This work investigates the hydrodynamics of jet wiping, a coating process in which a thin slot gas jet impinges on a coating film dragged by a moving strip; thus, reducing the coating thickness and developing a run-back flow. The interaction between...
Piezoelectric composites have found a wide range of applications in smart structures and devices and effective numerical methods should be developed to simulate not only the macroscopic coupled piezoelectric performances, but also the details of the...
Friction induced vibrations such as brake squealing, or juddering are still challenging topics in product engineering processes. So far, this topic was particularly relevant for the automobile industry because they were the main market for disc brake...
With the emergence of large-scale vision-language pre-training (VLP) models, remote sensing (RS) image–text retrieval is shifting from global representation learning to fine-grained semantic alignment. This review systematically examines two ma...
Medical image registration integrates information from different types of medical images to support and improve clinical diagnosis. Existing image registration approaches are mainly classified into two categories: model-driven methods and data driven...
Within planetary gear transmissions (PGTs), mode shapes and eigenfrequencies hold a crucial significance in operational reliability and efficacy. Mode shapes explain the unique motion patterns inherent in PGT systems. Conversely, eigenfrequencies des...
With the increasing demand for accurate multimodal data analysis in complex scenarios, existing models often struggle to effectively capture and fuse information across diverse modalities, especially when data include varying scales and levels of det...
Magnetic resonance imaging (MRI), a widely adopted modality in clinical practice, enables the acquisition of multi-contrast images from the same anatomical structure, commonly referred to as multimodal images. Integrating these diverse modalities is...
Multimodal sentiment analysis (MSA) utilizes complementary information from different modalities to achieve a more thorough and fine-grained understanding of human sentiment. To address the issues of high modality redundancy and insufficient refined...
The fusion of multi-modal medical images has great significance for comprehensive diagnosis and treatment. However, the large differences between the various modalities of medical images make multi-modal medical image fusion a great challenge. This p...
What if machines could seamlessly translate between the visual richness of images and the semantic depth of language with mathematical precision? This paper presents a theoretical and empirical analysis of five novel cross-modal Wasserstein adversari...
Accurate registration of optical and synthetic aperture radar (SAR) images is a fundamental prerequisite for multi-source remote sensing data fusion and analysis. However, due to the substantial differences in imaging mechanisms, optical–SAR im...
Multimodal medical image fusion integrates complementary information from heterogeneous imaging modalities to provide comprehensive visual support for clinical analysis. Most existing methods adopt an “encode–fuse–decode” para...
In order to effectively extract the key feature information hidden in the original vibration signal, this paper proposes a fault feature extraction method combining adaptive uniform phase local mean decomposition (AUPLMD) and refined time-shift multi...
Multimodal sentiment analysis (MSA) seeks to predict subjective human sentiments by utilizing information from multiple modalities. It has been applied in diverse scenarios. Recent studies suggest that MSA benefits from integrating diverse modalities...
In order to improve the fault diagnosis accuracy of bearings, an intelligent fault diagnosis method based on Variational Mode Decomposition (VMD), Composite Multi-scale Dispersion Entropy (CMDE), and Deep Belief Network (DBN) with Particle Swarm Opti...
Metal cutting is a complex process with strong randomness and nonlinear characteristics in its dynamic behavior, while tool wear or fractures will have an immediate impact on the product surface quality and machining precision. A combined prediction...
Rotating machinery is susceptible to harsh environmental interference, and fault signal features are challenging to extract, leading to difficulties in health status recognition. This paper proposes multi-scale hybrid features and improved convolutio...
The rising prevalence of skin lesions places a heavy burden on global health resources and necessitates an early and precise diagnosis for successful treatment. The diagnostic potential of recent multi-modal skin lesion detection algorithms is limite...
Understanding human mobility patterns is of great importance for urban planning, traffic management, and even marketing campaign. However, the capability of capturing detailed human movements with fine-grained spatial and temporal granularity is stil...
Understanding socio-ecological systems and the discovery, recovery and adaptation of land knowledge are key challenges for sustainable land use. The analysis of sustainable agricultural systems and practices, for instance, requires interdisciplinary...
Accurate land cover classification is critical for geographic information science. However, the fusion of hyperspectral and LiDAR data remains constrained by insufficient spectral-geometric coupling and limited scale representation. To address these...
The multi-layered and multi-scale refined structure of bamboo gives bamboo musical instruments a unique tonal quality. This study employed heat treatment to enhance the acoustic vibration stability of bamboo materials. The hammering method was subseq...
Background/Objectives: Emotions can be recognized through external behavioral cues and internal physiological signals. Owing to the inherently complex psychological and physiological nature of emotions, models relying on a single modality often suffe...
Background: Automatic sleep staging is fundamental to sleep architecture analysis and the diagnosis of sleep disorders. Sleep staging requires jointly modeling complementary information across modalities, multi-scale physiological features, and tempo...
Background: Multimodal breast ultrasound, including B-mode imaging, color Doppler flow imaging, and elastography, provides complementary information for lesion characterization. However, effectively integrating heterogeneous modalities remains challe...
Although significant progress has been made in sentiment analysis tasks based on image–text data, existing methods still have limitations in capturing cross-modal correlations and detailed information. To address these issues, we propose a Mult...
Structure detection (SD) has emerged as a critical technology for ensuring the safety and longevity of infrastructure, particularly in housing and civil engineering. Traditional SD methods often rely on manual inspections, which are time-consuming, l...
Segmentation of tomato fruits under complex lighting conditions remains technically challenging, especially in low illumination or overexposure, where RGB-only methods often suffer from blurred boundaries and missed small or occluded instances, and s...
Accurate tree counting from remote sensing data is essential for forest inventory, biomass estimation, carbon accounting, and ecological monitoring. However, existing approaches predominantly rely on airborne RGB imagery and often struggle in complex...
Aiming at the noise and nonlinear characteristics existing in the deformation monitoring data of concrete dams, this paper proposes a dam deformation prediction model based on a multi-scale adaptive kernel ensemble. The model incorporates Gaussian wh...
Multimodal Sentiment Analysis (MSA) aims to predict subjective human emotions by leveraging multimodal information. However, existing research inadequately utilizes explicit sentiment semantic information at the lexical level in text and overlooks no...
Drug–target affinity (DTA) prediction is one of the core components of drug discovery. Despite considerable advances in previous research, DTA tasks still face several limitations with insufficient multi-modal information of drugs, the inherent...
Medical image processing has been used in medical image analysis for many years and has achieved great success. However, one challenge is that medical image processing algorithms ineffectively utilize multi-modality characteristics to further extract...
Fine-grained building function identification (BFI) is essential for sustainable urban development, land-use analysis, and data-driven spatial planning. Recent progress in fully supervised semantic segmentation has advanced multimodal BFI; however, m...
The service conditions of underground coal mine equipment are poor, and it is difficult to accurately extract the fault characteristics of rolling bearings. In order to better improve the accuracy of the fault identification of rolling bearings, a fa...
Large volumes of images shared on social media have made image captioning an important tool for social hotspot identification and public opinion monitoring, where accurate visual–language alignment is essential for reliable analysis. However, e...
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...
Wearable sensor-based human activity recognition (HAR) has gained growing significance in healthcare monitoring and assisted living systems. Although considerable advances have been made in classifying dynamic movements, stationary activities—s...
Advancements in satellite sensor technology have enabled access to diverse remote sensing (RS) data from multiple platforms. Hyperspectral Image (HSI) data offers rich spectral detail for material identification, while LiDAR captures high-resolution...
Pavement cracks pose significant challenges to the driving stability and operational safety of automated guided vehicles (AGV) in industrial and logistics environments. The complex geometry of cracks and their nonlinear coupling with vehicle dynamics...
Super-resolution (SR) facilitates high-fidelity mapping beyond the spatial resolution capacity of clinical neuro-oncology MRI. While state-of-the-art deep learning algorithms have augmented this domain, such methods exclusively learn from spatial or...
Small-object detection in UAV remote sensing faces common challenges such as tiny target size, blurred features, and severe background interference. Furthermore, single imaging modalities exhibit limited representation capability in complex environme...
Background: Accurate segmentation of brain tumors from multi-modal magnetic resonance imaging (MRI) is essential for diagnosis, treatment planning, and therapy monitoring. However, this task remains challenging due to tumor heterogeneity, irregular b...
Edge detection, a cornerstone of computer vision, identifies intensity discontinuities in images, enabling applications from object recognition to autonomous navigation. This survey presents a mathematically grounded analysis of edge detection’...
An automated non-contact weight measurement method for ducks is beneficial for preventing the stress response of ducks and, thus, promoting their healthy development. We propose a two-stream bidirectional interaction network that depends on RGB-D pic...
A novel multimodal super-resolution framework is introduced, combining GAN-based synthesis, perceptual constraints, and joint low-rank sparsity regularization to noticeably enhance MR-PET image quality. The architecture integrates modality-specific R...
The widespread emergence of multimodal data on social platforms has presented new opportunities for sentiment analysis. However, previous studies have often overlooked the issue of detail loss during modal interaction fusion. They also exhibit limita...
This study presents a conceptual multi-scale characterization framework for analyzing the total losses in oil-immersed ONAN distribution transformers. Rather than proposing a real-time partial discharge detection system or a validated energy recovery...
Accurate cloud detection remains a significant challenge due to the spectral ambiguity between clouds and bright or heterogeneous surfaces (e.g., snow, desert). While multi-angle and polarization data offer rich information, the discriminative power...
of 6