As one of the supervised tensor learning methods, the support tensor machine (STM) for tensorial data classification is receiving increasing attention in machine learning and related applications, including remote sensing imaging, video processing, f...
This paper investigates refined Hardy-type inequalities on time scales associated with nonnegative kernels and superquadratic functions. Starting from a generalized Hardy operator, we derive an inequality involving explicit remainder terms through a...
The resolution of the products of numerical weather prediction is limited by the resolution of numerical models and computing resources, which can be improved accurately by a well-chosen interpolation algorithm. This paper is intended to improve the...
We develop a model-independent response-window formalism that describes how small perturbations of the cosmological expansion history around that of the standard
Kernel transfer learning (KTL), as a kind of statistical transfer learning (STL), has provided significant solutions for cross-domain condition monitoring and fault diagnosis of bearings due to its ability to capture relationships and reduce the gap...
Multi-kernel learning methods are essential kernel learning methods. Still, the base kernel functions in most multi-kernel learning methods only with select kernel functions with shallow structures, which are weak for large-scale uneven data. We prop...
In this paper, Jensen’s inequality and Fubini’s Theorem are extended for the function of several variables via diamond integrals of time scale calculus. These extensions are used to generalize Hardy-type inequalities with general kernels via diamond...
The construction of a similarity matrix is one significant step for the spectral clustering algorithm; while the Gaussian kernel function is one of the most common measures for constructing the similarity matrix. However, with a fixed scaling paramet...
We propose a new methodology to transform a time series into an ordered sequence of any entropic and information functionals, providing a novel tool for data analysis. To achieve this, a new algorithm has been designed to optimize the Probability Den...
Predicting the maneuvering motion of an unmanned surface vehicle (USV) plays an important role in intelligent applications. To more precisely predict this empirically, this study proposes a method based on the support vector regression with a mixed k...
In semantic segmentation tasks, large kernels and Atrous convolution have been utilized to increase the receptive field, enabling models to achieve competitive performance with fewer parameters. However, due to the fixed size of kernel functions, net...
2 March 2018
Based on grey language multi-attribute group decision making, a kernel and grey scale scoring function is put forward according to the definition of grey language and the meaning of the kernel and grey scale. The function introduces grey scale into t...
We develop incompressible magnetohydrodynamic (IMHD) energy budget equations with a spatial filtering kernel and estimate the scaling of the structure functions. The Politano-Pouquet law is recovered as an upper bound on the scale-to-scale energy tra...
We present a rigorous thermodynamic treatment of irreversible binary aggregation. We construct the Smoluchowski ensemble as the set of discrete finite distributions that are reached in fixed number of merging events and define a probability measure o...
Aiming at the typical faults in the coal mills operation process, the kernel extreme learning machine diagnosis model based on variational model feature extraction and kernel principal component analysis is offered. Firstly, the collected signals of...
Background: Maize, crucial for food, feed, and industry, is a model for genetic and breeding research. Kernel traits directly affect maize yield. This study developed the eBSRmap method to simplify gene cloning related to maize kernel traits. Methods...
This study exploits quantitative algorithms of Raman spectroscopy to assess, at the molecular scale, the nutritional quality of individual kernels of the Japanese short-grain rice cultivar Koshihikari in terms of amylose-to-amylopectin ratio, fractio...
A meshless, quasi-convex reproducing kernel particle framework for three-dimensional steady-state thermomechanical coupling problems is presented in this paper. A meshfree, second-order, quasi-convex reproducing kernel scheme is employed to approxima...
Deep learning methods have achieved remarkable success in remote sensing object detection. Existing object detection methods focus on integrating convolutional neural networks (CNNs) and Transformer networks to explore local and global representation...
Kernel-combination procedures used in classification often return only a combined kernel matrix on the training sample, rather than a kernel function that can be evaluated consistently at new points. This limitation is especially important for superv...
Classical Laplace-, Sumudu-, and Elzaki-type transforms are formulated within additive analytical frameworks and do not naturally accommodate multiplicative scaling structures arising in non-Newtonian calculus. Motivated by this limitation, this stud...
Part surface topography is characterized by complex multi-scale and multi-feature coupling, and accurate topography modeling is essential for predicting assembly precision in high-performance mechanical systems. Gaussian Process Regression (GPR) offe...
There is always an asymmetric phenomenon between traffic data quantity and unit information content. Labeled data is more effective but scarce, while unlabeled data is large but weaker in sample information. In an urban transportation assessment syst...
The classical absolute orientation method is capable of transforming tie points (TPs) from a local coordinate system to a global (geodetic) coordinate system. The method is based only on a unique set of similarity transformation parameters estimated...
Zero-shot learning (ZSL) holds significant promise for scaling image classification to previously unseen classes by leveraging previously acquired knowledge. However, conventional ZSL methods face challenges such as domain-shift and hubness problems....
This study introduces an improved YOLOv8 model tailored for detecting objects in road scenes. To overcome the limitations of standard convolution operations in adapting to varying targets, we introduce Adaptive Kernel Convolution (AKconv). AKconv dyn...
The integral equations with oscillatory kernels are of great concern in applied sciences and computational engineering, particularly for large-scale data points and high frequencies. Therefore, the interest of this work is to develop an accurate, eff...
As a green and sustainable trip mode, shared bicycles play an essential role in completing short-distance trips in cities. This paper proposes a method to analyze the impact of the urban built environment on the distribution of shared bicycles in a s...
In this paper, a support vector machine (SVM) technique has been applied to an antenna allocation system with multiple antennas in multiuser downlink communications. Here, only the channel magnitude information is available at the transmitter. Thus,...
Localization is fundamental to enable the use of autonomous mobile robots. In this work, we use magnetic-based localization. As Earth’s geomagnetic field is stable in time and is not affected by nonmagnetic materials, such as a large number of people...
Algorithms involving kernel functions, such as support vector machine (SVM), have attracted huge attention within the artificial learning communities. The performance of these algorithms is greatly influenced by outliers and the choice of kernel func...
Reliable attribution of artificial intelligence (AI)-generated text to a specific large language model (LLM) matters increasingly as LLMs proliferate, yet where quantum machine learning actually stands on this task has, to our knowledge, never been m...
Recently, deep neural network-based image compressed sensing methods have achieved impressive success in reconstruction quality. However, these methods (1) have limitations in sampling pattern and (2) usually have the disadvantage of high computation...
Fault localization (FL) is the task of identifying code locations responsible for bugs in software, and it is a prerequisite step in the bug-fixing process. FL in large-scale systems such as the Linux kernel involves three core challenges: First, the...
Remotely sensed morphological traits have been used to assess functional diversity of forests. This approach is potentially spatial-scale-independent. Lidar data collected from the ground or by drone at a high point density provide an opportunity to...
Aiming at the noise and nonlinear characteristics existing in the deformation monitoring data of concrete dams, this paper proposes a dam deformation prediction model based on a multi-scale adaptive kernel ensemble. The model incorporates Gaussian wh...
The convolutional neural networks (CNNs) functioning on geometric learning for the urban large-scale 3D meshes are indispensable because of their substantial, complex, and deformed shape constitutions. To address this issue, we proposed a novel Geome...
Feature extraction, aiming to simplify and optimize data features, is a typical hyperspectral image dimensionality reduction technique. As a kernel-based method, kernel minimum noise fraction (KMNF) transformation is excellent at handling the nonline...
Large convolution kernels offer better performance advantages. They can cover a wider area and capture a broader range of spatial information in a single convolution operation. This is of great importance when dealing with tasks that have significant...
Estimating the joint torques of lower limbs in human gait is a highly challenging task and of great significance in developing high-level controllers for lower-limb exoskeletons. This paper presents a dependent Gaussian process (DGP)-based learning a...
The pectic polysaccharides extracted from flaxseed (Linum usitatissiumum L.) mucilage and kernel were characterized as rhamnogalacturonan-I (RG-I). In this study, the conformational characteristics of RG-I fractions from flaxseed mucilage and kernel...
Research on visual encoding models for functional magnetic resonance imaging derived from deep neural networks, especially CNN (e.g., VGG16), has been developed. However, CNNs typically use smaller kernel sizes (e.g., 3 × 3) for feature extract...
Support vector regression (SVR) is a well-established kernel-based method for nonlinear regression. However, standard SVR formulations minimize absolute-error losses, which are not consistent with the scale-free, relative-accuracy criteria prevalent...
Since the lightweight convolutional neural network EfficientNet was proposed by Google in 2019, the series of models have quickly become very popular due to their superior performance with a small number of parameters. However, the existing convoluti...
This review develops an operator-selection framework in which equivalence, physical mechanism, mathematical domain, and independently measurable observables are examined before a fractional derivative is used. The principal result is a systematic ope...
This paper presents a Gaussian process based Bayesian inference system for the realization of intelligent surface measurement on multi-sensor instruments. The system considers the surface measurement as a time series data collection process, and the...
We address the challenging application of 3D pore scale reactive flow under varying geometry parameters. The task is to predict time-dependent integral quantities, i.e., breakthrough curves, from the given geometries. As the 3D reactive flow simulati...
The investigation of travel mode choice is an essential task in transport planning and policymaking for predicting travel demands. Typically, mode choice datasets are imbalanced and learning from such datasets is challenging. This study deals with im...
A balanced dataset is generally beneficial to underwater acoustic target recognition. However, the imbalanced class distribution is always meted out in a real scene. To address this, a weighted cross entropy loss function based on trigonometric funct...
Urban catering systems constitute an important subsystem of the complex urban system. They can reveal not only the impact of urban functional structure on the catering but also the behavioral patterns of individual catering points through the explora...
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