The management of complex, dynamic, and cross-domain resources in cyber-physical-human systems (CPHS) faces significant challenges under spatiotemporal dynamics, particularly resource state conflicts caused by rapid environmental changes and interdependent resource interactions. To address these challenges, this study proposes an integrated framework combining resource modeling and resource state adaptive

The large action space is a key challenge in reinforcement learning. Although hierarchical methods have been proven to be effective in addressing this issue, they are not fully explored. This paper combines domain knowledge with hierarchical concepts to propose a novel Hierarchical Reinforcement Learning framework based on macro actions (HRL-MA). This framework includes a macro action mapping model

Fleet formation identification in remote sensing is a significant focus in maritime surveillance. However, fleet may occur with different ship dense and noisy data due to the complex background and different satellite resolution, few studies have discussed formation identification considering the limits of sensing and application. This study introduces an effective fleet formation identification and

Cross-resolution Person Re-identification (ReID) faces the significant challenge of large resolution variance across different camera views in real surveillance systems. Most approaches based on super-resolution (SR) excessively rely on the SR images, which may lead to the loss of low-resolution (LR) information. Meanwhile, the region-agnostic SR could pose interference to ReID. For this, we propose

Open Set Domain Adaptation (OSDA) aims to minimize domain variation while distinguishing between known and unknown samples. However, existing OSDA methods, which rely on deep neural network classifiers, often lead to overconfident predictions and fail to clearly demarcate known from unknown samples. To address this limitation, we propose the Energy-based Open Set domain adaptation (EOS) method. EOS

Differential evolution (DE) is widely recognized as one of the most potent optimization algorithms, capable of effectively addressing a broad spectrum of optimization challenges. Nevertheless, even the most advanced variants of DE share some common challenges. This paper introduces a novel multi-stage semi-adaptive DE algorithm with enhanced diversity (MSA-DE), offering several key contributions: first

The rapid development of edge computing has an impact on the Internet of Vehicles (IoV). However, the high-speed mobility of vehicles makes the task offloading delay unstable and unreliable. Hence, this paper studies the task offloading problem to provide stable computing, communication and storage services for user vehicles in vehicle networks. The offloading problem is formulated to minimize cost

Condensation of large-scale finite element models while maintaining high prediction accuracy is crucial for efficient structural analysis and design. To this end, a novel Bayesian framework for model condensation and selection of master degrees of freedom (DOFs) is developed in this paper. The main idea behind it is to recast model condensation into the Bayesian full-field reconstruction problem. In

The conservative problem of the stability of symmetric nonlinear normal modes (NNMs) of sagged cables is analysed. Based on harmonic shape functions, the equations of motion for a conservative sagged cable are derived and nonlinear normal modes are calculated as a continuation of the linear modes, via the harmonic balance approach. Leveraging symmetry, we decouple the equations of motion, obtaining

Graph Convolutional Networks (GCNs) have achieved remarkable success in recommendation systems by leveraging higher-order neighborhoods. In recent years, multi-behavior recommendation has addressed the challenges of data sparsity and cold start problems to some extent. However, the introduction of noise from multi-behavior tasks into the user-item graph exacerbates the impact of noise from a few active

Accurate identification of tree species is essential for effective forestry management and conservation. Simple deep-learning models, such as AlexNet and VGG16, often struggle with fine-grained texture extraction and feature distinction, especially in complex environments. While more advanced models, such as ResNet34 and deeper architectures, offer superior feature extraction capabilities, they come

Effective identification of interdependence information between decision variables is crucial for variable grouping in large-scale global optimization (LSGO). This paper introduces a novel approach called FCA-G (Formal Concept Analysis-Driven Grouping) to solve LSGO problems. FCA, an effective tool for data analysis, is employed in this approach. The primary contribution involves transforming decision

For high-dimensional datasets, outlier objects can be effectively identified and extracted with the help of the coupling relationship between any two attributes. However, when all the coupling is used directly, there is a phenomenon of pseudo-correlation between attribute values that results in redundant coupling and affects the effectiveness of high-dimensional outlier detection. In this paper, a

Abstract Backdoor attacks pose a severe threat to the integrity of machine learning models, especially in real-world image classification tasks. In such attacks, adversaries embed malicious behaviors triggered by specific patterns in the training data, causing models to misclassify whenever the trigger is present. This paper introduces a novel, model-agnostic defense that systematically detects and

This study addresses the challenges of robotic localization and navigation in visually degraded environments, such as low illumination and adverse weather conditions, by proposing a novel thermal infrared visual SLAM (Simultaneous Localization and Mapping) algorithm. The research introduces a new infrared visual odometry that integrates feature-based methods with optical flow techniques, enhancing

Benchmark artifacts serve as an appropriate mean to represent quality measures in additively manufactured components. Especially witness specimens, which represent structural properties as a subtype of benchmark artifacts, are supposed to reproduce target stress states as they are critical for component failure.

Frugal wavelet transform (FrugWT) is a new version of discrete wavelet transform (DWT), acting as an efficient tool for detecting signal singularities. It is proven that the frugal wavelet transform usually performs better than other versions of the wavelet transforms in detecting singularities and local abrupt changes in the signals, specifically when we use wavelet functions with lower vanishing

With the increasing requirements of vehicle green level and safety level, deciding how to reasonably balance lightweight levels and crashworthiness objectives during the design phase has been a widespread concern in the field of passive safety. This study proposes an extended R-number-based multi-objective optimization method for optimal design of vehicle structures incorporating Non-dominated Sorting

In this research, we develop a numerical framework capable of evaluating the electrostriction effect on the electrostatic, finite deformation and viscoelastic response of dielectric elastomers. The principle of virtual work is employed to derive the governing equations and their weak form. The Zener rheological model is adopted for viscoelastic modeling. The constitutive equations incorporate the electrostriction

Accurate prediction of vessel trajectories in coastal areas poses a significant challenge due to the large number of irregular trajectories. Existing trajectory prediction studies predominantly employ recurrent neural network (RNN) and Transformer-based methods. However, the former often encounter challenges such as gradient vanishing or exploding, and the latter tend to focus on global temporal dependencies

Online recruitment services are rapidly transforming traditional hiring practices in the job market. Accurate person-job fit is crucial for intelligent recruitment. Previous studies on person-job fit fail to explore job seekers’ resume information from a multi-perspective approach, and neglect the sustainable learning of resume features. To address this, the present paper proposes a Graph-based Person-Job

The travelling thief problem (TTP) combines two NP-hard problems, traveling salesman problem (TSP) and knapsack problem (KP), which is more complicated for solving. In TTP, the salesman needs to choose the travel route and select the items at the same time to maximize the profit. Consequently, the resolution of TTP essentially encompasses two stages, including route planning and item selection. In

Gait analysis is an increasingly expanding research field, characterized by the application of non-invasive sensors and machine learning techniques across various domains. Using these advanced technologies, researchers can deep dive into understanding human gait and movement patterns, providing robust support for applications such as medical diagnosis, rehabilitation, and sports optimization. In this

Image super-resolution (SR) is a fundamental challenge in the field of computer vision. Recently, Convolutional Neural Network (CNN)-based methods for image SR have achieved significant progress across various SR tasks. However, most current research focuses on designing deeper and wider architectures, often sacrificing computational burden and speed in order to improve image SR quality. To achieve

Micro-expression (ME), as an involuntary and brief facial expression, holds significant potential applications in fields such as political psychology, lie detection, law enforcement, and healthcare. Most existing micro-expression spotting (MES) methods predominantly learn from optical flow features while neglecting the detailed information contained in RGB images. To address this issue, this paper

With the process of democratization of the network edge, hardware and software for networks are becoming available to the public, overcoming the confines of traditional cloud providers and network operators. This trend, coupled with the increasing importance of AI in 6G and beyond cellular networks, presents opportunities for innovative AI applications and systems at the network edge. While AI models

In this paper, an effective strategy is proposed for high-cycle fatigue life assessment under random excitation, with the generalized Lyapunov equation method (GLEM). Firstly, the Lyapunov equation method is generalized for odd spectral moments, which can be expressed in the Hilbert transform of the auto-correlation function. Then, a semi-analytic coefficient matrix of the Lyapunov equation is deduced

The traditional spectrum management paradigm is no longer sufficient to meet the increasingly urgent demand for efficient utilization of spectrum resources by Internet of Things (IoT) devices. Dynamic spectrum access, as an emerging solution, allows devices to intelligently select appropriate spectrum resources based on real-time demands and environmental changes. In this paper, we propose a dynamic

As a layer-wise construction method, 3D-printed concrete (3DPC) introduces weak interfaces between layers, resulting in different mechanical behavior in different directions. Most studies investigate this anisotropic behavior based on experimental specimen-scale samples and do not apply it in practical engineering. To address this need, the present paper develops a peridynamic (PD) method for the anisotropic

Conventional deep learning (DL) model compression methods affect all input samples equally. However, as samples vary in difficulty, a dynamic model that adapts computation based on sample complexity offers a novel perspective for compression and scaling. Despite this potential, existing dynamic techniques are typically monolithic and have model-specific implementations, limiting their generalizability

Most existing research on the identification of moving loads in the presence of uncertainties primarily focuses on parametric uncertainties related to the variability or the lack of knowledge of some parameters of the computational model. Such an approach does not allow the consideration of uncertainties related to modelling errors, for instance those related to the discretisation of the structure

In recent years, significant advancements have been made in computational methods for analyzing masonry structures. Within the Finite Element Method, two primary approaches have gained traction: Micro and Macro Scale modeling, and their subsequent integration via Multi-scale methods based on homogenization theory and the representative volume element concept. While Micro and Multi-scale approaches