Rotating machinery is a core component of modern industry, and its operational state directly affects system safety and reliability. In order to achieve intelligent fault diagnosis of bearings under complex working conditions, the health management o...
In a previous work we devised a framework to derive generalised gradient systems for an evolution equation from the large deviations of an underlying microscopic system, in the spirit of the Onsager–Machlup relations. Of particular interest is...
Consider the Euclidean space
This systematic review aimed to analyze the development and functionality of microfluidic concentration gradient generators (CGGs) for toxicological evaluation of different biological organisms. We searched articles using the keywords: concentration...
Image generation technology is currently one of the popular directions in computer vision research, especially regarding infrared imaging, bearing critical applications in the military field. Existing algorithms for generating infrared images from vi...
This study systematically investigates the flow characteristics, mixing efficiency, and concentration gradient generation (CGG) capabilities of three types of vertical-stepped main-channel microfluidic concentration gradient generators—the upwa...
A gradient-tracking generalized vertical coordinate (GT) for ocean models is proposed in the study. The movement of the grid height surfaces is based on a simple Arbitrary Lagrangian–Eulerian (ALE) method ensuring that the discrete representati...
In this paper, a gradient-based cuckoo search algorithm (GCS) is proposed to solve a reservoir-scheduling problem. The classical cuckoo search (CS) is first improved by a self-adaptive solution-generation technique, together with a differential strat...
Three-dimensional (3D) cell culture is considered more clinically relevant in mimicking the structural and physiological conditions of tumors in vivo compared to two-dimensional cell cultures. In recent years, high-throughput screening (HTS) in 3D ce...
We propose a new iterative method for solving a generalized Sylvester matrix equation
Microfluidic gradient generators (MGGs) provide a platform for investigating how cells respond to a concentration gradient or different concentrations of a specific chemical. Among these MGGs, those based on Christmas-tree-like structures possess adv...
Virtual testing is one of the key methods for evaluating the collision avoidance capabilities of autonomous ships, and test scenarios provide the fundamental basis for its implementation. However, generating test scenarios that are both diverse and p...
Regularization plans, acceleration mechanisms, and optimization strategies all have a significant impact on the convergence behavior of neural network training algorithms. A thorough analysis of a batch gradient method (BGM) improved with momentum an...
A microfluidic device capable of precise chemical control is helpful to mimic tumor microenvironments in vitro, which are closely associated with malignant progression, including metastasis. Cancer cells under a concentration gradient of oxygen and o...
High-quality short-term forecasts of wind farm generation are crucial for the dynamically developing renewable energy generation sector. This article addresses the selection of appropriate gradient-boosted decision tree models (GBDT) for forecasting...
By applying the weighted relaxation technique to the gradient-based iterative (GI) algorithm and taking proper weighted combinations of the solutions, this paper proposes the weighted, relaxed gradient-based iterative (WRGI) algorithm to solve the ge...
We report the possibility of achieving normal-incidence transmission at non-normal incidence angles using thin interfaces made of metasurface structures with an appropriately-designed positive spatial phase distributions. The reported effect represen...
We have developed a cast microfluidic chip for concentration gradient generation that contains a thin (~5 µm2 cross-sectional area) microchannel. The diffusion of diffused 185 nm ultraviolet (UV) light from an inexpensive low-pressure mercury l...
The Gerchberg–Saxton (GS) algorithm is a Fourier iterative algorithm that can effectively optimize phase-only computer-generated holograms (CGHs). This study proposes a new optimization technique for phase-only CGHs based on the gradient descent meth...
In this work, we explored, for the first time, to the best of our knowledge, the potential of stochastic gradient descent (SGD) to optimize random phase functions for application in non-iterative phase-only hologram generation. We defined and evaluat...
We introduce a gradient iterative scheme with an optimal convergent factor for solving a generalized Sylvester matrix equation
We derive a conjugate-gradient type algorithm to produce approximate least-squares (LS) solutions for an inconsistent generalized Sylvester-transpose matrix equation. The algorithm is always applicable for any given initial matrix and will arrive at...
Lidar-based models rely on an optimal relationship between the field and the lidar data for accurate predictions of forest attributes. This relationship may be altered by the variability in the stand growth conditions or by the temporal discrepancy b...
Thermoelectric systems configured in multistring arrays of thermoelectric generators (TEGs) represent a promising solution for energy harvesting in environments with non-uniform thermal gradients. However, the presence of multiple maximum power point...
This study presents a cost-based optimization model for the design of isolated foundations in cohesive soils. The optimization algorithm not only incorporates safety requirements in the form of ultimate limit state (ULS) and serviceability limit stat...
The photosynthetic cyanobacterium Synechococcus elongatus PCC7942 has recently gained great attention for its ability to directly convert CO2 into renewable chemicals upon genetic engineering. Thus, it is of great interest to increase the growth spee...
The research on fault diagnosis methods based on generative adversarial networks has achieved fruitful results, but most of the research objects are rolling bearings or gears, and the model test data are almost all derived from laboratory bench test...
The study compares model approaches in predictive modeling for claim frequency and severity within the cross-border cargo insurance domain. The aim is to identify the optimal model approach between generalized linear models (GLMs) and advanced machin...
This paper introduces a broad class of Mirror Descent (MD) and Generalized Exponentiated Gradient (GEG) algorithms derived from trace-form entropies defined via deformed logarithms. Leveraging these generalized entropies yields MD and GEG algorithms...
In this review, we consider state-of-the-art density functional theory (DFT) investigations of strongly correlated systems performed with the meta-generalized gradient approximation (meta-GGA) strongly constrained and appropriately normed (SCAN) func...
As one of the representative models in the field of image generation, generative adversarial networks (GANs) face a significant challenge: how to make the best trade-off between the quality of generated images and training stability. The U-Net based...
In this work, some properties of the general convolutional operators of general fractional calculus (GFC), which satisfy analogues of the fundamental theorems of calculus, are described. Two types of general fractional (GF) operators on a finite inte...
As part of our study, we investigate gradient Schouten harmonic solutions to sequential warped product manifolds. The main contribution of our work is an explanation of how it is possible to express gradient Schouten harmonic solitons on sequential w...
In this study, we investigate power generation by reverse electrodialysis in a dense silica membrane that is between two NaCl solutions with various combinations of concentrations. Each silica membrane is fabricated by depositing a silica layer on a...
Variable switching frequency in the finite control set model predictive control (FCS-MPC) method causes a negative impact on the converter efficiency and the design of the output filters. Several studies have addressed the problem, but they are eithe...
Federated learning (FL), as a distributed learning mechanism, can have model training completed without directly uploading original data, effectively reducing the risk of privacy leakage. However, through the shared gradient information, research sho...
A three-dimensional (3D) tumor spheroid model plays a critical role in mimicking tumor microenvironments in vivo. However, the conventional culture methods lack the ability to manipulate the 3D tumor spheroids in a homogeneous manner. To address this...
This paper is concerned with the real-time configuration of fault detection systems by exploiting an gradient optimization scheme. It is known that industrial processes may often encounter some uncertainties or changes of operating points and environ...
Titanium and titanium alloys have excellent physical properties and process properties and are widely used in the aviation industry, but their high-temperature oxidation resistance is poor, and there is a thermal barrier temperature of 600 °C, wh...
Wasserstein Generative Adversarial Networks (WGANs) have gained significant attention due to their theoretical foundations and effectiveness in generative modeling. However, training stability remains a major challenge, typically addressed through fi...
The present work aims to primarily provide a general representation of the solution of the simplified elastostatics version of Mindlin’s Form II first-strain gradient elastic theory, which converges to the solution of the corresponding classica...
In this paper, we propose a stochastic gradient descent algorithm, called stochastic gradient descent method-based generalized pinball support vector machine (SG-GPSVM), to solve data classification problems. This approach was developed by replacing...
This paper is a step towards developing a geometric understanding of a popular algorithm for training deep neural networks named stochastic gradient descent (SGD). We built upon a recent result which observed that the noise in SGD while training typi...
Distribution network data may encounter random missing data caused by abnormal conditions and continuous missing data caused by natural disasters during gathering, transmission, and conversion. To address these problems, this paper proposes a missing...
Energy harvesting has become a promising and alternative solution to conventional energy generation patterns to overcome the problem of supplying autonomous electrical systems. More particularly, thermal energy harvesting technologies have drawn a ma...
In order to minimize the disparity between visible and infrared modalities and enhance pedestrian feature representation, a cross-modality person re-identification method is proposed, which integrates modality generation and feature enhancement. Spec...
The high accuracy attainment, using less complex architectures of neural networks, remains one of the most important problems in machine learning. In many studies, increasing the quality of recognition and prediction is obtained by extending neural n...
There are a huge number, and abundant types, of microalgae in the ocean; and most of them have various values in many fields, such as food, medicine, energy, feed, etc. Therefore, how to identify and separation of microalgae cells quickly and effecti...
This work is devoted to the modeling and investigation of the architecture design for the delayed recurrent neural network, based on the delayed differential equations. The usage of discrete and distributed delays makes it possible to model the calcu...
Digital Elevation Models (DEMs) are commonly used for environment, engineering, and architecture-related studies. One of the most important factors for the accuracy of DEM generation is the process of spatial interpolation, which is used for estimati...
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