Parking occupancy prediction (POP) plays a vital role in many parking-related smart services for better parking management. However, an issue hinders its mass deployment: many parking facilities cannot collect enough data to feed data-hungry machine...
Deep learning has achieved good classification results in the field of traffic classification in recent years due to its good feature representation ability. However, the existing traffic classification technology cannot meet the requirements for the...
Due to the problem of insufficient dynamic human ear data, the Changchun University dynamic human ear (CCU-DE) database, which is a small sample human ear database, was developed in this study. The database fully considers the various complex situati...
Classification with a few labeled samples has always been a longstanding problem in the field of hyperspectral image (HSI) processing and analysis. Aiming at the small sample characteristics of HSI classification, a novel ensemble self-supervised fea...
Since the emergence of artificial intelligence and deep learning methods, the fault diagnosis of bearings in rotating machinery has gradually been realized, reducing the high costs of bearing faults. However, in the actual work of the equipment, faul...
Accurate and timely information on forest disturbance drivers is important for sustainable forest management, global carbon cycle accounting, and climate change response. However, forest disturbance classification is difficult due to two major challe...
Small sample learning ability is one of the most significant characteristics of the human brain. However, its mechanism is yet to be fully unveiled. In recent years, brain-inspired artificial intelligence has become a very hot research domain. Resear...
Total organic carbon (TOC) is a key parameter for geological sweet spot optimization and resource evaluation. However, accurate TOC prediction remains challenging under small-sample conditions because conventional physical and machine learning method...
Automated machining feature recognition is an essential component linking computer-aided design (CAD) and computer-aided process planning (CAPP). Deep learning (DL) has recently emerged as a promising method to improve machining feature recognition....
The accurate prediction of lithium-ion battery capacity is crucial for the safe and efficient operation of battery systems. Although data-driven approaches have demonstrated effectiveness in lifetime prediction, the acquisition of lifecycle data for...
The widely distributed “Step-type” landslides in the Three Gorges Reservoir (TGR) area have caused serious casualties and heavy economic losses. The prediction research of landslide displacement will be beneficial to the establishment of...
Post-stroke finger dysfunction severely impacts patients’ daily living abilities and quality of life. Traditional rehabilitation assessment methods face challenges such as high subjectivity, insufficient precision, and difficulty in capturing s...
Addressing the challenges of extreme sample scarcity, complex underwater optical environments, and significant variations in lesion scales in real-world aquaculture, this paper proposes a small-sample grass carp disease recognition method, namely Swi...
Aiming at the existing Direction of Arrival (DOA) methods based on neural network, a large number of samples are required to achieve signal-scene adaptation and accurate angle estimation. In the coherent signal environment, the problems of a larger a...
Accurate quality prediction can find and eliminate quality hazards. It is difficult to construct an accurate quality mathematical model for the production of small samples with high dimensionality due to the influence of quality characteristics and t...
Rolling bearings play a crucial role in industrial equipment, and their failure is highly likely to cause a series of serious consequences. Traditional deep learning-based bearing fault diagnosis algorithms rely on large amounts of training data; tra...
Palmprint recognition is becoming more and more common in the fields of security authentication, mobile payment, and crime detection. Aiming at the problem of small sample size and low recognition rate of palmprint, a small-sample palmprint recogniti...
Multi-camera networks have gained great interest in video-based surveillance systems for security monitoring, access control, etc. Person re-identification is an essential and challenging task in multi-camera networks, which aims to determine if a gi...
With the remarkable development of deep learning in the field of science, deep neural networks provide a new way to solve the Stefan problem. In this paper, deep neural networks combined with small sample learning and a general deep learning framewor...
In view of the differences in the applicability and prediction ability of different creep rupture life prediction models, we propose a creep rupture life prediction method in this paper. Various time–temperature parametric models, machine learn...
In order to improve the accuracy of the segmentation of buildings with small sample sizes, this paper proposes a building-segmentation network, ResFAUnet, with transfer learning and multi-scale feature fusion. The network is based on AttentionUnet. T...
Chinese local gazetteer maps contain visual evidence of historical cities and architecture, but their hand-drawn, low-texture, and small-sample characteristics make automatic recognition difficult. This study develops a transfer-learning workflow to...
A fault diagnosis method based on meta-learning and an improved multi-channel convolutional neural network (MAML-MCCNN-ISENet) was proposed to solve the problems of insufficient feature extraction and low fault type identification accuracy of vibrati...
Small experimental datasets make synthesis-condition modelling particularly sensitive to overfitting and data leakage. Here, a graph convolutional neural network (GCNN) was coupled with approximate attribution enhancement (AAE) and evaluated on 54 go...
Accurate ship motion prediction is important for navigation safety, onboard decision support, and shipborne digital twins. Yet data-driven models trained on one vessel often degrade when applied to different hull forms, speed regimes, and sea states....
Traditional federated learning relies heavily on mature datasets, which typically consist of large volumes of uniformly distributed data. While acquiring extensive datasets is relatively straightforward in academic research, it becomes prohibitively...
To address the practical engineering challenges of limited fault samples for high-voltage circuit breaker spring operating mechanisms and the inability of single features to fully reflect equipment status, this paper proposes a small-sample fault dia...
In continuous kraft pulping, key quality indicators such as Kappa number and pulp viscosity are usually measured offline at low frequency, which limits real-time quality monitoring and control. Although data-driven soft sensors have shown potential f...
Cross-domain classification with small samples is a more challenging and realistic experimental setup. Until now, few studies have focused on the problem of small-sample cross-domain classification between completely different hyperspectral images (H...
The concept of deep transfer learning has spawned broad research into fault diagnosis with small samples. A considerable covariate shift between the source and target domains, however, could result in negative transfer and lower fault diagnosis task...
Reliable prediction of monopile self-weight penetration depth is essential for drivability assessment and installation planning but remains challenging because of nonlinear pile–soil interaction and limited field data. This study compiles recor...
Underwater target classification methods based on deep learning suffer from obvious model overfitting and low recognition accuracy in the case of small samples and complex underwater environments. This paper proposes a novel classification network (E...
Accurate extraction of vegetation information from high-resolution remote sensing (RS) imagery is crucial for efficient urban ecological environment monitoring and land use management. However, due to the high cost of manual annotation in remote sens...
Change detection with heterogeneous remote sensing images (Hete-CD) plays a significant role in practical applications, particularly in cases where homogenous remote sensing images are unavailable. However, directly comparing bitemporal heterogeneous...
The utilization of deep learning in Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) has witnessed a recent surge owing to its remarkable feature extraction capabilities. Nonetheless, deep learning methodologies are often encumbered...
Tomatoes are a crop of significant economic importance, and disease during growth poses a substantial threat to yield and quality. In this paper, we propose IBSA_Net, a tomato leaf disease recognition network that employs transfer learning and small...
Data trading platforms play a crucial role in facilitating data circulation and promoting the sustainable allocation of data resources. Establishing a transparent, fair, and efficient pricing mechanism is key to ensuring the long-term stability and d...
Most current hyperspectral image classification (HSIC) models require a large number of training samples, and when the sample size is small, the classification performance decreases. To address this issue, we propose an innovative model that combines...
The Bayesian Network (BN) structure learning algorithm based on dynamic programming can obtain global optimal solutions. However, when the sample cannot fully contain the information of the real structure, especially when the sample size is small, th...
Three-dimensional (3D) face recognition has become a trending research direction in both industry and academia. However, traditional facial recognition methods carry high computational costs and face data storage costs. Deep learning has led to a sig...
This paper proposes a weakly supervised cross-domain person re-identification (Re-ID) method based on small sample data. In order to reduce the cost of data collection and annotation, the model design focuses on extracting and abstracting the informa...
Moving target detection is one of the most important tasks of radar systems. The clutter echo received by radar is usually strong and heterogeneous when the radar works in a complex terrain environment, resulting in performance degradation in moving...
Accurate workload prediction plays a key role in intelligent scheduling decisions on cloud platforms. There are massive amounts of short-workload sequences in the cloud platform, and the small amount of data and the presence of outliers make accurate...
Dynamic gesture recognition under small-sample conditions remains challenging due to the large variations caused by users, viewpoints, motion patterns, and hand configurations. This study proposes a lightweight dynamic gesture recognition framework t...
Deep neural networks are successful learning tools for building nonlinear models. However, a robust deep learning-based classification model needs a large dataset. Indeed, these models are often unstable when they use small datasets. To solve this is...
Existing intelligent fault diagnosis methods ignore the influence of sensors at different positions on the model fault diagnosis performance. Furthermore, the lack of interpretability leads to insufficient reliability of the model fault diagnosis res...
Meta-learning has demonstrated significant advantages in small-sample tasks and has attracted considerable attention in wind turbine fault diagnosis. However, due to extreme operating conditions and equipment aging, the monitoring data of wind turbin...
This paper addresses the challenges of anomaly detection in industrial components by proposing a two-stage deep-learning approach combining semantic segmentation and knowledge distillation. Traditional methods, such as manual inspection and machine v...
The prediction of the ablation rate of silicone rubber-based composites is of great significance to accelerate the development of flexible thermal protection materials. Herein, a method which combines uniform design experimentation, active learning,...
To address the challenges of ambiguous regional feature identification, high digital modeling costs, and limited regional adaptability of general-purpose AIGC tools in the micro-renewal of Western Sichuan Linpan, this study proposes a typology-constr...
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