Test-time adaptation (TTA) enhances model performance in target domains by dynamically adjusting parameters using unlabeled test data. However, existing TTA methods typically assume balanced data distributions, whereas real-world test data is often i...
The rapid spread of fires underscores the urgency of high-accuracy fire smoke detection for public safety, but early fires pose major challenges—small flame/smoke targets, blurred boundaries, low contrast, and complex background interference&md...
Point cloud semantic segmentation is a key technology for applications such as autonomous driving, robotics, and virtual reality. Current approaches are heavily reliant on local relative coordinates and simplistic attention mechanisms to aggregate ne...
Standard convolution sliding along a fixed direction in common convolutional neural networks (CNNs) is inconsistent with the direction of aerial targets, making it difficult to effectively extract features with high-aspect-ratio and arbitrary directi...
In deep learning-based speaker verification, the loss function plays a crucial role. Most systems rely on a single loss function, or simply sum multiple losses with manually adjusted weights, increasing experimental complexity and failing to fully le...
In recent years, large language models (LLMs) have shown an impressive ability in translating text to SQL queries. However, in real-world applications, standard loss functions frequently fail to capture the complexity of queries adequately. Therefore...
In dynamic and unstructured environments, the obstacle avoidance capabilities of Unmanned Aerial Vehicles (UAVs) are crucial for mission success. Traditional methods struggle with adaptability and effectiveness in unknown or changing scenes. In contr...
It is a great challenge to solve nonhomogeneous elliptic interface problems, because the interface divides the computational domain into two disjoint parts, and the solution may change dramatically across the interface. A soft constraint physics-info...
This study presents a comprehensive dynamic analysis of mechanical systems incorporating elastic joints and introduces an adaptive vibration actuator with an integrated transmission variator. The system’s behavior is modeled through kinematic a...
Physics-informed neural networks (PINNs) solve partial differential equations (PDEs) by embedding physical conditions as soft penalties into the loss function. However, the coexistence of multiple loss components often leads to gradient conflicts, de...
The core challenge of the Unique Continuity (UC) problem lies in inferring solutions across an entire domain using limited observational data, holding significant practical implications for multiphysics coupled models. Recently, physics-informed neur...
When PINNs solve the Navier–Stokes equations, the loss function has a gradient imbalance problem during training. It is one of the reasons why the efficiency of PINNs is limited. This paper proposes a novel method of adaptively adjusting the we...
European Union forest policy calls for closer-to-nature forest management, but natural disturbances and forest succession are ecological phenomena that are difficult to characterize and integrate into sustainable forest management practices. Therefor...
Infrared small-target segmentation (IRSTS) is crucial for a wide range of applications, including maritime search-and-rescue operations and intelligent traffic surveillance. However, current deep learning methods struggle with dynamic scale variation...
In this article, to study the influence of neural networks’ morphology symmetry, a mathematical model is developed that considers dynamic symmetry for diagnosing complex dynamic objects. The developed mathematical model includes the symmetric a...
Bitcoin transaction anomaly detection is essential for maintaining financial market stability. A significant challenge is capturing the dynamically evolving transaction patterns within transaction networks. Dynamic graph models are effective for char...
Due to issues with sample quality, there is an increasing interest in deep learning models that can handle noisy labels. Currently, the optimal way to deal with noisy labels is by combining robust active and passive loss functions. However, the weigh...
High-quality image restoration (IR) is a fundamental task in computer vision, aiming to recover a clear image from its degraded version. Prevailing methods typically employ a static inference pipeline, neglecting the spatial variability of image cont...
Autonomous driving (AD) significantly reduces road accidents, providing safer transportation while optimizing traffic flow for greater efficiency and smoothness. However, ensuring safe decision-making in dynamic and complex highway environments, espe...
The extensive deployment of quadrotors in complex environmental missions has revealed a critical challenge: degradation of trajectory tracking accuracy due to time-varying wind disturbances. Conventional model-based controllers struggle to adapt to n...
This article proposes an adaptive sliding mode fault-tolerant tracking control scheme for underactuated unmanned surface vehicles (USVs) that suffer from loss of effectiveness and increase in bias input when performing path tracking. First, the mathe...
Visual retrievers such as visual retrieval-augmented generation (RAG) have recently emerged as a powerful model for retrieving multimodal documents without the need to convert page images into text. Existing visual retrievers typically encode every d...
Text-to-image generation aims to create visually compelling images aligned with input prompts, but challenges such as subject mixing and subject neglect, often caused by semantic leakage during the generation process, remain, particularly in multi-su...
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...
Hyperspectral imaging (HSI) systems often suffer from complex noise degradation during the imaging process, significantly impacting downstream applications. Deep learning-based methods, though effective, rely on impractical paired training data, whil...
To address the challenges in multi-scale defect detection on railway track surfaces—such as the high likelihood of missing tiny defects, weak anti-interference capability in complex environments, and poor scale adaptability—this paper pro...
Most deep learning models suffer from the problems of large computational complexity and insufficient feature extraction. To achieve a dynamic balance and tradeoff between computational complexity and performance, an enhanced SVTR-based scene text re...
In the research on intelligent perception, dynamic emotion recognition has been the focus in recent years. Small samples and unbalanced data are the main reasons for the low recognition accuracy of current technologies. Inspired by circular convoluti...
Short bowel syndrome (SBS) develops when the remaining intestine is unable to sustain adequate nutrient and electrolyte absorption following extensive bowel resection. The condition is characterized by malabsorption and significant fluid losses which...
Neural rendering techniques aim to generate photorealistic images and accurate 3D geometries from multi-view images but often struggle with efficiency and geometric consistency in complex or dynamic scenes. Optimized Neural Radiance Fields (OptiNeRF)...
Fabric defect detection is a crucial step in ensuring product quality within the textile industry. However, current detection methods face challenges in processing efficiency for high-resolution images, detail recovery during upsampling, and the adap...
Microbes drive leaf litter decomposition, and their communities are adapted to the local vegetation providing that litter. However, whether these local microbial communities confer a significant home-field advantage in litter decomposition remains un...
The limited availability of pixel-level annotated medical images complicates training supervised segmentation models, as these models require large datasets. To deal with this issue, SemiSeg-CAW, a semi-supervised segmentation framework that leverage...
This paper proposes an Energy Adaptive Feedforward Neural Network (EANN). It uses multiple approximation techniques in the hardware implementation of the neuron unit. The used techniques are precision scaling, approximate multiplier, computation skip...
In this paper, we present a novel approach to state estimation in dynamic systems by combining Physics-Informed Neural Networks (PINNs) with an adaptive Unscented Kalman Filter (UKF). Recognizing the limitations of traditional state estimation method...
This article concentrates upon the issue of finite-time tracking control for a category of nonlinear systems in pure-feedback form with actuator faults and unmodeled dynamics, where the loss of effectiveness and bias fault are considered. Meanwhile,...
To improve the accuracy and stability of short-term traffic flow prediction in complex road networks and address the limitations of existing models in modeling spatiotemporal dependencies, this paper proposes a traffic flow prediction model based on...
Understanding the relationship between biodiversity and ecosystem functioning (BEF) is essential to comprehend the impacts of biodiversity changes on ecosystem functioning. This knowledge helps to detect and anticipate significant trends in global bi...
Accurate Value-at-Risk (VaR) forecasting is challenged by the non-stationary, fractal, and chaotic dynamics of financial markets. Standard deep learning models like LSTMs often rely on static internal mechanisms that fail to adapt to shifting market...
This study focuses on helicopter turboshaft engine innovative fault-tolerant fuzzy automatic control system development to enhance safety and efficiency in various flight modes. Unlike traditional systems, the proposed automatic control system incorp...
Background/Objectives: Medical image segmentation is a crucial task for diagnosis, treatment planning, and monitoring of cancer; however, it remains one of the toughest nuts to crack for Artificial Intelligence (AI)-based clinical applications. Deep-...
This study seeks to overcome the limitations of traditional gas well production peak forecasting approaches, particularly in the context of handling multi-dimensional features, complex dynamic behaviors, and non-linear relationships. Accordingly, a d...
Remote sensing imagery poses significant challenges for object detection due to the presence of objects at multiple scales, dense target overlap, and the complexity of extracting features from small targets. This paper introduces an innovative Adapti...
The printed circuit board (PCB), a core component of electronic products, is playing an increasingly critical role in quality defect detection. Traditional methods suffer from low efficiency and high missed detection rates, rendering them insufficien...
Longevity science has shifted from the pursuit of lifespan extension toward preservation of healthspan, resilience, and functional independence. The hallmarks of aging provide a powerful descriptive structure, but they do not by themselves specify ho...
In the field of intelligent connected vehicles, the precise and real-time identification of speed bumps is critically important for the safety of autonomous driving. To address the issue that existing visual perception algorithms struggle to simultan...
Recently, data-driven methods have been widely assessed by researchers in the field of power system transient stability assessment (TSA). The differences in prediction difficulty among the samples are ignored by most previous studies. To address this...
Integrated sensing and communication (ISAC) is a transformative technology for sixth-generation (6G) wireless networks. In this paper, we investigate end-to-end constellation mapping and demapping in ISAC systems, leveraging OFDM-based waveforms and...
Maximum power point tracking (MPPT) is a critical function for maximizing energy extraction in photovoltaic (PV) systems. Due to the inherently dynamic nature of the maximum power point under varying irradiance conditions, achieving fast convergence,...
Background: Physical activity is widely recommended for preserving musculoskeletal health and managing osteoarthritis-related pain, although its benefits are commonly framed in terms of muscle strengthening, weight control, and physical performance....
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