Adaptive Detection Research Articles

Microseismic activity characteristics and range evaluation of hydraulic fracturing in coal seam

Accurately evaluating hydraulic fracturing (HF) activity characteristics and impact range is critical to optimizing the HF scheme design. This study proposes an adaptive threshold peak detection method and a wavelet transform-support vector machine (WT-SVM) polarity analysis method based on microseismic (MS) monitoring data from a coal mine in Guizhou, China. The results show that the adaptive threshold peak detection method effectively improves the accuracy and stability of the P-wave initial arrival pickup for low signal-to-noise ratio (SNR) signals. The main frequency band of the MS signal of HF is focused on 150–300 Hz. The improved particle swarm optimization algorithm was used by introducing a random-weight strategy to determine the high-density impact areas of the HF within approximately 20 m on both sides of the fracturing borehole. Density cloud map analysis revealed a small area of the blank zone at the junction of the fracturing section, which provided a basis for fracturing parameter design and scheme optimization. The directional characteristics of the MS waveform were quantitatively described by polarity analysis, where the dip angle distribution ranged from 39° to 49°, the strike angle ranged from 61° to 74°, the slip angle was concentrated near 44°, and the stretch angle ranged from 61° to 74°. The WT-SVM proposed in this study overcame the effects of low SNR signals and the limitations of a small-area station network; 62% of the shear ruptures and 38% of the tensile ruptures of the HF were obtained. Shear cracks were the primary expression of the HF rupture type.

Inter-Domain Invariant Cross-Domain Object Detection Using Style and Content Disentanglement for In-Vehicle Images

The accurate detection of relevant vehicles, pedestrians, and other targets on the road plays a crucial role in ensuring the safety of autonomous driving. In recent years, object detectors based on Transformers or CNNs have achieved excellent performance in the fully supervised paradigm. However, when the trained model is directly applied to unfamiliar scenes where the training data and testing data have different distributions statistically, the model’s performance may decrease dramatically. To address this issue, unsupervised domain adaptive object detection methods have been proposed. However, these methods often exhibit decreasing performance when the gap between the source and target domains increases. Previous works mainly focused on utilizing the style gap to reduce the domain gap while ignoring the content gap. To tackle this challenge, we introduce a novel method called IDI-SCD that effectively addresses both the style and content gaps simultaneously. Firstly, the domain gap is reduced by disentangling it into the style gap and content gap, generating corresponding intermediate domains in the meanwhile. Secondly, during training, we focus on one single domain gap at a time to achieve inter-domain invariance. That is, the content gap is tackled while maintaining the style gap, and vice versa. In addition, the style-invariant loss is used to narrow down the style gap, and the mean teacher self-training framework is used to narrow down the content gap. Finally, we introduce a multiscale fusion strategy to enhance the quality of pseudo-labels, which mainly focus on enhancing the detection performance for extreme-scale objects (very large or very small objects). We conduct extensive experiments on four mainstream datasets of in-vehicle images. The experimental results demonstrate the effectiveness of our method and its superiority over most of the existing methods.

Adaptable and Interpretable Framework for Anomaly Detection in SCADA-based industrial systems

In this paper, we introduce an Adaptable and Interpretable Framework for Anomaly Detection (AID) designed for industrial systems utilizing IoT data streams on top of well-established SCADA systems. AID leverages dynamic conditional probability distribution modeling to capture the normal operation of dynamic systems and isolate the root causes of anomalies at the level of individual inputs. The self-supervised framework dynamically updates parameters of underlying model, allowing it to adapt to non-stationarity. AID interprets anomalies as significant deviations from conditional probability, encompassing interactions as well as both spatial and temporal irregularities by exposing them as features. Crucially, AID provides dynamic operating limits to integrate with existing alarm handling mechanisms in SCADA-based IoT systems. Two industrial-scale case studies demonstrate AID’s capabilities. The first study showcases AID’s effectiveness on energy storage system, adapting to changes, setting context-aware limits for SCADA, and ability to leverage a physics-based model. The second study monitors battery module temperatures, where AID identifies hardware faults, emphasizing its relevance to energy storage safety. A benchmark evaluation on real data shows that AID delivers comparable performance to other self-learning adaptable anomaly detection methods, with the significant advancement in diagnostic capabilities for improved system reliability and performance.

Hierarchical alignment network for domain adaptive object detection in aerial images

Domain Adaptive Object Detection (DAOD) alleviates the reliance on labeled data by transferring knowledge learned from labeled source domain to unlabeled target domain. Recent DAOD methods is modeled mainly based on ground-level images. Compared to ground-level images, aerial images suffer from scale variation and viewpoint diversity. This means that domain adaptive object detection in aerial images is a more challenging task. In this work, we address the domain shift in aerial images from two levels: 1) image-level shifts, such as weather, lighting, viewpoint, etc., 2) instance-level shifts, such as object appearance, scale, etc. Specifically, multiple domain-confusion classifiers are designed to learn image-level common knowledge of the source and target domains. Different levels of domain classifiers are further assigned adaptive weights to coordinate the transferability and discriminability of the adaptive detectors. Meanwhile, instance-level alignment is realized by forcing the intrinsic relationship between classes in both domains to be consistent. In addition, we perform instance-level alignment in different semantic-level feature layers to improve the scale awareness of the adaptation model. Extensive experimental results on VisDrone, UAVDT, DIOR and DOTA datasets demonstrate that our method achieves optimal detection performance in four domain adaption scenarios compared to other state-of-the-art methods, e.g., in Daytime → Night (VisDrone), the mAP50 is 23.5 %; in VisDrone → UAVDT, DIOR → UAVDT and DOTA → VisDrone, the AP50 of the car is 63.1 %, 46.6 % and 44.8 % respectively. Code will be available online (https://github.com/MaYou1997/HANet).

Adaptive Bernstein change detector for high-dimensional data streams

AbstractChange detection is of fundamental importance when analyzing data streams. Detecting changes both quickly and accurately enables monitoring and prediction systems to react, e.g., by issuing an alarm or by updating a learning algorithm. However, detecting changes is challenging when observations are high-dimensional. In high-dimensional data, change detectors should not only be able to identify when changes happen, but also in which subspace they occur. Ideally, one should also quantify how severe they are. Our approach, ABCD, has these properties. ABCD learns an encoder-decoder model and monitors its accuracy over a window of adaptive size. ABCD derives a change score based on Bernstein’s inequality to detect deviations in terms of accuracy, which indicate changes. Our experiments demonstrate that ABCD outperforms its best competitor by up to 20% in F1-score on average. It can also accurately estimate changes’ subspace, together with a severity measure that correlates with the ground truth.

Joint margin adaption and multiscale feature fusion for COVID-19 CT images segmentation

Segmenting COVID-19 from CT images remains a challenging task due to the structural characteristics, including complex and diverse infected regions, high inter-class similarity, and intra-class variability, which make it difficult to capture detailed semantic information such as edges and textures effectively. The present study introduces a novel methodology for segmenting COVID-19 CT images, consisting of three distinct stages. The proposed approach is primarily based on the margin adaptive deep supervised feature fusion network (MADFNet) architecture, which adopts an encoder-decoder framework. In this strategy, the segmentation outcomes obtained in the previous phase are utilized as supervised terms to facilitate the semantic segmentation process in the subsequent stage. In MADFNet, firstly, the channel edge detection and spatial edge detection modules capture the rich semantic information of edge context from different dimensions to obtain the fine texture information and semantic dependencies, respectively. Then, the encoder feature fusion module is designed to aggregate multiscale feature representations at the same level, facilitating information integration across different channels and enabling adaptive detection of structural features such as edges and textures. Finally, the decoder feature fusion module integrates multiscale feature dependencies from different levels to alleviate the semantic discrepancies of features at different levels and accurately locate lesion regions. The single-class segmentation Dice scores of MADFNet on the three experimental datasets are 0.831, 0.825, and 0.821, respectively, and the results of single-class as well as multi-class lesion segmentation on COVID-19 CT images validated the feasibility of our work.

Enhancing Credit Card Fraud Detection: An Ensemble Machine Learning Approach

In the era of digital advancements, the escalation of credit card fraud necessitates the development of robust and efficient fraud detection systems. This paper delves into the application of machine learning models, specifically focusing on ensemble methods, to enhance credit card fraud detection. Through an extensive review of existing literature, we identified limitations in current fraud detection technologies, including issues like data imbalance, concept drift, false positives/negatives, limited generalisability, and challenges in real-time processing. To address some of these shortcomings, we propose a novel ensemble model that integrates a Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), Bagging, and Boosting classifiers. This ensemble model tackles the dataset imbalance problem associated with most credit card datasets by implementing under-sampling and the Synthetic Over-sampling Technique (SMOTE) on some machine learning algorithms. The evaluation of the model utilises a dataset comprising transaction records from European credit card holders, providing a realistic scenario for assessment. The methodology of the proposed model encompasses data pre-processing, feature engineering, model selection, and evaluation, with Google Colab computational capabilities facilitating efficient model training and testing. Comparative analysis between the proposed ensemble model, traditional machine learning methods, and individual classifiers reveals the superior performance of the ensemble in mitigating challenges associated with credit card fraud detection. Across accuracy, precision, recall, and F1-score metrics, the ensemble outperforms existing models. This paper underscores the efficacy of ensemble methods as a valuable tool in the battle against fraudulent transactions. The findings presented lay the groundwork for future advancements in the development of more resilient and adaptive fraud detection systems, which will become crucial as credit card fraud techniques continue to evolve.

Adaptive and precise peak detection algorithm for fibre Bragg grating using generative adversarial network

Adaptive and precise peak detection algorithm for fibre Bragg grating using generative adversarial network

MLFA: Towards Realistic Test Time Adaptive Object Detection by Multi-level Feature Alignment.

Object detection methods have achieved remarkable performances when the training and testing data satisfy the assumption of i.i.d. However, the training and testing data may be collected from different domains, and the gap between the domains can significantly degrade the detectors. Test Time Adaptive Object Detection (TTA-OD) is a novel online approach that aims to adapt detectors quickly and make predictions during the testing procedure. TTA-OD is more realistic than the existing unsupervised domain adaptation and source-free unsupervised domain adaptation approaches. For example, self-driving cars need to improve their perception of new environments in the TTA-OD paradigm during driving. To address this, we propose a multi-level feature alignment (MLFA) method for TTA-OD, which is able to adapt the model online based on the steaming target domain data. For a more straightforward adaptation, we select informative foreground and background features from image feature maps and capture their distributions using probabilistic models. Our approach includes: i) global-level feature alignment to align all informative feature distributions, thereby encouraging detectors to extract domain-invariant features, and ii) cluster-level feature alignment to match feature distributions for each category cluster across different domains. Through the multi-level alignment, we can prompt detectors to extract domain-invariant features, as well as align the category-specific components of image features from distinct domains. We conduct extensive experiments to verify the effectiveness of our proposed method. Our code is accessible at https://github.com/yaboliudotug/MLFA.

Vehicle-Mounted Adaptive Traffic Sign Detector for Small-Sized Signs in Multiple Working Conditions

Vehicle-Mounted Adaptive Traffic Sign Detector for Small-Sized Signs in Multiple Working Conditions

YOLO-Adaptor: A Fast Adaptive One-Stage Detector for Non-Aligned Visible-Infrared Object Detection

YOLO-Adaptor: A Fast Adaptive One-Stage Detector for Non-Aligned Visible-Infrared Object Detection

Debiasing Frequency Adaptive Graph Neural Network-based Fraud Detector

Debiasing Frequency Adaptive Graph Neural Network-based Fraud Detector

Decision Fusion-Based Nonintrusive Load Identification Involving Adaptive Threshold Event Detection

Decision Fusion-Based Nonintrusive Load Identification Involving Adaptive Threshold Event Detection

Research on Domain Adaptive Object Detection Algorithms Based on Deep Learning

Research on Domain Adaptive Object Detection Algorithms Based on Deep Learning

Unsupervised Adaptive Fleet Battery Pack Fault Detection With Concept Drift Under Evolving Environment

Unsupervised Adaptive Fleet Battery Pack Fault Detection With Concept Drift Under Evolving Environment

Feature Selection based Performance Analysis of Machine Learning Algorithms in Network Intrusion Detection

Information and data security is one of the most challenging tasks for the massive-scale digital revolution all over the world. The very first step to secure our data is to identify invasive conduct and intrusive behavior. However, due to the high scalability of most modern systems and the complex nature of the attacks, the traditional detection system is less reliable. To overcome this challenge, it is necessary to build intelligent and adaptive intrusion detection technologies. That is why this research developed an intrusion detection system using commonly used ML algorithms and analyzed performance from different perspectives. In our pipeline, this exploration applied both supervised and unsupervised learning algorithms. The training and test data were split in multiple ways to evaluate the performance of the models. From the experimental results, it was found that the Light Gradient Boosting Machine (LightGBM) performs better in our context in terms of both precision and recall.

Adaptive Coherent Detection for Maritime Radar Range-Spread Targets in Correlated Heavy-Tailed Sea Clutter With Lognormal Texture

Adaptive Coherent Detection for Maritime Radar Range-Spread Targets in Correlated Heavy-Tailed Sea Clutter With Lognormal Texture

A Domain Adaptive Few-Shot SAR Ship Detection Algorithm Driven by the Latent Similarity between Optical and SAR Images

A Domain Adaptive Few-Shot SAR Ship Detection Algorithm Driven by the Latent Similarity between Optical and SAR Images

YoloOW: A Spatial Scale Adaptive Real-Time Object Detection Neural Network for Open Water Search and Rescue From UAV Aerial Imagery

YoloOW: A Spatial Scale Adaptive Real-Time Object Detection Neural Network for Open Water Search and Rescue From UAV Aerial Imagery

Real-Time Adaptive Anomaly Detection in Industrial IoT Environments

Real-Time Adaptive Anomaly Detection in Industrial IoT Environments