Output Error Research Articles

Subspace Identification of Bridge Frequencies Based on the Dimensionless Response of a Two-Axle Vehicle.

As an essential reference to bridge dynamic characteristics, the identification of bridge frequencies has far-reaching consequences for the health monitoring and damage evaluation of bridges. This study proposes a uniform scheme to identify bridge frequencies with two different subspace-based methodologies, i.e., an improved Short-Time Stochastic Subspace Identification (ST-SSI) method and an improved Multivariable Output Error State Space (MOESP) method, by simply adjusting the signal inputs. One of the key features of the proposed scheme is the dimensionless description of the vehicle-bridge interaction system and the employment of the dimensionless response of a two-axle vehicle as the state input, which enhances the robustness of the vehicle properties and speed. Additionally, it establishes the equation of the vehicle biaxial response difference considering the time shift between the front and the rear wheels, theoretically eliminating the road roughness information in the state equation and output signal effectively. The numerical examples discuss the effects of vehicle speeds, road roughness conditions, and ongoing traffic on the bridge identification. According to the dimensionless speed parameter Sv1 of the vehicle, the ST-SSI (Sv1 < 0.1) or MOESP (Sv1 ≥ 0.1) algorithm is applied to extract the frequencies of a simply supported bridge from the dimensionless response of a two-axle vehicle on a single passage. In addition, the proposed methodology is applied to two types of long-span complex bridges. The results show that the proposed approaches exhibit good performance in identifying multi-order frequencies of the bridges, even considering high vehicle speeds, high levels of road surface roughness, and random traffic flows.

Model reference output feedback tracking for nonlinear systems via LMIs

This work addresses the tracking problem for nonlinear systems with unmeasured variables. We offer a new design methodology that is composed of a two-degrees-of-freedom scheme. The reference model must track a time-varying signal, and the controller employed can be regarded as one-degree-of-freedom. Then, based on the error between the outputs of the reference model and the plant, another controller is designed to ensure that this mismatch converges to zero. The reference model and the system are described as Takagi–Sugeno (TS) fuzzy models with nonlinear consequents (N-TS), which allows for separating measured from unmeasured variables. An advantage of the proposed scheme is that the controller for the reference model can be carried out using several methodologies from the literature, whereas the controller that governs the output error can be static or dynamic, whose design is based on Linear Matrix Inequalities (LMIs). Two examples are used to illustrate the validity of the developed control methodology and to compare it to another recent approach in the literature.

Inline automatic quality control of 2D phase-contrast flow MRI for subject-specific scan time adaptation.

To develop an inline automatic quality control to achieve consistent diagnostic image quality with subject-specific scan time, and to demonstrate this method for 2D phase-contrast flow MRI to reach a predetermined SNR. We designed a closed-loop feedback framework between image reconstruction and data acquisition to intermittently check SNR (every 20 s) and automatically stop the acquisition when a target SNR is achieved. A free-breathing 2D pseudo-golden-angle spiral phase-contrast sequence was modified to listen for image-quality messages from the reconstructions. Ten healthy volunteers and 1 patient were imaged at 0.55 T. Target SNR was selected based on retrospective analysis of cardiac output error, and performance of the automatic SNR-driven "stop" was assessed inline. SNR calculation and automated segmentation was feasible within 20 s with inline deployment. The SNR-driven acquisition time was 2 min 39 s ± 67 s (aorta) and 3 min ± 80 s (main pulmonary artery) with a min/max acquisition time of 1 min 43 s/4 min 52 s (aorta) and 1 min 43 s/5 min 50 s (main pulmonary artery) across 6 healthy volunteers, while ensuring a diagnostic measurement with relative absolute error in quantitative flow measurement lower than 2.1% (aorta) and 6.3% (main pulmonary artery). The inline quality control enables subject-specific optimized scan times while ensuring consistent diagnostic image quality. The distribution of automated stopping times across the population revealed the value of a subject-specific scan time.

A posteriori error estimation for model order reduction of parametric systems

AbstractThis survey discusses a posteriori error estimation for model order reduction of parametric systems, including linear and nonlinear, time-dependent and steady systems. We focus on introducing the error estimators we have proposed in the past few years and comparing them with the most related error estimators from the literature. For a clearer comparison, we have translated some existing error bounds proposed in function spaces into the vector space $${\mathbb {C}}^n$$ C n and provide the corresponding proofs in $$\mathbb C^n$$ C n . Some new insights into our proposed error estimators are explored. Moreover, we review our newly proposed error estimator for nonlinear time-evolution systems, which is applicable to reduced-order models solved by arbitrary time-integration solvers. Our recent work on multi-fidelity error estimation is also briefly discussed. Finally, we derive a new inf-sup-constant-free output error estimator for nonlinear time-evolution systems. Numerical results for three examples show the robustness of the new error estimator.

Integrating core physics and machine learning for improved parameter prediction in boiling water reactor operations

This study introduces a novel method for enhancing Boiling Water Reactor (BWR) operation simulations by integrating machine learning (ML) models with conventional simulation techniques. The ML model is trained to identify and correct errors in low-fidelity simulation outputs, traditionally derived from core physics computations. These corrections aim to align the low-fidelity results closely with high-fidelity data. Precise predictions of nuclear reactor parameters like core eigenvalue and power distribution are crucial for efficient fuel management and adherence to technical specifications. Current high-fidelity transport calculations, while accurate, are impractical for real-time predictions due to extensive computational demands. Our approach, therefore, utilizes the standard two-step simulation process-assembly-level lattice physics calculations followed by whole-core nodal diffusion computations-to generate initial results, which are then refined using the ML-based error correction model. The methodology focuses on improving simulation accuracy in regular BWR operations rather than developing a universal ML predictor for reactor physics. By training an advanced neural network model on the difference in high-fidelity and low-fidelity simulations, the model can reduce the nodal power error from low-fidelity simulations to around 1% on average and the core eigenvalue down to under 100 pcm. This result is under the condition of the normal variations of control rod pattern and core flow rate changes in standard BWR operations used in the training and evaluation of the machine learning model. This work suggests a promising approach for achieving more accurate, computationally feasible simulation solutions in nuclear reactor operation and management.

Dynamic phasor measurement algorithm based on high-precision time synchronization

Ensuring the swift and precise tracking of power system signal parameters, especially the frequency, is imperative for the secure and stable operation of power grids. In instances of faults within the distribution network, abrupt changes in frequency may occur, presenting a challenge for existing algorithms that struggle to effectively track such signal variations. Addressing the need for enhanced performance in the face of frequency mutations, this paper introduces an innovative approach—the Covariance Reconstruction Extended Kalman Filter (CREKF) algorithm. Initially, the dynamic signal model of electric power is meticulously analyzed, establishing a dynamic signal relationship based on high-precision time source sampling tailored to the signal model’s characteristics. Subsequently, the filter gain, covariance matrix, and variance iteration equation are determined based on the signal relationship among three sampling points. In a final step, recognizing the impact of the covariance matrix on algorithmic tracking ability, the paper proposes a covariance matrix reset mechanism utilizing hysteresis induced by output errors. Through extensive verification with simulated signals, the results conclusively demonstrate that the CREKF algorithm exhibits superior measurement accuracy and accelerated tracking speed when confronted with mutating signals.

A regularized orthogonal activated inverse-learning neural network for regression and classification with outliers

A novel regularized orthogonal activated inverse-learning (ROAIL) neural network is proposed and investigated for reducing the impact of outliers in regression and classification fields. The proposed ROAIL network does not require extensive iterative computations. Instead, it can achieve the desired results with a single step of computation, allowing for the efficient acquisition of network weights. By extending the Gegenbauer polynomials to a multi-variate version, and integrating the ℓ2 regularization and Welsch loss function into the orthogonal activated inverse-learning framework, two forms of ROAIL are obtained, i.e., ℓ2 norm ROAIL (ℓ2-ROAIL) and Welsch-ROAIL (W-ROAIL). ℓ2-ROAIL neural network is proposed to minimize the empirical and structural risk simultaneously since taking the structural risk as a part of loss function can effectively reduce the complexity of the model and thus improve the generalization ability. W-ROAIL neural network further improves the robustness of the ℓ2-ROAIL neural network by replacing the original two-norm in loss function with Welsch function. The Welsch function can determine the weights of each sample according to its output error, and influence of outliers could be weakened since the weights of outliers would be reduced. Both regression and classification experiments show that W-ROAIL neural network has strong ability to suppress the influence of outliers.

Sampling-based event-triggered iterative learning control in nonlinear hyperbolic distributed parameter systems

This article explores a sampling-based event-triggered iterative learning control (SETILC) method for achieving precise control in nonlinear hyperbolic distributed parameter systems. Firstly, by designing a Lyapunov function regarding the system output error, reasonable triggering conditions are derived to selectively update the system control input. Subsequently, a sampling-based event-triggered iterative learning control algorithm is proposed to further enhance the control performance of the system while avoiding the occurrence of Zeno phenomena. A condition sufficient for ensuring the convergence of the system’s tracking error to zero is presented, and its convergence is proven. Finally, the proposed method’s effectiveness is verified through numerical simulations.

Exploring the potential of ChatGPT as an adjunct for generating diagnosis based on chief complaint and cone beam CT radiologic findings.

This study aimed to assess the performance of OpenAI's ChatGPT in generating diagnosis based on chief complaint and cone beam computed tomography (CBCT) radiologic findings. 102 CBCT reports (48 with dental diseases (DD) and 54 with neoplastic/cystic diseases (N/CD)) were collected. ChatGPT was provided with chief complaint and CBCT radiologic findings. Diagnostic outputs from ChatGPT were scored based on five-point Likert scale. For diagnosis accuracy, the scoring was based on the accuracy of chief complaint related diagnosis and chief complaint unrelated diagnoses (1-5 points); for diagnosis completeness, the scoring was based on how many accurate diagnoses included in ChatGPT's output for one case (1-5 points); for text quality, the scoring was based on how many text errors included in ChatGPT's output for one case (1-5 points). For 54N/CD cases, the consistence of the diagnosis generated by ChatGPT with pathological diagnosis was also calculated. The constitution of text errors in ChatGPT's outputs was evaluated. After subjective ratings by expert reviewers on a five-point Likert scale, the final score of diagnosis accuracy, diagnosis completeness and text quality of ChatGPT was 3.7, 4.5 and 4.6 for the 102 cases. For diagnostic accuracy, it performed significantly better on N/CD (3.8/5) compared to DD (3.6/5). For 54N/CD cases, 21(38.9%) cases have first diagnosis completely consistent with pathological diagnosis. No text errors were observed in 88.7% of all the 390 text items. ChatGPT showed potential in generating radiographic diagnosis based on chief complaint and radiologic findings. However, the performance of ChatGPT varied with task complexity, necessitating professional oversight due to a certain error rate.

A Module-Level Configuration Methodology for Programmable Camouflaged Logic

Logic camouflage is a widely adopted technique that mitigates the threat of intellectual property (IP) piracy and overproduction in the integrated circuit (IC) supply chain. Camouflaged logic achieves functional obfuscation through physical-level ambiguity and post-manufacturing programmability. However, discussions on programmability are confined to the level of logic cells/gates, limiting the broader-scale application of logic camouflage. In this work, we propose a novel module-level configuration methodology for programmable camouflaged logic that can be implemented without additional hardware ports and with negligible resources. We prove theoretically that the configuration of the programmable camouflaged logic cells can be achieved through the inputs and netlist of the original module. Further, we propose a novel lightweight ferroelectric FET (FeFET)-based reconfigurable logic gate (rGate) family and apply it to the proposed methodology. With the flexible replacement and the proposed configuration-aware conversion algorithm, this work is characterized by the input-only programming scheme as well as the combination of high output error rate and point-function-like defense. Evaluations show an average of &gt;95% of the alternative rGate location for camouflage, which is sufficient for the security-aware design. We illustrate the exponential complexity in function state traversal and the enhanced defense capability of locked blackbox against Boolean Satisfiability (SAT) attacks compared with key-based methods. We also preserve an evident output Hamming distance and introduce negligible hardware overheads in both gate-level and module-level evaluations under typical benchmarks.

A fusion algorithm for HRG output errors based on improved support function and MDTW

To improve the accuracy of hemispherical resonator gyro (HRG) output errors, a novel fusion algorithm is proposed based on improved support function and multi-dimensional dynamic time warping. By analyzing the temperature characteristics and big data characteristics of HRG, the eigenvectors are initially selected, and the correlation and redundancy are later removed by Kernel principal component analysis. To obtain potential relationships between different time series, an improved support function based on grey incidence analysis theory is brought in to reduce the computational complexity, and the genetic algorithm is introduced to identify the optimal parameters rather than manually setting them. To accurately quantify how similar or different a time series is compared with another, multi-dimensional dynamic time warping which utilizes Mahalanobis distance is introduced instead of dynamic time warping. Experiment results show that the proposed algorithm performs better in both accuracy and universality compared with the other three comparative methods.

Assessing quality assurance of multi-leaf collimator using the structural similarity index.

This study aims to evaluate the viability of utilizing the Structural Similarity Index (SSI*) as an innovative imaging metric for quality assurance (QA) of the multi-leaf collimator (MLC). Additionally, we compared the results obtained through SSI* with those derived from a conventional Gamma index test for three types of Varian machines (Trilogy, Truebeam, and Edge) over a 12-week period of MLC QA in our clinic. To assess sensitivity to MLC positioning errors, we designed a 1cm slit on the reference MLC, subsequently shifted by 0.5-5mm on the target MLC. For evaluating sensitivity to output error, we irradiated five 25cm×25cm open fields on the portal image with varying Monitor Units (MUs) of 96-100. We compared SSI* and Gamma index tests using three linear accelerator (LINAC) machines: Varian Trilogy, Truebeam, and Edge, with MLC leaf widths of 1, 0.5, and 0.25mm. Weekly QA included VMAT and static field modes, with Picket fence test images acquired. Mechanical uncertainties related to the LINAC head, electronic portal imaging device (EPID), and MLC during gantry rotation and leaf motion were monitored. The Gamma index test started detecting the MLC shift at a threshold of 4mm, whereas the SSI* metric showed sensitivity to shifts as small as 2mm. Moreover, the Gamma index test identified dose changes at 95MUs, indicating a 5% dose difference based on the distance to agreement (DTA)/dose difference (DD) criteria of 1mm/3%. In contrast, the SSI* metric alerted to dose differences starting from 97MUs, corresponding to a 3% dose difference. The Gamma index test passed all measurements conducted on each machine. However, the SSI* metric rejected all measurements from the Edge and Trilogy machines and two from the Truebeam. Our findings demonstrate that the SSI* exhibits greater sensitivity than the Gamma index test in detecting MLC positioning errors and dose changes between static and VMAT modes. The SSI* metric outperformed the Gamma index test regarding sensitivity across these parameters.

Optimal state filters for networked iterative learning control systems with data losses and noises

SummaryData losses and noises in both forward and feedback channels significantly impact the convergence of networked iterative learning control (ILC) systems. To address this issue, this article considers a class of linear time‐invariant objects controlled by proportional ILC controllers, an optimal state filter is then designed at the ILC controller side that aims to guarantee the convergence of the input transmitted by ILC controllers. First, two data transmission processes are introduced to account for the effects of data losses and noises. Second, a filtering model is established utilizing only the object information and the aforementioned data transmission processes. Third, the optimal state filter is designed on the basis of the orthogonal projection principle. This filtered state facilitates the acquisition of actual output errors, thus improving the convergence of the input transmitted by ILC controllers. Simulation results demonstrate the effectiveness of the proposed state filters.

Adaptive Practical Predefined-Time Control for Uncertain Teleoperation Systems With Input Saturation and Output Error Constraints

In this paper, the synchronization tracking control issue is investigated for uncertain teleoperation systems with input saturation and output error constraints. To this end, an adaptive practical predefined-time control scheme integrating backstepping recursive design, predefined time theory, prescribed performance function, and the anti-saturation auxiliary system, is developed for the first time. A fixed-time extended state observer is utilized to eliminate the negative influence of the lumped uncertainty, and the anti-saturation auxiliary system is constructed to address the input saturation. In contrast to finite/fixed-time controllers, the upper bound of convergence time can be obtained in advance by adjusting a single control parameter. The results indicate that the error signals can converge into a small region of the zero domain within a user-defined time and that the output error never violates the prescribed performance boundary. Simulations and experiments are performed on a teleoperation platform made up of two Phantom Touch robots to verify the effectiveness and practicality of the developed controller.

Simulation analysis of school road traffic characteristics

This study aims to identify key factors and sensitive intervals affect the school road traffic characteristics. We collect traffic data from the parking area and the school road (400-700 meters). The simulation is calibrated to ensure the error of outputs are within 1.5%. A sensitivity analysis method is proposed, it makes the multifactor comparable. The sensitivity factors of vehicle delay, queue length, and average speed are 1.44, 2.03, and 0.28 in school road, and the bottleneck road are 3.07, 4.44, and 0.65. The traffic indicators change more concentrated in bottleneck road but greater in school road. 6 scenarios are created to analyze school road traffic characteristics. Traffic flow (TF), number of parking spaces (NPS), and stopping time (ST) are selected as variables. Scenarios 1-3 are univariate, and scenarios 4-6 are bivariate. TF is the key factor with a sensitivity interval of [1300,1700].

Prescribed time bipartite output consensus tracking for heterogeneous multi-agent systems with external disturbances

This paper investigates the bipartite output consensus tracking problem for disturbed agent networks where the agents have different state dimensions. A prescribed time robust coordination control scheme is proposed, which consists of a leader-state observer and an output-regulation-based robust controller that both converge within a preset time. Time-scaling functions with preset parameters T1 and T2 respectively take effect in the observer and the controller, and by computing the output regulation matrix which turns the output errors of followers tracking the heterogeneous leader into their state errors, the model-mismatched agents could reach bipartite output tracking consensus within a total duration of T1+T2. Moreover, neural networks are deployed to estimate and eliminate the unknown disturbances. Thorough Lyapunov-based derivations approve the preset-time convergence and robustness for the controller. Numerical simulations are performed to verify the efficacy of the proposed control scheme.

Predictor‐based continuous‐discrete nonlinear observer design subject to aperiodic sampled and delayed measurement

AbstractIn this paper, a predictor‐based continuous‐discrete nonlinear observer is proposed for a class of nonaffine Lipschitz nonlinear systems with aperiodic sampled delayed output measurements and external disturbance. Firstly, this study introduces a class of delayed sampling hybrid nonlinear systems. By employing Lyapunov techniques and trajectory‐based stability theory, sufficient conditions are established for ensuring input‐to‐state stability of the delayed sampling hybrid nonlinear system with respect to external disturbances. Then the predictor‐based continuous‐discrete nonlinear observer is designed which consists of a continuous‐time observer, a compensating injector and a predictor. The continuous‐time observer is utilized to obtain continuous and delay‐free state estimation. The compensating injector is designed to compensate for output errors that occur between sampling instants. Furthermore, the predictor is employed to obtain delay‐free output error information, which is then utilized by the continuous‐time observer for feedback correction. The proposed observer's exponential input‐to‐state property against the disturbance is proved via the proposed hybrid system stability theory. The effectiveness of the proposed observers has been demonstrated through numerical simulations and performance comparisons with the zero‐order holder‐based observer and the output predictor‐based observer designs in order to highlight the effectiveness and advantages of the proposed observer.

Cooperative parallel operation of multiple actuators

This paper studies parallel operation of identical actuators cooperatively driving a common plant. The objective of the parallel operation is to regulate the output of the plant accurately while allowing the actuators to fairly share the input of the plant. To address the parallel operation problem, it is assumed that the plant and the actuators are modeled by general linear systems as opposed to the existing results. Further it is assumed that the controllers for the actuators share their local information with each other through communication. A feedback control law for each actuator is proposed by using the plant output error measurement and the exchanged local information. Assuming the existence of the spanning tree in the graph representing the communication topology among the controllers, it is shown that the output regulation and plant input sharing properties can be achieved by the proposed control law. The design of the proposed control law is demonstrated by a numerical example.

Two-dimensional model-free Q-learning-based output feedback fault-tolerant control for batch processes

For batch processes with partial actuator failures and unknown system dynamics, an innovative two-dimensional (2D) model-free Q-learning algorithm is proposed to obtain the optimal controller's gains, achieving output feedback fault-tolerant control. First, a 2D linear model is constructed to describe batch processes with partial actuator failures. Then, the state increments in the batch direction and the output errors in the time direction are used as novel state variables to construct a multi-degree-of-freedom model. Second, a 2D Bellman equation is proposed through a connection between a 2D value and a 2D Q functions. Next, a 2D off-policy model-free Q-learning algorithm is highlighted, which incorporates target policies into a multi-degree-of-freedom model and focuses on using policy iteration to solve the fault-tolerant tracking control problem. The robustness analysis rigorously proves the stability of the closed-loop system. Lastly, the simulation results of the holding stage prove the feasibility and effectiveness of the presented algorithm.

High-factor interpolation method based on space-time modulation and a Kalman filter for optical encoders.

A high-factor interpolation method based on space-time modulation and a Kalman filter for optical encoders is proposed. Space-time modulation employs a reference time signal to modulate the output displacement signal of the optical encoder into a displacement space-time signal. Subsequently, high-frequency pulse signals are used for interpolation, which detect the phase of the reference time signal and the displacement space-time signal to obtain displacement information from the optical encoder output. The interpolation factor of this method depends on the frequencies of the high-frequency pulse signal and the reference time signal, and is independent of the moving speed. A Kalman filter is employed to estimate the velocity, compensating for time lag errors in the displacement information output by space-time modulation to improve the real-time performance of displacement output. The proposed method is simple and effective, which can be implemented on an FPGA. The effectiveness of the proposed method is verified through simulation and experimentation.