To address the difficulty in obtaining a large number of labeled jamming signals in complex electromagnetic environments, this paper proposes a small-sample communication jamming signal recognition method based on WDCGAN-SA (Wasserstein Deep Convolut...
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...
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...
Condition documentation for traditional stone carvings requires comparable and repeatable records, yet field images are often limited by small samples, class imbalance, multi-defect co-occurrence, and diffuse defect boundaries. This study uses 643 fi...
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...
Although automatic target recognition (ATR) models based on data-driven algorithms have achieved excellent performance in recent years, the synthetic aperture radar (SAR) ATR model often suffered from performance degradation when it encountered a sma...
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...
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....
Automatic modulation recognition (AMR) is a key technique in modern communications. We propose LM-GDMAF, which integrates lightweight Mamba modules with a GDMAF fusion module. The lightweight Mamba modules efficiently extract temporal features, while...
As a key technology for wireless signal identification, modulation recognition plays an important role in the fields of unmanned aerial vehicle (UAV) communications, low-altitude spectrum management, etc. However, the accuracy of modulation recogniti...
A novel hand gesture recognition framework based on surface electromyography (sEMG) is proposed for soldier operational scenarios under small-sample conditions. The framework integrates Empirical Mode Decomposition (EMD) for signal reconstruction, hi...
The growing complexity of electrical infrastructure has elevated partial discharge (PD) detection to a crucial methodology for ensuring power system safety. Current PD pattern recognition approaches encounter persistent challenges in low-data scenari...
As voice cloning technology rapidly advances, the risk of personal voices being misused by malicious actors for fraud or other illegal activities has significantly increased, making the collection of speech data increasingly challenging. To address t...
At present, synthetic aperture radar (SAR) automatic target recognition (ATR) has been deeply researched and widely used in military and civilian fields. SAR images are very sensitive to the azimuth aspect of the imaging geomety; the same target at d...
In the breeding scene, limited by the small number of samples and environmental interference such as illumination occlusion, sika deer behavior recognition still faces challenges such as insufficient feature representation and weak cross-scale modeli...
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...
Speech emotion recognition often encounters the problems of data imbalance and redundant features in different application scenarios. Researchers usually design different recognition models for different sample conditions. In this study, a speech emo...
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 sentiment prediction technology, businesses can quickly look at user reviews to find ways to improve their products and services. We present the BertBilstm Multiple Emotion Judgment (BBMEJ) model for small-sample emotion prediction tasks to solv...
In China, tillage depth is a core performance indicator for certificating tillage machinery. The current manual measurement in field suffers from high subjectivity and poor traceability. This study proposed a tillage depth detection method called MSK...
The research of Synthetic aperture radar (SAR) target recognition plays a significant role in military and civilian fields. However, for small sample SAR target recognition, there are some problems that need to be solved urgently, including low recog...
In order to resolve the problem that the sample of image for internal detection of DN100 buried gas pipeline microleakage is single and difficult to identify, a recognition method of microleakage image of the pipeline internal detection robot is prop...
Traffic signs are updated quickly, and there image acquisition and labeling work requires a lot of manpower and material resources, so it is difficult to provide a large number of training samples for high-precision recognition. Aiming at this proble...
Intelligent pest detection has become a critical challenge in precision agriculture. Addressing the challenge of distinguishing between aphids, thrips, whiteflies, beet armyworms, spodopetra frugiperda, and spider mites during strawberry growth, this...
The acquisition of target data for underwater acoustic target recognition (UATR) is difficult and costly. Although deep neural networks (DNN) have been used in UATR, and some achievements have been made, the performance is not satisfactory when recog...
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...
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...
In special industrial fields such as electric submersible pump (ESP) wells, named entity recognition (NER) often suffers from low accuracy and incomplete entity recognition due to the scarcity of high-quality corpora and the prevalence of rare words...
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...
As artificial intelligence develops, deep learning algorithms are increasingly being used in the field of dorsal hand vein (DHV) recognition. However, deep learning has high requirements regarding the number of samples, and current DHV datasets have...
Methods such as transfer learning and attention mechanisms play an important role in small-sample image classification tasks. However, the conventional transfer method retains too much prior knowledge of the source domain and cannot learn the feature...
Background: Electroencephalography (EEG) has been widely used in Attention Deficit Hyperactivity Disorder (ADHD) recognition, but existing methods still suffer from limitations in dynamic modeling, small-sample adaptability, and training stability. T...
Currently, few-shot setting and entity nesting are two major challenges in named-entity recognition (NER). Compared to English, Chinese NER not only has issues such as complex grammatical structures, polysemy, and entity nesting but also faces low-re...
The accurate segmentation of apple leaf disease spots is the key to identifying the classification of apple leaf diseases and disease severity. Therefore, a DeepLabV3+ semantic segmentation network model with an actors spatial pyramid pool module (AS...
The Nielsen Report points out that credit card fraud caused business losses of USD 28.65 billion globally in 2019, with the US accounting for more than one-third of the high share, and that insufficient identification of credit card fraud has brought...
Named Entity Recognition (NER) is a fundamental and pivotal stage in the development of various knowledge-based support systems, including knowledge retrieval and question-answering systems. In the domain of pig diseases, Chinese NER models encounter...
Respiratory diseases pose a significant threat to intensive pig farming, and cough recognition serves as a key indicator for early intervention. However, its practical application is constrained by the scarcity of labeled samples and the complex acou...
Rotating machinery has been widely used in industries, but it often faces a high incidence of sudden failures under harsh operating conditions. Therefore, ensuring its safety and reliability is of utmost importance. However, fault diagnosis frequentl...
Communication radiation source individual identification technology is an essential technique in electronic reconnaissance and a crucial link in electronic warfare support measures. Nevertheless, when the sample set encounters complex circumstances,...
To address the problem of the fault mode discrimination accuracy of marine rotating machinery under the conditions of noise interference and limited training samples, a fault diagnosis method based on a particle swarm optimization-driven kernelized c...
Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR), most algorithms of which have employed and relied on sufficient training samples to receive a strong discriminative classification model, has remained a challenging task in recent yea...
To address the challenge of incomplete fault feature extraction in rolling bearing fault diagnosis under small-sample conditions, this paper proposes a Temporal-Graph Convolutional Fusion Network (T-GCFN). The method enhances diagnostic robustness th...
Human sitting posture recognition is critical for smart seating, ergonomic monitoring, and healthcare systems. However, existing deep learning approaches typically rely on highly complex network architectures that are computationally expensive, hinde...
The limited availability of in-situ images of the lunar surface significantly hinders the performance improvement of intelligent algorithms, such as scientific target point-of-interest recognition. To address the low diversity of images generated by...
Background: Epilepsy is a chronic neurological disorder that affects individuals across all age groups. Early detection and intervention are crucial for minimizing both physical and psychological distress. However, the unpredictable nature of seizure...
To address the challenges of small-sample rock fine classification—such as overfitting caused by limited sample size and the increased complexity resulting from high inter-class similarity—this study proposes a Stacking ensemble method ta...
To tackle the challenges of relative attitude adaptability and limited sample availability in underwater moving target recognition for active sonar, this study focuses on key aspects such as feature extraction, network model design, and information f...
Ceramic surfaces are directly related to product quality and safety in industry, and any minor defects may affect performance. Therefore, surface defect detection has important practical significance. Traditional detection methods have limitations, w...
Recognizing targets through side-scan sonar (SSS) data by deep learning-based techniques has been particularly challenging. The primary challenge stems from the difficulty and time consumption associated with underwater acoustic data acquisition, whi...
Background: Metabolomics data is inherently characterized by high dimensionality and small sample size. Existing multi-objective particle swarm optimization (MOPSO)-based feature selection algorithms suffer from two critical limitations: they general...
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