Small Sample Learning Research Articles

Research on the Individual Identification of Communication Radiation Sources in Complex Circumstances

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, such as class imbalance or a small sample, the classification network model, driven by big data, disrupts the symmetry between the recognition effect and the quantity of the datasets, leading to suboptimal recognition performance. Thus, it is requisite to optimize the existing models and algorithms to better propose more representative fingerprint features. This paper references the speech signal recognition model multivariate long short-term memory–fully convolutional network (MLSTM-FCN), and ameliorates the recognition algorithm and training strategy for the two scenarios of class imbalance and a small sample. It puts forward a communication radiation source individual identification method based on MLSTM-FCN incremental random feature concatenation and a communication radiation source individual identification method based on meta-learning. Proceeding from improving the class imbalance issue among features and small-sample learning, the experimental results under various signal-to-noise ratios demonstrate that the proposed methods have superior recognition effects and higher accuracy.

Few-Shot Learning Method Based on Data Enhancement and Transfer Learning

With the significant progress of deep learning technology in many fields, the dependence of model training on a large amount of labeled data is increasingly prominent. However, in many practical application scenarios, especially in tasks with high labeling costs, data scarcity often occurs. This realistic challenge has promoted the rise of Few-Shot Learning (FSL) technology, which seeks to achieve effective learning of models with extremely limited samples. This article provides a comprehensive overview of the theoretical background, key technologies of FSL, to explore its potential and effectiveness in solving the problem of small-sample learning. In the method overview section, this article pays special attention to FSL strategies based on data augmentation and transfer learning. By reviewing and analyzing these methods, this article aims to provide some theoretical support and technical references for further exploration in this field and hopes to contribute to solving the problem of data scarcity and promote the sustainable development of this field.

Leveraging patent classification based on deep learning: The case study on smart cities and industrial Internet of Things

With the trends of technology convergence and technology interdisciplinarity, technology-field (TF) resolution and classification of patents have gradually been challenged. Whether for patent applicants or for patent examiners, more precisely labeling the TF for a certain patent is important for technological searches. However, determining the TF of a patent may be difficult and may even involve the strategic behavior of patenting, which can cause noise in patent classification systems (PCSs). In addition, some specific patents could contain more TFs than claimed or be assigned questionable IPC codes; subsequently, in a regular search for technology/patents, information could be missed. Considering the advantages of deep learning compared with traditional machine learning algorithms in areas such as natural language processing (NLP), text classification and text sentiment analysis, this paper investigates several popular deep learning models and proposes a large-scale multilabel regression (MLR) model to handle specific patent analyses under situations of small sample learning. To verify the proposed MLR model for patent classification, the case study on smart cities and industrial Internet of Things (IIoT) is conducted. The MLR experiments on the TF resolution of smart cities and IIoT have yielded moderate results compared with those of the latest patent classification studies, which also rely on deep learning and the large language models (LLMs), which include RCNN, Bi-LSTM, BERT and GPT-4 etc. Therefore, the proposed MLR model with a customized loss function could be moderately effective for patent classification within a specific technology theme, could have implications for patent classification and the TF resolution of patents, and could further enrich methodologies for patent mining and informetrics based on artificial intelligence (AI).

Virtual sample generation for small sample learning: A survey, recent developments and future prospects

Virtual sample generation (VSG) technology aims to generate virtual samples based on real samples, in order to expand the size of the datasets and improve model performance. However, there is limited research summarizing VSG technology, which motivates this paper. In recent years, VSG technology has grown as a crucial tool for augmenting datasets and enhancing model performance, particularly in the fields like image recognition, medicine, and quality control where small datasets are common issues. This paper aims to provide an updated review of VSG technology, focusing on three key techniques which are important for small sample analysis studies, including sampling-based, information diffusion-based, and Generative Adversarial Networks (GANs)-based technology. In this review, we seek to identify the key trends in this field and to provide insights regarding the opportunities and challenges.

Augmented pre-training-based carbon emission accounting method using electricity data under small-sample condition

IntroductionAccurate and rapid carbon accounting method for the power industry is crucial to support China’s low-carbon transformation. Currently, carbon emission accounting methods are based on slowly updated fuel statistics or expensive monitoring equipment, resulting in high costs and delays in carbon emission estimation. Power data offers high real-time availability, accuracy, and resolution, and exhibits a strong correlation with carbon emissions. These characteristics provide a pathway for achieving rapid and precise annual carbon emission accountings. However, carbon emission data inherently exhibits small sample characteristics, making these methods less effective in small sample conditions and leading to lower accounting accuracy.MethodsTherefore, this paper proposes an augmented pre-training-based “electricity-to-carbon” method under small sample conditions.ResultsThis approach utilizes the correlation between electricity and carbon data as well as the autocorrelation characteristics of carbon emission data to construct a machine learning-based electricity-carbon fitting model for rapid and accurate carbon emission estimation. To address the challenges of small sample learning, this paper introduces an interpolation pre-training method to optimize the model’s hyperparameters and conserve samples for model training, thereby improving the model’s generalization and robustness.DiscussionCase studies on a real dataset verifies the effectiveness of the proposed method. The findings of this study can promote the development of carbon measurement technology and facilitate the low-carbon transition of developing countries.

Siamese Neural Network and Multimodal Data Fusion Approach for Small-Sample Learning in Industrial Soft Sensor Modeling

Siamese Neural Network and Multimodal Data Fusion Approach for Small-Sample Learning in Industrial Soft Sensor Modeling

Design and Research on Identification of Typical Tea Plant Diseases Using Small Sample Learning

Tea plants are susceptible to diseases during their growth. These diseases seriously affect the yield and quality of tea. The effective prevention and control of diseases requires accurate identification of diseases. With the development of artificial intelligence and computer vision, automatic recognition of plant diseases using image features has become feasible. As the support vector machine (SVM) is suitable for high dimension, high noise, and small sample learning, this paper uses the support vector machine learning method to realize the segmentation of disease spots of diseased tea plants. An improved Conditional Deep Convolutional Generation Adversarial Network with Gradient Penalty (C-DCGAN-GP) was used to expand the segmentation of tea plant spots. Finally, the Visual Geometry Group 16 (VGG16) deep learning classification network was trained by the expanded tea lesion images to realize tea disease recognition.

A Fault Diagnosis Method for Pumped Storage Unit Stator Based on Improved STFT-SVDD Hybrid Algorithm

Stator faults are one of the common issues in pumped storage generators, significantly impacting their performance and safety. To ensure the safe and stable operation of pumped storage generators, a stator fault diagnosis method based on an improved short-time Fourier transform (STFT)-support vector data description (SVDD) hybrid algorithm is proposed. This method establishes a fault model for inter-turn short circuits in the stator windings of pumped storage generators and analyzes the electrical and magnetic states associated with such faults. Based on the three-phase current signals observed during an inter-turn short circuit fault in the stator windings, the three-phase currents are first converted into two-phase currents using the principle of equal magnetic potential. Then, the STFT is applied to transform the time-domain signals of the stator’s two-phase currents into frequency-domain signals, and the resulting fault current spectrum is input into the improved SVDD network for processing. This ultimately outputs the diagnosis result for inter-turn short circuit faults in the stator windings of the pumped storage generator. Experimental results demonstrate that this method can effectively distinguish between normal and faulty states in pumped storage generators, enabling the diagnosis of inter-turn short circuit faults in stator windings with low cross-entropy loss. Through analysis, under small data sample conditions, the accuracy of the proposed method in this paper can be improved by up to 7.2%. In the presence of strong noise interference, the fault diagnosis accuracy of the proposed method remains above 90%, and compared to conventional methods, the fault diagnosis accuracy can be improved by up to 6.9%. This demonstrates that the proposed method possesses excellent noise robustness and small sample learning ability, making it effective in complex, dynamic, and noisy environments.

Current computational tools for protein lysine acylation site prediction.

As a main subtype of post-translational modification (PTM), protein lysine acylations (PLAs) play crucial roles in regulating diverse functions of proteins. With recent advancements in proteomics technology, the identification of PTM is becoming a data-rich field. A large amount of experimentally verified data is urgently required to be translated into valuable biological insights. With computational approaches, PLA can be accurately detected across the whole proteome, even for organisms with small-scale datasets. Herein, a comprehensive summary of 166 in silico PLA prediction methods is presented, including a single type of PLA site and multiple types of PLA sites. This recapitulation covers important aspects that are critical for the development of a robust predictor, including data collection and preparation, sample selection, feature representation, classification algorithm design, model evaluation, and method availability. Notably, we discuss the application of protein language models and transfer learning to solve the small-sample learning issue. We also highlight the prediction methods developed for functionally relevant PLA sites and species/substrate/cell-type-specific PLA sites. In conclusion, this systematic review could potentially facilitate the development of novel PLA predictors and offer useful insights to researchers from various disciplines.

Angle Estimation Using Learning-Based Doppler Deconvolution in Beamspace with Forward-Looking Radar

The measurement of the target azimuth angle using forward-looking radar (FLR) is widely applied in unmanned systems, such as obstacle avoidance and tracking applications. This paper proposes a semi-supervised support vector regression (SVR) method to solve the problem of small sample learning of the target angle with FLR. This method utilizes function approximation to solve the problem of estimating the target angle. First, SVR is used to construct the function mapping relationship between the echo and the target angle in beamspace. Next, by adding manifold constraints to the loss function, supervised learning is extended to semi-supervised learning, aiming to improve the small sample adaptation ability. This framework supports updating the angle estimating function with continuously increasing unlabeled samples during the FLR scanning process. The numerical simulation results show that the new technology has better performance than model-based methods and fully supervised methods, especially under limited conditions such as signal-to-noise ratio and number of training samples.

Online multi-scenario impedance spectra generation for batteries based on small-sample learning

Online multi-scenario impedance spectra generation for batteries based on small-sample learning

Unlocking freeform structured surface denoising with small sample learning: Enhancing performance via physics-informed loss and detail-driven data augmentation

Denoising plays a vital role in freeform structured surface metrology. Traditional techniques, such as Gaussian and partial differential equation-based diffusion filters, often involve a time-consuming calibration process, particularly for complex surfaces. The main challenge lies in automating the denoising operation while accurately preserving features for varied surface textures. To address this challenge, an automatic approach PI-DnCNN based on small sample learning is presented in this paper. Denoising convolutional neural network (DnCNN) is employed as the basic architecture of this approach, due to its effectiveness in tackling mix-level Gaussian noise and adapting to small training datasets. Acknowledging the constraints of limited datasets, a novel physics-informed denoising loss function marrying filtering techniques is proposed to improve model performance. Additionally, a hybrid data augmentation strategy is developed to enhance the recognition of complex components. The paper also reports a set of experiments to demonstrate the presented approach in terms of performance over conventional techniques, enhancements with limited sample sizes, and applicability in general image denoising. The experiment results suggest that the presented approach consistently achieves higher average scores compared to traditional filters and emerges superior compared to the conventional DnCNN loss across different dataset sizes. In addition, the proposed loss also shows effectiveness in general image denoising, which suggests the robustness and universality of the approach.

Virtual Power Plant Transaction Management Using Small Sample Learning Through Cloud Computing Based Noise Reduction by Machine Learning

Virtual Power Plant Transaction Management Using Small Sample Learning Through Cloud Computing Based Noise Reduction by Machine Learning

Research on abnormal diagnosis model of electric power measurement based on small sample learning

Research on abnormal diagnosis model of electric power measurement based on small sample learning

Simultaneous Determination of Four Catechins in Black Tea via NIR Spectroscopy and Feature Wavelength Selection: A Novel Approach.

As a non-destructive, fast, and cost-effective technique, near-infrared (NIR) spectroscopy has been widely used to determine the content of bioactive components in tea. However, due to the similar chemical structures of various catechins in black tea, the NIR spectra of black tea severely overlap in certain bands, causing nonlinear relationships and reducing analytical accuracy. In addition, the number of NIR spectral wavelengths is much larger than that of the modeled samples, and the small-sample learning problem is rather typical. These issues make the use of NIRS to simultaneously determine black tea catechins challenging. To address the above problems, this study innovatively proposed a wavelength selection algorithm based on feature interval combination sensitivity segmentation (FIC-SS). This algorithm extracts wavelengths at both coarse-grained and fine-grained levels, achieving higher accuracy and stability in feature wavelength extraction. On this basis, the study built four simultaneous prediction models for catechins based on extreme learning machines (ELMs), utilizing their powerful nonlinear learning ability and simple model structure to achieve simultaneous and accurate prediction of catechins. The experimental results showed that for the full spectrum, the ELM model has better prediction performance than the partial least squares model for epicatechin (EC), epicatechin gallate (ECG), epigallocatechin (EGC), and epigallocatechin gallate (EGCG). For the feature wavelengths, our proposed FIC-SS-ELM model enjoys higher prediction performance than ELM models based on other wavelength selection algorithms; it can simultaneously and accurately predict the content of EC (Rp2 = 0.91, RMSEP = 0.019), ECG (Rp2 = 0.96, RMSEP = 0.11), EGC (Rp2 = 0.97, RMSEP = 0.15), and EGCG (Rp2 = 0.97, RMSEP = 0.35) in black tea. The results of this study provide a new method for the quantitative determination of the bioactive components of black tea.

A kind of multi-dot ensemble regression AI detector for lubricating oil additive content based on lambert-beer law

In this work, we propose a Multi-dot Ensemble Regression AI detector (MER) based on the Lambert-Beer law. We pre-trained a model using the infrared spectral data of target additives collected in advance to detect the target additives in unknown oil samples. The algorithm's feasibility was validated by assessing the content of additives in a series of simulated commercial oil samples that were not part of the training set. We established models for three common lubricating oil additives (anti-friction, anti-wear, and antioxidant agents), demonstrating their effectiveness in oil sample detection. Additionally, by comparing with other algorithms, we established the superiority of MER in small-sample learning scenarios.

Contrasting YOLOv7, SSD, and DETR on Insulator Identification under Small-sample Learning

Background:: Daily inspections of insulators are necessary because they are indispensable components for power transmission lines. Using deep learning to monitor insulators is a newly developed method. However, most deep learning-based detection methods rely on a large training sample set, which consumes computing resources and increases the workload of sample labeling. The selection of learning models to monitor insulators becomes problematic. Objective:: Through comparative analysis, a model suitable for small-sample insulator learning is found to provide a reference for the research and application of insulator detection. Methods:: This paper compares some of the latest deep learning models, YOLOv7, SSD, and DETR, for insulator detection based on small-sample learning. The small sample here means that the number of samples and their proportion to the total sample are relatively small. Two public insulator image sets, InsulatorDataSet with 600 insulator images and Transmission-line-pictures (TLP) with 1230 insulator images in the natural background are selected to test the performance of these models. Results:: Tests on two public insulator image sets, InsulatorDataSet and TLP, show that the recognition rates of YOLOv7, DETR, and SSD are arranged from high to low. The DETR and the YOLOv7 have stable performance, while the SSD lacks stable performance in terms of the learning time and recognition rate. Conclusion:: The in-domain and cross-domain scenario tests show that YOLOv7 is more suitable for insulator detection under small-sample conditions among the three models.

SLKIR: A framework for extracting key information from air traffic control instructions Using small sample learning

In air traffic control (ATC), Key Information Recognition (KIR) of ATC instructions plays a pivotal role in automation. The field's specialized nature has led to a scarcity of related research and a gap with the industry's cutting-edge developments. Addressing this, an innovative end-to-end deep learning framework, Small Sample Learning for Key Information Recognition (SLKIR), is introduced for enhancing KIR in ATC instructions. SLKIR incorporates a novel Multi-Head Local Lexical Association Attention (MHLA) mechanism, specifically designed to enhance accuracy in identifying boundary words of key information by capturing their latent representations. Furthermore, the framework includes a task focused on prompt, aiming to bolster the semantic comprehension of ATC instructions within the core network. To overcome the challenges posed by category imbalance in boundary word and prompt discrimination tasks, tailored loss function optimization strategies are implemented, effectively expediting the learning process and boosting recognition accuracy. The framework's efficacy and adaptability are demonstrated through experiments on two distinct ATC instruction datasets. Notably, SLKIR outperforms the leading baseline model, W2NER, achieving a 3.65% increase in F1 score on the commercial flight dataset and a 12.8% increase on the training flight dataset. This study is the first of its kind to apply small-sample learning in KIR for ATC and the source code of SLKIR will be available at: https://github.com/PANPANKK/ATC_KIR.

Multi-source domain transfer learning with small sample learning for thermal runaway diagnosis of lithium-ion battery

Data-driven thermal runaway diagnosis based on small amounts of thermal runaway data often struggles to produce satisfactory accuracy. However, in actual application scenarios, obtaining real thermal runaway data has a high cost. To this end, we propose a diagnostic method for multi-source domain transfer learning with few-shot learning (MDTL-FSL), which combines the ideas of small sample learning and adversarial learning. Use multiple different but related thermal runaway case data to obtain common diagnostic knowledge to achieve thermal runaway diagnosis. First of all, in order to avoid negative migration of the algorithm model caused by large differences in data distribution between multi-source domains, a data distribution difference measurement method under segmented pressure differences is proposed. This measurement method is simple and effective from multi-source domains. Select the source domain with a small difference in data distribution from the target domain. Secondly, the adversarial learning idea is integrated into multi-source domain transfer learning to learn temporal invariant features. Then, due to the small number of samples in each source domain in the multi-source domain under the thermal runaway scenario, a source domain reorganization mechanism was designed to find the decision boundary based on meta-learning ideas to achieve small-sample learning in the multi-source domain. Finally, we used cells produced by different battery manufacturers to trigger thermal runaway. By comparison and verification with other data-driven algorithms, the results show that the MDTL-FSL model proposed in this article has higher accuracy. At the same time, we use batteries of different types and capacities to trigger thermal runaway under different working conditions. The MDTL-FSL algorithm can issue early warnings before thermal runaway occurs, thereby effectively ensuring the safe operation of the energy storage system.

MAML–SGD: a reliable airline rescheduling algorithm for small-sample learning based on MAML and SGD

MAML–SGD: a reliable airline rescheduling algorithm for small-sample learning based on MAML and SGD