Search Results (260)

Exploring the design of beneficial nonlinear restoring force structures has become a highly popular topic due to their extensive applications in energy harvesting, actuation, energy absorption, robotics, etc. However, the current literature lacks a systematic review and classification that addresses the design, modeling, and parameter identification of nonlinear restoring forces. Thus, the present paper provides a thorough examination of the latest advancements in the design of nonlinear restoring forces, as well as modeling and parameter identification in contemporary beneficial nonlinear designs. The seven design methodologies, namely magnetic coupling, oblique spring linkages, static or dynamic preloading, metamaterials, bio-inspired, MEMS (Micro-Electromechanical Systems) manufacturing, and dry friction applied approaches, are classified. The polynomial, hysteretic, and piecewise linear models are summarized for nonlinear restoring force characterization. The system parameter identification methods covering restoring force surface, Hilbert transform, time-frequency analysis, nonlinear subspace identification, unscented Kalman filter, optimization algorithms, physics-informed neural networks, and data-driven sparse regression are reviewed. Moreover, possible enhancement strategies for nonlinear system identification of nonlinear restoring forces are presented. Finally, broader implications and future directions for the design, characterization, and identification of nonlinear restoring forces are discussed. Full article

Linear cultural heritage is characterized by complex cross-regional and multi-level features, facing severe challenges of spatial resource fragmentation and an imbalance in cultural and tourism functions. However, existing research lacks quantitative analysis regarding the non-linear driving mechanisms of spatial distribution and the misalignment of culture–tourism coupling. In this study, we construct an integrated identification–explanation–coupling–governance (IECG) theoretical framework. Taking The Great Tea Road (Jinzhong Section) as a case study, our framework integrates the CCSPM, XGBoost-SHAP machine learning interpreter, and Geodetector to systematically quantify the spatial structure of heritage and the level of culture–tourism integration. The results indicate that, (1) in terms of spatial patterns, the study area exhibits an unbalanced agglomeration characteristic of “dual-primary and dual-secondary cores,” with high-density areas showing significant orientation along rivers and roads; (2) regarding driving mechanisms, the machine learning model reveals a significant “non-linear threshold effect,” with 83% of driving factors (e.g., elevation and distance to transportation) exhibiting non-linear fluctuations in their influence on heritage distribution; and, (3) in terms of culture–tourism coupling, the overall coupling coordination degree (CCD) is low (mean 0.38), indicating significant “resource–facility” spatial misalignment. The modern number of public cultural facilities (NCF) is identified as the primary obstacle restricting the transformation of high-grade heritage into tourism products. Based on these findings, we propose adaptive zoning governance strategies. This research not only theoretically clarifies the complexity of the social–ecological system of linear heritage but also provides a generalizable quantitative method for the digital protection and sustainable tourism planning of cross-regional cultural heritage. Full article

Wind power systems often introduce interfering DC components that distort power measurements and threaten grid stability. To address these issues, this paper proposes a novel delayed sampling-based grid parameter estimation method that explicitly accounts for DC disturbances. By transforming the estimation problem into a linear regression form via nonlinear algebraic transformation, an adaptive recursive identification algorithm is developed to estimate grid frequency, amplitude, phase, and DC component simultaneously. Rigorous stability analysis is provided to guarantee convergence and robustness of the estimator in the presence of DC components. Experimental results demonstrate fast transient response and zero steady-state error, validating the effectiveness of the proposed method for real-time grid parameter estimation. Full article

This study presents a cost-efficient Hardware-in-the-Loop platform for liquid-level process control education, designed to bridge the gap between theoretical learning and real-world industrial practice. The proposed system integrates NI myRIO and NI myDAQ hardware with LabVIEW-based real-time simulation and controller implementation, enabling flexible experimentation across a range of linear and nonlinear tank models. Through real-time controllers, students can design, tune, and validate classical digital controllers while gaining hands-on experience with real-time process dynamics. Experimental results from Model-in-the-Loop and Hardware-in-the-Loop configurations confirm the high accuracy between simulated and hardware responses, with low normalized root mean square error (NRMSE < 0.07) and high normalized cross-correlation (NCC > 0.99) between MIL and HIL responses. Additionally, learning outcomes were assessed using rubrics and student perception surveys aligned with ABET criteria. The platform successfully satisfies ABET student outcomes (SO1, SO2, SO7) by promoting modeling, system identification, and real-time implementation skills. Student surveys reveal high satisfaction mean = 5.44 and a Cronbach’s α of 0.91367, highlighting enhanced engagement, flexibility, and confidence in control system design. This work demonstrates an adaptable, scalable educational solution that strengthens engineering competencies while keeping implementation costs low. Full article

Urban Water utilities in low- and middle-income countries face systemic challenges, including data scarcity, institutional fragmentation, and aging infrastructure, that constrain the applicability of conventional benchmarking tools reliant on peer comparisons. This study introduces and validates the Index of Sustainability of Water Supply Systems (ISA), an autonomous diagnostic framework that evaluates sustainability without external references. The ISA integrates 49 indicators across economic, social, and environmental dimensions, transforming raw utility data into standardized quality scores through non-linear conversion functions and weighted aggregation. When applied to 14 urban water systems in southern Ecuador, the ISA revealed severe sustainability deficits: all scored between 25 and 43 on a 0–100 scale, with 71% classified as poor and 29% as deficient. Key weaknesses included inadequate cost recovery, network renewal below 0.2%/year, lack of wastewater treatment, limited watershed protection, intermittent supply under 12 h/day, and persistent water quality issues. A critical failure was an Infrastructure Leakage Index > 38 in 7 of 14 systems. The ISA’s autonomous design enabled identification of systemic vulnerabilities, including governance gaps and environmental deficits. These results confirm the ISA’s practical utility as an equitable, actionable diagnostic tool for utilities and regulators to prioritize interventions and advance SDG 6 in data-constrained settings. Full article

The Maximum Entropy (MaxEnt) model, integrated with ArcGIS (a geographic information system), was employed to project potential species distribution under current conditions and future climate scenarios (SSP1–2.6, SSP2–4.5, SSP5–8.5) for the 2050s, 2070s, and 2090s. Model optimization involved testing 1160 parameter combinations. The optimized model (FC = LQ, RM = 0.1) exhibited significantly improved predictive performance, with an average AUC of 0.967. Under current conditions, the estimated core suitable habitat spans 35.62 × 10⁴ km², primarily located in southern China. Future projections indicated a non-linear trajectory: an initial contraction of total suitable area by mid-century, followed by a substantial expansion by the 2090s, particularly under high-emission scenarios. Simultaneously, the distribution centroid shifted northwestward. The primary factors influencing distribution were the annual mean temperature (Bio1, 41.1%) and the precipitation of the coldest quarter (Bio19, 20.0%). These findings establish a critical scientific basis for developing climate-adaptive conservation strategies, including the identification of priority climate refugia in Fujian province, China, and planning for assisted migration to northwestern regions. Full article

Intelligent Fault Diagnosis (IFD) systems are integral to predictive maintenance and real-time monitoring but often encounter challenges such as data scarcity, non-linearity, and changing operational conditions. To address these challenges, we propose an enhanced transfer learning framework that integrates the Universal Adaptation Network (UAN) with Spectral-normalized Neural Gaussian Process (SNGP), WideResNet, and attention mechanisms, including self-attention and an outlier attention layer. UAN’s flexibility bridges diverse fault conditions, while SNGP’s robustness enables uncertainty quantification for more reliable diagnostics. WideResNet’s architectural depth captures complex fault patterns, and the attention mechanisms focus the diagnostic process. Additionally, we employ Optuna for hyperparameter optimization, using a structured study to fine-tune model parameters and ensure optimal performance. The proposed approach is evaluated on benchmark datasets, demonstrating superior fault identification accuracy, adaptability to varying operational conditions, and resilience against data anomalies compared to existing models. Our findings highlight the potential of advanced machine learning techniques in IFD, setting a new standard for applying these methods in complex diagnostic environments. Full article

This paper addresses the problem of stabilizing an inverted pendulum actuated by a reaction wheel, a system relevant for robotic balancing platforms and aerospace applications. The study compares several state-space representations of the system and examines their implications for controller synthesis and parameter identification. A unified nonlinear model formulation is introduced, enabling a structural Lyapunov-based robustness analysis that reveals how variations in the gravitational gain affect closed-loop stability. Control strategies based on pole placement and Linear Quadratic Regulator (LQR) design are implemented and compared across the different representations. The analysis highlights a robustness–fidelity trade-off between model complexity and sensitivity to parameter uncertainty, providing insight that extends beyond the specific laboratory setup. Theoretical results are validated on a real laboratory platform. The controllers are evaluated in both upright and downward equilibrium configurations, and the influence of parameter shifts is assessed experimentally using global identification and performance indices. The work offers general modeling and robustness guidelines for reaction-wheel-based stabilization systems and related underactuated nonlinear mechanisms. Full article

Individual beliefs are a critical factor in understanding human action and behavior. Certain beliefs, such as irrational beliefs and burnout, influence all forms of learning and social interaction within the school environment, primarily limiting both individual and collective development. The former are associated with the inherent human tendency to adhere to habits and behaviors not strictly dictated by rationality, often stemming from irrational thoughts held by the individual. The latter, examined within the framework of the Job Demands–Resources Theory, pertain to occupational characteristics that differentially affect employees’ well-being, job demands, and available resources. The present study aims to investigate the role of these variables in relation to teachers’ Innovative Work Behavior, a recurring, multi-stage process oriented toward the implementation of new ideas within the school context. The sample consisted of 337 preschool educators who completed self-report questionnaires. Multiple linear regression analysis indicated that both irrational beliefs (positively) and the dimension of work disengagement (negatively) significantly influenced innovative work behavior, underscoring the distinct contributions of personal belief systems and burnout dimensions. Furthermore, a hierarchical cluster analysis revealed both heterogeneity among educators and common, distinct response patterns. The identification of five different clusters suggests that the examined characteristics and the underlying beliefs represent individual traits that change dynamically, leaving open the possibility of nonlinear relationships present in the workplace. Five profiles were identified, namely Disengaged-Low Innovators, Resilient-Balanced Innovators, Adaptive Innovators, Strained but Innovative Innovators, and Belief-Driven Innovators, which highlight the complex ways in which disengagement, exhaustion, and irrational beliefs combine to shape innovative work behavior. The findings are interpretable within the framework of contemporary theories in organizational psychology and management and can be utilized by educational principals to enhance school climate and teacher performance. Full article

Accurate fault localization in rail transit train cables is hindered by impedance mismatch, which induces overshoot interference and attenuates reflected signals, causing traditional peak-detection methods to fail. This study proposes a novel traveling wave dual-peak identification method to address this challenge. The approach employs signal polarity normalization to eliminate phase inversion, Gaussian-weighted filtering to suppress noise and distortion, and local extrema screening to robustly isolate incident and reflected wave peaks amidst complex backgrounds including overshoot oscillations and electromagnetic crosstalk. A dual-Gaussian model is optimized via nonlinear fitting to precisely quantify peak arrival times while compensating for waveform broadening. Fault distance is derived from the optimized time difference and wave velocity. Experimental validation across single-core coaxial, twin-core coaxial, and harness cables with open/short-circuit faults at multiple distances confirms the method’s effectiveness. Results demonstrate strong linear relationships between time differences and fault distances for all cable types, with successful peak identification achieved even under severe signal attenuation or strong coupling interference. This method significantly enhances localization accuracy for rail transit cable systems under impedance mismatch conditions. Full article

To address the issue of hysteresis nonlinearity adversely affecting the tracking accuracy of piezoelectric actuators, an improved particle swarm optimization (PSO) algorithm is proposed to improve the accuracy of hysteresis model parameter identification. Additionally, a discrete-time linear quadratic optimal tracking (DLQT) control strategy incorporating hysteresis compensation is developed to improve tracking performance. This study employs the Hammerstein model to characterize the nonlinear hysteresis behavior of piezoelectric actuators. Regarding parameter identification, the conventional PSO algorithm tends to suffer from premature convergence and being trapped in local optima. To address this, a cross-variation mechanism is introduced to enhance population diversity and improve global search ability. Furthermore, adaptive and dynamically adjustable inertia weights are designed based on evolutionary factors to balance exploration and exploitation, thereby enhancing convergence and identification accuracy. The inertia weights and learning factors are adaptively adjusted based on the evolutionary factor to balance local and global search capabilities and accelerate convergence. Benchmark function tests and model identification experiments demonstrate the improved algorithm’s superior convergence speed and accuracy. In terms of control strategy, a hysteresis compensator based on an asymmetric hysteresis model is designed to improve system linearity. To address the issues of incomplete hysteresis compensation and low tracking accuracy, a DLQT controller is developed based on hysteresis compensation. Hardware-in-the-loop tracking control experiments using single and composite frequency reference signals show that the relative error is below 3.3% in the no-load case and below 4.5% in the loaded case. Compared with the baseline method, the proposed control strategy achieves lower root-mean-square error and maximum steady-state error, demonstrating its effectiveness. Full article

The design of informatively rich input signals is essential for accurate system identification, yet classical Fisher-information-based methods are inherently local and often inadequate in the presence of significant model uncertainty and non-linearity. This paper develops a Bayesian approach that uses the mutual information (MI) between observations and parameters as the utility function. To address the computational intractability of the MI, we maximize a tractable MI lower bound. The method is then applied to the design of an input signal for the identification of quasi-linear stochastic dynamical systems. Evaluating the MI lower bound requires the inversion of large covariance matrices whose dimensions scale with the number of data points N. To overcome this problem, an algorithm that reduces the dimension of the matrices to be inverted by a factor of N is developed, making the approach feasible for long experiments. The proposed Bayesian method is compared with the average D-optimal design method, a semi-Bayesian approach, and its advantages are demonstrated. The effectiveness of the proposed method is further illustrated through four examples, including atomic sensor models, where input signals that generate a large amount of MI are especially important for reducing the estimation error. Full article

Early prediction of weaning outcomes in mechanically ventilated patients has significant potential to influence the duration of treatment as well as associated morbidity and mortality. This study aimed to investigate the utility of signal analysis using electromyographic diaphragm (EMG) and electrocardiography (ECG) signals to classify the success or failure of weaning in mechanically ventilated patients. Electromyographic signals of 40 subjects were recorded using 5-channel surface electrodes placed around the diaphragm muscle, along with an ECG recording through a 3-lead Holter system during extubation. EMG and ECG signals were recorded from mechanically ventilated patients undergoing weaning trials. Linear and nonlinear signal analysis techniques were used to assess the interaction between diaphragm muscle activity and cardiac activity. Supervised machine learning algorithms were then used to classify the weaning outcomes. The study revealed clear differences in diaphragmatic and cardiac patterns between patients who succeeded and failed in the weaning trials. Successful weaning was characterised by a higher ECG-derived respiration amplitude, whereas failed weaning was characterised by an elevated EMG amplitude. Furthermore, successful weaning exhibited greater oscillations in diaphragmatic muscle activity. Spectral analysis and parameter extraction identified 320 parameters, of which 43 were significant predictors of weaning outcomes. Using seven of these parameters, the Naive Bayes classifier demonstrated high accuracy in classifying weaning outcomes. Surface electromyographic and electrocardiographic signal analyses can predict weaning outcomes in mechanically ventilated patients. This approach could facilitate the early identification of patients at risk of weaning failure, allowing for improved clinical management. Full article

Modeling mobile robots is crucial to odometry estimation, control design, and navigation. Classical state-space models (SSMs) have traditionally been used for system identification, while recent advances in deep learning, such as Long Short-Term Memory (LSTM) networks, capture complex nonlinear dependencies. However, few direct comparisons exist between these paradigms. This paper compares two multivariate modeling approaches for a differential drive robot: a classical SSM and an LSTM-based recurrent neural network. Both models predict the robot’s linear (v) and angular ($\omega$) velocities using experimental data from a five-minute navigation sequence. Performance is evaluated in terms of prediction accuracy, odometry estimation, and computational efficiency, with ground-truth odometry obtained via a SLAM-based method in ROS2. Each model was tuned for fair comparison: order selection for the SSM and hyperparameter search for the LSTM. Results show that the best SSM is a second-order model, while the LSTM used seven layers, 30 neurons, and 20-sample sliding windows. The LSTM achieved a FIT of 93.10% for v and 90.95% for $\omega$, with an odometry RMSE of 1.09 m and 0.23 rad, whereas the SSM outperformed it with FIT values of 94.70% and 91.71% and lower RMSE (0.85 m, 0.17 rad). The SSM was also more resource-efficient (0.00257 ms and 1.03 bytes per step) compared to the LSTM (0.0342 ms and 20.49 bytes). The results suggest that SSMs remain a strong option for accurate odometry with low computational demand while encouraging the exploration of hybrid models to improve robustness in complex environments. At the same time, LSTM models demonstrated flexibility through hyperparameter tuning, highlighting their potential for further accuracy improvements with refined configurations. Full article

To address the challenges of transient voltage stability in modern power systems with high renewables penetration, this paper proposes a multiple stability margin indexes-oriented online risk evaluation and adjustment framework based on a digital twin platform. The System Voltage Deviation Index (S_VDI) is first introduced as a quantitative metric to assess transient voltage stability from time-domain simulation results, capturing the system’s dynamic response under large disturbances. An arbitrary Polynomial Chaos (aPC) expansion combined with Sobol sensitivity analysis is then employed to model the nonlinear relationship between S_VDI and uncertain inputs such as wind power, photovoltaic output, and dynamic load variations, enabling accurate identification of key nodes influencing stability. Furthermore, an emergency control optimization model is developed that jointly considers voltage, frequency, and rotor angle stability margins, as well as the economic costs of load shedding, with a trajectory sensitivity-based local linearization technique applied to enhance computational efficiency. The proposed method is validated on a hybrid AC/DC test system (CSEE-VS), and results show that, compared with a traditional control strategy, the optimized approach reduces total load shedding from 322.59 MW to 191.40 MW, decreases economic cost from 229.18 to 178.11, and improves the transient rotor angle stability index from 0.31 to 0.34 and the transient frequency stability index from 0.3162 to 1.511, while maintaining acceptable voltage stability performance. These findings demonstrate that the proposed framework can accurately assess online operational risks, pinpoint vulnerable nodes, and generate cost-effective, stability-guaranteeing control strategies, showing strong potential for practical deployment in renewable-integrated power grids. Full article