Search Results (25,079)

In order to consider the effect of fabric anisotropy in the analysis of geotechnical boundary value problems, this study proposes a modified model based on a fabric-based modified Cam-clay model, which can account for the anisotropic response of soil. The major modification of the original model aims to simplify the equations for numerical implementation by replacing the SMP strength criterion with the Lade’s strength criterion. This model comprehensively considers the inherent anisotropy, induced anisotropy, and three-dimensional strength characteristics of soil. The model is first numerically implemented using the elastic trial–plastic correction method, and then it is encapsulated into the FLAC^3D 6.0 software, and tested through conventional triaxial, embankment loading, and tunnel excavation experiments. Numerical simulation results indicate that considering anisotropy and three-dimensional strength in geotechnical engineering analysis is necessary. By accounting for the interaction between microstructure and macroscopic anisotropy, the model can more accurately represent soil behavior, providing significant advantages for geotechnical analysis. Full article

Aiming at the problems of a six-degree-of-freedom robotic arm in a three-dimensional multi-obstacle space, such as low sampling efficiency and path search failure, an improved fast extended random tree (RRT*) algorithm for robotic arm path planning method (abbreviated as HP-APF-RRT*) is proposed. The algorithm generates multiple candidate points per iteration, selecting a sampling point probabilistically based on heuristic values, thereby optimizing sampling efficiency and reducing unnecessary nodes. To mitigate increased search times in obstacle-dense areas, an artificial potential field (APF) approach is integrated, establishing gravitational and repulsive fields to guide sampling points around obstacles toward the target. This method enhances path search in complex environments, yielding near-optimal paths. Furthermore, the path is simplified using the triangle inequality, and redundant intermediate nodes are utilized to further refine the path. Finally, the simulation experiment of the improved HP-APF-RRT* is executed on Matlab R2022b and ROS, and the physical experiment is performed on the NZ500-500 robotic arm. The effectiveness and superiority of the improved algorithm are determined by comparing it with the existing algorithms. Full article

Due to advances in big data technology, deep learning, and knowledge engineering, biological sequence visualization has been extensively explored. In the post-genome era, biological sequence visualization enables the visual representation of both structured and unstructured biological sequence data. However, a universal visualization method for all types of sequences has not been reported. Biological sequence data are rapidly expanding exponentially and the acquisition, extraction, fusion, and inference of knowledge from biological sequences are critical supporting technologies for visualization research. These areas are important and require in-depth exploration. This paper elaborates on a comprehensive overview of visualization methods for DNA sequences from four different perspectives—two-dimensional, three-dimensional, four-dimensional, and dynamic visualization approaches—and discusses the strengths and limitations of each method in detail. Furthermore, this paper proposes two potential future research directions for biological sequence visualization in response to the challenges of inefficient graphical feature extraction and knowledge association network generation in existing methods. The first direction is the construction of knowledge graphs for biological sequence big data, and the second direction is the cross-modal visualization of biological sequences using machine learning methods. This review is anticipated to provide valuable insights and contributions to computational biology, bioinformatics, genomic computing, genetic breeding, evolutionary analysis, and other related disciplines in the fields of biology, medicine, chemistry, statistics, and computing. It has an important reference value in biological sequence recommendation systems and knowledge question answering systems. Full article

Niobium pentoxide (T-Nb₂O₅) is a promising anode material for dual-ion batteries due to its high lithium capacity and fast ion storage and release mechanism. However, T-Nb₂O₅ suffers from the disadvantages of poor electrical conductivity and fast cycling capacity decay. Herein, a nitrogen-doped three-dimensional porous carbon (RMF) was prepared for loading niobium pentoxide to construct a composite system with excellent electrochemical performance. The obtained T-Nb₂O₅/RMF composites have a well-developed pore structure and a high specific surface area of 1568.5 m² g⁻¹, which could effectively increase the contact area between the material and electrolyte, improving the electrode reaction and lithium-ion transfer diffusion. Nitrogen doping increased surface polarity, creating more active sites and accelerating the electrode reaction rate. The introduction of T-Nb₂O₅ imparted high power density and excellent cycling stability to the battery. The composites exhibited good electrochemical performance when used as dual-ion battery anode, with a stable cycle life of 207.2 mA h g⁻¹ at 1 A g⁻¹ current density after 650 cycles and great rate performance of 181.5 mA h g⁻¹ at 5A g⁻¹ was also obtained. This work provides the possibility for applying T-Nb₂O₅/RMF as an anode for a high-performance dual-ion battery. Full article

The primary purpose of this work is to provide a new fractional boundary element method (BEM) formulation to solve thermal stress wave propagation problems in anisotropic materials. In the Laplace domain, the fundamental solutions to the governing equations can be identified. Then, the boundary integral equations are constructed. The Caputo fractional time derivative was used in the formulation of the considered heat conduction equation. The three-block splitting (TBS) iteration approach was used to solve the resulting BEM linear systems, resulting in fewer iterations and less CPU time. The new TBS iteration method converges rapidly and does not involve complicated computations; it performs better than the two-dimensional double successive projection method (2D-DSPM) and modified symmetric successive overrelaxation (MSSOR) for solving the resultant BEM linear system. We only studied a special case of our model to compare our findings to those of other articles in the literature. Because the BEM results are so consistent with the finite element method (FEM) findings, the numerical results demonstrate the validity, accuracy, and efficiency of our proposed BEM formulation for solving three-dimensional thermal stress wave propagation problems in anisotropic materials. Full article

The transient plane heat source method (TPS), also known as the hot disc method, is an experimental method for determining the thermal transport properties of materials. The method’s main element is a sensor made of a nickel metal strip in the shape of a double helix, which is inserted into an insulating polymer film. In this work, we used the finite element method to create a three-dimensional model of the sensors and compared the simulated and experimentally recorded mean temperature rise data. The volume mean temperature rise of the sensor, as determined through simulation, exhibits a high level of resemblance with the corresponding experimental data. Additionally, temperature rise curves of several other materials are also simulated based on the model and the thermal performance parameters are calculated from these data. In the meantime, this paper presents an evaluation and discussion of the current density distribution of the sensor and the temperature distribution during the testing of the sample. This simulation has the potential to be utilized for future geometry and parameter estimate optimization, and provides a theoretical reference for detector design. Full article

This study proposes a novel rolling bearing fault diagnosis technique based on a synchrosqueezing wavelet transform (SWT) and a transfer residual convolutional neural network (TRCNN) designed to address the difficulties of feature extraction caused by the non-stationarity of fault signals, as well as the issue of low fault diagnosis accuracy resulting from small sample quantities. This approach transforms the one-dimensional vibration signal into time–frequency diagrams using an SWT based on complex Morlet wavelet basis functions, which redistributes (squeezes) the values of the wavelet coefficients at different localized points in a time–frequency plane to the estimated instantaneous frequencies. This allows the energy to be more fully concentrated in actual corresponding frequency components. This strategy improves both the time–frequency aggregation and the resolution, which better reflects the eigenvalues of non-stationary signals. In this process, transfer learning and a residual structure are used in the training of a convolutional neural network. The resulting time–frequency diagrams, acquired using the steps discussed above, are then input to the TRCNN for diagnosis. A series of validation experiments confirmed that applying the TRCNN structure made it possible to achieve high diagnostic accuracy, even when training the network with only a small number of fault samples, as all 12 fault types from the test dataset were diagnosed correctly. Further simulation experiments demonstrated that our proposed method improved fault diagnosis accuracy compared to that of conventional techniques (with increases of 1.74% over RCNN, 1.28% over TCNN, 1.62% over STFT, 1.73% over WT, 2.83% over PWVD, and 1.39% over STFA-PD). In addition, diagnostic accuracy reached 100% during the application of three-time transfer learning, validating the effectiveness of the proposed method for rolling bearing fault diagnosis. Full article

Lab-on-a-Chip devices based on tilted-angle standing surface acoustic waves (tasSAWs) emerged as a promising technology for multidimensional particle separation, highly selective in particle size and acoustic contrast factor. For this active separation method, a tailored acoustic field is used to focus and separate particles on stationary pressure nodes by means of the acoustic radiation force. However, additional non-linear acoustofluidic phenomena, such as the acoustically induced fluid flow or dielectrophoretic effects, are superimposed on the separation process. To obtain a particle separation of high quality, control parameters that can be adjusted during the separation process as well as design parameters are available. The latter are specified prior to the separation and span a high-dimensional parameter space, ranging from the acoustic wavelength to the dimensions and materials used for the microchannel. In this paper, the physical mechanisms to control and design tasSAW-based separation devices are reviewed. By combining experimental, semi-analytical, and numerical findings, a critical channel height and width are derived to suppress the influence of the acoustically induced fluid flow. Dealing with the three-dimensional nature of the separation process, particles are focused at different height levels of equal force balance by implementing a channel cover of high acoustic impedance while achieving an approx. three-times higher acoustic pressure. Using this improved channel design, the particle shape is identified as an additional separation criterion, rendering the continuous acoustofluidic particle separation as a multidimensional technology capable of selectively separating microparticles below 10 μm with regard to size, acoustic contrast, and shape. Full article

Background/Objectives: Three-dimensional motion analysis is often used to evaluate improvements or decrements in movement patterns in athletes. The purpose of this study was to evaluate the reliability of joint flexion/extension angles of the pitching elbow and bilateral knees and hips in softball pitchers. Methods: Fourteen softball pitchers (17.9 ± 2.3 years) were tested in one session consisting of four sets of five consecutive fastballs and a second session of two sets of five fastballs. The magnitude of systematic bias and within-subject variation was calculated between pitches. An iterative intraclass correlation coefficient (ICC) process was used to determine intra- and inter-session reliability, standard error of measurement and minimal detectable change. Results: Reductions in within-subject variation were observed for all variables when the number of pitches used in calculations was increased. Intra-session ICC values ranged from an average of 0.643 for pitching elbow to 0.989 for stride leg knee. Inter-session ICC values ranged from an average of 0.663 for pitching elbow to 0.996 for stride leg knee. Conclusions: Joint flexion/extension angles during the softball windmill pitch can be measured with good to high reliability using three-dimensional motion analysis. Biomechanical analysis can be confidently used to detect changes in the pitching motion over the course of a season or following an intervention. Full article

Three-dimensional object detection based on deep neural networks (DNNs) is widely used in safety-related applications, such as autonomous driving. However, existing research has shown that 3D object detection models are vulnerable to adversarial attacks. Hence, the improvement on the robustness of deep 3D detection models under adversarial attacks is investigated in this work. A deep autoencoder-based anomaly detection method is proposed, which has a strong ability to detect elaborate adversarial samples in an unsupervised way. The proposed anomaly detection method operates on a given Light Detection and Ranging (LiDAR) scene in its Bird’s Eye View (BEV) image and reconstructs the scene through an autoencoder. To improve the performance of the autoencoder, an augmented memory module with typical normal patterns recorded is introduced. It is designed to help the model to amplify the reconstruction errors of malicious samples with normal samples negligibly affected. Experiments on several public datasets show that the proposed anomaly detection method achieves an AUC of 0.8 under adversarial attacks and improves the robustness of 3D object detection. Full article

The ability to segment moving objects from three-dimensional (3D) LiDAR scans is critical to advancing autonomous driving technology, facilitating core tasks like localization, collision avoidance, and path planning. In this paper, we introduce a novel deep neural network designed to enhance the performance of 3D LiDAR point cloud moving object segmentation (MOS) through the integration of image gradient information and the principle of motion consistency. Our method processes sequential range images, employing depth pixel difference convolution (DPDC) to improve the efficacy of dilated convolutions, thus boosting spatial information extraction from range images. Additionally, we incorporate Bayesian filtering to impose posterior constraints on predictions, enhancing the accuracy of motion segmentation. To handle the issue of uneven object scales in range images, we develop a novel edge-aware loss function and use a progressive training strategy to further boost performance. Our method is validated on the SemanticKITTI-based LiDAR MOS benchmark, where it significantly outperforms current state-of-the-art (SOTA) methods, all while working directly on two-dimensional (2D) range images without requiring mapping. Full article

Hyperspectral images are high-dimensional data containing rich spatial, spectral, and radiometric information, widely used in geological mapping, urban remote sensing, and other fields. However, due to the characteristics of hyperspectral remote sensing images—such as high redundancy, strong correlation, and large data volumes—the classification and recognition of these images present significant challenges. In this paper, we propose a band selection method (GE-AP) based on multi-feature extraction and the Affine Propagation Clustering (AP) algorithm for dimensionality reduction of hyperspectral images, aiming to improve classification accuracy and processing efficiency. In this method, texture features of the band images are extracted using the Gray-Level Co-occurrence Matrix (GLCM), and the Euclidean distance between bands is calculated. A similarity matrix is then constructed by integrating multi-feature information. The AP algorithm clusters the bands of the hyperspectral images to achieve effective band dimensionality reduction. Through simulation and comparison experiments evaluating the overall classification accuracy (OA) and Kappa coefficient, it was found that the GE-AP method achieves the highest OA and Kappa coefficient compared to three other methods, with maximum increases of 8.89% and 13.18%, respectively. This verifies that the proposed method outperforms traditional single-information methods in handling spatial and spectral redundancy between bands, demonstrating good adaptability and stability. Full article

Hydrogels are widely utilized in industrial and scientific applications owing to their ability to immobilize active molecules, cells, and nanoparticles. This capability has led to their growing use in various biomedical fields, including cell culture and transplantation, drug delivery, and tissue engineering. Among the available synthesis techniques, ionizing-radiation-induced fabrication stands out as an environmentally friendly method for hydrogel preparation. In alignment with the current requirements for cleaner technologies, developing hydrogels using gamma and electron beam irradiation technologies represents a promising and innovative approach for their biomedical applications. A key advantage of these methods is their ability to synthesize homogeneous three-dimensional networks in a single step, without the need for chemical initiators or catalysts. Additionally, the fabrication process is controllable by adjusting the radiation dose and dose rate. Full article

Electric vehicles (EVs) wireless charging is enabled by inductive power transfer (IPT) technology, which eliminates the need for physical connections between the vehicle and the charging station, allowing power to be transmitted without the use of cables. However, in the present wireless charging equipment, the power transfer still needs to be improved. In this work, we present a power transfer structure using a unique “DD circular (DDC) power pad”, which mitigates the two major obstacles of wireless EV charging, due to the mitigating power of electromagnetic field (EMF) leakage emissions and the increase in misalignment tolerance. We present a DDC power pad structure, which integrates features from both double D(DD) and circular power pads. We first build a three-dimensional electromagnetic model based on the DDC structure. A detailed analysis is performed of the electromagnetic characteristics, and the device parameters regarding the power transfer efficiency, coupling coefficient, and mutual inductance are also presented to evaluate the overall performance. Then, we examine the performance of the DDC power pad under various horizontal and vertical misalignment circumstances. The coupling coefficients and mutual inductance, as two essential factors for effective power transmission under dynamic circumstances, are investigated. The findings of misalignment effects on coupling efficiency indicate that the misalignment does not compromise the DDC pad’s robust performance. Therefore, our DDC power pad structure has a better electromagnetic characteristic and a higher misalignment tolerance than conventional circular and DD pads. In general, the DDC structure we present makes it a promising solution for wireless EV charging systems and has good application prospects. Full article

Global optimization problems, prevalent across scientific and engineering disciplines, necessitate efficient algorithms for navigating complex, high-dimensional search spaces. Drawing inspiration from the resilient and adaptive growth strategies of moss colonies, the moss growth optimization (MGO) algorithm presents a promising biomimetic approach to these challenges. However, the original MGO can experience premature convergence and limited exploration capabilities. This paper introduces an enhanced bio-inspired algorithm, termed crisscross moss growth optimization (CCMGO), which incorporates a crisscross (CC) strategy and a dynamic grouping parameter, further emulating the biological mechanisms of spore dispersal and resource allocation in moss. By mimicking the interwoven growth patterns of moss, the CC strategy facilitates improved information exchange among population members, thereby enhancing offspring diversity and accelerating convergence. The dynamic grouping parameter, analogous to the adaptive resource allocation strategies of moss in response to environmental changes, balances exploration and exploitation for a more efficient search. Key findings from rigorous experimental evaluations using the CEC2017 benchmark suite demonstrate that CCMGO consistently outperforms nine established metaheuristic algorithms across diverse benchmark functions. Furthermore, in a real-world application to a three-channel reservoir production optimization problem, CCMGO achieves a significantly higher net present value (NPV) compared to benchmark algorithms. This successful application highlights CCMGO’s potential as a robust and adaptable tool for addressing complex, real-world optimization challenges, particularly those found in resource management and other nature-inspired domains. Full article