Dynamic Estimation Research Articles

Instance Segmentation and 3D Pose Estimation of Tea Bud Leaves for Autonomous Harvesting Robots

In unstructured tea garden environments, accurate recognition and pose estimation of tea bud leaves are critical for autonomous harvesting robots. Due to variations in imaging distance, tea bud leaves exhibit diverse scale and pose characteristics in camera views, which significantly complicates the recognition and pose estimation process. This study proposes a method using an RGB-D camera for precise recognition and pose estimation of tea bud leaves. The approach first constructs an for tea bud leaves, followed by a dynamic weight estimation strategy to achieve adaptive pose estimation. Quantitative experiments demonstrate that the instance segmentation model achieves an mAP@50 of 92.0% for box detection and 91.9% for mask detection, improving by 3.2% and 3.4%, respectively, compared to the YOLOv8s-seg instance segmentation model. The pose estimation results indicate a maximum angular error of 7.76°, a mean angular error of 3.41°, a median angular error of 3.69°, and a median absolute deviation of 1.42°. The corresponding distance errors are 8.60 mm, 2.83 mm, 2.57 mm, and 0.81 mm, further confirming the accuracy and robustness of the proposed method. These results indicate that the proposed method can be applied in unstructured tea garden environments for non-destructive and precise harvesting with autonomous tea bud-leave harvesting robots.

A Purely Real-Valued Fast Estimator of Dynamic Harmonics for Application in Embedded Monitoring Devices in Power-Electronic Grids

Dynamic harmonic estimation is important for the monitoring and control of power-electronic grids. But the high-precision dynamic harmonic estimation algorithms usually have a heavy computational burden and occupy a large memory space, making them difficult to implement in the embedded platform. Thus, the motivation of this paper lies in providing an estimator with low computational complexity and less storage space consumption. A purely real-valued fast dynamic harmonics estimator is proposed. Firstly, a purely real-valued estimation model is established based on the Taylor series expansion on the time-varying amplitude and phase angle. Secondly, the estimation filter bank is computed in the least-squares sense, and the corresponding estimation error is theoretically analyzed. Finally, the purely real-valued fast dynamic harmonics estimator is designed. The advantage includes significantly reducing the computational complexity and memory space consumption while maintaining high-precision estimation. The testing results show that the proposed estimator can achieve the highest harmonics estimation precision under dynamic conditions. The frequency error, magnitude error, and phase angle error are less than 5 × 10−2 Hz, 7 × 10−1%, and 8 × 10−2 degrees, respectively, which verifies the advantage of high-precision estimation. The proposed estimator achieves a computational speed-up of approximately 430, 396, and 330 times compared to the Prony method, ESPRIT method, and iterative Taylor Fourier transform method, respectively. The computational load rate for executing the proposed estimator on the embedded prototype using C6748 DSP for estimating 50 harmonics is approximately only 2.05%, which verifies the advantage of a low computational load rate.

Effects of density dependence in growth and natural mortality on FMSY and maximum sustainable yield

Abstract In fisheries management, it is generally assumed that density-dependent processes are confined to the pre-recruit stage of a fish. Consequently, current calculations of fishing mortality reference points include density dependence (DD) solely in recruitment dynamics. However, several studies have reported the importance of DD in growth and natural mortality (M). In this study, we tested the effect of DD on estimates of fishing mortality producing maximum sustainable yields (FMSY), comparing these estimates to those resulting from constant growth and M. We simulated fish populations with a wide range of life-history traits (LHTs), applying either constant or density-dependent growth and M and derived FMSY for each stock. Results showed that including DD in growth and M led to higher FMSY and often also higher yield estimates (MSY). The effect of DD was found to be greater in growth than in M. Additionally, FMSY estimates derived from surplus production models fitted to generated stocks with density-dependent growth and/or M were generally closer to true FMSY, compared to those from age-based long-term predictions assuming constant growth and M. However, due to the inherent uncertainties in estimates of stock dynamics and biological reference points (BRPs), caution is suggested before implementing DD in FMSY calculations. Equilibrium yield curves relating production to stock size were right-skewed, shape parameters from production models were slightly below 1 for scenarios assuming constant growth and M, but declined to 0.5 with increasing DD.

Impact of foreign direct investment on economic growth in developing countries: The role of institutional quality

This paper empirically examines the impact of institutional quality on FDI-driven economic growth in 135 developing countries from 1996–2020. Given that improved institutions lower business costs, reduce uncertainty, and attract FDI inflow, the study hypothesizes that enhancing institutional quality will likely foster higher economic growth in developing countries through FDI. We apply dynamic and static panel estimation techniques for this investigation. The research employs six measures of institutional quality from the World Governance Indicators (WGI) and their interaction with FDI inflow to identify key institutional quality indicators for developing countries. Results indicate a positive and significant effect of FDI on economic growth in developing countries. Moreover, enhancements in three specific institutional quality indicators—government effectiveness, regulatory quality, and the rule of law—strengthen FDI’s impact on economic growth. The study also underscores the greater significance of institutional quality in driving FDI-led economic growth in developing countries, compared to its limited impact on all countries. Ultimately, the study recommends that policymakers in developing countries devise effective strategies to enhance government effectiveness, regulatory quality, and the rule of law to expedite economic growth through FDI.

High-fidelity and adaptive forward-phase-based vibration sensing using a Wiener filter in DSCM systems under commercial ECLs.

In this Letter, we propose and experimentally validate a high-fidelity and adaptive forward-phase-based vibration sensing using a Wiener filter (WF). In commercial coherent digital subcarrier multiplexing (DSCM) systems under external cavity lasers (ECLs), frequency-domain pilot tones (FPTs) in subcarrier intervals are employed for dynamic frequency offset estimation (FOE), carrier phase estimation (CPE), and polarization demultiplexing. The phase estimated by the CPE module is processed with the WF to achieve high-fidelity extraction of the vibration-induced phase. Compared to traditional bandpass filter (BPF)-based phase extraction, the WF method effectively suppresses sensing background noise caused by laser phase noise, improving sensing signal-to-noise ratio (SSNR). Furthermore, without knowing vibration frequency and waveform information, the WF method can adaptively detect various vibration waveforms, including amplitude-modulated continuous wave (AMCW) and frequency-modulated continuous wave (FMCW). In experiments, for 10 kHz sinusoidal vibration detection, the WF method demonstrated an 8 dB SSNR improvement compared to the BPF method. The experiments further verified that both AMCW and FMCW vibrations can be detected adaptively with the WF method using the same filter parameters as for sinusoidal vibrations.

A State Estimation of Dynamic Parameters of Electric Drive Articulated Vehicles Based on the Forgetting Factor of Unscented Kalman Filter with Singular Value Decomposition

In this paper, a state estimation method of distributed electric drive articulated vehicle dynamics parameters based on the forgetting factor unscented Kalman filter with singular value decomposition (SVD-UKF) is proposed. The 7DOF nonlinear dynamics model of a distributed electric drive articulated vehicle is established. The unscented Kalman filter algorithm is the foundation, with singular value decomposition replacing the Cholesky decomposition. A forgetting factor is introduced to dynamically adapt the observation noise covariance matrix in real time, resulting in a centralized parameter state estimator for the articulated vehicle. The proposed parameter state estimation method based on the forgetting factor SVD-UKF is simulated and compared with the unscented Kalman filter (UKF) estimation method. Key dynamic parameters are estimated, such as the lateral and longitudinal velocities and accelerations, angular velocity, articulated angle, wheel speeds, and longitudinal and lateral tire forces of both the front and rear vehicle bodies. The results show that the proposed forgetting factor SVD-UKF method outperforms the traditional UKF method. Furthermore, a prototype vehicle test is conducted to compare the estimated values of various dynamic parameters with the actual values, demonstrating minimal errors. This verifies the effectiveness of the proposed dynamic parameter estimation method for articulated vehicles.

Integrated learning‐based estimation and nonlinear predictive control of an ammonia synthesis reactor

AbstractThis paper presents an advanced machine learning‐based framework designed for predictive modeling, state estimation, and feedback control of ammonia synthesis reactor dynamics. A high‐fidelity two‐dimensional multiphysics model is employed to generate a comprehensive dataset that captures variable dynamics under various operational conditions. Surrogate long short‐term memory neural networks are trained to enable real‐time predictions and model‐based control. Additionally, a feedforward neural network is developed to estimate the outlet ammonia concentration and hotspot temperature using spatially distributed temperature readings, thereby addressing the challenges associated with real‐time concentration and maximum temperature measurements. The machine learning‐based predictive modeling and state estimation methods are integrated into a model predictive control architecture to regulate ammonia synthesis. Simulation results demonstrate that the machine learning surrogates accurately represent the nonlinear process dynamics with minimal discrepancy while reducing optimization costs compared to the high‐fidelity model, ensuring adaptability and effective guidance of the reactor to desired set points.

Intelligent Saturation Power Limit Load Distribution Algorithm (ISPLLDA) for Cooperative Manipulators Applications

Cooperative manipulators face challenges related to an inadequate distribution of external loads and a decrease in Dynamic Load Carrying Capacity (DLCC). Understanding the impact of optimal load distribution on power consumption, load carrying capacity, and gripper error is crucial. This paper presents the Intelligent Saturation Power Limit Load Distribution Algorithm (ISPLLDA), a novel method that achieves optimal external load distribution. ISPLLDA dynamically distributes the external load among manipulators based on torque-bearing capacity and actuator position. Additionally, nonlinearity in system dynamics introduces uncertainties, leading to incorrect DLCC evaluation, increased error, and higher actuator power consumption. To address this, a Radial Basis Function Neural Network (RBFNN) accurately determines actuator saturation limits in the presence of uncertainty, enabling correct estimation of system dynamics and external disturbances. ISPLLDA ensures near-simultaneous saturation of all manipulators' actuators, maximizing their capacity utilization. The proposed method is validated through simulations and experimental tests on cooperative manipulators. Results demonstrate a 17% increase in load-carrying capacity, as well as more than 35% improvement in error and torque indexes compared to the Lagrange multipliers method.

Dynamics of access to finance, financial development and sustainable economic development in developing economies

This paper assesses the interconnectedness between access to finance (ATF), financial development (FD) and sustainable economic development (SED). The study employed a sample of world developing economies and obtained data from the World Development Indicator Database (WDI) from 2010 to 2022. The paper incorporates the static and dynamic panel estimation models to test the proposed connection. The findings show the favourable impact of FD on SED suggesting that financial expansion across developing countries plays a crucial role in enhancing sustainable development. Additionally, a robust financial system facilitates the effective mobilization and allocation of cash towards investments in eco-friendly initiatives and infrastructure, expediting the preservation of natural resources and the protection of the environment. However, access to finance weakens the connection between FD and SED which implies that the limited access to finance in developing nations can increase the income gap, limit opportunities for entrepreneurship and wealth creation, and hinder efforts to address social inequality and alleviate poverty. Consequently, this limits the development of financial services necessary for sustainable economic growth. The findings have crucial policy implications for the financial regulators and policymakers of developing nations around the world. Such that the key to achieving long-term economic growth and bettering the lives of people in developing nations lies in removing barriers to financial inclusion and advancing inclusive financial development.

Dynamic reliability estimation of onshore wind turbine tower based on frequency-domain method

With the upscaling of wind turbine, the issue of dynamic reliability of wind turbine towers has become increasingly evident. However, the complexity and nonlinearity of wind turbine dynamics present significant challenges in evaluating their dynamic reliability under stochastic loads, requiring substantial computational resources. In this paper, a computationally cost-effective frequency-domain method is proposed as a means of estimating the dynamic reliability of an onshore wind turbine tower. An analytical approach is employed to obtain the power spectral density (PSD) of the rated wind speed and load on the rotating blades. Secondly, a dynamic model of onshore horizontal axis onshore wind turbine is established using flexible multi-body dynamics (MBD). The linear time-varying dynamic equation is transformed into a linear time-invariant form through the application of statistical linearization method. Subsequently, complex modal analysis is used to employ the displacements and internal forces of the tower. Moreover, the first-passage reliability and fatigue reliability of the wind turbine tower are determined through power spectral analysis. A numerical example of a 5-MW wind turbine is presented to demonstrate that the proposed method yields results in good agreement with Monte Carlo simulations (MCS), thereby demonstrating its effectiveness. The reliability analysis of the 5-MW wind turbine reveals that over a 20-year design life, the first-passage reliability is 0.9997 when adopting fore-after displacement at the tower top as the failure criterion, and the fatigue reliability of the tower base is 0.9445.

Breaking New Ground in Monocular Depth Estimation with Dynamic Iterative Refinement and Scale Consistency

Monocular depth estimation (MDE) is a critical task in computer vision with applications in autonomous driving, robotics, and augmented reality. However, predicting depth from a single image poses significant challenges, especially in dynamic scenes where moving objects introduce scale ambiguity and inaccuracies. In this paper, we propose the Dynamic Iterative Monocular Depth Estimation (DI-MDE) framework, which integrates an iterative refinement process with a novel scale-alignment module to address these issues. Our approach combines elastic depth bins that adjust dynamically based on uncertainty estimates with a scale-alignment mechanism to ensure consistency between static and dynamic regions. Leveraging self-supervised learning, DI-MDE does not require ground truth depth labels, making it scalable and applicable to real-world environments. Experimental results on standard datasets such as SUN RGB-D and KITTI demonstrate that our method achieves state-of-the-art performance, significantly improving depth prediction accuracy in dynamic scenes. This work contributes a robust and efficient solution to the challenges of monocular depth estimation, offering advancements in both depth refinement and scale consistency.

Too Much Finance and Top-Income Concentration: A Dynamic Panel Data Analysis

ABSTRACT This study investigates the effect of financial development on top-income concentration and income inequality. We conduct a dynamic panel estimation using the GMM method for 138 countries including developed and developing economies from 1970 TO –2017. We find that financial development has a nonlinear U-shaped effect on the top 1% and top 10% income shares and Gini coefficients. Private credit reduces inequality when it is under a threshold value, while it raises income inequality above the threshold value. This suggests that too much finance is beneficial only for the top-income group, but detrimental to income distribution for the entire population.

An Adaptive Penalized Weighted Least Squared Approach for Detecting and Mitigating Cyberattacks on Dynamic State Estimation

An Adaptive Penalized Weighted Least Squared Approach for Detecting and Mitigating Cyberattacks on Dynamic State Estimation

GNEP based dynamic segmentation and motion estimation for neuromorphic imaging

GNEP based dynamic segmentation and motion estimation for neuromorphic imaging

Interval Dynamic Harmonic High-Resolution State Estimation for Distribution Networks based on Multi-Source Measurement Data Fusion

Interval Dynamic Harmonic High-Resolution State Estimation for Distribution Networks based on Multi-Source Measurement Data Fusion

Dynamic estimation of the soil environmental carrying capacity for Benzo(a)pyrene in an industrial city, China: Insight from both duration and rate of regional emission.

An in-depth investigation of the maximum environmental load is crucial for soil security and pollution prevention. This research focused on soil environmental carrying capacity (SECC) for different risk receptors in a Chinese industrial city. By determining risk threshold for various land use types, we integrated mass balance and iterative models to capture dynamic net input fluxes with spatial heterogeneity. This enabled quantitative characterization of Benzo(a)pyrene (BaP) SECC through top-down and bottom-up approaches (corresponding to duration (D) and rate of regional emission, respectively). The thresholds were in the order of agricultural land<residential land<forest<industrial land<park. The top-down analysis showed D increased ∼1.5x with a 5% input flux decline until 2031. The bottom-up analysis suggested industrial emissions decreased by approximately 10% as the pollution control period was extended from 20 to 50 years. Both methods showed that at maximum background values (C0), D was ∼4x and the industrial emission rate was ∼10% higher than at minimum C0. SECC values near industrial areas significantly decreased, even reaching negative values, signifying complete carrying capacity loss. This study provided an approach to the dynamics of SECC under diverse scenarios, aiding informed decision-making for sustainable land management.

Unknown input filtering under full accessibility attacks

This study initially addresses the state estimation problem for discrete-time linear time-invariant systems under the influence of both exogenous attacks and random noises. As the filtering side, we make no prior assumptions or have any prior knowledge about the nature of attacks. We employ a specific unknown input filtering approach, which has been examined in prior research, to simultaneously estimate both the system states and attacks. Differing from existing works, our emphasis on full accessibility attacks, a particular class of unknown inputs, reveals that the dynamic estimation gains of the adopted filtering reduce directly to static ones. The second contribution of this study is to mitigate the influence of attacks by utilizing the estimated attack signals. Under this strategy, the deviation between nominal states and attacked states is characterized by evaluating the upper bound of the covariance matrix of the errors.

Mapping the fine spatial distribution of global offshore surface seawater mariculture using remote sensing big data

ABSTRACT Against the backdrop of near-saturation in global marine capture fisheries, current mariculture production now exceeds one-third of capture production and continues to hold significant growth potential. However, the lack of spatially detailed global offshore surface seawater mariculture (OSSM) data hampers scientific understanding and management from a spatial perspective. In this study, utilizing multi-source remote sensing time-series imagery, we achieve the world’s first spatially detailed mapping of both raft and cage mariculture types. The results show that (1) in 2020, the total global OSSM area was 20,079.97 km², comprising 85.04% raft mariculture and 14.96% cage mariculture. 95.33% of the OSSM was concentrated in Asia. (2) The interpreted national OSSM areas align well with the FAO production statistics trends. Obtaining these areas using remote sensing offers greater timeliness compared to production statistics, which is advantageous for dynamic monitoring and production estimation. (3) Through analysis of global pond aquaculture data, it was found that most coastal areas worldwide had a single-type of aquaculture. However, in parts of Asia, regions with both OSSM and pond aquaculture coexisted. The data acquired in this study, characterized by detailed spatial information, provide significant insights for constructing spatial models of mariculture, researching resource-environment effects, and informing management practices.

The Impact of Firm‐Level Political Risk on Eco‐Innovation: The Moderating Effect of CEO Power

ABSTRACTThis study examines the impact of firm‐level political risk on eco‐innovation at the firm level, particularly emphasizing the moderating role of CEO power in this relationship. Using a dataset from 33 countries from 2006 to 2022, we employ two‐step dynamic panel data estimations to address endogeneity concerns. The findings highlight a positive impact of political risk on eco‐innovation, which is further strengthened in the presence of a powerful CEO. This evidence implies that effective leadership from CEOs can assist firms in navigating political risks and advancing sustainable initiatives. The results remain robust across various specifications, including alternative measurements for firm‐level political risk. The study highlights the crucial role of CEOs in managing political risks and facilitating eco‐innovative practices within firms.

Splash on a liquid pool: coupled cavity–sheet unsteady dynamics

Splashes from impacts of drops on liquid pools are ubiquitous and generate secondary droplets important for a range of applications in healthcare, agriculture and industry. The physics of splash continues to comprise central unresolved questions. Combining experiments and theory, here we study the sequence of topological changes from drop impact on a deep, inviscid liquid pool, with a focus on the regime of crown splash with developing air cavity below the interface and crown sheet above it. We develop coupled evolution equations for the cavity–crown system, leveraging asymptotic theory for the cavity and conservation laws for the crown. Using the key coupling of sheet and cavity, we derive similarity solutions for the sheet velocity and thickness profiles, and asymptotic prediction of the crown height evolution. Unlike the cavity whose expansion is opposed by gravitational effects, the axial crown rise is mostly opposed by surface tension effects. Moreover, both the maximum crown height and the time of its occurrence scale as ${\textit {We}}^{5/7}$ . We find our analytical results to be in good agreement with our experimental measurements. The cavity–crown coupling achieved enables us to obtain explicit estimates of the crown splash spatio-temporal unsteady dynamics, paving the way to deciphering ultimate splash fragmentation.