Multiscale enhanced sampling (MSES) allows for an enhanced sampling of all-atom protein structures by coupling with the accelerated dynamics of the associated coarse-grained (CG) model. In this paper, we propose an MSES extension to replace the CG mo...
This paper proposes DCGAN-YOLOv8n, an integrated framework that significantly advances small-sample target detection by synergizing generative adversarial feature enhancement with multi-scale representation learning. The model’s core contributi...
To understand functions of biomolecules such as proteins, not only structures but their conformational change and kinetics need to be characterized, but its atomistic details are hard to obtain both experimentally and computationally. Here, we review...
The medical decision-making system is an advanced system for patients that can assist doctors in their medical work. Osteosarcoma is a primary malignant tumor of the bone, due to its specificity, such as its blurred borders, diverse tumor morphology,...
Lithological mapping with multispectral remote sensing remains challenging when diagnostic spectral information is limited and reliable labeled samples are scarce. This problem is particularly relevant when convolutional neural networks (CNNs) are ap...
Hemerocallis fulva, essential to urban ecosystems and landscape design, faces challenges in disease detection due to limited data and reduced accuracy in complex backgrounds. To address these issues, the Hemerocallis fulva leaf disease dataset (HFLD-...
In complex industrial scenarios, high-quality fault data of rotating machinery are scarce and costly to collect. Therefore, small sample fault diagnosis needs further research. To solve this problem, in this work is proposed a minimum variance auxili...
In recent years, deep learning methods have achieved remarkable success in hyperspectral image classification (HSIC), and the utilization of convolutional neural networks (CNNs) has proven to be highly effective. However, there are still several crit...
To address the challenges of underwater target detection, including complex background interference, light attenuation, severe occlusion, and overlap between targets, as well as the wide-scale variation in objects, we propose MAS-YOLOv11, an improved...
Change detection in high-resolution optical remote sensing images is essential for dynamic surface monitoring and land resource management. However, existing methods still face challenges in representing multiscale change information, accurately loca...
Casting defects in turbine blades can significantly reduce an aero-engine’s service life and cause secondary damage to the blades when exposed to harsh environments. Therefore, casting defect detection plays a crucial role in enhancing aircraft...
Coalbed methane, an abundant clean energy resource in China, is gaining significant attention. Electric submersible progressive cavity pumps, ideal for downhole extraction with high solids content, are vital in coalbed methane operations. Current fau...
Characterizing instability in gas–liquid flows is difficult because flow dynamics interact across multiple scales. In this work, we develop an integrated framework that combines multi-resolution analysis with composite multiscale equiprobable s...
Multiscale fuzzy entropy (MFE), as an enhanced multiscale sample entropy (MSE) method, is an effective nonlinear method for measuring the complexity of time series. In this paper, an improved MFE algorithm termed composite interpolation-based multisc...
High-resolution multispectral remote sensing imagery is widely used in critical fields such as coastal zone management and marine engineering. However, obtaining such images at a low cost remains a significant challenge. To address this issue, we pro...
Pine wilt disease is extremely ruinous to forests. It is an important to hold back the transmission of the disease in order to detect diseased trees on UAV imagery, by using a detection algorithm. However, most of the existing detection algorithms fo...
To address the issues of model overfitting under scarce samples and poor segmentation performance on slender objects in the task of semantic segmentation of remote sensing hyperspectral images, this paper proposes a hyperspectral image semantic segme...
Employing the multi-scale strategy in hyperspectral image (HSI) classification enables the exploration of complex land-cover structures with diverse shapes. However, existing multi-scale methods still have limitations for fine feature extraction and...
Numerous deep learning-based object detection methods have achieved excellent performance. However, the performance on small-size object detection and positive and negative sample imbalance problems is not satisfactory. We propose a multi-scale featu...
To address the limitations in multi-scale feature processing and illumination sensitivity of existing steel surface defect detection algorithms, we proposed ADP-YOLOv8-n, enhancing accuracy and computational efficiency through advanced feature fusion...
Small object detection in traffic sign applications often faces challenges like complex backgrounds, blurry samples, and multi-scale variations. Existing solutions tend to complicate the algorithms. In this study, we designed an efficient and simple...
Accurate segmentation of ship targets in high-resolution remote sensing images is crucial for maritime monitoring, traffic management and naval security. However, existing methods struggle to simultaneously address extreme scale variations in ships a...
Rolling bearings act as key parts in many items of mechanical equipment and any abnormality will affect the normal operation of the entire apparatus. To diagnose the faults of rolling bearings effectively, a novel fault identification method is propo...
To address the limitations of traditional methods in adapting to complex operating conditions, this paper proposes a fault diagnosis approach combining multi-scale empirical mode decomposition (MS-EMD) and a one-dimensional convolutional neural netwo...
In hyperspectral imaging (HSI) classification tasks, Convolutional Neural Network (CNN) and 3-D Swin Transformer (3DST) methods have shown strong HSI classification performance, yet CNN is limited by local receptive fields that restrict global–...
Compared to traditional real-valued neural networks, which process only amplitude information, complex-valued neural networks handle both amplitude and phase information, leading to superior performance in polarimetric synthetic aperture radar (PolSA...
Botnets pose a significant challenge in network security by leveraging Domain Generation Algorithms (DGA) to evade traditional security measures. Extracting DGA domain samples is inherently complex, and the current DGA detection models often struggle...
The anti-reflection of transparent material surfaces has attracted great attention due to its potential applications. In this paper, a single-step controllable method based on an infrared femtosecond laser is proposed for self-generation multiscale a...
With the continuous advancement of deep learning technology, researchers have made further progress in the hyperspectral image (HSI) classification domain. We propose a double-branch multi-scale residual network (DBMSRN) framework for HSI classificat...
In complex industrial environments, vibration and acoustic signals are often corrupted by strong noise, resulting in non-stationary and low signal-to-noise ratio (SNR) characteristics that hinder effective feature extraction and degrade model general...
Deep learning-based ship-detection methods have recently achieved impressive results in the synthetic aperture radar (SAR) community. However, numerous challenging issues affecting ship detection, such as multi-scale characteristics of the ship, clut...
Orbital maneuver detection is a core component of space situational awareness. The multi-scale characteristics of satellite orbital behavior and sample imbalance issues lead to challenges in existing methods, including insufficient feature adaptation...
Visual place recognition (VPR) aims to localize a query image by matching its visual representation against a geotagged database. One major challenge in VPR is to learn place representations that remain robust under appearance changes, viewpoint vari...
Real-time classroom behavior monitoring is an important component of intelligent education systems, yet it remains challenging due to dense student layouts, frequent occlusions, and small-scale behavior targets. To address these challenges, this pape...
For surface crack detection in in-service concrete buildings, this study proposes DCFYOLO, a lightweight crack instance segmentation algorithm improved from YOLO11n-seg, which enhances feature representation and multi-scale contextual fusion to impro...
Graph convolutional networks (GCNs) have obtained remarkable performance in skeleton-based action recognition. However, previous approaches fail to capture the implicit correlations between joints and handle actions across varying time intervals. To...
Down-sampling-based video compression frameworks have shown great potential in improving compression efficiency in modern sensing and imaging systems. However, existing methods ignore critical spatial and temporal redundancy, and treat all frames uni...
Hyperspectral images (HSIs) demonstrate significant potential in agricultural applications, but conventional classification methods face challenges in adequately extracting complex spatial–spectral features. While deep learning approaches provi...
Hyperspectral image classification (HSIC) is one of the most important research topics in the field of remote sensing. However, it is difficult to label hyperspectral data, which limits the improvement of classification performance of hyperspectral i...
Hyperspectral imaging (HSI) offers rich spectral and spatial data, beneficial for a variety of applications. However, challenges persist in HSI classification due to spectral variability, non-linearity, limited samples, and a dearth of spatial inform...
Measurement of grasp configurations (position, orientation, and width) in unstructured environments is critical for robotic systems. Accurate and robust prediction relies on rich multi-scale object representations; however, detail loss and fusion con...
Surface defect detection plays an important role in particleboard quality control. But it still faces challenges in detecting coexisting multi-scale defects and weak texture defects. To address these issues, this study proposed PBD-YOLO (Particleboar...
Recently, with the remarkable advancements of deep learning in the field of image processing, convolutional neural networks (CNNs) have garnered widespread attention from researchers in the domain of hyperspectral image (HSI) classification. Moreover...
Industrial visual inspection relies heavily on unsupervised anomaly detection due to the scarcity of annotated defect samples. However, existing methods struggle to balance global structural consistency and local defect sensitivity, leading to limite...
Few-shot semantic segmentation has attracted much attention because it requires only a few labeled samples to achieve good segmentation performance. However, existing methods still suffer from insufficient contextual information and unsatisfactory ed...
Recognizing and classifying natural or artificial geo-objects under complex geo-scenes using remotely sensed data remains a significant challenge due to the heterogeneity in their spatial distribution and sampling bias. In this study, we propose a de...
In high-resolution remote sensing images, the combination of complex farmland plot features and limitations of manual and traditional classification methods hinders large-scale, automated, and precise farmland plot extraction. Key challenges include...
Remote sensing image classification is one of the fundamental tasks in the field of remote sensing and plays a critical role in Earth observation applications. However, the inherent multi-scale characteristics of this task pose significant challenges...
Soils play a central role in ecosystem functioning, and thus, mapped soil property information is indispensable to supporting sustainable land management. Digital Soil Mapping (DSM) provides a framework to spatially estimate soil properties. However,...
The evaluation of rock porosity and the mercury injection capillary pressure (MICP) curve is fundamental for oil and gas exploration and production. Digital rock (DR) technology, incorporating 3D micro-CT imaging and numerical methods, has been widel...
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