height occur during radar acquisition. Specifically, our methodology begins by employing a semi-empirical inversion model directly derived from the random volume over ground (RVoG) formulation to establish the relationship between forest canopy height, volume scattering energy and wave extinction. Subsequently, PolSAR decomposition techniques are used to extract canopy volume scattering energy. Additionally, machine learning is employed to generate a spatially continuous extinction coefficient product, utilizing sparse LiDAR samples for assistance. Finally, with the derived inversion model and the resulting model parameters (i.e., volume scattering power and extinction coefficient), forest canopy height can be estimated. The performance of the proposed forest height inversion method is illustrated with L-band NASA/JPL UAVSAR from AfriSAR data conducted over the Gabon Lope National Park and airborne LiDAR data. Compared to high-accuracy airborne LiDAR data, the obtained forest canopy height from the proposed approach exhibited higher accuracy (R2 = 0.92, RMSE = 6.09 m). The results demonstrate the potential and merit of the synergistic combination of PolSAR (volume scattering power) and sparse LiDAR (forest height) measurements for forest height estimation. Additionally, our approach achieves good performance in forest height estimation, with accuracy comparable to that of the multi-baseline PolInSAR-based inversion method (RMSE = 5.80 m), surpassing traditional PolSAR-based methods with an accuracy of 10.86 m. Given the simplicity and efficiency of the proposed method, it has the potential for large-scale forest height estimation applications when only single-temporal dual-polarization acquisitions are available.

Model estimation of eastern oyster larval performance from food quantity and quality measures in western Mississippi Sound

Oyster Crassostrea virginica population recovery is critical in degraded estuarine systems, such as Mississippi Sound, USA, where repeated mass mortality events have depleted local oyster stocks. Owing to multiple recent die-offs, the western Mississippi Sound oyster population is recruitment-limited; population growth is constrained by the entry of new individuals into the extant population. Therefore, oyster recovery requires an adequate supply of larvae capable of timely development, growth, and successful metamorphosis. Larval performance and settlement potential are influenced by ambient temperature, salinity, and food supply. Food quantity is important to larvae, but so is food quality, as larvae require a balanced diet of lipids, proteins, and carbohydrates to develop and survive through metamorphosis. In this study, in situ environmental and food conditions during the 2021 and 2022 spawning seasons from 7 oyster reefs in western Mississippi Sound were integrated into an established biochemically based larval performance model to estimate periods facilitative of successful metamorphosis. In 2021, model-estimated larval survivorship was suppressed through much of the spawning season by prolonged, extremely low salinity (&lt;5 ppt) and inadequately balanced food supply. Higher seasonal salinity and more balanced food composition increased model-estimated larval survivorship in 2022, despite lower total food content, suggesting larval performance was primarily governed by the quality of available food. Model-estimated settlement windows were compared to settlement windows derived from concomitant field observations of recruitment. Strong agreement between model-estimated and observed settlement windows validates the effectiveness of the model and informs on the underlying causes of recruitment limitation in western Mississippi Sound.

Rapid distance estimation of odor sources by electronic nose with multi-sensor fusion based on spiking neural network

The inference of odor source locations holds great potential value in chemical leak detection, explosive searches and flammable substance detection. Sensor response and derivative changes are common indicators used to estimate source distance. However, most studies often rely on single-feature methods using a single sensor. In this study, a novel distance indicator based on a spiking neural network (SNN) that integrates multiple sensors is proposed. SNN was introduced for effective multi-sensor data fusion, which can provide accurate and rapid source distance estimation. The indicator's accuracy and quick detection capabilities were tested using a publicly available wind tunnel dataset and compared with single-sensor indicators. Additionally, an olfactory robot platform was constructed to test the method's real-time performance. Results demonstrate that this method effectively utilizes sensor arrays to estimate source distance, with an average root mean square error (RMSE) of less than 0.1 m across different wind speeds, outperforming single-sensor-based indicators. Additionally, the model exhibits good distance estimation performance within a relatively short detection time, with an average RMSE of 0.118 m. Practical testing on a mobile robot platform confirms the applicability of this distance indicator for real-time distance monitoring. In conclusion, the proposed distance indicator enables effective distance estimation for odor sources, providing a promising method for odor source localization and olfactory navigation.

A robust M-estimator for Gaussian ARMA time series based on the Whittle approximation

In the frequency domain, the Whittle approach using the classical periodogram is traditionally used to estimate the parameters of a stationary time series. The M-periodogram has recently become an alternative tool for analyzing the dependence of time series. It is particularly useful for dealing with outliers and heavy-tailed noise. This paper proposes an alternative to the standard Whittle approach, the M-Whittle estimator, built from the M-periodogram. We show that the proposed method is a consistent estimator of the true parameters of an ARMA process. A finite sample investigation is carried out to assess the performance of the estimator in the scenarios of contaminated and uncontaminated time series. As expected, for the uncontaminated data, the M-Whittle estimator performs similarly to the classical Whittle approach. However, the superiority of the first method is clear in terms of root mean squared error when the series has additive outliers. Two applications are considered to illustrate the methodologies in real data contexts. Regardless of whether or not the data are contaminated by additive outliers, the results presented here strongly motivate using the M-Whittle estimator in practical problems.

A Multi-Mechanism Fusion Method for Robust State of Charge Estimation via Bidirectional Long Short-Term Memory Model with Mixture Kernel Mean p-Power Error Loss Optimized by Golden Jackal Optimization Algorithm

Abstract Accurate state of charge (SOC) estimation is crucial for effective battery management in various applications. The bidirectional long short-term memory (BiLSTM) as an outstanding nonlinear regression model can be used for SOC estimation. This work develops a novel multi-mechanism fusion method based on BiLSTM to further enhance its estimation performance for SOC, in which the convolutional neural network (CNN), attention mechanism, and mixture kernel mean p-power error (MKMPE) loss are introduced into the BiLSTM framework for addressing different issues. First, the introduction of CNN components aims to extract essential features from battery data, enhancing the model's comprehension of complex information. Then, the attention mechanism is used to further refine the model's perceptual ability and a robust MKMPE loss is introduced into the BiLSTM framework to replace its original mean squared error loss, and a novel robust model is developed to suppress non-Gaussian noise interference. Finally, some key hyperparameters of the proposed model are fine-tuned using the golden jackal optimization algorithm, resulting in improved estimation performance. Comparative numerical experiments are meticulously conducted in various cases to evaluate the performance of the proposed method, and the experiment results demonstrate that it can perform outstanding effectiveness in handling non-Gaussian noise scenarios.

Statistical Identification of Independent Shocks with Kernel-based Maximum Likelihood Estimation and an Application to the Global Crude Oil Market

Independent component analysis has emerged as a promising approach for revealing structural relationships in multivariate dynamic systems, particularly in scenarios with limited knowledge of causal patterns. This article introduces a robust kernel-based maximum likelihood (KML) estimation method that accommodates the distributional characteristics of the structural sources of data variation. Our Monte Carlo study demonstrates the superior performance of the KML estimator compared to existing approaches for independence-based identification. Moreover, the proposed method enables partial identification and dimension reduction even in the presence of dependent shocks. We illustrate the benefits of our approach by applying it to the global oil market model of Kilian, highlighting its ability to capture unmodeled higher-order dependence between oil supply and speculative oil demand shocks.

Estimation of Source Range and Location Using Ship-Radiated Noise Measured by Two Vertical Line Arrays with a Feed-Forward Neural Network

Machine learning-based source range estimation is a promising method for enhancing the performance of tracking both the dynamic and static positions of targets in the underwater acoustic environment using extensive training data. This study constructed a machine learning model for source range estimation using ship-radiated noise recorded by two vertical line arrays (VLAs) during the Shallow-water Acoustic Variability Experiment (SAVEX-15), employing the Sample Covariance Matrix (SCM) and the Generalized Cross Correlation (GCC) as input features. A feed-forward neural network (FNN) was used to train the model on the acoustic characteristics of the source at various distances, and the range estimation results indicated that the SCM outperformed the GCC with lower error rates. Additionally, array tilt correction using the array invariant-based method improved range estimation accuracy. The impact of the training data composition corresponding to the bottom depth variation between the source and receivers on range estimation performance was also discussed. Furthermore, the estimated ranges from the two VLA locations were applied to localization using trilateration. Our results confirm that the SCM is the more appropriate feature for the FNN-based source range estimation model compared with the GCC and imply that ocean environment variability should be considered in developing a general-purpose machine learning model for underwater acoustics.

Nonstationary A/B Tests: Optimal Variance Reduction, Bias Correction, and Valid Inference

We develop an analytical framework to appropriately model and adequately analyze A/B tests in presence of nonparametric nonstationarities in the targeted business metrics. A/B tests, also known as online randomized controlled experiments, have been used at scale by data-driven enterprises to guide decisions and test innovative ideas to improve core business metrics. Meanwhile, nonstationarities, such as the time-of-day effect and the day-of-week effect, can often arise nonparametrically in key business metrics involving purchases, revenue, conversions, customer experiences, and so on. First, we develop a generic nonparametric stochastic model to capture nonstationarities in A/B test experiments, where each sample represents a visit or action associated with a time label. We build a practically relevant limiting regime to facilitate analyzing large-sample estimator performances under nonparametric nonstationarities. Second, we show that ignoring or inadequately addressing nonstationarities can cause standard A/B test estimators to have suboptimal variance and nonvanishing bias, therefore leading to loss of statistical efficiency and accuracy. We provide a new estimator that views time as a continuous strata and performs poststratification with a data-dependent number of stratification levels. Without making parametric assumptions, we prove a central limit theorem for the proposed estimator and show that the estimator attains the best achievable asymptotic variance and is asymptotically unbiased. Third, we propose a time-grouped randomization that is designed to balance treatment and control assignments at granular time scales. We show that when the time-grouped randomization is integrated to standard experimental designs to generate experiment data, simple A/B test estimators can achieve asymptotically optimal variance. A brief account of numerical experiments are conducted to illustrate the analysis. This paper was accepted by Baris Ata, stochastic models and simulation. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2022.01205 .

Using a Triple Sensor Collocation Approach to Evaluate Small-Holder Irrigation Scheme Performances in Northern Ethiopia

This study uses a triple-sensor collocation approach to evaluate the performance of small-holder irrigation schemes in the Zamra catchment of Northern Ethiopia. Crop water productivity (CWP), as an integrator of biomass production and water use, was used to compare the overall efficiencies of three types of irrigation systems: traditional and modern diversions, and dam-based irrigation water supply. Farmer-reported data often rely on observations, which can introduce human estimation and measurement errors. As a result, the evaluation of irrigation scheme performance has frequently been insufficient to fully explain crop water productivity. To overcome the challenges of using one single estimation method, we used a triple-sensor collocation approach to evaluate the efficiency of three small-scale irrigation schemes, using water productivity as an indicator. It employed three independent methods: remotely sensed data, a model-based approach, and farmer in-situ estimates to assess crop yields and water consumption. To implement the triple collocation appraisal, we first applied three independent evaluation methods, i.e., remotely sensed, model-based, and farmer in-situ estimates of crop yields and water consumption, to assess the crop water productivities of the systems. Triple-sensor collocation allows for the appraisal and comparison of estimation errors of measurement sensor systems, and enables the ranking of the estimators by their quality to represent the de-facto unknown true value, in our case: crop yields, water use, and its ratio CWP, in small-holder irrigated agriculture. The study entailed four main components: (1) collecting in-situ information and data from small-holder farmers on crop yields and water use; (2) derivation of remote sensing-based CWP from the FAO WaPOR open database and time series; (3) evaluation of biomass, crop yields and water use (evapotranspiration) using the AquaCrop model, integrating climate, soil data, and irrigation management practices; (4) performing and analysis of a categorical triple collocation analysis of the independent estimator data and performance ranking of the three sensing and small-holder irrigation systems. Maize and vegetables were used as main crops during three consecutive irrigation seasons (2017/18, 2018/19, 2019/20). Civil war prevented further field surveying, in-situ research, and data collection. The results indicate that remote sensing products are performed best in the modern and dam irrigation schemes for maize. For vegetables, AquaCrop performed best in the dam irrigation scheme.

A TMSBL underwater acoustic channel estimation method based on dictionary learning denoising

The shallow sea underwater acoustic channel exhibits a significant sparse multipath structure. The temporally multiple sparse Bayesian learning (TMSBL) algorithm can effectively estimate this sparse multipath channel. However, the complexity of the algorithm is high, the signal-to-noise ratio (SNR) of shallow-sea underwater acoustic communication is low, and the estimation performance of the TMSBL algorithm is greatly affected by noise. To address this problem, an improved TMSBL underwater acoustic channel estimation method based on a dictionary learning noise reduction algorithm is proposed. Firstly, the K-Singular Value Decomposition (K-SVD) dictionary learning method is used to reduce the noise of the received pilot matrix, reducing the influence of noise on the signal. Then, the Generalized Orthogonal Matching Pursuit (GOMP) channel estimation method is combined to obtain a priori information such as the perceptual matrix and hyperparameter matrix for TMSBL channel estimation; and the noise variance is obtained by using the null subcarrier calculation instead of iteratively updating the noise variance in the TMSBL, to improve the estimation accuracy and reduce the algorithmic complexity. Finally, the TMSBL channel estimation method is used to estimate the underwater acoustic channels of different symbols jointly. The simulation results show that the normalized mean square error of the channel estimation of the improved TMSBL method is reduced by about 92.2% compared with the TMSBL algorithm, obtaining higher estimation accuracy; running time is reduced by about 45.6%, and there is also better performance in terms of the running speed, which provides a reference for underwater acoustic channel estimation.

TVIE-based fault-tolerant model predictive control for trajectory tracking of mobile robot

This paper investigates the trajectory tracking control issue for a linear parameter-varying (LPV) system of a wheeled mobile robot (WMR) with actuator fault and constraints, where a time-varying intermediate estimator (TVIE)-based fault-tolerant model predictive control (MPC) method is proposed. To obtain the real system with actuator fault, based on the existing traditional IE, an optimized objective function is designed to update its observer gain online, which results in a TVIE. A new estimation-based predictive model in MPC is designed by introducing the estimations of error state and fault from the TVIE. Moreover, compared with the existing IE or extended state observer (ESO), the estimation performance of TVIE is better. Then the MPC optimization strategy is used to design fault-tolerant controllers. To guarantee stability and recursive feasibility, the bimodal MPC strategy is used, where a terminal penalty and a terminal constraint are included. Finally, the effectiveness and superiority of the proposed approach is illustrated in experiment.

A simple model for the estimation of turbofan engine performance in all airborne phases of flight

Abstract The overall efficiency of a turbofan engine may be expressed as a function of the Mach number, flight level and one other parameter. This may be either the net thrust, the turbine entry temperature or the fuel flow rate. Using basic aero-thermodynamic principles, dimensional analysis, normalisation and curve fitting, five approximate and “near universal” relations have been identified for engines having bypass ratios between 1 and 13. These relations contain five independent characteristic engine parameters. When these parameters are known, the relations form the basis of an estimation method for engine overall efficiency that is simple, fast, open source, completely transparent and, as new information appears, capable of further refinement. Since the empirical relations presented in this analysis are valid for Mach numbers greater than 0.2, the method is applicable to all airborne phases of flight. For a given aircraft, if the flight trajectory is specified in sufficient detail for the variation of net thrust with Mach number and flight level to be determined, only three of the five relations, together with the value of engine overall efficiency at a single reference condition, are needed to estimate the overall efficiency at every point on the trajectory. Comparisons with the data used in this analysis suggest that the accuracy is better than ±5% in most cases. In the completely general case, two additional engine characteristic parameters, one a total temperature ratio and the other a Mach number, are introduced. If these are known, both engine overall efficiency and net thrust can be expressed as functions of Mach number, flight level and turbine entry temperature. This allows the method to be used for the estimation of operating limits in the various phases of flight and in simplified optimisation studies, e.g. finding the environmentally optimum flight trajectory. In previous work, estimates of engine overall efficiency at the “design optimum” condition have been estimated for 53 aircraft and engine combinations. It is shown that the ‘design optimum’ condition is an appropriate choice for the engine reference condition. Updated and revised values for the relevant parameters for these 53 examples, together with estimates for the two additional engine characteristic parameters, are given in tabular form.

Environmental safety performance dynamics and estimation of the deterioration of leachate containment elements: Implications for the lifespan of hazardous waste landfills

In the coming decades, the failure and expiration of over 100,000 landfills involving billions of tons of waste not only threaten global water security but also lead to a sudden increase in the global demand for solid waste disposal. Therefore, further research is needed on the ways in which landfill performance is degraded, their life expectancy, the factors that influence them and the ways in which they are influenced. A study estimated the performance degradation and expected lifespan of a modern hazardous waste landfill in China. The landfill experienced slow performance degradation in early years (0–12 years). But during mid-term (12–62 years) due to the rapid degradation of the liner material, the rate of leakage from the containment layer will increase by a factor of 4.8–27.1, leading to its earliest failure after about 38 years. This indicates the need for measures to prevent rapid performance degradation in the middle of the landfill's service life and to strengthen monitoring of performance indicators and environmental medium pollution in the later period (after 62 years) to respond to potential scrapping and pollutant leakage problems. Differences in construction quality and regional climate can cause differences in the lifespan of landfill facilities by up to 20 and 30 times, highlighting the importance of scenario-based failure and lifespan prediction for designing reasonable monitoring plans and preparing response measures to prevent potential groundwater pollution and to meet the sudden increase in demand for secondary waste disposal.