Deep convolutional neural networks (CNNs) are successful in single-image super-resolution. Traditional CNNs are limited to exploit multi-scale contextual information for image reconstruction due to the fixed convolutional kernel in their building mod...
The Internet of things (IoT) enables mobile devices to connect and exchange information with others over the Internet with a lot of applications in consumer, commercial, and industrial products. With the rapid development of machine learning, IoT wit...
Controlled cooling technology is widely used in hot-rolled steel plate production lines. The final cooling temperature directly affects the microstructure and properties of steel plates, but cooling and heat transfer constitutes a nonlinear process,...
Recently, deep learning technology was successfully applied to mechanical fault diagnosis. The convolutional neural network (CNN), as a prevalent deep learning model, occupies a place in intelligent fault diagnosis, which reduces the need for human f...
Haze removal is a pre-processing step that operates on at-sensor radiance data prior to the physically based image correction step to enhance hazy imagery visually. Most current haze removal methods focus on point-to-point operations and utilize info...
Drowsy driving is one of the major causes of traffic accidents, injuries, and deaths on roads worldwide. One of the best physiological signals that are useful in detecting a driver’s drowsiness is electroencephalography (EEG), a kind of brain s...
This paper proposes an advanced deep convolutional neural network model for motor bearing fault detection that was designed to overcome the limitations of traditional models in feature extraction, accuracy, and generalization under complex operating...
The classification of point clouds is a basic task in airborne laser scanning (ALS) point cloud processing. It is quite a challenge when facing complex observed scenes and irregular point distributions. In order to reduce the computational burden of...
Efficient bearing fault diagnosis not only extends the operational lifespan of rolling bearings but also reduces unnecessary maintenance and resource waste. However, current deep learning-based methods face significant challenges, particularly due to...
Vegetation segmentation plays a crucial role in accurately monitoring and analyzing vegetation cover, growth patterns, and changes over time, which in turn contributes to environmental studies, land management, and assessing the impact of climate cha...
Affected by the hardware conditions and environment of imaging, images generally have serious noise. The presence of noise diminishes the image quality and compromises its effectiveness in real-world applications. Therefore, in real-world application...
In synthetic aperture radar (SAR) image change detection, it is quite challenging to exploit the changing information from the noisy difference image subject to the speckle. In this paper, we propose a multi-scale spatial pooling (MSSP) network to ex...
In order to achieve impact load localization of complex structures such as ships, this paper proposes a multi-scale feature fusion convolutional neural network (MSFF-CNN) method for impact load localization. An end-to-end machine learning model is us...
The number of leaves in maize plant is one of the key traits describing its growth conditions. It is directly related to plant development and leaf counts also give insight into changing plant development stages. Compared with the traditional solutio...
Remote sensing image scene classification (RSISC) plays a vital role in remote sensing applications. Recent methods based on convolutional neural networks (CNNs) have driven the development of RSISC. However, these approaches are not adequate conside...
Supervised deep learning methods have been widely utilized in hyperspectral image (HSI) classification tasks. However, acquiring a large number of reliably labeled samples to train deep networks is not always possible in practical HSI applications du...
A brain tumor is the growth of abnormal cells in certain brain tissues with a high mortality rate; therefore, it requires high precision in diagnosis, as a minor human judgment can eventually cause severe consequences. Magnetic Resonance Image (MRI)...
Bridge Health Monitoring (BHM) systems generate nonstationary time-series data that pose challenges for accurate structural state prediction. This study proposes a novel neural network-based method for predicting bridge states, the Temporal Multi-Sca...
Fire detection remains a challenging task due to varying fire scales, occlusions, and complex environmental conditions. This paper proposes the CN2VF-Net model, a novel hybrid architecture that combines vision Transformers (ViTs) and convolutional ne...
Mapping land surface water automatically and accurately is closely related to human activity, biological reproduction, and the ecological environment. High spatial resolution remote sensing image (HSRRSI) data provide extensive details for land surfa...
Background/Objectives: Vision Transformers (ViTs) and convolutional neural networks (CNNs) have demonstrated remarkable performances in image classification, especially in the domain of medical imaging analysis. However, ViTs struggle to capture high...
Remote sensing data are becoming increasingly important for quantifying long-term changes in land surfaces. Optical sensors onboard satellite platforms face a tradeoff between temporal and spatial resolutions. Spatiotemporal fusion models can produce...
To address the limitations of traditional methods in adapting to complex operating conditions, this paper proposes a fault diagnosis approach combining multi-scale empirical mode decomposition (MS-EMD) and a one-dimensional convolutional neural netwo...
Deep learning models combining spectral and spatial features have been proven to be effective for hyperspectral image (HSI) classification. However, most spatial feature integration methods only consider a single input spatial scale regardless of var...
Efficient stock status analysis and forecasting are important for stock market participants to be able to improve returns and reduce associated risks. However, stock market data are replete with noise and randomness, rendering the task of attaining p...
In the context of industrial robot maintenance and assembly, workers often suffer from work-related musculoskeletal disorders (WRMSDs). This paper proposes a multi-scale, multi-stage pose recognition method (MMARM-CNN) based on convolutional neural n...
The prediction of circular RNA (circRNA)-drug associations plays a crucial role in understanding disease mechanisms and identifying potential therapeutic targets. Traditional methods often struggle to cope with the complexity of heterogeneous network...
Rotating machinery is susceptible to harsh environmental interference, and fault signal features are challenging to extract, leading to difficulties in health status recognition. This paper proposes multi-scale hybrid features and improved convolutio...
Extracting fault features in mechanical fault diagnosis is challenging and leads to low diagnosis accuracy. A novel fault diagnosis method using multi-scale convolutional neural networks (MSCNN) and extreme learning machines is presented in this rese...
This research introduces an original approach to time series forecasting through the use of multi-scale convolutional neural networks with Transformer modules. The objective is to focus on the limitations of short-term load forecasting in terms of co...
Airborne electromechanical actuators (EMAs) play a key role in the flight control system, and their health condition has a considerable impact on the flight status and safety of aircraft. Considering the multi-scale feature of fault signals and the f...
The full waveform inversion at this stage still has many problems in the recovery of deep background velocities. Velocity modeling based on end-to-end deep learning usually lacks a generalization capability. The proposed method is a multi-scale convo...
License plate detection (LPD) is the first and key step in license plate recognition. State-of-the-art object-detection algorithms based on deep learning provide a promising form of LPD. However, there still exist two main challenges. First, existing...
Compared with traditional machine learning algorithms, the convolutional neural network (CNN) has an excellent automatic feature learning ability and can complete the nonlinear representation from original data input to output by itself. However, the...
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...
In response to the challenges posed by complex and variable operating conditions of rolling bearings and the limited availability of labeled data, both of which hinder the effective extraction of key fault features and reduce diagnostic accuracy, thi...
Advancements in machine learning have led to an increased interest in applying deep reinforcement learning techniques to investment decision-making problems. Despite this, existing approaches often rely solely on single-scaling daily data, neglecting...
The accurate segmentation of retinal images is a basic step in screening for retinopathy and glaucoma. Most existing retinal image segmentation methods have insufficient feature information extraction. They are susceptible to the impact of the lesion...
Deep learning models with convolutional operators have received widespread attention for their good image denoising performance. However, since the convolutional operation prefers to extract local features, the extracted features may lose some global...
Cybersecurity in modern vehicles has received increased attention from the research community in recent years. Intrusion Detection Systems (IDSs) are one of the techniques used to detect and mitigate cybersecurity risks. This paper proposes a novel i...
In order to solve the problem in which most currently existing research focuses on the binary tactile attributes of objects and ignores identifying the strength level of tactile attributes, this paper establishes a tactile data set of the strength le...
Daily acquisition of large amounts of aerial and satellite images has facilitated subsequent automatic interpretations of these images. One such interpretation is object detection. Despite the great progress made in this domain, the detection of mult...
In recent years, spatial-temporal graph convolutional networks have played an increasingly important role in skeleton-based human action recognition. However, there are still three major limitations to most ST-GCN-based approaches: (1) They only use...
In recent years, deep learning methods have achieved remarkable success in hyperspectral image classification (HSIC), and the utilization of convolutional neural networks (CNNs) has proven to be highly effective. However, there are still several crit...
Soil is a critical natural resource that requires continuous monitoring to support sustainable agriculture. Among soil properties, pH is an essential indicator because it strongly affects nutrient availability and biological activity. Visible–N...
Fault diagnosis methods for rolling bearings are frequently constrained to the automatic extraction of single-scale features from raw vibration signals, overlooking crucial information embedded in data of other scales, which often results in unsatisf...
Ship-radiated noise classification is critical in ocean acoustics. Recently, the feature extraction method combined with time–frequency spectrograms and convolutional neural networks (CNNs) has effectively described the differences between vari...
Accurate crop insect pest identification in fields is useful to control pests and beneficial to agricultural yield and quality. However, it is a difficult and challenging problem due to the crop insect pests being small with various sizes, postures,...
In view of the limitations of existing rotating machine fault diagnosis methods in single-scale signal analysis, a fault diagnosis method based on multi-scale permutation entropy (MPE) and multi-channel fusion convolutional neural networks (MCFCNN) i...
Visible image quality is very susceptible to changes in illumination, and there are limitations in ship classification using images acquired by a single sensor. This study proposes a ship classification method based on an attention mechanism and mult...
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