Sample Generation Method Research Articles

Classification and Identification of Frequency-Hopping Signals Based on Jacobi Salient Map for Adversarial Sample Attack Approach.

Frequency-hopping (FH) communication adversarial research is a key area in modern electronic countermeasures. To address the challenge posed by interfering parties that use deep neural networks (DNNs) to classify and identify multiple intercepted FH signals-enabling targeted interference and degrading communication performance-this paper presents a batch feature point targetless adversarial sample generation method based on the Jacobi saliency map (BPNT-JSMA). This method builds on the traditional JSMA to generate feature saliency maps, selects the top 8% of salient feature points in batches for perturbation, and increases the perturbation limit to restrict the extreme values of single-point perturbations. Experimental results in a white-box environment show that, compared with the traditional JSMA method, BPNT-JSMA not only maintains a high attack success rate but also enhances attack efficiency and improves the stealthiness of the adversarial samples.

Dynamic reliability assessment for high CFRDs incorporating ground motion and material parameter variability via GPDEM

The randomness of earthquake and material parameters significantly impacts the seismic response and dynamic reliability of geotechnical engineering structures. In this study, a dynamic reliability analysis method for high concrete face rockfill dams (CFRDs) under the influence of random factors is proposed by combining random sample generation methods with the generalized probability density evolution method (GPDEM). Firstly, the spectral representation-random function method is employed to achieve dimensionality reduction expression for intensity-frequency non-stationary ground motion. Sensitivity analysis is then used to identify the random variables of the rockfill constitutive model, and the improved generalized F-discrepancy (GF-discrepancy) method is utilized to generate random material parameters. Then, the random samples of earthquake and material parameter are introduced into the finite element calculation of CFRD, conducting a series of dynamic finite element calculations. Finally, GPDEM is used to derive probability information and dynamic reliability from the deterministic time history responses. The results indicate that random factors contribute to the variability of seismic response and dynamic reliability. Stochastic ground motion significantly affects extreme values, while variability in material parameters has a more pronounced influence on the overall process of seismic response. These findings provide valuable references for the seismic design of CFRDs.

A new discrepancy for sample generation in stochastic response analyses of aerospace problems with uncertain parameters

Good distribution of samples and weights can improve the computational accuracy and efficiency in the stochastic response analyses of aerospace problems with uncertain parameters. This work proposes a new generalized L2 discrepancy based on a general point (GL2D-GP) for generating samples and their corresponding weights. The proposed GL2D-GP is an extension of the existing discrepancy by introducing the non-same weights and a smaller box to measure probability errors. Minimizing the GL2D-GP can yield a weight optimization formula that generates a set of optimal non-identical weights for a given sample set. Through minimizing the GL2D-GP assigned to the set of optimal non-same weights, a new sample and weight generation method is developed. In the proposed method, the samples can be easily generated in terms of the generalized Halton formula with a series of optimal permutation vectors which are found by the intelligent evolutionary algorithm. Once the sample set is obtained, the optimal weights can be generated in terms of the weight optimization formula. Five numerical examples are presented to verify the high accuracy, efficiency, and strong robustness of the proposed sample generation method based on GL2D-GP.

Research on carotid artery plaque anomaly detection algorithm based on ultrasound images

Carotid artery plaque is a key factor in stroke and other cardiovascular diseases. Accurate detection and localization of carotid artery plaque are essential for early prevention and treatment of diseases. However, current carotid artery ultrasound image anomaly detection algorithms face several challenges, such as scarcity of anomaly data in carotid arteries and traditional convolutional neural networks (CNNs) overlooking long-distance dependencies in image processing. To address these issues, we propose an anomaly detection algorithm for carotid artery plaques based on ultrasound images. The algorithm innovatively introduces an anomaly sample pair generation method to increase dataset diversity. Moreover, it employs an improved adaptive recursive gating pyramid pooling module to extract image features. This module significantly enhances the model’s capacity for high-order spatial interactions and adaptive feature fusion, thereby greatly improving the neural network’s feature extraction ability. The algorithm uses a Sigmoid layer to map each pixel’s feature vector to a probability distribution between 0 and 1, and anomalies are detected through probability threshold binarization. Experimental results show that our algorithm’s AUROC index reached 90.7% on a carotid artery dataset, improving by 2.1% compared to the FPI method. This research is expected to provide robust support for the early prevention and treatment of cardiovascular diseases.

An automated sample generation method by integrating phenology domain optical-SAR features in rice cropping pattern mapping

Accurate spatio-temporal information on rice cropping patterns is vital for predicting grain production, managing water resource and assessing greenhouse gas emissions. However, current automated mapping of rice cropping patterns at regional scale is heavily constrained by insufficient training samples and frequent cloudy weathers in major rice-producing areas. To tackle this challenge, we proposed a Phenology domain Optical-SAR feature inTegration method to Automatically generate single (SC-Rice) and double cropping Rice (DC-Rice) sample (POSTAR) for efficient and refined rice mapping. POSTAR includes three major steps: (1) generating a potential rice map using a phenology- and object-based classification method with optical data (Sentinel-2 MSI) to select candidate rice samples; (2) employing K-means to identify SC- and DC-Rice candidate samples according to unique SAR-based (Sentinel-1 SAR) phenological features; (3) implementing a two-step refinement strategy to filter high-confidence SC- and DC-Rice samples, maintaining a balance between intraclass phenological variance and sample purity. Test areas selected for validation include the Dongting Lake plain and Poyang Lake plain in South China, as well as Fujin county located in the Sanjiang plain of North China. POSTAR proved effective in producing reliable SC- and DC-Rice samples, achieving a high spectral correlation similarity (>0.85) and low dynamic time wrapping distance (<8.5) with field samples. Applying POSTAR-derived samples to random forest classifier yielded an overall accuracy of 89.6%, with F1 score of 0.899 for SC-Rice and 0.938 for DC-Rice in the Dongting Lake plain. Owing to the incorporation of knowledge-based optical and SAR phenological features, POSTAR exhibited strong spatial transferability, achieving an overall accuracy of 96.0% in the Poyang Lake plain and 97.8% in the Fujin county. These results demonstrated the effectiveness of the POSTAR method in accurately mapping rice cropping patterns without extensive field visits, providing valuable insights for crop monitoring in large, diverse, and cloudy regions through the integration of optical and SAR data.

A Novel Sample Generation Method for Deep Learning Lithological Mapping with Airborne TASI Hyperspectral Data in Northern Liuyuan, Gansu, China

High-resolution and thermal infrared hyperspectral data acquired from the Thermal Infrared Airborne Spectrographic Imager (TASI) have been recognized as efficient tools in geology, demonstrating significant potential for rock discernment. Deep learning (DL), as an advanced technology, has driven substantial advancements in lithological mapping by automatically extracting high-level semantic features from images to enhance recognition accuracy. However, gathering sufficient high-quality lithological samples for model training is challenging in many scenarios, posing limitations for data-driven DL approaches. Moreover, existing sample collection approaches are plagued by limited verifiability, subjective bias, and variation in the spectra of the same class at different locations. To tackle these challenges, a novel sample generation method called multi-lithology spectra sample selection (MLS3) is first employed. This method involves multiple steps: multiple spectra extraction, spectra combination and optimization, lithological type identification, and sample selection. In this study, the TASI hyperspectral data collected from the Liuyuan area in Gansu Province, China, were used as experimental data. Samples generated based on MLS3 were fed into five typical DL models, including two-dimensional convolutional neural network (2D-CNN), hybrid spectral CNN (HybridSN), multiscale residual network (MSRN), spectral-spatial residual network (SSRN), and spectral partitioning residual network (SPRN) for lithological mapping. Among these models, the accuracy of the SPRN reaches 84.03%, outperforming the other algorithms. Furthermore, MLS3 demonstrates superior performance, achieving an overall accuracy of 2.25–6.96% higher than other sample collection methods when SPRN is used as the DL framework. In general, MLS3 enables both the quantity and quality of samples, providing inspiration for the application of DL to hyperspectral lithological mapping.

An optimized dynamic model improved deep discriminative transfer learning network for fault detection in rotation vector reducers

In recent years, recognized fault types using artificial intelligence (AI) models has gradually become one of the mainstream directions in the field of mechanical fault diagnosis. However, it is very difficult to obtain relatively complete fault samples, which limits the application of AI models for complex mechanical systems, such as rotation vector (RV) reducers. To address this issue, physical model-based fault sample generation methods attracted many attentions but still an open problem: the difference in fault samples between numerical simulation and measurement of a physical system needs to be well decreased. Therefore, this article proposes an improved deep discriminative transfer learning network (IDDTLN) for RV reducer fault diagnosis. The presented network is driven by an optimized dynamic model. Firstly, the measurement normal signal and whale optimization algorithm (WOA) are utilized to update the dynamic model parameters of the RV reducer. Secondly, the updated numerical model is employed to calculate simulation fault samples. Finally, simulation samples and unlabeled measurement fault samples are used to train IDDTLN. The trained IDDTLN can accurately identify the unknow measurement fault samples. The data obtained from RV reducer test rigs are utilized to explore the feasibility of the proposed method, and the classification accuracies reach 99.8 %.

A noise generative network to reduce the gap between simulation and measurement signals in mechanical fault diagnosis

Data-driven artificial intelligence models play an important role in mechanical fault diagnosis. Generally, it is difficult to collect relative complete fault samples, which limits the application of artificial intelligence models for complex mechanical systems. To address this issue, numerical model-based or called physical model-based fault sample generation methods attracted many attentions but still an open problem: the difference in fault samples between numerical simulation and measurement of a physical system needs to be well decreased. Therefore, a noise generative network (NGN) for mechanical fault classification is developed. First, the NGN is trained using the simulation and measurement normal samples. The simulation fault samples are further fed into the trained NGN to obtain more solid simulation fault samples with minor differences from measurements of mechanical systems with faults. Second, deep convolutional neural network is trained by solid simulation fault samples, and the test samples of unknown fault types will be finally recognized. Finally, validation experiments using rotation vector reducers, bearings, and rotors classified test samples with average accuracies of 99.5%, 95.7%, and 99.9%, respectively. They indicate that the proposed NGN model effectively reduces the difference between the simulation and measurement samples to lead more precision results for fault classification in mechanical systems.

Automatic PCB Sample Generation and Defect Detection Based on ControlNet and Swin Transformer.

In order to improve the efficiency and accuracy of multitarget detection of soldering defects on surface-mounted components in Printed Circuit Board (PCB) fabrication, we propose a sample generation method using Stable Diffusion Model and ControlNet, as well as a defect detection method based on the Swin Transformer. The method consists of two stages: First, high-definition original images collected in industrial production and the corresponding prompts are input to Stable Diffusion Model and ControlNet for automatic generation of nonindependent samples. Subsequently, we integrate Swin Transformer as the backbone into the Cascade Mask R-CNN to improve the quality of defect features extracted from the samples for accurate detection box localization and segmentation. Instead of segmenting individual components on the PCB, the method inspects all components in the field of view simultaneously over a larger area. The experimental results demonstrate the effectiveness of our method in scaling up nonindependent sample datasets, thereby enabling the generation of high-quality datasets. The method accurately recognizes targets and detects defect types when performing multitarget inspection on printed circuit boards. The analysis against other models shows that our improved defect detection and segmentation method improves the Average Recall (AR) by 2.8% and the mean Average Precision (mAP) by 1.9%.

Audio Recognition of the Percussion Sounds Generated by a 3D Auto-Drum Machine System via Machine Learning

Audio Recognition of the Percussion Sounds Generated by a 3D Auto-Drum Machine System via Machine Learning

Sample generation method for marine diesel engines based on FEM simulation and DCGAN

Sample generation method for marine diesel engines based on FEM simulation and DCGAN

A High-Quality Sample Generation Method for Improving Steel Surface Defect Inspection.

Defect inspection is a critical task in ensuring the surface quality of steel plates. Deep neural networks have the potential to achieve excellent inspection accuracy if defect samples are sufficient. Nevertheless, it is very different to collect enough samples using cameras alone. To a certain extent, generative models can alleviate this problem but poor sample quality can greatly affect the final inspection performance. A sample generation method, which employs a generative adversarial network (GAN), is proposed to generate high-quality defect samples for training accurate inspection models. To improve generation quality, we propose a production-and-elimination, two-stage sample generation process by simulating the formation of defects on the surface of steel plates. The production stage learns to generate defects on defect-free background samples, and the elimination stage learns to erase defects on defective samples. By minimizing the differences between the samples at both stages, the proposed model can make generated background samples close to real ones while guiding the generated defect samples to be more realistic. Experimental results show that the proposed method has the ability to generate high-quality samples that can help train powerful inspection models and thereby improve inspection performance.

Attacks on Continual Semantic Segmentation by Perturbing Incremental Samples

As an essential computer vision task, Continual Semantic Segmentation (CSS) has received a lot of attention. However, security issues regarding this task have not been fully studied. To bridge this gap, we study the problem of attacks in CSS in this paper. We first propose a new task, namely, attacks on incremental samples in CSS, and reveal that the attacks on incremental samples corrupt the performance of CSS in both old and new classes. Moreover, we present an adversarial sample generation method based on class shift, namely Class Shift Attack (CS-Attack), which is an offline and easy-to-implement approach for CSS. CS-Attack is able to significantly degrade the performance of models on both old and new classes without knowledge of the incremental learning approach, which undermines the original purpose of the incremental learning, i.e., learning new classes while retaining old knowledge. Experiments show that on the popular datasets Pascal VOC, ADE20k, and Cityscapes, our approach easily degrades the performance of currently popular CSS methods, which reveals the importance of security in CSS.

Study on rapid prediction of low concentration o-nitrotoluene in mononitrotoluene mixture by near infrared spectroscopy combined with novel calibration strategies

The determination of the o-nitrotoluene (o-MNT) content in separation process of mononitrotoluene (MNT) is of interest, since it affects the purity of m-nitrotoluene (m-MNT) and p-nitrotoluene (p-MNT). However, the analytical techniques traditionally used for its content determination are tedious and time consuming. Therefore, we explored the analysis of spectral data based on near-infrared spectroscopy (NIRS) and chemometrics, and extracted the spectral features of the o-MNT based on the interval selection algorithm. The calibration models for the o-MNT content based on samples with different concentration ranges were developed by PLS. Among them, the calibration model based on samples with 0.01–0.5 % concentration range has the best prediction performance. The calibration model was established with the determination coefficient of prediction (R2) of 0.959, root mean squared error of prediction (RMSEP) of 0.011 and ratio of standard deviation of the calibration set to standard error of prediction (RPD) of 4.899 for o-MNT. It is sufficient to meet the fast detection needs of the o-MNT content for process control in chemical industry. In addition, in order to reduce the demand of the model on samples and corresponding reference values, we explored the virtual sample generation method based on background difference compensation. The calibration model based on virtual samples was established with R2 of 0.74, RMSEP of 0.028 and RPD of 1.951. This study shows that the method based on NIRS and chemometrics has strong prediction performance for o-MNT in separation process of MNT, which is a guideline for controlling product purity of m-MNT and p-MNT. And the virtual sample generation method proposed in this study can significantly reduce the sample demand of calibration model.

Spatial domain transfer: Cross-regional paddy rice mapping with a few samples based on Sentinel-1 and Sentinel-2 data on GEE

The global population is on the rise, leading to an increased demand for food resources. Accurate mapping and monitoring of paddy rice fields have become crucial for effective food management and yield estimation. Several large-scale paddy rice mapping products with medium-to-high resolution have been made by using threshold-based phenological methods. The main shortcoming is that paddy rice varies in different regions due to differences in climate and planting habits, which results in the limitations of fixed phenological indicator thresholds for large-scale paddy rice mapping. Therefore, paddy rice samples from different regions are needed to account for regional differences between paddy rice and improve paddy rice mapping accuracy. To address two key shortcomings in existing sample generation methods (the non-robustness of DTW distance and the limitation of fixed phenological indicator thresholds for generating samples) and the three difficulties of cross-regional sample generation (different planting times, growth cycles, and planting intensities), this study proposed a novel approach for cross-regional paddy rice sample generation based on spatial domain transfer, achieving cross-regional paddy rice mapping. The accuracy results (OA of 95.16–97.54%) in three study regions with different climatic and topographic conditions in China and improved accuracy (OA improvement of 5.89–19.06%) compared with the method of NESEA-Rice10, proved the effectiveness of the proposed method and the potential of the proposed method in large-scale paddy rice mapping. The proposed method can obtain high-accuracy paddy rice mapping results in regions without paddy rice samples and be applied to the mapping of other crops and land cover types.

Improvements to adversarial training for text classification tasks

Although deep learning models show powerful performance, they are still easily deceived by adversarial samples. Some methods for generating adversarial samples have the drawback of high time loss, which is problematic for adversarial training, and the existing adversarial training methods are difficult to adapt to the dynamic nature of the model, so it is still challenging to study an efficient adversarial training method. In this paper, we propose an adversarial training method, the core of which is the improved adversarial sample generation method AGFAT for adversarial training and the improved dynamic adversarial training method AGFAT-DAT. AGFAT uses a word frequency-based approach to identify significant words, filter replacement candidates, and use an efficient semantic constraint module as a means to reduce the time of adversarial sample generation; AGFAT-DAT is a dynamic adversarial training approach that uses a cyclic attack on the model after adversarial training and generates adversarial samples for adversarial training again. It is demonstrated that the proposed method can significantly reduce the generation time of adversarial samples, and the adversarial-trained model can also effectively defend against other types of word-level adversarial attacks.

Research on micro parallax adversarial sample generation method based on texture sensitive region

Deep neural networks have been extensively applied in fields such as image classification, object detection, and face recognition. However, research has shown that adversarial samples with subtle perturbations can effectively deceive these networks. Existing methods for generating such adversarial images often lack stealth and robustness. In this study, we present an enhanced attack strategy based on traditional Generative Adversarial Networks (GANs). We integrate image texture into the unsupervised training scheme, guiding the model to focus perturbations in high-texture areas. We also introduce a dynamic equilibrium training strategy that employs Differential Evolution algorithms to adaptively adjust both network weight parameters and the training ratio between the generator and discriminator, achieving a self-balancing training process. Further, we propose an image local optimization algorithm to eliminate perturbations in non-sensitive areas through weighted filtering. The model is validated using benchmark datasets such as MNIST, ImageNet and SVHN. Through extensive experimental evaluations, our approach shows a 4.93% improvement in attack success rate against conventional models and a 10.23% increase against defense models compared to state-of-the-art attack methods.

A Reinforcement Learning-based ELF Adversarial Malicious Sample Generation Method

A Reinforcement Learning-based ELF Adversarial Malicious Sample Generation Method

XAI Driven Intelligent IoMT Secure Data Management Framework.

The Internet of Medical Things (IoMT) has transformed traditional healthcare systems by enabling real-time monitoring, remote diagnostics, and data-driven treatment. However, security and privacy remain significant concerns for IoMT adoption due to the sensitive nature of medical data. Therefore, we propose an integrated framework leveraging blockchain and explainable artificial intelligence (XAI) to enable secure, intelligent, and transparent management of IoMT data. First, the traceability and tamper-proof of blockchain are used to realize the secure transaction of IoMT data, transforming the secure transaction of IoMT data into a two-stage Stackelberg game. The dual-chain architecture is used to ensure the security and privacy protection of the transaction. The main-chain manages regular IoMT data transactions, while the side-chain deals with data trading activities aimed at resale. Simultaneously, the perceptual hash technology is used to realize data rights confirmation, which maximally protects the rights and interests of each participant in the transaction. Subsequently, medical time-series data is modeled using bidirectional simple recurrent units to detect anomalies and cyberthreats accurately while overcoming vanishing gradients. Lastly, an adversarial sample generation method based on local interpretable model-agnostic explanations is provided to evaluate, secure, and improve the anomaly detection model, as well as to make it more explainable and resilient to possible adversarial attacks. Simulation results are provided to illustrate the high performance of the integrated secure data management framework leveraging blockchain and XAI, compared with the benchmarks.

A transferred hybrid surrogate model integrating Gaussian membership virtual sample generation for small sample prediction: Applications in metal tube bending

The high-performance virtual sample generation (VSG) method has been extensively introduced to solve the problem of small sample sizes. Data distribution information is a key element of current VSG methods at the data-driven level. Herein, we propose an improved VSG method with a Gaussian distribution and explore the relationship between the Gaussian function and data expansion. To obtain more feasible virtual samples, information expanded based on the Gaussian membership function (GMIE) was established. For further improvement, a hybrid surrogate model based on transfer (THSM) is proposed, which differs from the general hybrid surrogate model (HSM) methods that only mix single models. Using a prevailing evaluation method, our proposed method, which combines a Gaussian membership function with a hybrid surrogate model, outperforms other competing approaches in 12 numerical cases owing to its feasibility and efficacy. Additionally, the proposed approach is applied to a metal tube rotary draw bending (RDB) prediction problem to illustrate its ability to support complex engineering designs.