Indoor localization is an important issue for indoor location-based services. As opposed to the other indoor localization approaches, the radio frequency (RF) based approaches are low-energy solutions with simple implementation. The kernel learning h...
Energy consumption forecasting plays an important role in energy management, conservation, and optimization in manufacturing companies. Aiming at the tin smelting process with multiple types of energy consumption and a strong coupling with energy con...
Imbalanced classification remains a fundamental challenge in machine learning, where the Area Under the ROC Curve (AUC) is widely used for threshold-independent ranking evaluation, especially in AUC maximization studies. Existing AUC maximization met...
The accurate prediction of electricity-heat-cooling-gas loads on the demand side in the integrated energy system (IES) can provide significant reference for multiple energy planning and stable operation of the IES. This paper combines the multi-task...
Objectives: Cancer, characterized by its profound complexity and heterogeneity, arises from a multitude of molecular disruptions. The pursuit of identifying distinct cancer subtypes is driven by the need to stratify patients into clinically coherent...
This paper proposes an intelligent fault classification and location identification method for microgrids using discrete orthonormal Stockwell transform (DOST)-based optimized multi-kernel extreme learning machine (MKELM). The proposed method first e...
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...
Single-cell RNA sequencing (scRNA-seq) has recently brought new insight into cell differentiation processes and functional variation in cell subtypes from homogeneous cell populations. A lack of prior knowledge makes unsupervised machine learning met...
Considering various fault states under severe working conditions, the comprehensive feature extraction from the raw vibration signal is still a challenge for the diagnosis task of rolling bearing. To deal with strong coupling and high nonlinearity of...
This study aimed to investigate whether there are structural differences in the brains of professional artists who received formal training in the visual arts and non-artists who did not have any formal training or professional experience in the visu...
Nonintrusive Load Monitoring (NILM) models often suffer from significant performance degradation when deployed across different households and datasets, primarily because of distribution discrepancies. To address this challenge, this study proposes a...
Rolling bearings are critical components in industrial machinery, and their failures can lead to equipment downtime or safety hazards, making accurate fault diagnosis vital. While data-driven intelligent methods perform well with sufficient labeled d...
As a spontaneous facial expression, a micro-expression can reveal the psychological responses of human beings. Thus, micro-expression recognition can be widely studied and applied for its potentiality in clinical diagnosis, psychological research, an...
Inthis paper, a distributed semi-supervised partial multi-label learning (dS2PML) algorithm is proposed, which can be used to address the problem of distributed classification of partially multi-labeled data and unlabeled data. In this algorithm, we...
Interferometric Synthetic Aperture Radar (InSAR) is a widely used remote sensing technology for Earth observation, enabling the detection and measurement of ground deformation through the generation of interferograms. However, phase noise remains a c...
Robust Remaining Useful Life (RUL) forecasting is indispensable for condition-based maintenance in rotating machinery. Nevertheless, realizing high predictive precision constitutes an arduous endeavor, primarily complicated by the highly nonlinear an...
Extracting road from high resolution remote sensing (HRRS) images is an economic and effective way to acquire road information, which has become an important research topic and has a wide range of applications. In this paper, we present a novel metho...
Conventional supervised machine learning is widely used for intrusion detection without packet payload inspection, showing good accuracy in detecting known attacks. However, these methods require large labeled datasets, which are scarce due to privac...
This study aims to improve the prediction accuracy of reference evapotranspiration under limited meteorological factors. Based on the commonly recommended PSO-ELM model for ET0 prediction and addressing its limitations, an improved QPSO algorithm and...
Achieving deep learning-based bearing fault diagnosis heavily relies on large labeled training samples. However, in real industry applications, labeled data are scarce or even impossible to obtain. In this study, we addressed a challenging few-shot b...
Crop straw-to-grain ratio (SGR) estimation underpins regional straw resource assessment, yet national inventories rely on fixed coefficients that ignore structured variation across variety, environment, and phenotype. We introduce a Structured Multi-...
The traditional power load forecasting learning method has problems such as overfitting and incomplete learning of time series information when dealing with complex nonlinear data, which affects the accuracy of short–medium term power load fore...
This study proposes adaptive Studentized multi-kernel maximum mean discrepancy (MMD) for cross-condition machinery fault diagnosis. To account for sampling variation in mini-batch alignment, the multi-kernel MMD objective is normalized by empirical v...
To ensure the reliable operation of power converters and prevent catastrophic failures, this paper proposes a novel online fault diagnosis strategy for a four-level converter. The core of this strategy is an optimized multi-kernel extreme learning ma...
It is difficult to determine the main microscopic factors controlling reservoir quality due to the strong microscopic heterogeneity of tight sandstone reservoirs, which also makes it difficult to distinguish dominant reservoirs. At the same time, the...
With the widespread use of electric vehicles (EVs), the charging behavior of these resources has brought a large amount of load growth to the grid, leading to a series of problems such as increased peak valley load difference and line flow violation....
Alzheimer’s disease (AD) is a significant neurological disorder characterized by progressive cognitive decline and memory loss. One essential task is understanding the molecular mechanisms underlying brain disorders of AD. Detecting biomarkers...
In recent years, the correntropy instead of the mean squared error has been widely taken as a powerful tool for enhancing the robustness against noise and outliers by forming the local similarity measurements. However, most correntropy-based models e...
Urban land cover classification for high-resolution images is a fundamental yet challenging task in remote sensing image analysis. Recently, deep learning techniques have achieved outstanding performance in high-resolution image classification, espec...
Ball screw feed systems are critical components of CNC machine tools, and their health condition directly affects positioning accuracy and operational reliability. However, variations in rotational speed under changing operating conditions can cause...
Welding technology plays a crucial role in manufacturing, aerospace, construction, and military industries, where the quality of welds directly impacts the safety and reliability of overall structures. Therefore, developing a real-time welding defect...
Deep learning has been demonstrated to be a powerful nonlinear modeling method with end-to-end optimization capabilities for hyperspectral Images (HSIs). However, in real classification cases, obtaining labeled samples is often time-consuming and lab...
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...
Accurate fault diagnosis of power transformers is critical for maintaining grid reliability, yet conventional dissolved gas analysis (DGA) methods face challenges in feature representation and high-dimensional data processing. This paper presents an...
Compared to fault diagnosis across operating conditions, the differences in data distribution between devices are more pronounced and better aligned with practical application needs. However, current research on transfer learning inadequately address...
Deep learning models have been widely used in data-driven bridge structural damage diagnosis methods in recent years. However, these methods require training and test datasets to satisfy the same distribution, which is difficult to satisfy in practic...
Gas leak detection in oil and gas processing facilities is a critical component of the safety production monitoring system. Non-contact detection technology based on infrared imaging has emerged as a vital real-time monitoring method due to its rapid...
The gearbox is a critical component in modern industrial systems, directly determining the operational reliability of machinery. Therefore, effective fault diagnosis is essential to ensure its proper functioning. Modern diagnostic approaches often em...
Glaucoma is a serious eye disease that can cause permanent blindness and is difficult to diagnose early. Optic disc (OD) and optic cup (OC) play a pivotal role in the screening of glaucoma. Therefore, accurate segmentation of OD and OC from fundus im...
Deep learning using an end-to-end convolutional neural network (ConvNet) has been applied to several electroencephalography (EEG)-based brain–computer interface tasks to extract feature maps and classify the target output. However, the EEG anal...
Currently, huge quantities of remote sensing images (RSIs) are becoming available. Nevertheless, the scarcity of labeled samples hinders the semantic understanding of RSIs. Fortunately, many ground-level image datasets with detailed semantic annotati...
Recently, artificial intelligence (AI) including machine learning (ML) and deep learning (DL) models has been commonly employed for the automated disease diagnosis process. AI in biological and biomedical imaging is an emerging area and will be a fut...
A brain-computer interface (BCI) translates a user’s thoughts such as motor imagery (MI) into the control of external devices. However, some people, who are defined as BCI illiteracy, cannot control BCI effectively. The main characteristics of...
Prognostics is necessary to ensure the reliability and safety of lithium-ion batteries for hybrid electric vehicles or satellites. This process can be achieved by capacity estimation, which is a direct fading indicator for assessing the state of heal...
Deep learning (DL)-based object detection algorithms have gained impressive achievements in natural images and have gradually matured in recent years. However, compared with natural images, remote sensing images are faced with severe challenges due t...
The improvement of data-driven soft sensor modeling methods and techniques for the industrial process has strongly promoted the development of the intelligent process industry. Among them, ensemble learning is an excellent modeling framework. Accurac...
Single-image super-resolution is of great importance as a low-level computer-vision task. Recent approaches with deep convolutional neural networks have achieved impressive performance. However, existing architectures have limitations due to the less...
Deep learning-based fault diagnosis of induction motors often degrades under varying operating conditions because labeled fault data are limited and source and target domains may follow different distributions. To address this issue, this paper propo...
SAR ship detection is of great significance in marine safety, fisheries management, and maritime traffic. At present, many deep learning-based ship detection methods have improved the detection accuracy but also increased the complexity and computati...
The transition toward sustainable and resilient new-type power systems requires robust diagnostic frameworks for terminal power supply units to ensure continuous grid stability. To ensure the resilience of modern power systems, this paper proposes a...
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