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Abstract Facial Expression Recognition (FER) demonstrates significant value in practical scenarios such as intelligent human–computer interaction. However, conventional FER methods often struggle to balance performance...

Abstract GIS (Gas-insulated switchgear) is a critical component of modern power grids, and accurate awareness of its internal insulation condition is essential to power system safety. PD (Partial Discharge) is a key indi...

As a key element of human settlements and fundamental features in maps, buildings play a critical role in urban planning and environmental monitoring. Automatically extracting buildings from remote sensing images is of s...

This work introduces a novel geometrical and analytical modeling framework for predicting the stiffness properties of knitted composites, a class of materials known for their complex architectures and versatile mechanica...

Tensor network formalisms have emerged as powerful tools for simulating quantum state evolution. While widely applied in the study of optical quantum circuits, such as boson sampling, existing tensor network approaches f...

Abstract Efficiently and accurately extracting key information from videos has become a core challenge in the current computer vision. Traditional methods rely on manual features and shallow models, making it difficult t...

With advances in aerospace technology, high-resolution optical imagery from satellites, such as China’s Gaofen series, is vital for climate monitoring and agriculture. However, cloud cover significantly impedes th...

Abstract The Broad Learning System (BLS) provides an effective framework for nonlinear mapping, offering advantages over traditional deep neural networks through its expanded input node architecture. While BLS has demons...

Moving towards a sustainable future, optimising energy systems and resources involves increasingly sophisticated decision-making processes that require balancing technical efficiency with societal, economic and environme...

Recently, in the image super-resolution (SR) domain, Transformers have outperformed Convolution Neural Networks (CNNs) with reduced computational complexity and parameters by modeling long-range dependencies input-depend...

Aiming at the problem of power quality disturbance (PQD) classification, classification sub-models based on one-dimension CNN in the time domain (original signal), frequency domain (Fourier transform) and time-frequency ...

Digital health increasingly relies on artificial intelligence for clinical decision-making, patient management, and distributed learning, yet two persistent challenges remain: the computational complexity of optimization...

Abstract Multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) systems assisted by reconfigurable intelligent surfaces (RIS) have become a viable way to improve next-generation wireless networks. Howe...

Buildings serve as a key indicator of human land use, and building change detection plays a crucial role in urban growth and shrinkage monitoring, land use planning, and urban development assessment and management. Curre...

Formulation of the problem. In scientific research in sociology, education, physics, chemistry and other sciences, methods of correlation analysis are used. Correlation coefficients are used to determine the existence of...

Convolutional neural networks (CNNs) and visual Transformers have become the dominant approaches for hyperspectral image (HSI) classification due to their powerful feature representation capabilities. However, CNNs are l...

This study addresses the under-explored dynamic characteristics of unmanned ships in trajectory planning by constructing a constraint space in the navigation state-phase plane. First, we employ a path-velocity decoupling...

Soilless cultivation systems such as hydroponics rely on continuous and accurate monitoring of physicochemical parameters to maintain optimal plant growth conditions. In practice, however, hydroponic operations are const...

In response to the limitations of traditional planning methods for Autonomous Surface Vehicles (ASVs), which heavily rely on predefined map representations and neglect kinematic feasibility, this paper proposes a novel k...

BACKGROUND: In a previous paper, we introduced MUSCLE, a new program for creating multiple alignments of protein sequences, giving a brief summary of the algorithm and showing MUSCLE to achieve the highest scores reporte...

Argumentation systems have substantially evolved in the past few years, resulting in adequate tools to model some forms of common sense reasoning. This has sprung a new set of argument-based applications in diverse are...

Multi-objective evolutionary algorithms (MOEAs) that use non-dominated sorting and sharing have been criticized mainly for: (1) their O(MN/sup 3/) computational complexity (where M is the number of objectives and N is th...

Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resol...

A new algorithm called Mersenne Twister (MT) is proposed for generating uniform pseudorandom numbers. For a particular choice of parameters, the algorithm provides a super astronomical period of 2 19937 −1 and 623-dime...