Adaptive Detection Research Articles

Domain Adaptive Driver Distraction Detection Based on Partial Feature Alignment and Confusion-Minimized Classification

Domain Adaptive Driver Distraction Detection Based on Partial Feature Alignment and Confusion-Minimized Classification

Designing hollow-core PCF sensors for high-performance terahertz detection of NaCN and KCN

Cyanide is very poisonous and raises environmental problems because of its industrial application and potential as a terrorist weapon. Given CN's toxicity and possible hazard to people, an effective and adaptive detection approach is needed. This specification suggests using a PCF to build a terahertz Hexagonal Core and Curved rectangular air holes sensor to detect NaCN and KCN. The recently created PCF analysis, which was promptly delivered, reveals an RS concentration of 99.62 % for NaCN and a maximum concentration of KCN is 99.08 %. In addition, we analyzed the Confinement Loss (CL) at a value of 5.88 × 10−09 dB/m and 2.07 × 10−05 dB/m, as well as EML at values of 0.0020 cm−1 and 0.0026 cm−1, accordingly, about these hazardous substances. The designed detector can identify NaCN and KCN at low concentrations even with small RI shifts due to its high sensitivity. Real-time NaCN and KCN detection and monitoring through nerve reflexes is essential for life-threatening conditions. It can selectively work in NaCN and KCN, ensuring accurate detection even in complex chemical compositions. Additionally, its tiny size allows for emergency use.

JFDI: Joint Feature Differentiation and Interaction for domain adaptive object detection

In unsupervised domain adaptive object detection, learning target-specific features is pivotal in enhancing detector performance. However, previous methods mostly concentrated on aligning domain-invariant features across domains and neglected integrating the specific features. To tackle this issue, we introduce a novel feature learning method called Joint Feature Differentiation and Interaction (JFDI), which significantly boosts the adaptability of the object detector. We construct a dual-path architecture based on we proposed feature differentiate modules: One path, guided by the source domain data, utilizes multiple discriminators to confuse and align domain-invariant features. The other path, specifically tailored to the target domain, learns its distinctive characteristics based on pseudo-labeled target data. Subsequently, we implement an interactive enhanced mechanism between these paths to ensure stable learning of features and mitigate interference from pseudo-label noise during the iterative optimization. Additionally, we devise a hierarchical pseudo-label fusion module that consolidates more comprehensive and reliable results. In addition, we analyze the generalization error bound of JFDI, which provides a theoretical basis for the effectiveness of JFDI. Extensive empirical evaluations across diverse benchmark scenarios demonstrate that our method is advanced and efficient.

Generative AI in Fintech: Advancing Risk Assessment and Fraud Detection in Digital Payment Technologies

This article explores the transformative potential of artificial intelligence (AI), particularly generative AI, in enhancing risk assessment and fraud detection within digital payment technologies. As financial fraud evolves in sophistication, traditional detection methods often fall short. We propose a novel approach leveraging generative AI to create synthetic data that mimics real-world scenarios, thereby training more comprehensive and adaptive fraud detection models. This article addresses limitations in historical datasets, improves the accuracy of predictive models, and reduces false positives. Furthermore, we demonstrate how this approach enables proactive detection of emerging fraud trends, allowing fintech firms to stay ahead in the ongoing arms race against fraudsters. Our findings suggest that integrating generative AI into fraud detection systems can significantly enhance the security and reliability of digital payment platforms, potentially revolutionizing risk management in the fintech industry. This article contributes to the growing body of knowledge on AI applications in financial security and provides a foundation for future developments in this critical area.

Optimization of Machine Learning-Based Automatic Target Detection and Locking System on Robots

Background: In recent years, the world of robotics has made significant progress in improving the operational capabilities of robots through target detection and locking systems. These systems play a crucial role in improving the efficiency and effectiveness of critical applications such as defense, security, and industrial automation. However, the main challenge faced is the limitations of the existing system in adapting to unstable environmental conditions and dynamic changes in targets. Objective: This research aims to overcome these challenges by developing a more adaptive and responsive target detection and locking system by integrating two leading machine learning technologies: Convolutional Neural Networks (CNN) for target detection and Long Short-Term Memory (LSTM) for target tracking. Methods: This study uses a quantitative approach to evaluate the effectiveness of the integration of CNNs and LSTMs in target detection and locking systems. Results: The results of the study showed a detection accuracy rate of 95% and a locking accuracy of 90%. The system is proven to be able to adapt to changing operational conditions in real-time and provide consistent performance in a variety of complex and dynamic scenarios. Conclusion: The conclusion of this study is that the integration of CNN and LSTM technologies in target detection and locking systems in robots significantly improves the performance and efficiency of the system, enabling a wider and more complex application.

Virtual Array-Based Signal Detection and Carrier Frequency Offset Estimation in a Multistatic Collaborative Passive Detection System

To tackle the problem of difficult signal detection and carrier frequency synchronization faced by wireless communication among stations of the multistatic passive detection system in interference environments, an adaptive signal detection and carrier frequency offset (CFO) estimation method based on a virtual array is proposed in this paper. This is a data-aided method that utilizes a training sequence composed of three segments of sub-training sequences with different symbols. This method first uses spatial spectrum estimation to obtain the coarse frequency estimations of interference signals and CFO from virtual array signals constructed from the first two sub-training sequences. Then, beamforming is conducted accordingly on the virtual array signals constructed from the third sub-training sequence to suppress the in-band interferences and protrude the expected signal. Finally, improved performance of signal detection and CFO estimation is obtained with the beamformed signals. Simulation experiments show that a missed detection probability as low as 1 × 10−4, with a false detection probability of 1 × 10−3, can be obtained under a signal-to-interference ratio (SIR) of −10 dB and Eb/N0 of 1 dB. Moreover, the proposed method can also simultaneously achieve a CFO estimation error that is lower than 3%, with the condition of Eb/N0 being as low as −5 dB under different SIRs. Simulation results validate the proposed method and demonstrate the promising application prospects of the proposed method in networked passive detection scenarios.

Research on Network Intrusion Detection Technology Based on Artificial Intelligence

As network intrusion becomes more and more complicated and network characteristics become more and more diverse, the traditional network intrusion detection technology has some problems such as high false alarm rate, poor adaptability and low detection rate. The article provides an in-depth analysis of network intrusion detection techniques based on artificial intelligence to better solve these problems. In recent years, we have been witnessing great progress in image recognition, speech recognition, and natural language processing. The powerful advantage of artificial intelligence in processing large-scale and complex data provides new ideas for the processing of multi-attribute intrusion data. The introduction of AI technology in network intrusion detection can effectively improve the detection accuracy and reduce the false alarm rate and omission rate. Based on this, the article analyses the artificial intelligence network intrusion detection technology and discusses the network security management based on artificial intelligence for the reference of related personnel.

An adaptive detection framework based on artificial immune for IoT intrusion detection system

Given the continual evolution of new network attack methodologies, defenders face the imperative of constantly upgrading security defenses. Current security technologies, albeit effective against known threats, often fall short in handling the intricacies of diverse and novel attacks. Artificial immunity-based network anomaly detection offers a promising avenue by dynamically adapting to evolving threats. However, prevailing algorithms in this domain suffer from low detection rates, limited adaptability, and extended detector generation times. This study aims to tackle these challenges by introducing a high-efficiency network anomaly detection framework, emphasizing both high-dimensional feature selection and adaptive detector generation. Our approach begins with an enhanced dual-module hybrid high-dimensional feature selection method, leveraging evolutionary principles. Furthermore, we introduce a self-sample clustering algorithm based on fuzzy clustering during the tolerance stage, enhancing detector tolerance efficiency. Additionally, an adaptive detector generation scheme is devised. It divides the non-boundary sub-population based on non-self differences and evolution, while employing the red fox optimization algorithm in the boundary region. This adaptive approach dynamically adjusts detector positions and radii to derive optimal detectors. Through comprehensive validation using well-established IoT and network anomaly datasets, our proposed artificial immunity-based IoT intrusion detection framework exhibits superior performance. It achieves higher classification accuracy and lower error rates compared to current state-of-the-art machine learning and artificial immunity algorithms.

Highly robust thermal infrared and visible image registration with canny and phase congruence detection

When a dual-optical vehicle-mounted camera is photographed, visible and thermal infrared (VI-T) images cannot be registered, and realising fine matching of the images to solve for the optimal affine parameters is thus key to this research. Visible and thermal image matching is of great interest owing to the presence of challenging scale transformations, nonlinear radial aberrations, and robustness under illumination conditions. Thus, this paper presents a novel algorithm, namely, Highly Robust Thermal Infrared and Visible Image Registration with Canny and Phase Congruence Detection (HRCP). Firstly, the image is pre-processed, and feature point detection is performed by combining adaptive thresholding GW-Canny edge detection and phase coherence (PC), which increases the edge information. Secondly, SURF and FAST feature detection at the maximum distance of PC maps can obtain stable and reliable feature points, which are robust to scale invariance and rotation invariance. Finally, the optimal affine parameter is solved using the least square method with several iterations. Compared to existing matching methods, we find that VI-T image matching is particularly sensitive to both lighting factors and scale-transformed nonlinear radial distortion and that HRCP is robust in performing matching. For the randomly selected dataset, the average image matching time is reduced by 3.265 s, the number of correct image matches increases by 44 pairs on average, the root mean square error improves to 1.830, and the average error improves to 2.815. The proposed method not only improves the inaccuracy of radiation-insensitive feature transform matching in both low and high light, but also achieves a higher accuracy and an increase in the number of feature matching points, which facilitates robust VI-T image registration.

An unsupervised embedding method based on streaming videos for process monitoring in repetitive production systems

Advanced production systems, such as multi-step assembly processes, predominantly comprise of repetitive operations. The repetitive manual or human–robot integrated production operations call for new real-time process management technologies such as the increasing use of sensors and the development of system intelligence. Conventional process monitoring and management methods, which are often labor-intensive, fall short in providing immediate and actionable insights. To tackle this limitation, we develop an unsupervised embedding method to automatically delineate the process into different stages and predict real-time progress information. We propose a Contrastive Variational Autoencoder as a feature extractor to adeptly embed repetitive processes into a Gaussian Mixture Model. Based on the extracted features, we propose an adaptive change-point detection and an Iterative Dynamic Time Wrapping algorithm to identify and segment multiple standardized process stages automatically. Theoretically, we establish the asymptotic optimality of the detected change-points associated with the given precision of image and feature extractors, ensuring the high-quality process stage separation and labeling. The proposed method autonomously extracts essential features encapsulating progress information from a limited set of unlabelled process videos. Through four diverse case studies including production of an actual aircraft spoiler, our method exhibits very promising performance. Specifically, it achieves an average of 98.14% accuracy in predicting production progress and 0.9202 area under the curve in predicting progress deviation across three distinct production environments. The proposed process monitoring method in repetitive production systems has the potential to significantly improve productivity, promote standardization of repetitive operations, and predict production deviations.

Spatial Adaptive Improvement Detection Network for Corroded Bolt Detection in Tunnels

Spatial Adaptive Improvement Detection Network for Corroded Bolt Detection in Tunnels

Optically enhanced organic phototransistors for adaptive image processing under complex light conditions

Effectively handling massive information in complex lighting conditions is crucial for security-focused machine vision, as it relies on precise image feature detection and encryption/decryption to ensure integrity and confidentiality. Traditional hardware, primarily based on complementary metal-oxide-semiconductor (CMOS) technology, struggles with the complexity of processing tasks around the clock. To address this challenge, a ternary organic heterostructure visuomorphic phototransistor was tailored for adaptive critical image processing. The light coupling induced by the porous heterostructural layer enhances the light absorption efficiency by 2.5-fold. Our phototransistor exhibits bidirectional photoresponse across the visible and near-infrared (NIR) spectral regions, enabling adaptive edge detection in varying lighting conditions with precision above 85 %. The phototransistor array facilitates image encryption/decryption with a superior accuracy of 12 %/90 % compared to existing counterparts. This ternary organic hetero-integration combines multiple response modes, enabling compact and efficient critical feature processing.

Adaptive Detection for Multiantenna Ambient Backscatter Communications System With MFSK Modulation

Adaptive Detection for Multiantenna Ambient Backscatter Communications System With MFSK Modulation

A novel framework for low-contrast and random multi-scale blade casting defect detection by an adaptive global dynamic detection transformer

The radiographic inspection plays a crucial role in ensuring the casting quality for improving the service life under harsh environments. However, due to the low-contrast between the defects and the image background, the random spatial position distribution, random shapes and aspect ratios of the defects, the development of an accurate defect automatic detection system is still challenging. To address these issues, this paper proposes a novel framework for low-contrast and random multi-scale casting defect detection, which is referred to as adaptive global dynamic detection transformer (AGD-DETR). A novel defect-aware data augmentation method is first proposed to adaptively highlight the feature of the low-contrast defect boundary. A multi-attentional pyramid feature refinement (MPFR) module is then established to refine and fuse the multi-scale defect features of random sizes. Afterwards, a novel global dynamic receptive fusion-transformer (GDRF-Transformer) detection scheme is designed to perform the global perception and feature dynamic extraction of complex internal casting defects. It includes 4D-anchor query and cross-layer box update strategy, query rectification by prior information of defect aspect ratio, and global adaptive-feed forward network (GA-FFN). A dataset comprising turbine blade casting defect radiographic (TBCDR) images, is used to demonstrate the high efficiency of the proposed AGD-DETR. The obtained results show that the proposed method can accurately capture the spatial position distributions and complex defect shapes. Furthermore, it outperforms existing state-of-the-art defect detection methods.

Using Machine Learning Models to Detect the Increasing Threats of Financial Fraud in the Cyberspace

In the dynamic landscape of the financial sector, the escalating menace of financial fraud presents pervasive implications for businesses and consumers alike. Particularly, detecting credit card fraud in real- time transactions has become a pivotal concern within the financial industry. This abstract delves into the critical role of data mining in addressing the complexities of credit card fraud detection, shedding light on the multifaceted challenges that confront this domain. The realm of financial business is increasingly besieged by the spectre of financial fraud, necessitating robust measures to combat its detrimental effects. As the sophistication and prevalence of fraudulent activities continue to evolve, the imperative of deploying effective strategies for fraud detection becomes more pronounced. Applying data mining techniques in this context is paramount in identifying and mitigating credit card fraud. Leveraging advanced data mining methodologies is essential for scrutinising live transactions and discerning anomalous patterns indicative of fraudulent behaviour. Credit card fraud detection poses formidable challenges, primarily attributable to two compelling factors. Firstly, the inherent dynamism of normal and fraudulent behavioural profiles engenders a perpetual need for adaptive and responsive detection mechanisms. Secondly, the highly imbalanced nature of credit card fraud data sets further complicates accurately identifying fraudulent activities, necessitating nuanced approaches to discern anomalies amidst voluminous transactional data effectively. In light of the foregoing, this abstract underscore the criticality of data mining in addressing the intricate landscape of credit card fraud detection, emphasising the need for agile and sophisticated methodologies to navigate the evolving nature of fraudulent behaviours and the skewed distribution of fraud-related data sets. By comprehensively elucidating these challenges, this abstract provides a foundational understanding of the nuanced complexities inherent in combatting financial fraud through the lens of data mining.

An adaptive transfer fault detection method for rotary machine with multi-sensor information fusion

An adaptive transfer fault detection method for rotary machine with multi-sensor information fusion

An Explainable Feature Selection Framework for Web Phishing Detection with Machine Learning

In the evolving landscape of cyber threats, phishing attacks pose significant challenges, particularly through deceptive webpages designed to extract sensitive information under the guise of legitimacy. Conventional and machine learning (ML)-based detection systems struggle to detect phishing websites owing to their constantly changing tactics. Furthermore, newer phishing websites exhibit subtle and expertly concealed indicators that are not readily detectable. Hence, effective detection depends on identifying the most critical features. Traditional feature selection (FS) methods often struggle to enhance ML model performance and instead decrease it. To combat these issues, we propose an innovative method using eXplainable AI (XAI) to enhance FS in ML models and improve the identification of phishing websites. Specifically, we employ SHapley Additive exPlanations (SHAP) for global perspective and aggregated local interpretable model-agnostic explanations (LIME) to determine specific localized patterns. The proposed SHAP and LIME-aggregated feature selection (SLA-FS) framework pinpoints the most informative features, enabling more precise, swift, and adaptable phishing detection. Applying this approach to an up-to-date web phishing dataset, we evaluate the performance of three ML models before and after FS to assess their effectiveness. Our findings reveal that random forest (RF), with an accuracy of 97.41% and XGBoost (XGB) at 97.21% significantly benefit from the SLA-FS framework, while KNN lags. Our framework increases the accuracy of RF and XGB by 0.65% and 0.41%, respectively, outperforming traditional filter or wrapper methods and any prior methods evaluated on this dataset, showcasing its potential.

CRRFNet: An adaptive traffic object detection method based on camera and radar radio frequency fusion

A large number of studies have proved that camera and radar fusion is a useful and economical solution for traffic object detection. However, how to improve the reliability and robustness of fusion methods is still a huge challenge. In this paper, an adaptive traffic object detection method based on a camera and radar radio frequency Network (CRRFNet) is proposed, to solve the problem of robust and reliable traffic object detection in noisy or abnormal scenes. Firstly, two different deep convolution modules are designed for extracting features from the camera and radar; Secondly, the camera and radar features are catenated, and a deconvolution module is built for upsampling; Thirdly, the heatmap module is used to compress redundant channels. Finally, the objects in the Field of View (FoV) are predicted by location-based Non-Maximum Suppression (L-NMS). In addition, a data scrambling technique is proposed to alleviate the problem of overfitting to a single sensor by the fusion method. The existing Washington University Camera Radar (CRUW) dataset is improved and a new dataset named Camera-Radar Nanjing University of Science and Technology Version 1.0 (CRNJUST-v1.0) is collected to verify the proposed method. Experiments show that CRRFNet can detect objects by using the information of radar and camera at the same time, which is far more accurate than a single sensor method. Combined with the proposed data scrambling technology, CRRFNet shows excellent robustness that can effectively detect objects in the case of interference or single sensor failure.

A Step-Wise Domain Adaptation Detection Transformer for Object Detection under Poor Visibility Conditions

A Step-Wise Domain Adaptation Detection Transformer for Object Detection under Poor Visibility Conditions

Adaptive cyclic content ratiogram: a new signal decomposition method for bearing concurrent fault diagnosis

Fast kurtogram (FK) has been proven to be an effective tool for resonance frequency band detection, which is widely used in bearing fault diagnosis. However, FK is not robust to impulsive noise, and its frequency band segmentation rule is fixed, which leads to over-decomposition or under-decomposition of the fault resonance frequency band in its signal decomposition results. Therefore, an adaptive cyclic content ratiogram is proposed in this paper. Firstly, based on the energy distribution of vibration signals on different frequency components, the frequency spectral segmentation is performed adaptively, and multiple sub-signals containing different frequency components are obtained. Secondly, the ratio of cyclic content (RCC), which cannot only more accurately characterize the cyclostationarity of bearing fault impacts but also be insensitive to impulsive noise, is applied to evaluate the fault feature information contained in each sub-signal separately. In the meanwhile, considering that fault characteristic frequency information is required in the process of RCC evaluation, the proposed method performs adaptive fault characteristic frequency detection for each sub-signal based on the envelope spectrum. The RCC maximization is used to locate the fault resonance frequency band. Also, combined with the estimated fault characteristic frequencies, the proposed method can achieve the extraction of concurrent fault features. Simulation and experimental data verify the effectiveness of the proposed method.