Dynamic Estimation Research Articles

Dynamic State Estimation for Distribution Networks Based on Adaptive Set Membership Filter Under Unknown But Bounded Noise Environments

Dynamic State Estimation for Distribution Networks Based on Adaptive Set Membership Filter Under Unknown But Bounded Noise Environments

Effects of Landscape Heterogeneity and Disperser Movement on Seed Dispersal

The primacy of endozoochory for the maintenance and expansion of many woody plant populations is well known, but seed dispersal is not well understood for most species. This is especially true for rare species, where small population size and low fruit production can limit field- or observation-based experiments. Additionally, the effect of environmental heterogeneity on disperser movement is rarely investigated but has been shown to improve estimates of plant population spatial patterns and dynamics. We used simulation experiments to explore the effects of environmental heterogeneity and disperser movement on Lindera subcoriacea seed dispersal, a rare shrub from the southeastern United States with avian-dispersed seeds. Our experiments incorporated environmental heterogeneity and simulated disperser movement for five bird species, based on either landscape permeability or straight path rules. We anticipated that permeability-based movement would result in greater dispersal distances and seed dispersal effectiveness, which characterizes both quantity and quality. Generally, we did not find differences in seed dispersal between permeability and straight path experiments. However, we did find that permeability-based experiments had greater deposition into suitable habitat during flight (23 vs. 1%). These rare but longer distance depositions may be especially important for plants that are influenced by gap or interpopulation dynamics. We also found consistently greater dispersal into high quality habitats regardless of disperser species in permeability experiments, implying that incorporating species-specific assessments of landscape utilization (occupancy) could influence the effectiveness of seed dispersal. Our study suggests that including environmental heterogeneity in seed dispersal models can provide additional insights not provided by avian parameters (e.g., gut capacity, seed retention time, and flight speed) commonly used to inform dispersal models.

Safe adaptive output‐feedback optimal control of a class of linear systems

AbstractThe objective of this research is to enable safety‐critical systems to simultaneously learn and execute optimal control policies in a safe manner to achieve complex autonomy. Learning optimal policies via trial and error, that is, traditional reinforcement learning, is difficult to implement in safety‐critical systems, particularly when task restarts are unavailable. Safe model‐based reinforcement learning techniques based on a barrier transformation have recently been developed to address this problem. However, these methods rely on full‐state feedback, limiting their usability in a real‐world environment. In this work, an output‐feedback safe model‐based reinforcement learning technique based on a novel barrier‐aware dynamic state estimator has been designed to address this issue. The developed approach facilitates simultaneous learning and execution of safe control policies for safety‐critical linear systems. Simulation results indicate that barrier transformation is an effective approach to achieve online reinforcement learning in safety‐critical systems using output feedback.

MusselCF, a user-friendly toolbox to estimate the physiological carbon footprint of mussels in suspended aquaculture

Interest on the potential CO2 sequestration of marine bivalve aquaculture has increased during the last decade. However, there is still some controversy about which biological processes are involved and how to estimate their contribution to the carbon footprint of bivalve aquaculture. This work considers the dissolved inorganic carbon, CO2 and alkalinity fluxes linked to flesh and shell growth, calcification, respiration, faeces egestion, and ammonia excretion, accounting also for the RDOC production associated to these processes. We have developed an algorithm for a dynamic estimation of these fluxes based on a net production DEB growth model for mussels. The resulting model has been implemented in Python to create a toolbox with a graphical user interface. This toolbox allows the selection of different culture strategies, in terms of seeding date, seed size and culture length, and consequently analyzes the carbon footprint and impact on the carbonate chemistry of seawater of aquaculture management.

Site Quality Models and Fuel Load Dynamic Equation Systems Disaggregated by Size Fractions and Vegetative States in Gorse and High Heath Shrublands in Galicia (NW Spain)

Compatible model systems were developed for estimating fuel load dynamics in Ulex europaeus (gorse) and in Erica australis (Spanish heath) dominated shrub communities at stand level. The models were based on intensive, detailed destructive field sampling and were fitted simultaneously to fulfill the additivity principle. The models enable, for the first time, estimation of the biomass dynamics of the total shrub layer, size fractions and vegetative stage, with reasonably good accuracy. The approach used addresses the high variability in shrub biomass estimates by using a site index (SI) based on biomass levels at a reference age of 10 years. Analysis of the effect of climatic variables on site index confirmed the preference of gorse for mild temperatures and the ability of high heath communities to tolerate a wider range of temperatures. In the gorse communities, SI tended to increase as summer rainfall and the mean temperature of the coldest month increased. However, in the heath communities, no relationships were observed between SI and any of the climatic variables analyzed. The study findings may be useful for assessing and monitoring fuel hazards, updating fuel mapping, planning and implementing fuel reduction treatments and predicting fire behavior, among other important ecological and biomass use-related applications.

Dynamic event-triggered state estimation for discrete-time delayed switched neural networks with constrained bit rate

In this paper, a class of discrete-time delayed switched neural networks with dynamic event-triggered mechanism (DETM) and constrained bit rate is considered. In order to reduce the transmission frequency and alleviate the unnecessary resource loss between sensor and estimator, a DETM is proposed. The data transmission from sensor to estimator is realized through constrained bit rate channel. Therefore, in order to reflect the bandwidth allocation rules of accessible neurone nodes, a bit rate constraint model is introduced and an encoding-decoding mechanism is developed. This paper is concerned with the strategy of average dwell time (ADT) and linear matrix inequality, then sufficient conditions for the exponential ultimate boundedness of switched neural networks with DETM and constrained bit rate are proposed. Finally, an example is given to prove the effectiveness of the results.

Analysis of the impacts of financial development on economic growth in East Africa: How do the institutional qualities matter?

The financial sector plays a vital role in fostering economic growth, regardless of a nation's development stage. However, East Africa, compared to other regions, faces unique challenges in advancing its financial landscape. While the existing body of research acknowledges the significance of financial development in promoting economic growth, some studies need more regional specificity, focusing on broader frameworks that may neglect specific factors like the quality of institutions within East Africa. This research addresses this gap by investigating the relationship between financial development and economic growth in 18 East African countries from 1995 to 2021. Employing dynamic panel estimation (GMM) to address potential Endogeneity and reverse causality concerns, the study delves into the moderating effect of institutional quality on this relationship. Factors like government stability, rule of law, presence of the military in politics, and corruption control measure institutional quality.Additionally, the study controls for other relevant factors influencing economic growth. Data sourced from reputable international organizations such as the World Bank, International Monetary Fund, and relevant risk assessment institutions serves as the basis for analysis. The findings reveal that the impact of financial development on economic growth in East Africa is multifaceted. While financial development itself demonstrates a positive influence on growth, this effect is significantly amplified in countries with stronger institutional frameworks. This suggests that robust institutions act as a catalyst, maximizing the positive impact of financial development on economic growth.

Improved estimation of semiparametric dynamic copula models with filtered nonstationarity

This paper considers estimation of short-run dynamics in time series that contain a nonstationary component. We assume that appropriate preliminary methods can be applied to the observed time series to separate short-run elements from long-run slowly evolving secular components, and focus on estimation of the short-run dynamics based on the filtered data. We use a flexible copula-generated Markov model to capture the nonlinear temporal dependence in the short-run component and study estimation of the copula model. Using the rescaled empirical distribution of the filtered data as an estimator of the marginal distribution, Chen et al. (2022) proposed a simple, yet flexible, two-step estimation procedure for the copula model. The two-step estimator works well when the tail dependence is small. However, simulations reveal that the two-step estimator may be biased in finite samples in the presence of tail dependence. To improve the performance of short-term dynamic analysis in the presence of tail dependence, we propose in this paper a pseudo sieve maximum likelihood (PSML) procedure to jointly estimate the residual copula parameter and the invariant density of the filtered residuals. We establish the root-n consistency and asymptotic distribution of the PSML estimator of any smooth functional of the residual copula parameter and invariant residual density. We further show that the PSML estimator of the residual copula parameter is asymptotically normal, with the limiting distribution independent of the filtration. Simulations reveal that in the presence of strong tail dependence, compared to the two-step estimates of Chen et al. (2022), the proposed PSML estimates have smaller biases and smaller mean squared errors even in small samples. Applications to nonstationary macro-finance and climate time series are presented.

Dynamic Event-Triggered Sensor Fault Interval Estimation and Accommodation for Aeroengine

This paper concerns with the dynamic event-triggered fault interval estimation and accommodation for aeroengine sensors. Firstly, a dynamic event-triggered based robust augmented state observer is designed to reconstruct sensor fault for an aeroengine subject to external disturbance and measurement noise. Next, the event-triggered parameters and fault-tolerant controller (FTC) are co-designed based on the obtained observer gains to save communication resources. Moreover, the interval estimation of sensor fault is achieved via reachability analysis. Eventually, the designed scheme is tested with aeroengine Hardware-in-the-loop (HIL) experiment, which illustrates its potential in aeronautical engineering.

Real-time measurements and modeling of sodium combustion aerosol dynamics in test chamber to improve the evaluation of SFR containment aerosol behaviour

The initial size distribution and morphological parameters of sodium aerosols are critical in evaluating the accidental suspended aerosol behaviour in Sodium-cooled Fast Reactor (SFR) containment. Mass-based measurements were more familiar in characterizing the sodium aerosols. Real-time number size distribution measurements are carried out in this study. The sensitivity analysis of sodium aerosol effective density (ρe) in deriving the actual number size distributions from the measured Aerodynamic Particle Size Distributions (APSD) and predicting suspended aerosol dynamics is presented. Tests are conducted in a 1 m3 chamber at 47 ± 3% RH for different initial mass concentrations (M0) of 0.1, 1, and 2.9 g/m3. The initial APSDs measured just after the generation completions are observed to be polydisperse with the count median aerodynamic diameter (CMAD) < 1 μm. The literature reported ρe values of sodium aerosols, 2.27, 1.362, and 0.61 g/cm3 are used to derive mobility equivalent PSDs from APSD in each test. The real-time number concentration decay and size growth for four different PSDs are measured and compared with the estimate using nodal method-based code to ascertain the actual parameters. The validated parameters CMD = 0.66 μm, σg = 1.96, ρe = 1 g/cm3 and χ = 1 are used for improved estimation of sodium aerosol dynamics in Indian SFR containment with M0 = 4 g/m3 for severe accident scenarios.

Optimizing dynamic aperture studies with active learning

Dynamic aperture is an important concept for the study of non-linear beam dynamics in circular accelerators. It describes the extent of the phase-space region where a particle's motion remains bounded over a given number of turns. Understanding the features of dynamic aperture is crucial for the design and operation of such accelerators, as it provides insights into nonlinear effects and the possibility of optimising beam lifetime. The standard approach to calculate the dynamic aperture requires numerical simulations of several initial conditions densely distributed in phase space for a sufficient number of turns to probe the time scale corresponding to machine operations. This process is very computationally intensive and practically outside the range of today's computers. In our study, we introduced a novel method to estimate dynamic aperture rapidly and accurately by utilising a Deep Neural Network model. This model was trained with simulated tracking data from the CERN Large Hadron Collider and takes into account variations in accelerator parameters such as betatron tune, chromaticity, and the strength of the Landau octupoles. To enhance its performance, we integrate the model into an innovative Active Learning framework. This framework not only enables retraining and updating of the computed model, but also facilitates efficient data generation through smart sampling. Since chaotic motion cannot be predicted, traditional tracking simulations are incorporated into the Active Learning framework to deal with the chaotic nature of some initial conditions. The results demonstrate that the use of the Active Learning framework allows faster scanning of the configuration parameters without compromising the accuracy of the dynamic aperture estimates.

The Estimation of Battery State of Charge using Corny Network

State of charge (SOC) estimation of lithium-ion batteries has been extensively studied and the estimation accuracy was mainly investigated through the development of various battery models and dynamic estimation algorithms. All battery models, however, contain inherent model bias due to the simplifications and assumptions, which cannot be effectively addressed through the development of various conventional computation and intelligent computation. Consequently, some existing methods performed battery SOC estimation using conventional and intelligent computation have not very accurate to predict the SOC battery characteristics. There some drawbacks in employment deep learning to estimate SOC battery, such as complicated algorithm or network, over fitting and so on. The proposed method, the Corny architecture has narrow layers design. This design has low cost computation and prevent over fitting. The result shows the accuracy of method is very high. The predicted and targeted values are almost merged in a single line. The RMSE and MAX error indexes are very low. That the accuracy of the model is acceptable. The electric vehicle battery can estimate to life longer and more reliable to perform mobility task. Finally, this method also show the accuracy of estimation SOC battery of electric vehicle can be solved by narrow learning layers.

Dynamic estimation method for pulsar periods based on photon energy distribution folding and image template matching

The accuracy of the pulsar period estimation directly affects the restoration effect of the signal profile. A more accurate pulsar profile will help improve the accuracy of pulsar delay estimation and thereby improve the performance of X-ray pulsar navigation. This paper proposes a pulsar period estimation method based on photon energy distribution folding and image template matching (PETM). This method uses the probability distribution information of photon energy for weighted epoch folding. The one-dimensional (1D) profile information was converted into two-dimensional (2D) image information through reverse space-filling curve (SFC) encoding. Then, a feature matching was performed between the target structure and the template structure. At the same time, the criterion of Pearson correlation coefficient (PCC) was used to quantitatively evaluate the matching effect to estimate the optimal period. The simulation results show that the period estimation accuracy of the PETM method is significantly improved, as compared with the traditional \( method. This work also analyzes the folding effect based on the photon energy distribution model and conducts simulation experiments and comparisons on influencing factors, such as noise interference and data quality. At the same time, we also specifically demonstrated the effectiveness of the PETM method for the glitch phenomenon (i.e., a sudden change in period) of pulsar periods. Finally, we also used China's XPNAV-1 satellite to conduct experiments and analysis of the actual observation data of PSR B0531+21 pulsar within a fixed period of time. The results show that the period estimation accuracy of this method is 4.8190$ns$, which is 50.23&lt;!PCT!&gt; higher than the traditional \( method. The method proposed in this article has the advantages of high estimation accuracy, stable estimation performance, strong anti-interference ability, and excellent dynamic period estimation performance. Therefore, it can further improve the navigation performance of X-ray pulsars.

Assessing infrastructure and trade connectivity through network analysis evidence from BRI countries

ABSTRACT The Belt and Road Initiative (BRI) is the world’s largest infrastructure investment that aims to improve economic integration and connectivity throughout Asia, Europe, and Africa. This paper investigates the influence of infrastructure development on trade connectivity in BRI countries. This paper employs network analysis to visualize and quantify the trade connectivity among BRI countries in 2013 and 2020 using the World Integrated Trade Solution (WITS) data. The study then empirically examines the impact of infrastructure development on trade connectivity from 1990 to 2020 for 76 BRI countries using a dynamic estimator. The dynamic model demonstrates how infrastructure investment can bolster trade connectivity. The results of the network analysis indicate that trade connectivity has significantly improved within BRI countries, in Asia and Europe while it lags behind in African and Central Asian regions.We suggest that that China should channel additional resources toward enhancing the connectivity of African and Central Asian nations.

In vivo bistatic dual-aperture ultrasound imaging and elastography of the abdominal aorta.

Introduction: In this paper we introduce in vivo multi-aperture ultrasound imaging and elastography of the abdominal aorta. Monitoring of the geometry and growth of abdominal aortic aneurysms (AAA) is paramount for risk stratification and intervention planning. However, such an assessment is limited by the lateral lumen-wall contrast and resolution of conventional ultrasound. Here, an in vivo dual-aperture bistatic imaging approach is shown to improve abdominal ultrasound and strain imaging quality significantly. By scanning the aorta from different directions, a larger part of the vessel circumference can be visualized. Methods: In this first-in-man volunteer study, the performance of multi-aperture ultrasound imaging and elastography of the abdominal aortic wall was assessed in 20 healthy volunteers. Dual-probe acquisition was performed in which two curved array transducers were aligned in the same imaging plane. The transducers alternately transmit and both probes receive simultaneously on each transmit event, which allows for the reconstruction of four ultrasound signals. Automatic probe localization was achieved by optimizing the coherence of the trans-probe data, using a gradient descent algorithm. Speckle-tracking was performed on the four individual bistatic signals, after which the respective axial displacements were compounded and strains were calculated. Results: Using bistatic multi-aperture ultrasound imaging, the image quality of the ultrasound images, i.e., the angular coverage of the wall, was improved which enables accurate estimation of local motion dynamics and strain in the abdominal aortic wall. The motion tracking error was reduced from 1.3 mm ± 0.63 mm to 0.16 mm ± 0.076 mm, which increased the circumferential elastographic signal-to-noise ratio (SNRe) by 12.3 dB ± 8.3 dB on average, revealing more accurate and homogeneous strain estimates compared to single-perspective ultrasound. Conclusion: Multi-aperture ultrasound imaging and elastography is feasible in vivo and can provide the clinician with vital information about the anatomical and mechanical state of AAAs in the future.

A fast dynamic pose estimation method for vision-based trajectory tracking control of industrial robots

Vision-based robotic trajectory tracking control is considered a promising technology. However, the slow sampling rate and latency of the vision sensor enormously limit the tracking performance. To conquer the issues, this paper proposes a dual-space error-state Kalman filter (DS-ESKF). By combining the encoder measurement with the vision measurement, the end-effector’s pose between adjacent vision measurements is restored, and the pose estimation cycle is synchronized with the control cycle. The critical distinguishing of DS-ESKF is that the encoder-driven error-state kinematics for the industrial robot is reconstructed. The experimental results on the Staubli TX60 industrial robot show that compared with the existing dual-rate Kalman filters (DR-KF), DS-ESKF can reduce estimation errors by about 50 % and have robust estimation performance. By applying the trajectory tracking control scheme combining DS-ESKF with a simple PID controller to Staubli TX60, the tracking accuracy is significantly improved (±0.11 mm for position and ± 0.05°for orientation).

Labor Productivity Convergence among Eurozone Member Countries

This paper analyzes the evolution of labor productivity among the Eurozone’s member countries between 1999 and 2019, using a dynamic panel estimate. The main findings indicate that, since the adoption of the single currency in 1999, both productivity per worker and productivity per hour followed different routes among Eurozone countries. Convergence among the founding countries stagnated after 2008, probably because of the Global Financial Crisis (GFC). But in the countries that joined the Eurozone several years after its creation, convergence did not slow down; indeed, towards the end of the period, the convergence of labor productivity among new entrants did not slow down. This paper focuses on measuring convergence in labor productivity and should be continued with a diagnosis of the likely causes of its stagnation. In this sense, it opens a line of research whose findings will contribute to the design of international integration policy between regions.

A new robust dynamic state estimation approach for power systems with non-Gaussian noise

The Gaussian noise distribution is typically used in dynamic state estimation (DSE) but it is not always true in practice because of abnormal system inputs, impulsive noise and measurement outliers. In this paper, a new robust DSE approach based on a new robust Lp norm based estimator and the cubature Kalman filter (CKF) is developed for power systems with non-Gaussian noise statistics. The Lp norm based estimator is derived from the Lp norm formula and the quadratic formula in order to alleviate the impacts from bad data and outliers. The proposed Lp-CKF DSE approach exhibits good accuracy because a new estimation error covariance is obtained by using the influence function. The robustness of the proposed Lp-CKF DSE approach is verified by performing simulations on a generator in the IEEE 39-bus system.

Impediments in attracting greenfield investment: does a stable absorption rate of the European cohesion policy make a difference?

ABSTRACT This paper seeks to investigate how the absorption rate of the European Cohesion Policy (ECP) funds and its dynamics are related to the creation of greenfield foreign direct investment (FDI) projects in the European Union (EU). A dynamic panel estimation based on 28 recipient countries in 2007–2016 reveals that a high ECP funds absorption rate alone does not ensure the creation of greenfield FDI. However, stable absorption (i.e. low dynamics in the ECP funds absorption rate) seems to promote greenfield investment. The large year-to-year changes in the ECP funds absorption rate appear to indicate a non-stable business environment that may hamper new investment, calling for corrective mechanisms to prevent the inefficient rapid withdrawal of funds after the eligibility period.

Dynamic Reserve Calculation Method of Fractured-Vuggy Reservoir Based on Modified Comprehensive Compression Coefficient

The low comprehensive compressibility coefficient characteristic of fracture-vuggy reservoirs often leads to imprecise dynamic reserve calculations. This study introduces a novel method for estimating dynamic reserves, which incorporates a modified comprehensive compressibility coefficient to enhance accuracy. This methodology has been applied to 23 wells in the Tahe Oilfield, resulting in error rates substantially lower than those associated with traditional techniques, thereby markedly enhancing the accuracy of dynamic reserve estimations. Specifically, for karst cave and fracture-vuggy reservoirs, the error rate in dynamic reserve calculations is reduced to under 10%, surpassing conventional methods by more than fivefold. In the case of fractured reservoirs, despite minor fluctuations in error rates due to stress sensitivity, diversion capacity, and channel variations, the proposed method still demonstrates a significant reduction in error rates compared to standard practices.