Existing intelligent fault diagnosis methods have been widely developed and proven to be effective in monitoring the operating status of key mechanical components. However, centrifugal fans, as important equipment in energy and manufacturing industri...
Omission of frequency-dependent hydraulic resistance (skin friction) during modelling of the water hammer phenomenon is unacceptable. This resistance plays a major role when the transient liquid flow occurs in rigid-walled pipes (steel, copper, etc.)...
Bistatic airborne radar is widely studied because of its separated geometric configuration, offering flexible imaging ability for its receiver. However, when the bistatic platforms work in a synthetic aperture radar (SAR) mode, the bistatic system sh...
Carbon market prices are jointly shaped by policy interventions, energy market fluctuations, and macroeconomic dynamics, and thus exhibit pronounced nonlinearity, non-stationarity, localized abrupt changes, and time-varying uncertainty. From an infor...
Three-dimensional inversion of airborne transient electromagnetic (ATEM) data is computationally demanding because it requires repeated forward simulations and sensitivity calculations. Previous Krylov-subspace studies have substantially improved the...
In this paper, a novel 3D vector field convolution (VFC)-based B-spline deformation model is proposed for accurate and robust cartilage segmentation. Firstly, the anisotropic diffusion method is utilized for noise reduction, and the Sinc interpolatio...
The objective of basic oxygen furnace (BOF) steelmaking is to achieve molten steel with final carbon content, temperature, and phosphorus content meeting the requirements. Accurate prediction of the above properties is crucial for end-point control i...
This study proposes an advanced short-term Direct Normal Irradiance (DNI) prediction model for Concentrated Solar Power (CSP) systems, integrating a convolutional neural network (CNN) with a fitted clear-sky DNI model. Leveraging all-sky images and h...
With the continuous optimization of energy structures, wind power generation has become the dominant new energy source. The strong random fluctuation of natural wind will bring challenges to power system dispatching, so it is necessary to predict win...
During steel production, various defects often appear on the surface of the steel, such as cracks, pores, scars, and inclusions. These defects may seriously decrease steel quality or performance, so how to timely and accurately detect defects has gre...
Alzheimer’s disease (AD) is a neurological disease that affects numerous people. The condition causes brain atrophy, which leads to memory loss, cognitive impairment, and death. In its early stages, Alzheimer’s disease is tricky to predic...
Separating foliage and woody components can effectively improve the accuracy of simulating the forest eco-hydrological processes. It is still challenging to use deep learning models to classify canopy components from the point cloud data collected in...
This manuscript presents a study of the Stress-Driven integral Model (SDM) for the bending response of Bernoulli–Euler nanobeams. Unlike conventional approaches that reformulate the nonlocal integral problem into an equivalent differential form...
Over the past few decades, a new area of reliability known as classes of life distributions has developed as a result of the creation of metrics for evaluating the success or failure of reliability. This paper proposes a new reliability class-test st...
Human action recognition has a wide range of applications, including Ambient Intelligence systems and user assistance. Starting from the recognized actions performed by the user, a better human–computer interaction can be achieved, and improved...
Extensive medical research has revealed evidence of a strong association between hippocampus atrophy and age-related diseases such as Alzheimer’s disease (AD). Therefore; segmentation of the hippocampus is an important task that can help clinic...
Using kernel methods, Lepski and Willer study a convolution structure density model and establish adaptive and optimal
In light of the increasing level of correlation and dependence between the crude oil markets and the external influencing factors in the related financial markets, we propose a new multivariate empirical decomposition convolutional neural network mod...
Stock market forecasting has always been researched extensively in Social Sciences. In this research, Fuzzy time series with deep learning is widely adopted to create a fuzzy convolutional neural network integration model as this model enhances the f...
This paper introduces a convolution model with non-singular rational kernels in which coefficients are considered complex. An interlacing property of the poles and zeros in these rational kernels permits the accurate approximation of the power law fu...
Convolutional neural networks (CNNs) are extensively utilized in computer vision; however, they pose challenges in terms of computational time and storage requirements. To address this issue, one well-known approach is filter pruning. However, fine-t...
Although correlation filter (CF)-based visual tracking algorithms have achieved appealing results, there are still some problems to be solved. When the target object goes through long-term occlusions or scale variation, the correlation model used in...
This study introduces a hybrid point cloud compression method that transfers from octree-nodes to voxel occupancy estimation to find its lower-bound bitrate by using a Binary Arithmetic Range Coder. In previous attempts, we demonstrated that our entr...
This study presents a unique method of building a surrogate model using a graph convolutional network (GCN) for mesh-based structure-borne noise analysis of a fluid–structure coupled system. Structure-borne noise generated from irregular shape...
Precise identification of cancer subtypes from whole slide images (WSIs) is pivotal in tailoring patient-specific therapies. Under the weakly supervised multiple instance learning (MIL) paradigm, existing techniques frequently fall short in simultane...
Binary convolutional neural networks (BCNN) have shown good accuracy for small to medium neural network models. Their extreme quantization of weights and activations reduces off-chip data transfer and greatly reduces the computational complexity of c...
Abstract text summarization aims to offer a highly condensed and valuable information that expresses the main ideas of the text. Most previous researches focus on extractive models. In this work, we put forward a new generative model based on convolu...
Convolutional neural networks (CNNs) with log-mel audio representation and CNN-based end-to-end learning have both been used for environmental event sound recognition (ESC). However, log-mel features can be complemented by features learned from the r...
The method based on convolution neural networks (CNNs) has been widely developed and applied to residual life prediction, and many excellent results have been achieved. However, CNN models can only learn feature information relative to size, and it i...
Proper plant leaf disease (PLD) detection is challenging in complex backgrounds and under different capture conditions. For this reason, initially, modified adaptive centroid-based segmentation (ACS) is used to trace the proper region of interest (RO...
The rapid progress in artificial intelligence technologies has significantly impacted the global economy, driving transformative changes in manufacturing and giving rise to intelligent manufacturing. In this context, multivariable time-series data ha...
The efficient and accurate prediction of urban travel demand, which is a hot topic in intelligent transportation research, is challenging due to its complicated spatial-temporal dependencies, dynamic nature, and uneven distribution. Most existing for...
Face recognition (FR) uses a passive approach to person authentication that avoids face-to-face contact. Among different FR techniques, most FR approaches place little emphasis on reducing powerful cryptography and instead concentrate on increasing r...
In the context of rapid industrialization, efficiently detecting metal corrosion areas has become a critical task in preventing material damage. Unlike conventional semantic segmentation targets, metal corrosion characteristics vary significantly in...
19 February 2020
The machine comprehension research of clinical medicine has great potential value in practical application, but it has not received sufficient attention and many existing models are very time consuming for the cloze-style machine reading comprehensio...
Dynamic risk prediction is an important statistical technique for detecting temporal changes in risk and provides quantitative support for early risk identification in clinical decision-making, industrial process monitoring, and financial anomaly det...
Convolutional neural networks (CNNs) have transformed the landscape of image analysis and are widely applied across various fields. With their widespread adoption in fields like medical diagnosis and autonomous driving, CNNs have demonstrated powerfu...
This paper presents a new approach for modelling nonlinear dynamic processes (NDP). It is based on a nonlinear autoregressive with exogenous (NARX) inputs model structure and a deep convolutional fuzzy system (DCFS). The DCFS is a hierarchical fuzzy...
Heat exchangers are widely used process equipment in industrial sectors, making the study of their temperature dynamics particularly appealing due to the nonlinearities involved. Model-free approaches enable the use of input and output data to genera...
Rapid landslide motions control impact area, flow velocity, deposition pattern, and, in extreme cases, are a river-blocking hazard; therefore, reliable dynamic simulations are of direct importance to engineering–geological hazard assessments. D...
To address the limitations of low modeling accuracy in physics-based methods—which often lead to poor vehicle-tracking performance and high energy consumption—this paper proposes an intelligent vehicle modeling and trajectory tracking con...
Accurate generation–load forecasting is essential for the stability and efficiency of modern power systems. However, large-scale renewable integration and diverse user demand introduce strong nonlinearity and uncertainty, making probabilistic f...
Birdsong is a valuable indicator of rich biodiversity and ecological significance. Although feature extraction has demonstrated satisfactory performance in classification, single-scale feature extraction methods may not fully capture the complexity o...
Drug discovery, which aids to identify potential novel treatments, entails a broad range of fields of science, including chemistry, pharmacology, and biology. In the early stages of drug development, predicting drug–target affinity is crucial....
The current paper presents a hyper parameterization optimization process for a convolutional neural network (CNN) applied to pipe burst locations in water distribution networks (WDN). The hyper parameterization process of the CNN includes the early s...
Count data are encountered in real-life dealings. More understanding of such data and the extraction of important information about the data require some statistical analysis or modeling. One innovative technique to increase the modeling flexibility...
Nowadays, Convolutional Neural Networks (CNNs) are the mainstream model in the bearing fault diagnosis area. Aiming at the problems of “incomplete capture of multiscale fault features and insufficient use of key time and frequency domain inform...
Stroke detection using medical imaging plays a crucial role in early diagnosis and treatment planning. In this study, we propose a Convolutional Neural Network (CNN)-based model for detecting strokes from brain Computed Tomography (CT) images. The mo...
Seawater density is an important physical property in oceanography that affects the accuracy of calculations such as gravity fields and tidal potentials and the calibration of acoustic and optical oceanographic sensors. In related studies, constant d...
Purpose: The pulmonary function test holds significant clinical value in assessing the severity, prognosis, and treatment efficacy of respiratory diseases. However, the test is limited by patient compliance, thereby limiting its practical application...
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