Search Results (150)

At temperatures higher than 5 °C in the cooling chambers of refrigeration systems, bacteria multiply rapidly on fresh fishes, thereby leading to an increased risk of foodborne diseases. Maintaining the storage temperature within the recommended bounds of 0 °C and 5 °C is needed to maintain food safety and quality. This study presents model predictive control of a data-driven medium-temperature cold storage system using subspace system identification techniques. The identified linear model presents a holistic view of the whole system, with each subsystem cohesively linked together. The data-driven model was developed from synthetic data derived from a high-fidelity simulation benchmark model of a supermarket refrigeration system from Aalborg University, Denmark. The benchmark model consists of a medium-temperature closed display case, the suction manifold, and the compressor rack. The data of the expansion valve, suction pressure, compressor capacity, heat transfer rate, and ambient temperature were taken as inputs, while the data of the air and goods temperatures were taken as outputs based on expert knowledge. A linear model predictive controller was designed to control the temperature outputs of the identified linear model, and the outputs were compared with the proportional–integral dead band control used in the benchmark. Simulation results for 24 h showed that the model predictive controller was able to achieve an air temperature and a goods temperature within the recommended temperature range of 0 °C and 5 °C that guarantees safe storage of fresh fishes. These results imply that a reduced-order model of a commercial refrigeration system that is robust, reliable, and stable can be developed and controlled to achieve the goal of food safety, thereby guaranteeing food security and reducing costs. Full article

Stochastic Subspace Identification (SSI) theory offers the distinct advantage of extracting modal parameters directly from operational ambient excitations without requiring artificial force, ensuring completely true boundary conditions and providing extensive field measurement data. In this study, we systematically investigate the operational modal characteristics of Electric Multiple Units (EMUs) in the Chinese high-speed railway network under multi-dimensional coupling conditions, including wide speed ranges, axle load perturbations, air spring faults, and coupled operation. The results reveal that while car body modal frequencies remain largely insensitive to operating speed—indicating negligible effects of aerodynamic stiffness—they exhibit distinct sensitivities to mass and boundary variations. Specifically, an increase in axle load induces a significant attenuation (exceeding 5%) in low-order vertical bending frequencies, conforming to the dynamic mass law. Conversely, air spring deflation triggers a sharp increase in boundary stiffness, resulting in a 13.6% surge in torsional modal frequency, which serves as a critical indicator for fault diagnosis. Furthermore, coupled operation is found to primarily enhance system damping. Based on these findings, we establish a “condition-modal” vehicle sensitivity matrix, quantifying dynamic evolution mechanisms under complex boundaries and providing a vital baseline for monitoring the structural health of railway vehicles and conducting intelligent maintenance. Full article

A data-driven system identification algorithm that utilizes eigenvectors is presented. The eigenvectors are extracted from a unified solution space comprising both input and output subspaces. To expand the input subspace, a higher-order subspace from the input subspaces is augmented with the measured input subspace; this higher-order subspace exhibits additional cross-correlations with both the input and output subspaces, thus producing more informative eigenvectors and linearizing the system. The extracted eigenvectors are then deployed to sequentially project new input snapshots first onto the input subspace and subsequently onto the output subspace to predict the output. The algorithm effectively reconstructs the original governing equations of a quasi-stationary dynamic system, providing an inference that the original system is a series of data projections via eigenvectors, and also implying the possibility of reconstructing the low-rank governing equation with a limited number of eigenvectors, thus yielding a linearized representation of the system from the data. Notably, identifying the system from the well-expanded, high-dimensional nonlinear solution space requires only a limited duration of data snapshots, indicating that the essential spatial features manifested by the original governing equation are determined rapidly. Full article

The growing penetration of power electronics and nonlinear loads in industrial electrical networks has increased the dynamic complexity of these systems, exceeding the analysis capabilities of traditional approaches based on quasi-stationary models. In this context, this paper presents a methodology for the dynamic characterization of an industrial electrical network based on high-resolution synchrophasor measurements obtained using a microPMU. The proposed approach is based on the identification of a linear dynamic model in state space using subspace techniques based on real data recorded during a short-duration transient event. The results show that the identified model is capable of adequately capturing local underdamped dynamics and reproducing the temporal response observed in the measurements. This evidences the presence of dynamic modes associated with the interaction between the network and power electronics-based devices. Similarly, the stability analysis of the identified model demonstrates its consistency and robust gains in temporal variations within the analysis window. Overall, the results confirm that the combination of microPMU and data-based modeling techniques is an effective tool for improving dynamic observability and understanding the transient behavior of industrial power grids, complementing classical analysis and simulation methods. Full article

Structural Health Monitoring (SHM) of large-scale civil infrastructure is essential to ensure safety, minimise maintenance costs, and support informed decision-making. Unsupervised anomaly detection has emerged as a powerful tool for identifying deviations in structural behaviour without requiring labelled damage data. The study initially reproduces and implements a state-of-the-art methodology that combines local density estimation through the Cumulative Distance Participation Factor (CDPF) with Semi-parametric Extreme Value Theory (SEVT) for thresholding, which serves as an essential baseline reference for establishing normal structural behaviour and for benchmarking the performance of the proposed anomaly detection framework. Using modal frequencies extracted via Stochastic Subspace Identification from the Z24 bridge dataset, the baseline method effectively identifies structural anomalies caused by progressive damage scenarios. However, its performance is constrained when dealing with subtle or non-linear deviations. To address this limitation, we introduce an innovative ensemble anomaly detection framework that integrates two complementary unsupervised methods: Principal Component Analysis (PCA) and Autoencoder (AE) are dimensionality reduction methods used for anomaly detection. PCA captures linear patterns using variance, while AE learns non-linear representations through data reconstruction. By leveraging the strengths of these techniques, the ensemble achieves improved sensitivity, reliability, and interpretability in anomaly detection. A comprehensive comparison with the baseline approach demonstrates that the proposed ensemble not only captures anomalies more reliably but also provides improved stability to environmental and operational variability. These findings highlight the potential of ensemble-based unsupervised methods for advancing SHM practices. Full article

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

Offshore wind turbines (OWTs) operate under harsh marine conditions involving strong winds, waves, and salt-laden air, which increase the risk of excessive vibrations and structural failures such as tower collapse. To ensure structural safety and achieve effective vibration control, accurate modal parameter identification is essential. In this study, a vibration monitoring system was developed, and the Bayesian Spectral Decomposition (BSD) method was applied for the operational modal analysis of a 5.5 MW monopile OWT. The monitoring system consisted of ten uniaxial accelerometers mounted at five elevations along the tower, with two orthogonally oriented sensors at each level to capture horizontal vibrations. Due to continuous nacelle yawing, the measured accelerations were projected onto the structural fore–aft (FA) and side–side (SS) directions prior to modal analysis. Two days of vibration and SCADA data were collected: one under rated rotor speed and another including one hour of idle state. Data preprocessing involved outlier removal, low-pass filtering, and directional projection. The obtained data were divided into 20-min segments, and the BSD approach was applied to extract the primary modal parameters in both FA and SS directions. Comparison with results from the Stochastic Subspace Identification (SSI) technique showed strong consistency, verifying the reliability of the BSD method and its advantage in uncertainty quantification. The results indicate that the identified modal frequencies remain relatively stable under both rated and idle conditions, whereas the damping ratios increase with wind speed, with a more significant growth observed in the FA direction. Full article

This study investigates the practical value of angular-velocity measurements in the dynamic identification of pedestrian footbridges, addressing the need for reliable yet cost-effective diagnostics for slender civil structures. A comprehensive experimental campaign on a steel footbridge in Gdynia combined ambient vibration tests, forced excitation (light and heavy shakers), and controlled pedestrian loading. Synchronous translational accelerations and rotational velocities from MEMS sensors enabled evaluation of both bending and torsional responses. Three identification techniques—Peak Picking (PP), Frequency Domain Decomposition (FDD), and Stochastic Subspace Identification (SSI)—were applied and compared with a validated beam–shell FEM developed in SOFiSTiK. The results show that rotational data improve mode-shape interpretation and classification, notably resolving a coupled torsional–vertical mode (VT₂) that was ambiguous in acceleration-only analyses. The fundamental frequency of 3.1 Hz places the bridge in a resonance-prone range; field tests confirmed predominantly vertical response, with horizontal accelerations < 0.05 m/s² and peak vertical accelerations exceeding comfort class CL3 during synchronised walking of six pedestrians (≈2.55 m/s²) and jumping (up to 3.61 m/s²). Overall, the outcomes highlight that low-cost gyroscopic sensing offers substantial benefits for structural system identification and mode-shape characterization, enriching acceleration-based diagnostics and strengthening the basis for subsequent analyses. By reducing the financial and material demands of vibration testing, the proposed approach contributes to more sustainable assessment and maintenance of pedestrian bridges, aligning with resource-efficient monitoring strategies in civil infrastructure. Full article

To address the issue of low accuracy in the dynamic modal decomposition (DMD) method used for operational modal analysis (OMA) under noise conditions of aerospace structures, an enhanced identification approach is proposed in this paper, which integrates subspace orthogonal projection with DMD to better determine the modal properties of linear mechanical systems with noisy observations. Subspace orthogonal projection applied to the Hankelized matrix is utilized for denoising observation signals. Compact singular value decomposition (SVD) is employed on the projection matrix in order to acquire the optimal realization of system matrix. Subsequently, DMD is introduced to reduce the dimensionality of the system matrix and extract the dominant modal features. The effectiveness and practicality of the proposed method are confirmed through numerical and experimental examples. The proposed method demonstrates marginally improved identification accuracy in modal frequency and enhanced performance in damping ratios when compared to representative OMA methods under different white noise conditions. Full article

Reliable estimation of operational modal parameters is essential in structural health monitoring (SHM), particularly when these parameters serve as damage-sensitive features. Modern distributed monitoring systems, often employing digital MEMS accelerometers, must account for timing uncertainties across sensor networks. Clock irregularities can lead to non-deterministic sampling, introducing uncertainty in the identification of modal parameters. In this paper, the effects of timing variability throughout the network are propagated to the final modal quantities through a Monte-Carlo-based framework. The modal parameters are identified using the covariance-driven stochastic subspace identification (SSI-COV) algorithm. A finite element model of a steel cantilever beam serves as a test case, with timing irregularities modeled probabilistically to simulate variations in sensing node clock stability. The results demonstrate that clock variability at both intra-node and inter-node levels significantly influences mode shape estimation and introduces systematic biases in the identified natural frequencies and damping ratios. The confidence intervals are calculated, showing increased uncertainty with greater timing irregularity. Furthermore, the study examines how clock variability impacts damage detection, offering metrological insights into the limitations of distributed vibration-based SHM systems. Overall, the findings offer guidance for designing and deploying monitoring systems with independently timed nodes, aiming to enhance their reliability and robustness. Full article

Accurate finite element (FE) models are essential for the safety assessment of civil engineering structures. However, obtaining reliable model parameters for existing bridges remains challenging due to the inability to conduct static load tests without disrupting traffic flow. To address this, this study proposes an FE model updating framework that integrates the response surface method and the nutcracker optimization algorithm (NOA). This framework is characterized by the incorporation of ambient vibration data into parameter optimization, thereby enhancing model accuracy. The stochastic subspace identification method is first adopted to extract the bridge’s natural frequencies from vibration data. The response surface method is then employed to construct a response surface function that approximates the FE model. The NOA is subsequently applied to iteratively optimize this response surface function, ensuring rapid convergence and the precise adjustment of the FE model parameter. To validate the effectiveness of the proposed framework, a continuous beam–arch composite bridge with a span of 204.783 m was selected as a case study. The results indicate that the proposed method reduced the average frequency error from 5.58% to 2.75% by updating the model parameters. While the whale optimization algorithm required 21 iterations and the grey wolf optimizer needed 41 iterations to converge near the minimum, the NOA achieved this in merely 13 iterations, demonstrating the NOA’s superior convergence speed. Furthermore, the NOA significantly outperformed both the whale optimization algorithm and the grey wolf optimizer in reducing the error of the first transverse vibration frequency. Full article

The underwater environment severely degrades image quality by absorbing and scattering light. This causes significant challenges, including non-uniform illumination, low contrast, color distortion, and blurring. These degradations compromise the performance of critical underwater applications, including water quality monitoring, object detection, and identification. To address these issues, this study proposes a detail-oriented hybrid framework for underwater image enhancement that synergizes the strengths of traditional image processing with the powerful feature extraction capabilities of unsupervised deep learning. Our framework introduces a novel multi-scale detail enhancement unit to accentuate structural information, followed by a Latent Low-Rank Representation (LatLRR)-based simplification step. This unique combination effectively suppresses common artifacts like oversharpening, spurious edges, and noise by decomposing the image into meaningful subspaces. The principal structural features are then optimally combined with a gamma-corrected luminance channel using an unsupervised MU-Fusion network, achieving a balanced optimization of both global contrast and local details. The experimental results on the challenging Test-C60 and OceanDark datasets demonstrate that our method consistently outperforms state-of-the-art fusion-based approaches, achieving average improvements of 7.5% in UIQM, 6% in IL-NIQE, and 3% in AG. Wilcoxon signed-rank tests confirm that these performance gains are statistically significant (p < 0.01). Consequently, the proposed method significantly mitigates prevalent issues such as color aberration, detail loss, and artificial haze, which are frequently encountered in existing techniques. Full article

The accurate identification of modal parameters is essential for structural health monitoring (SHM), as it provides critical insights into the presence of damage or degradation within the structure. A promising technique, stochastic subspace identification (SSI) has numerous advantages in operational modal analysis (OMA), particularly in implementing automated OMA. Hence, an improved procedure is proposed in this study, addressing the size of the SSI matrix, the estimation of system order, and the removal of spurious modes for automated modal analysis. A general instruction for user-defined parameters is first reviewed and summarized. Subsequently, a proposed procedure is then introduced and framed into three steps. Key advances include the preliminary identification of fundamental frequency, which helps the overall automated work, adequately assigning the size of the SSI matrix, which can improve decomposition, and a decay function, which provides a good estimation of system order. To demonstrate and verify the procedure, a numerical simulation of a ten-story shear-type building structure and two field datasets, collected from reinforced concrete (RC) frames in Taiwan, are utilized. Consequently, the results suggest that the proposed three-step procedure based on SSI can facilitate automated OMA for continuous and long-term SHM, in terms of autonomously adjusting user-defined parameters. Full article

For the longitudinal tire force estimation problem of four-wheel independent drive electric vehicles (4WIDEVs), traditional model-based observers have limitations such as high modeling complexity and strong parameter sensitivity, while pure data-driven methods are susceptible to noise interference and have insufficient generalization ability. Therefore, this study proposes a joint estimation framework that integrates data-driven and modified recursive subspace identification algorithms. Firstly, based on the electromechanical coupling mechanism, an electric drive wheel dynamics model (EDWM) is constructed, and multidimensional driving data is collected through a chassis dynamometer experimental platform. Secondly, an improved proportional integral observer (PIO) is designed to decouple the longitudinal force from the system input into a state variable, and a subspace identification recursive algorithm based on correction term with forgetting factor (CFF-SIR) is introduced to suppress the residual influence of historical data and enhance the ability to track time-varying parameters. The simulation and experimental results show that under complex working conditions without noise and interference, with noise influence (5% white noise), and with interference (5% irregular signal), the mean and mean square error of longitudinal force estimation under the CFF-SIR algorithm are significantly reduced compared to the correction-based subspace identification recursive (C-SIR) algorithm, and the comprehensive estimation accuracy is improved by 8.37%. It can provide a high-precision and highly adaptive longitudinal force estimation solution for vehicle dynamics control and intelligent driving systems. Full article

Gait recognition, as a non-contact biometric technology, offers unique advantages in scenarios requiring long-distance identification without active cooperation from subjects. However, existing gait recognition methods predominantly rely on single-modal data, which demonstrates insufficient feature expression capabilities when confronted with complex factors in real-world environments, including viewpoint variations, clothing differences, occlusion problems, and illumination changes. This paper addresses these challenges by introducing a multi-modal gait recognition network based on channel shuffle regulation and spatial-frequency joint learning, which integrates two complementary modalities (silhouette data and heatmap data) to construct a more comprehensive gait representation. The channel shuffle-based feature selective regulation module achieves cross-channel information interaction and feature enhancement through channel grouping and feature shuffling strategies. This module divides input features along the channel dimension into multiple subspaces, which undergo channel-aware and spatial-aware processing to capture dependency relationships across different dimensions. Subsequently, channel shuffling operations facilitate information exchange between different semantic groups, achieving adaptive enhancement and optimization of features with relatively low parameter overhead. The spatial-frequency joint learning module maps spatiotemporal features to the spectral domain through fast Fourier transform, effectively capturing inherent periodic patterns and long-range dependencies in gait sequences. The global receptive field advantage of frequency domain processing enables the model to transcend local spatiotemporal constraints and capture global motion patterns. Concurrently, the spatial domain processing branch balances the contributions of frequency and spatial domain information through an adaptive weighting mechanism, maintaining computational efficiency while enhancing features. Experimental results demonstrate that the proposed GaitCSF model achieves significant performance improvements on mainstream datasets including GREW, Gait3D, and SUSTech1k, breaking through the performance bottlenecks of traditional methods. The implications of this research are significant for improving the performance and robustness of gait recognition systems when implemented in practical application scenarios. Full article