Mode pairing is a crucial step for the stability of any model-updating strategy based on experimental modal parameters. Automatically establishing a stable and assertive correspondence between numerical and experimental modes, in many cases, proves t...
Vital defect information present in the magnetic field data of oil and gas pipelines can be perceived by developing such non-parametric algorithms that can extract modal features and performs structural assessment directly from the recorded signal da...
The conventional eigensystem realization algorithm with data correlation (ERA/DC) combines the impulse response or free response data of a structural system with the concept of correlation function to identify the modal parameter of the structural sy...
Noise, vibration and harshness (NVH) problems are critical issues to be tackled for wind turbine drivetrains. Tracking the behavior of modal parameters of the machines’ fundamental modes during operation it is of high interest to validate compl...
In multimodal collaborative learning, the gradient dynamics of heterogeneous modalities face significant challenges due to the curvature heterogeneity of parameter manifolds and mismatches in phase evolution. Traditional Euclidean optimization method...
An offshore wind turbine (OWT) is a complex structure that consists of different parts (e.g., foundation, tower, drivetrain, blades, et al.). The last decade, there has been continuous trend towards larger machines with the goal of cost reduction. Mo...
In real-world fire scenarios, complex lighting conditions and smoke interference significantly challenge the accuracy and robustness of traditional fire detection systems. Fusion of complementary modalities, such as visible light (RGB) and infrared (...
The modal and vibration-noise response characteristics of plate structures are closely related to their boundary effects, and the analytical modeling and solution of the dynamics of plate structures with complex boundary conditions can reveal mechani...
The working environment of rotating machines is complex, and their key components are prone to failure. The early fault diagnosis of rolling bearings is of great significance; however, extracting the single scale fault feature of the early weak fault...
Offshore wind turbines are prone to structural damage over time due to environmental factors, which increases operational costs and the risk of accidents. Early detection of structural damage through monitoring systems can help reduce maintenance cos...
Multimodal object detection leverages the complementary characteristics of visible (RGB) and infrared (IR) imagery, making it well-suited for challenging scenarios such as low illumination, occlusion, and complex backgrounds. However, most existing f...
Harmonic resonance challenges have intensified in modern power grids, primarily due to the high penetration of converter-based offshore wind energy. Traditional modal analysis methods conducted in the abc reference frame are often constrained by comp...
During UAV aerial photography tasks, influenced by flight altitude and imaging mechanisms, the target in images often exhibits characteristics such as small size, complex backgrounds, and small inter-class differences. Under single optical modality,...
In modal identification, the value of the model parameters and the associated uncertainty depends on the quality of the measurements. The maximum likelihood estimator (
Integrated blisk–shaft rotors represent a critical advancement in aero-engine design, offering enhanced structural integrity and weight reduction. However, their complex dynamic behavior under inherent material uncertainties poses significant c...
Obtaining the modal parameters of a tire with ground contact and rolling conditions represents a challenge due to the complex vibration characteristic behaviors that cause the distortion of the tire’s symmetry and the bifurcation phenomena of t...
The performance of Multimodal Sentiment Analysis (MSA) is commonly hindered by two major bottlenecks: the complexity and redundancy associated with supervised feature disentanglement and the coarse granularity of static fusion mechanisms. To systemat...
A new, non-destructive methodology is proposed in this work in order to determine the mechanical properties of membrane using vibro-acoustic tests. This procedure is based on the dynamic analysis of the behavior of the membrane. When the membrane is...
Multi-modal object tracking (MMOT) has received widespread attention for the ability to overcome single-sensor perception limitations. However, existing methods encounter several critical challenges. Representation learning and generalization capabil...
In order to realize the accurate prediction of the vibration fatigue life of the beam in service, a loose coupling analysis method is proposed to carry out vibration fatigue analysis of a beam with an initial crack. In modal analysis, the initial cra...
RGB-infrared (RGB-IR) object detection leverages complementary information from these two modalities to substantially enhance perception in complex environments, which is particularly beneficial for reliable detection under adverse imaging conditions...
Semantic segmentation of multi-modal remote sensing images is an important branch of remote sensing image interpretation. Multi-modal data has been proven to provide rich complementary information to deal with complex scenes. In recent years, semanti...
Contactless vital sign monitoring has emerged as a transformative healthcare technology, enabling the assessment of vital signs without physical contact with the human body. This review comprehensively reviews the rapidly evolving landscape of this f...
Due to the difficulty of obtaining statistical energy parameters of complex structures and the complexity of modeling connection and model verification, the hybrid FE-SEA model has many problems in modeling complex structures. Therefore, in order to...
We propose Heterogeneous Dual-path Contrastive Architecture (HDCA) for action recognition. Our model involves a spatial pathway and a temporal pathway; these two pathways employ distinct backbone networks and input formats, tailored to the specific p...
Some problems exist in classical optimization algorithms to solve multi-modal optimization problems and other complex systems. A Dynamic Niches-based Improved Hybrid Breeding Optimization (DNIHBO) algorithm is proposed to address the multi-modal opti...
The development of emotionally intelligent computers depends on emotion recognition based on richer multimodal inputs, such as text, speech, and visual cues, as multiple modalities complement one another. The effectiveness of complex relationships be...
A method for dynamic finite element (FE) model updating based on correlated mode auto-pairing and adaptive evolution screening (CMPES) is proposed to overcome difficulties in pairing inaccurate analytical modal data and incomplete experimental modal...
This paper investigates system identification algorithms for modal identification of frame structures, such as a suspension bridge and an overhead transmission line-crossing frame, using ambient vibration measurements. The modal identification proced...
Multi-modal object re-identification (ReID) is a challenging task that seeks to identify objects across different image modalities by leveraging their complementary information. Traditional CNN-based methods are constrained by limited receptive field...
High-frequency optical dynamic measurement can realize multiple measurement points covering the whole surface of the thin-walled structure, which is very useful for obtaining high-resolution spatial information for damage localization. However, the n...
Object detection in remote sensing images is crucial for airport management, hazard prevention, traffic monitoring, and more. The precise ability for object localization and identification enables remote sensing imagery to provide early warnings, mit...
The efficient use of energy in electrical systems has become a relevant topic due to its environmental impact. Parameter identification in induction motors and capacitor allocation in distribution networks are two representative problems that have st...
Wearable laser Doppler devices are susceptible to complex noise interferences, such as Gaussian white noise, baseline drift, and motion artifacts, with motion artifacts significantly impacting clinical diagnostic accuracy. Addressing the limitations...
The paper presents an original method concerning the problem of vibration reduction in the general case while milling large-size and geometrically complex details with the use of an innovative approach to the selection of spindle speed. A computation...
Multimodal research is an emerging field of artificial intelligence, and the analysis of dangerous driving behavior is one of the main application scenarios in the field of multimodal fusion. Aiming at the problem of data heterogeneity in the process...
The rapid development of deep neural networks has attracted significant attention in the infrared and visible image fusion field. However, most existing fusion models have many parameters and consume high computational and spatial resources. This pap...
We focus on a Bayesian inference framework for finite element (FE) model updating of a long-span cable-stayed bridge using long-term monitoring data collected from a wireless sensor network (WSN). A robust Bayesian inference method is proposed which...
Most current emotion recognition methods are often limited to a single- or dual-modality approach, neglecting the rich information embedded in other modalities. This limitation hampers the accurate identification of complex or subtle emotional expres...
This paper proposes a methodology to process and interpret the complex signals acquired from the health monitoring of civil structures via scale-space empirical wavelet transform (EWT). The FREEVIB method, a widely used instantaneous modal parameters...
Prototype vibration response data of high arch dam discharge structures inevitably mix various noises under the discharge excitation, which adversely affects the accuracy of the working modal identification of the structure. To effectively filter noi...
Audio–visual learning seeks to achieve holistic scene understanding by integrating auditory and visual cues. Early research focused on fully fine-tuning pre-trained models, incurring high computational costs. Consequently, recent studies have a...
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...
In fluid mechanics, modal decomposition, deeply intertwined with the concept of symmetry, is an essential data analysis method. It facilitates the segmentation of parameters such as flow, velocity, and pressure fields into distinct modes, each exhibi...
The quantitative diagnosis of rolling bearings is essential to automating maintenance decisions. Over recent years, Lempel–Ziv complexity (LZC) has been widely used for the quantitative assessment of mechanical failures as one of the most valua...
Considering that the occurrence and spread of diseases are closely related to the planting environment, a tomato disease diagnosis method based on Multi-ResNet34 multi-modal fusion learning based on residual learning is proposed for the problem of li...
Simultaneous Localization and Mapping (SLAM) algorithms are crucial for enabling agents to estimate their position in unknown environments. In autonomous navigation systems, these algorithms need to operate in real-time on devices with limited resour...
To address information isolation and incomplete parameter extraction among multi-modal data (e.g., drawings, text, and tables) in the operation and maintenance stage of buildings, this paper proposes a multi-modal data parsing, automatic parameter ex...
Photovoltaic (PV) systems play a significant role in renewable energy production. Due to the nonlinear and multi-modal nature of PV models, using accurate model parameters is crucial. In recent years, metaheuristic algorithms have been utilized to es...
With the rapid development of new power systems, power usage stations are becoming more diverse and complex. Fine-grained management of demand-side power load has become increasingly crucial. To address the accurate load forecasting needs for various...
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