Adaptive State Research Articles

Single cell profiling of tubular epithelial cells under adaptive state in urine sediment from early and advanced diabetic kidney disease patients

IntroductionTubular epithelial cells under adaptive state (aTEC) have been frequently reported in the injured kidney closely associated with disease progression. However, whether aTEC is present in the urine of Diabetic Kidney Disease (DKD) patients and its clinical implication have not been assessed. MethodsUrine samples from early and advanced DKD patients were collected and subjected to single cell RNA sequencing (scRNA-seq). Kidney single nucleus RNA-seq, spatial scRNA-seq and bulk RNA-seq datasets were employed to reconstruct their local environment and to delineate differences between shed cells and local residence. ResultsMost urinary TEC in DKD patients are under adaptive states. While the composition of urinary TEC is consistent in early and advanced DKD, higher ratio of aTEC under progenitor and fibrosis state is defined in early DKD. Trajectory inference reveals some aTEC are early derivatives of injured PT. Spatial mapping reveals proliferative and fibrosis aTEC reside close to glomerulus region. Systemic evaluation of different states of urinary aTEC in patients of diverse-cause kidney injury suggests that the ratio of progenitor/proliferative aTEC and fibrosis aTEC is of diagnostic value. ConclusionUrine is an underestimated source of aTEC providing us non-invasive manner to interrogate the injured state of tubule. The ratio of fibrosis aTEC and progenitor/proliferative aTEC in urine features tubular injury state and may help improve DKD diagnosis.

A robust adaptive error state Kalman filter for MEMS IMU attitude estimation under dynamic acceleration

Accurate estimation of attitude (pitch, roll) is a prerequisite for ensuring safe navigation during vehicle control execution. In dynamic environments, the presence of acceleration often degrades the accuracy and robustness of attitude estimation using traditional algorithms with Micro-Electro-Mechanical Systems (MEMS) Inertial Measurement Unit (IMU). This paper proposes a robust adaptive error state Kalman filter (RAESKF) algorithm for attitude estimation of MEMS IMU under dynamic acceleration conditions. The RAESKF algorithm decomposes the true attitude Direction Cosine Matrix (DCM) into nominal and error components. An error state Kalman filter is utilized to fuse real-time measurements from MEMS gyroscope and accelerometer, estimating the error component, which is then used to correct the nominal attitude DCM. Furthermore, within the error state Kalman filtering framework, a dynamic acceleration robust adaptive adjustment strategy is implemented. This strategy relies on real-time data monitoring, threshold-based decision-making, and switching mechanisms. It adaptively selects between dynamic acceleration model compensation and online adjustment of the measurement noise covariance matrix. The primary objective of this strategy is to mitigate the impact of disturbance induced by dynamic acceleration on attitude estimation, thereby enhancing the accuracy and robustness. Laboratory rotation experiments have demonstrated that the algorithm improved pitch and roll measurement accuracy by at least 8.81% and 11.69%, respectively, under rotational conditions. In dynamic acceleration conditions, pitch and roll measurement accuracy improved by at least 42.76% and 67.21%, respectively.

Inspection robot GPS outages localization based on error Kalman filter and deep learning

In urban environments, inspection robots face complex terrain and variable motion states, posing high demands on their positioning systems. Although the integration of Micro-Electro-Mechanical Systems Inertial Navigation Systems (MEMS-INS) with the Global Positioning System (GPS) provides continuous positioning information, high buildings and tunnels in cities can block GPS signals, leading to signal interruptions and increased positioning errors. During GPS outages, MEMS-INS gradually accumulates errors, severely affecting positioning accuracy. To address this issue, this paper proposes an adaptive error state Kalman Filter (AESKF), which employs an adaptive mechanism to eliminate the noise impact of MEMS-INS and reduce reliance on the process model. Additionally, a deep learning framework based on the Self-Attention mechanism of the Transformer and a custom loss function Long Short-Term Memory (LSTM) module is proposed to predict position increments of the inspection robot. Combining AESKF with Transformer-LSTM achieves optimized positioning accuracy of the inspection robot during GPS outages in dynamic urban environments. Simulation and practical experimental results demonstrate that the combination of AESKF and Transformer-LSTM significantly improves positioning accuracy. Compared to other mature methods, the Root Mean Square Error (RMSE) of positioning is reduced by up to 83.64 % in the north direction and 89.56 % in the east direction. When the GPS signal interruption lasts for 10 s and 60 s, the maximum position error standard deviation (STD) is 0.1186 m and 1.0417 m, respectively.

A thermodynamic framework for nonequilibrium self-assembly and force morphology tradeoffs in branched actin networks.

Branched actin networks are involved in a variety of cellular processes, most notably the formation of lamellipodia in the leading edge of the cell. These systems adapt to varying loads through force dependent assembly rates that allow the network density and material properties to be modulated. Recent experimental work has described growth and force feedback mechanisms in these systems. Here, we consider the role played by energy dissipation in determining the kind of growth-force-morphology curves obtained in experiments. We construct a minimal model of the branched actin network self assembly process incorporating some of the established mechanisms. Our minimal analytically tractable model is able to reproduce experimental trends in density and growth rate. Further, we show how these trends depend crucially on entropy dissipation and change quantitatively if the entropy dissipation is parametrically set to values corresponding to a quasistatic state. Finally, we also identify the potential energy costs of adaptive behavior by branched actin networks, using insights from our minimal models. We suggest that the dissipative cost in the system beyond what is necessary to move the load may be necessary to maintain an adaptive steady state. Our results hence show how constraints from stochastic thermodynamics and non-equilibrium thermodynamics may bound or constrain the structures that result in such force generating processes.

Fuzzy Adaptive State Estimation of Distributed Drive Electric Vehicles with Random Missing Measurements and Unknown Process Noise

Accurate estimation of sideslip angle and vehicle velocity is crucial for effective control of distributed drive electric vehicles. However, as these states are not directly measured, Kalman-based approaches utilizing in-vehicle sensors have been developed to estimate them. Unfortunately, existing methods tend to ignore the impact of data loss on estimation performance. Furthermore, the process noise, which changes dynamically due to varying driving conditions, is not adequately considered. In response to these constraints, we propose a novel method called the fuzzy adaptive fault-tolerant extended Kalman filter (FAFTEKF). Initially, a fault-tolerant EKF is devised to handle missing measurements. Additionally, a fuzzy logic system that dynamically updates the process noise matrix, is built to improve estimation accuracy under different driving conditions. Extensive experimental results validate the superiority of the FAFTEKF over the traditional EKF across various scenarios with different degrees of data loss.

Phenotypically plastic drug-resistant chronic myeloid leukaemia cell line displays enhanced cellular dynamics in a zebrafish xenograft model.

Understanding the mechanisms by which cancer cells switch between different adaptive states and evade therapeutic interventions is essential for clinical management. In this study, the invivo cellular dynamics of a new chronic myeloid leukaemia cell line displaying altered phenotype and resistance to tyrosine kinase inhibitors were investigated in correlation with their parental cells for invasiveness/metastasis, angiogenic potential and population kinetics. We showed that the cells exhibiting drug resistance and plastic phenotype possess an increased capacity for invasion compared to their parental cells, that exposure to imatinib mesylate has the potential to enhance cellular motility and that in a leukaemic cell population, even a minority of plastic cells exhibit improved migratory ability. Furthermore, we show that these plastic cells have angiogenic and extravasation potential. The present study provides significant insights into the cellular dynamics displayed by a TKI-resistant, phenotypically plastic CML cell line, using a zebrafish (Danio rerio) xenograft model.

Adaptive Phase Estimation with Squeezed Vacuum Approaching the Quantum Limit

Phase estimation plays a central role in communications, sensing, and information processing. Quantum correlated states, such as squeezed states, enable phase estimation beyond the shot-noise limit, and in principle approach the ultimate quantum limit in precision, when paired with optimal quantum measurements. However, physical realizations of optimal quantum measurements for optical phase estimation with quantum-correlated states are still unknown. Here we address this problem by introducing an adaptive Gaussian measurement strategy for optical phase estimation with squeezed vacuum states that, by construction, approaches the quantum limit in precision. This strategy builds from a comprehensive set of locally optimal POVMs through rotations and homodyne measurements and uses the Adaptive Quantum State Estimation framework for optimizing the adaptive measurement process, which, under certain regularity conditions, guarantees asymptotic optimality for this quantum parameter estimation problem. As a result, the adaptive phase estimation strategy based on locally-optimal homodyne measurements achieves the quantum limit within the phase interval of [0,π/2). Furthermore, we generalize this strategy by including heterodyne measurements, enabling phase estimation across the full range of phases from [0,π), where squeezed vacuum allows for unambiguous phase encoding. Remarkably, for this phase interval, which is the maximum range of phases that can be encoded in squeezed vacuum, this estimation strategy maintains an asymptotic quantum-optimal performance, representing a significant advancement in quantum metrology.

Automatic clutch control using ADRC with continuous adaptive extended state observer

Automatic clutch control using ADRC with continuous adaptive extended state observer

Matchability and Uncertainty-Aware Iterative Disparity Refinement for Stereo Matching

After significant progress in stereo matching, the pursuit of robust and efficient ill-posed-region disparity refinement methods remains challenging. To further improve the performance of disparity refinement, in this paper, we propose the matchability and uncertainty-aware iterative disparity refinement neural network. Firstly, a new matchability and uncertainty decoder (MUD) is proposed to decode the matchability mask and disparity uncertainties, which are used to evaluate the reliability of feature matching and estimated disparity, thereby reducing the susceptibility to mismatched pixels. Then, based on the proposed MUD, we present two modules: the uncertainty-preferred disparity field initialization (UFI) and the masked hidden state global aggregation (MGA) modules. In the UFI, a multi-disparity window scan-and-select method is employed to provide a further initialized disparity field and more accurate initial disparity. In the MGA, the adaptive masked disparity field hidden state is globally aggregated to extend the propagation range per iteration, improving the refinement efficiency. Finally, the experimental results on public datasets show that the proposed model achieves a reduction up to 17.9% in disparity average error and 16.9% in occluded outlier proportion, respectively, demonstrating its more practical handling of ill-posed regions.

Robust adaptive asymmetric state constraint control of brush DC motor systems driving a one-link robot manipulator: A feasibility-condition-free method

In this paper, we investigate the tracking control problem for a class of brush direct current (DC) motor systems driving a one-link robot manipulator subject to asymmetric full-state constraints. By constructing a state-dependent nonlinear transformation function (NTF), we present an adaptive robust dynamic surface control (DSC) strategy that can directly address both symmetric and asymmetric state constraints, so that there is no need to convert the problem of state constraint into the constraints on tracking errors as necessitated by the Barrier Lyapunov Function (BLF)-based existing works. Furthermore, by employing the first-order filter and constructing a new coordinate transformation, the demanding feasibility condition imposed on the BLF methods is removed, allowing the designer more freedom to select design parameters. Moreover, it is worth mentioning that under the proposed nonlinear transformation function the extra condition on the constraining function is not required. The effectiveness of the proposed control is verified via the Simulation results.

Sensorless Control of Variable Flux Memory Machines Based on Improved Extended EMF Model and Adaptive Extended State Observer

Sensorless Control of Variable Flux Memory Machines Based on Improved Extended EMF Model and Adaptive Extended State Observer

State Space Model Reference Adaptive Control for a Class of Second-Degree Fractional Order Systems with Different Eigenvalues

This study proposes an adaptive control synthesis for a class of second-degree fractional order systems with different eigenvalues in the state-space domain. The proposed fractional order adaptive controller is a generalization of the MRAC controller for the class of scalar fractional order systems. In order to control the fractional order plant, an adaptive state space feedback controller is applied based on the error between the system output and a chosen reference model using a fractional adaptation law to make the fractional order plant track the fractional order reference model. We show that the resulting adaptive regulator is able to stabilize the fractional order second degree system with a satisfying performance. A simulation example illustrating these performance properties is provided along with a comparison with a fractional order sliding mode control (FOSMC) to demonstrate the superiority of the proposed control scheme.

Adaptive polynomial Kalman filter for nonlinear state estimation in modified AR time series with fixed coefficients

AbstractThis article presents a novel approach for adaptive nonlinear state estimation in a modified autoregressive time series with fixed coefficients, leveraging an adaptive polynomial Kalman filter (APKF). The proposed APKF dynamically adjusts the evolving system dynamics by selecting an appropriate autoregressive time‐series model corresponding to the optimal polynomial order, based on the minimum residual error. This dynamic selection enhances the robustness of the state estimation process, ensuring accurate predictions, even in the presence of varying system complexities and noise. The proposed methodology involves predicting the next state using polynomial extrapolation. Extensive simulations were conducted to validate the performance of the APKF, demonstrating its superiority in accurately estimating the true system state compared with traditional Kalman filtering methods. The root‐mean‐square error was evaluated for various combinations of standard deviations of sensor noise and process noise for different sample sizes. On average, the root‐mean‐square error value, which represents the disparity between the true sensor reading and estimate derived from the adaptive Kalman filter, was 35.31% more accurate than that of the traditional Kalman filter. The comparative analysis highlights the efficacy of the APKF, showing significant improvements in state estimation accuracy and noise resilience.

Human Electroretinography Shows Little Polarity Specificity Following Full-Field Ramp Adaptation.

The ramp aftereffect, a visual phenomenon in which perception of light changes dynamically after exposure to sawtooth-modulated light, was first described in 1967. Despite decades of psychophysical research, location and mechanisms of its generation remain unknown. In this study, we investigated a potential retinal contribution to effect formation with specific emphasis on on-/off-pathway involvement. A 100ms flash electroretinogram (ERG) was employed to probe the adaptive state of retinal neurons after presentation of stimuli that were homogenous in space but modulated in time following a sawtooth pattern (upward or downward ramps at 2Hz). Additionally, a psychophysical nulling experiment was performed. Psychophysics data confirmed previous findings that the ramp aftereffect opposes the adapting stimuli in ramp direction and is stronger after upward ramps. The ERG study revealed significant changes of activity in every response component in the low-frequency range (a-wave, b-wave, on-PhNR, d-wave and off-PhNR) and high-frequency range (oscillatory potentials) in amplitudes, peak times, or both. The changes are neither specific to the on- or off-response nor antagonistic between ramp directions. With downward ramp adaptation, effects were stronger. Neither amplitudes nor peak times were correlated with perception strength. Amplitudes and peak times were uncorrelated, and the effect diminished over time, ceasing almost completely with three seconds. Despite abundant effects on retinal responses, the pattern of adaptational effects was not specific to the sawtooth nature of adaptation. Although not ruling out retinal contributions the present findings favor post-retinal mechanisms as the primary locus of the ramp aftereffect.

Hybrid modeling for vehicle lateral dynamics via AGRU with a dual-attention mechanism under limited data

A precise vehicle dynamics model is critical for simulation and algorithm testing. Neural networks have been widely used to build high-fidelity vehicle dynamics models due to the excellent learning ability. However, data starvation is a common phenomenon in neural networks. With limited data, it is difficult for neural networks to achieve precise predictions. To address these problems, a hybrid model combining physics and dual attention neural networks is developed to model vehicle lateral dynamics. First, due to the interpretability of the physical model, linear lateral dynamics model is regarded as a prior model. However, due to the imperfect prior knowledge, there are residuals between the prior and the actual vehicle dynamics. Therefore, neural networks are used to characterize the residuals to achieve recalibration of vehicle dynamics model. Modeling vehicle residual dynamics with neural networks is a time series forecasting problem. The GRU with a dual attention mechanism and adaptive initial hidden states (DA-AGRU) is designed to capture spatial and temporal correlations in the vehicle dynamics data. In particular, considering the unique auto regressive structure of the hybrid model, a spatial attention mechanism with a feature fusion module is designed, so as to globally compute the weights of different channel features. The dataset used to train and validate the model is recorded from a vehicle platform, and the experimental results show that the proposed hybrid model can accurately predict vehicle dynamics states in a data-scarce environment.

Co-estimation of state-of-charge and capacity for series-connected battery packs based on multi-method fusion and field data

Accurate state-of-charge (SOC) and capacity estimations are of great importance for the performance management, predictive maintenance, and safe operation of lithium-ion battery packs in electric vehicles (EVs). However, it is quite challenging to estimate real-world large-sized EV battery packs due to the unpredictable operating profiles and large measurement disturbances. This article proposes an adaptive onboard SOC and capacity co-estimation framework, which incorporates a multi-timescale hierarchy and integrates multiple individual methods adaptively to practical driving profiles. First, this framework considers the most evident inconsistency between battery cells and periodically screens the weakest cells in a long timescale (week-level). Subsequently, the SOC of the battery pack is accurately estimated in a short timescale (in real-time) based on multi-method fusion. Finally, the capacity of the battery pack is periodically calibrated in a medium timescale (minute-level) based on an adaptive state filter and reliable SOC estimation. Both the laboratory and field data were used for validation, and the results demonstrated the proposed method achieved accurate SOC and capacity estimations of large-sized EV battery packs, with the maximum root mean squared errors of <0.7 % and <3.2 %, respectively, and it was run five times faster than the multi-cell model-based method.

Adaptive control for a class of stochastic nonlinear time-delay systems with unknown control coefficients

This paper mainly investigates the adaptive state feedback control problem for a class of stochastic nonlinear systems with time-delay and unknown control coefficients. The proposed control scheme leverages the dynamic gain technology and Nussbaum function to develop a state feedback controller, aiming to simplify its implementation and eliminate the influence of unidentified time-varying control coefficients. Through the utilization of suitable Lyapunov–Krasovskii (L–K) functionals and stochastic stability theories, it is demonstrated that the designed control mechanism guarantees the boundedness in probability for all closed-loop signals, ultimately regulating the state of the plant to zero. The availability and superiority of the developed adaptive management strategy are further certified through simulation results.

Prescribed-time adaptive state observer approach for distributed output-feedback formation tracking of networked uncertain underactuated surface vehicles

Prescribed-time adaptive state observer approach for distributed output-feedback formation tracking of networked uncertain underactuated surface vehicles

Chromatic adaptation for different viewing media through achromatic matches and neutrality ratings.

Many corresponding color datasets have been collected under different illumination conditions over these years, providing adequate data for developing chromatic adaptation transforms (CAT). Nevertheless, these datasets exhibit notable, systematical discrepancies in visual data, probably stemming from their differences in the experiment setup and methodology. This necessitates a comprehensive examination of how the experiment-related factors influence the outcomes, accompanied by thorough discussions to establish theoretical references for the effective classification of datasets. Horizontal comparisons among these datasets indicate the influence of two crucial factors-viewing medium and experimental methods-on chromatic adaptation, albeit without systematic investigations. Additionally, the underlying mechanism contributing to the disparities observed among different media remains unclear. To address these limitations, this study selected three different media - surface colors, self-luminous colors, and independently illuminated surface colors, and two experimental methods - the achromatic matching and neutrality rating method. The results confirm the significant influence of the viewing medium on the adaptation state. Two crucial factors emerge as significant contributors: the color correlation with global illumination and the nature of the surface medium (reflective or self-luminous).

Observer-based adaptive memory event-triggered consensus tracking control for high-speed train under DoS attacks

This paper focuses on the event-triggered consensus tracking of high-speed trains under denial-of-service (DoS) attacks. A novel anti-disturbance control strategy is proposed, based on an adaptive memory state observer and a disturbance observer. A memory is introduced to buffer system output information which enhancing the precision of the state observer, and the gust disturbance during the high-speed train operation is estimated by the disturbance observer. The Linear Matrix inequality technique is used to obtain the observer feedback coefficient and the controller gain, and the Lyapunov theory is employed to demonstrate the controller’s consistency tracking performance. Applying the memory to the event-triggered mechanism, a new adaptive memory event-triggered scheme is designed to better ensure system performance and effectively prevent the occurrence of Zeno behavior. To achieve precise identification of DoS attacks in the event-triggered environment, a new detection algorithm is proposed. Finally, simulation examples are used to validate the effectiveness and practicality of the proposed scheme.