The elastic characterization of beam sections is a complex problem, especially for non-homogeneous beams composed of anisotropic materials. Over the past four decades, several viable solutions have been proposed to address this important scientific and engineering challenge. These solutions either require a dedicated formulation for section discretization or, if based on conventional finite element

This paper presents a novel approach for optimizing extruded thin-walled beams. The main idea of the proposed approach is to formulate the problem’s design variables as line segments with unknown thicknesses. This is attained by viewing the beam’s cross-section as a set of connected line segments, representing the flat folded plates that form its geometry. The design space is defined based on the ground

Adversarial examples generated by perturbing raw data with carefully designed, imperceptible noise have emerged as a primary security threat to artificial intelligence systems. In particular, black-box adversarial attack algorithms, which only rely on model input and output to generate adversarial examples, are easy to implement in real scenarios. However, previous research on black-box attacks has

Convolutional neural networks (CNNs) are constrained in their capacity to model geometric transformations due to their fixed geometric structure. To overcome this problem, researchers introduce deformable convolution, which allows the convolution kernel to be deformable on the feature map. However, deformable convolution may introduce irrelevant contextual information during the learning process and

This study aims to enhance the operational stability and efficiency of flexible blades in variable-pitch vertical-axis wind turbines, providing significant engineering applications. Currently, research on blade dynamic behavior often simplifies blades to slender beam models, yet this approach has limitations in analyzing blades of small and medium-sized vertical-axis wind turbines, particularly for

A bond-based peridynamic model is established for geometrically exact beams under Simo-Reissner hypothesis. In the framework of the special Euclidean group, a set of nonlocal deformation measures are obtained from the relative position of two cross-sectional centroids and the relative rotation of cross-sectional frames. These measures capture the tension, shearing, bending and torsion of the bond,

In this work, we consider the decentralized non-convex online optimization problem over an undirected network. To solve the problem over a communication-efficient manner, we propose a novel quantized decentralized adaptive momentum gradient descent algorithm based on the adaptive momentum estimation methods, where quantified information is exchanged between agents. The proposed algorithm not only can

The identification of vulnerabilities in smart contracts is necessary for ensuring their security. As a pre-trained language model, BERT has been employed in the detection of smart contract vulnerabilities, exhibiting high accuracy in tasks. However, it has certain limitations. Existing methods solely depend on features extracted from the final layer, thereby disregarding the potential contribution

This article focuses on a new application-oriented variant of vehicle routing problem. This problem comes from the daily distribution scenarios of a real-world logistics company. It is a large-scale (with customer sizes up to 2000), many-objective (with six objective functions) NP-hard problem with six constraints. Then, a local search with chain search path strategy (LS-CSP) is proposed to effectively

Auscultation plays a pivotal role in early respiratory and pulmonary disease diagnosis. Despite the emergence of deep learning-based methods for automatic respiratory sound classification post-Covid-19, limited datasets impede performance enhancement. Distinguishing between normal and abnormal respiratory sounds poses challenges due to the coexistence of normal respiratory components and noise components

Many hysteresis models have been proposed and applied in engineering practices to describe and predict complex hysteretic behaviors observed in various engineering systems. However, selection of suitable hysteresis model usually costs extra time. In this paper, a symbolic deep learning (SDL) based method is proposed to fully describe the complex hysteresis behavior of structural systems and generate

Reservoir oil-bearing recognition is the process of predicting reservoir types based on well logging data, which determines the accuracy of recognition. However, the original well logging data is multidimensional and contains potential noise, which can influence the performance of sequent processing, such as clustering and classification. It is crucial to obtain key low-dimensional features and study

Semantic segmentation of urban scenes is essential in urban traffic analysis and road condition information acquisition. The semantic segmentation model with good performance is the key to applying high-resolution urban locations. However, the types of these images are diverse, and the spatial relationships are complex. It is greatly affected by weather and light. Objects of different scales pose significant

General type-2 fuzzy logic systems (GT2 FLSs) based on the \(\alpha\)-planes representation of general T2 fuzzy sets (FSs) have become more accessible to FL investigators in recent years. Type reduction (TR) is the most important block for GT2 FLSs. Here the weighted type-reduction algorithms based on the Newton and Cotes quadrature formulas of numerical methods of integration technique are first given

Time series forecasting plays a crucial role across various sectors, including energy, transportation, meteorology, and epidemiology. However, existing models often struggle with capturing long-term dependencies and managing computational efficiency when handling complex and extensive time series data. To address these challenges, this paper introduces the BSAformer model, which leverages a unique

Remote sensing image pairs of different time phases have complex and changeable semantic contents, and traditional convolutional registration methods are challenging in modeling subtle local changes and global large-scale deformation differences in detail. This results in poor registration performance and poor feature representation. To address these problems, a semantically enhanced dual-graph perception

Feature selection is a common method for improving classification performance. Selecting features for high-dimensional data is challenging due to the large search space. Traditional feature ranking methods that search for top-ranked features cannot remove redundant and irrelevant features and may also ignore interrelated features. Evolutionary computation (EC) techniques are widely used in feature

Sample augmentation, especially sample generation is conducive for addressing the challenge of training robust image and video object detection models based on the deep learning. Still, the existing methods lack sample editing capability and suffer from annotation work. This paper proposes an image sample generation method based on key box points detection and Generative adversarial network (GAN),

In this paper, we present a novel localization algorithm for mobile robots navigating in complex planar environments, a critical capability for various real-world applications such as autonomous driving, robotic assistance, and industrial automation. Although traditional methods such as particle filters and extended Kalman filters have been widely used, there is still room for assessing the capabilities

Evolutionary multi-objective algorithms have been applied to beam angle optimization (called BAO) for generating diverse trade-off radiotherapy treatment plans. However, their performance is not so effective due to the ignorance of using the specific clinical knowledge that can be obtain intuitively by clinical physicist. To address this issue, we suggest a pattern mining based evolutionary multi-objective

Although skeleton-based gesture recognition using supervised learning has achieved promising results, the reliance on extensive annotated data poses significant costs. This paper addresses the challenge of semi-supervised skeleton-based gesture recognition, to effectively learn feature representations from labeled and unlabeled data. To resolve this problem, we propose a novel multimodal multilevel

Multi-objective evolutionary optimization is widely utilized in industrial design. Despite the success of multi-objective evolutionary optimization algorithms in addressing complex optimization problems, research focusing on input disturbances remains limited. In many manufacturing processes, design parameters are vulnerable to random input disturbances, resulting in products that often perform less

The booming development of the Internet of Things (IoT) has led to an explosion of web services, making it more inconvenient for users to choose satisfactory services among numerous options. Therefore, ensuring quality of service (QoS) in a service-oriented IoT environment is crucial, highlighting QoS prediction as a prominent research focus. However, issues related to information credibility, user

In order to reduce the burden of DBA, the knob tuning method based on reinforcement learning has been proposed and achieved good results in some cases. However, the performance of these solutions is not ideal as the workload features are not considered enough. To address these issues, we propose a database tuning system called ADWTune. In this model, ADWTune employs the idea of multiple sampling to

With the rapid growth in both the variety and volume of data on networks, especially within social networks containing vast multimedia data such as text, images, and video, there is an urgent need for efficient methods to retrieve helpful information quickly. Due to their high computational efficiency and low storage costs, unsupervised deep cross-modal hashing methods have become the primary method

This paper introduces DOAMRL, a novel meta-reinforcement learning (meta-RL) method that extends the Model-Agnostic Meta-Learning (MAML) framework. The method addresses a key limitation of existing meta-RL approaches, which struggle to effectively use suboptimal demonstrations to guide training in sparse reward environments. DOAMRL effectively combines reinforcement learning (RL) and imitation learning

Driver action recognition is crucial for in-vehicle safety. We argue that the following factors limit the related research. First, spatial constraints and obstructions in the vehicle restrict the range of motion, resulting in similar action patterns and difficulty collecting the full body posture. Second, in skeleton-based action recognition, establishing the joint dependencies by the self-attention

Multimodal sentiment analysis (MSA) has garnered significant attention for its immense potential in human-computer interaction. While cross-modality attention mechanisms are widely used in MSA to capture inter-modality interactions, existing methods are limited to pairwise interactions between two modalities. Additionally, these methods can not utilize the causal relationship to guide attention learning

This study introduces a novel implementation of the inverse finite element method (iFEM) for full-field mode shape reconstruction of plate and shell structures under random vibration. The proposed methodology, termed iFEM-MoRe (Mode Reconstruction), seamlessly integrates classical iFEM with Fourier transformation using Welch’s estimation method. By processing dynamic strain measurements, iFEM-MoRe

Negative Poisson’s ratio (NPR) materials are attractive for their unique mechanical properties. Especially lightweight structures made of NPR materials have potential application in the aviation industry. The purpose of this study is to propose a lightweight structure with NPR materials and optimize it with its performance and mass as the objectives. In this study, buckling prediction and structural

Real-time reconstruction of antenna element displacements using limited strain data is essential for mitigating the degradation of phased array radar systems’ electrical performance caused by antenna deformations. This study presents a data-driven framework for efficiently and accurately reconstructing antenna element displacements. The proposed approach combines finite element simulations with a multilayer

The aim of this paper is to present a numerical procedure for reproducing the crack growth in two-dimensional (2D) cohesive solids. Two approaches are proposed to determine the direction of crack propagation. One approach is based on the evaluation of the nonlocal stress around the tip of the fracture, while the other on the computation of the J-integral. Once the direction of crack propagation has

The rise of social media has led to an increase in hate speech. Hate speech is generally described as a deliberate act of aggression aimed at a particular group, intended to harm or marginalize them based on specific attributes of their identity. While positive interactions in diverse communities can greatly enhance confidence, it is important to acknowledge that negative remarks such as hate speech

This paper introduces a computational approach for identifying the optimal driving-path for stable state transformations (SST) of multistable tensegrity structures. In order to capture the morphological changes of the tensegrity structure when the driving-nodes are actuated along arbitrary driving-path, a tracking method is firstly introduced. Then, the concept of segmenting-nodes is introduced to

In this study, we develop a multi-layered shell finite element model capable of incorporating interlayer slips for both linear and nonlinear analyses. We derive the total Lagrangian formulation of the shell element to allow for large displacements and large rotations. The shell element effectively represents in-plane interlayer slips within the shell kinematics framework, allowing straightforward modeling

JointCloud computing supports large-scale resource consolidation and collaboration among multiple cloud service providers to provide users with powerful performance and adequate services. In the face of exponential scaling of resources, monitoring is an indispensable part of effective resource management. Monitoring provides methods for reviewing and managing the performance status of JointCloud resources

Efficient and accurate evaluation of the rare event probability is a crucial yet challenging task for the design of static and dynamic structures with uncertainties. This study establishes a novel level-wise representative points increment strategy for direct probability integral method (DPIM), which calculates accurately rare event probabilities (less than 10−3). Firstly, the two advantages of partitioning

Steel-Concrete Composite (SCC) connections in steel buildings are inherently complex in terms of internal stress distributions and failure modes that are important to characterize for effective design and performance assessment. In this context, numerical results obtained from a multiscale lattice discrete particle model are presented to examine its efficacy in characterizing the response of SCC connections

Finite element (FE) modelling is widely recognised as the most powerful and foremost computational technique for analysing complex structural systems due to its highly efficient modelling and simulation capabilities. Despite the strengths, its computational demands restrict its ability of performing instantaneous computations and present substantial challenges for achieving real-time analyses results

Internet of Things is significantly advancing the development of modern interconnected networks. Coordinated with cloud computing, this technology becomes even more powerful, cost-effective, and reliable. These advancements are rapidly being integrated into modern healthcare through innovations such as smart ambulances, remote monitoring systems, and smart hospitals, enhancing tracking, analysis, and

Existing multi-damage localization methods usually need to be trained using labeled data obtained from various damage cases, and such methods can identify multiple damages with high accuracy. However, it’s extremely challenging to obtain labeled data from engineered structures under various damage states, especially in multiple damages case. Thus, damage localization methods that need to be trained

Identification of the material properties of reinforced concrete (RC) systems is a challenging but crucial part of the strength analysis and condition assessment. However, the heterogeneous nature of concrete poses complexities in determining its material properties accurately. A combination of finite element model updating with metaheuristic optimization algorithms has emerged as one method to determine

In this study, an upgraded version of a Double Tuned Mass Damper (UDTMD) is developed to mitigate the dynamic response of structures under earthquake loadings. This device is a passive vibration absorber combined by two masses, two springs and a dashpot. Like a Tuned Mass Damper (TMD) or Double Tuned Mass Damper (DTMD), the UDTMD has notable advantages such as stability, cost-effective and low maintenance

The resilience optimization of the built environment under extreme events with building structures and interdependent physical infrastructure systems is inefficient due to the large action spaces of pre-hazard mitigation alternatives. Hence, a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) based resilience optimization framework is proposed herein to enhance the resilience of the built environment

Curved fibered structures are ubiquitous in nature and the mechanical behavior of these materials is of pivotal importance in the biomechanics and mechanobiology fields. We develop a multiscale formulation to characterize the macroscopic mechanical nonlinear behavior from the microstructure of fibered matrices. From the analysis of the mechanics of a randomly curved single fiber, a fibered matrix model