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Anomaly-Focused Augmentation Method for Industrial Visual Inspection

IEEE Access 2025年 13卷 89118-89139页

作者： Lara, Melvin A. Hameed, Ibrahim A. Koziorek, Jiri Faculty of Electrical Engineering and Computer Science Department of Cybernetics and Biomedical Engineering Ostrava70800 Czech Republic Department of Mechanical Engineering and Technology Management Ås1433 Norway

In industrial inspection, the detection of surface defects—such as scratches, dents, or other defects—is crucial for ensuring product quality. However, the limited availability of annotated images of such defects poses challenges for developing reliable detection models. This paper proposes a novel image augmentation algorithm to address this limitation by generating synthetic imperfections from a small sample set. Unlike traditional approaches that augment entire images, the proposed approach isolates defects and applies spatial transformations (e.g., rotation, shear) before blending them into background images. This allows for the creation of diverse training datasets. The algorithm employs a 2D binary map to encode the spatial relationship between defect and background regions of images and then applies an AI-driven model to generate new realistic blended images. By leveraging a generator-discriminator framework, the augmentation process is iteratively refined during training to maintain the style and texture of the original imperfections. Experimental results show that the proposed method significantly enhances the performance of defect detection models compared to existing augmentation techniques while preserving the realism of the generated images. © 2013 IEEE.

关键词： Photointerpretation

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Robust Graph Autoencoder-Based Detection of False Data Injection Attacks Against Data Poisoning in Smart Grids

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1287-1301页

作者： Takiddin, Abdulrahman Ismail, Muhammad Atat, Rachad Davis, Katherine R. Serpedin, Erchin Department of Electrical and Computer Engineering Texas A and M University College StationTX77843 United States Department of Computer Science Tennessee Tech University CookevilleTN38505 United States Department of Electrical and Computer Engineering Program Texas A and M University Qatar Doha23874 Qatar

Machine learning-based detection of false data injection attacks (FDIAs) in smart grids relies on labeled measurement data for training and testing. The majority of existing detectors are developed assuming that the adopted datasets for training have correct labeling information. However, such an assumption is not always valid as training data might include measurement samples that are incorrectly labeled as benign, namely, adversarial data poisoning samples, which have not been detected before. Neglecting such an aspect makes detectors susceptible to data poisoning. Our investigations revealed that detection rates (DRs) of existing detectors significantly deteriorate by up to 9-29% when subject to data poisoning in generalized and topology-specific settings. Thus, we propose a generalized graph neural network-based anomaly detector that is robust against FDIAs and data poisoning. It requires only benign datasets for training and employs an autoencoder with Chebyshev graph convolutional recurrent layers with attention mechanism to capture the spatial and temporal correlations within measurement data. The proposed convolutional recurrent graph autoencoder model is trained and tested on various topologies (from 14, 39, and 118-bus systems). Due to such factors, it yields stable generalized detection performance that is degraded by only 1.6-3.7% in DR against high levels of data poisoning and unseen FDIAs in unobserved topologies. Impact Statement-Artificial Intelligence (AI) systems are used in smart grids to detect cyberattacks. They can automatically detect malicious actions carried out bymalicious entities that falsifymeasurement data within power grids. Themajority of such systems are data-driven and rely on labeled data for model training and testing. However, datasets are not always correctly labeled since malicious entities might be carrying out cyberattacks without being detected, which leads to training on mislabeled datasets. Such actions might degrade the d

关键词： Cybersecurity

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Recent advances in tip-enhanced Raman spectroscopy probe designs

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Nano Research 2023年第4期16卷 5555-5571页

作者： Da Xu Boqun Liang Yaodong Xu Ming Liu Department of Electrical and Computer Engineering University of California-RiversideRiversideCalifornia 92521USA Materials Science and Engineering program University of California-RiversideRiversideCalifornia 92521USA

Tip-enhanced Raman spectroscopy(TERS)imaging is a super-resolution imaging technique that features the merits of both surface-enhanced Raman spectroscopy(SERS)and scanning probe microscopy(SPM),such as the high chemical sensitivity from the former and the nanoscale spatial resolution from the *** advantages make TERS an essential nanospectroscopic characterization technique for chemical analysis,materials science,bio-sensing,*** probes,the most critical factor determining the TERS imaging quality,are expected to provide a highly confined electromagnetic hotspot with a minimized scattering background for the generation of Raman signals with high spatial *** two decades of development,numerous probe design concepts have been proposed and *** review provides a comprehensive overview of the state-of-the-art TERS probe designs,from the working mechanism to the practical *** start with reviewing the recent development of TERS configurations and the corresponding working mechanisms,including the SPM platforms,optical excitation/collection techniques,and probe preparation *** then review the emerging novel TERS probe designs,including the remote-excitation probes,the waveguide-based nanofocusing probes,the metal-coated nanofocusing probes,the nanowire-assisted selective-coupling probes,and the tapered metal-insulator-metal *** discussion focuses on a few critical aspects,including the surface-plasmon-polariton(SPP)hotspot excitation technique,conversion efficiency,working frequency,and *** the end,we review the latest TERS applications and give a perspective on the future of TERS.

关键词： tip-enhanced Raman spectroscopy plasmonics scanning probe microscopy nanoscale imaging nanophotonics nanofocusing

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Hardware Accelerator for a Power Efficient Single-lead Dry-electrode ECG Wearable Design 46

Hardware Accelerator for a Power Efficient Single-lead Dry-e...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Abdou, Abdelrahman Krishnan, Sridhar Toronto Metropolitan University Department of Electrical Computer and Biomedical Engineering Toronto Canada

ISBN: (纸本)9798350371499

Single-lead electrocardiographic (ECG) monitoring wearables are becoming candidate technologies for long-term remote monitoring applications. Current wearable disadvantages include high power consumption from computational complex pre-processing leading to low battery life. A hardware (HW) architecture for dry electrode-based ECG signal processing to increase wearable longevity is proposed. The technology is based off an analog-front end (AFE) chip combined with a field programmable gated arrays (FPGA)-based optimized cubic Hermite interpolation approach for signal processing. This system is deployed on a FPGA board featuring a single-core processor. The architecture uses 0.01 W, utilizes 0.67% and 0.44% of available look-up-tables (LUTs) and flip-flops (FFs) components on FPGA and performed real-time signal processing. Signal quality indexes (SQIs) and signal to noise ratios (SNR) information are computed where the HW processed signals showed an average SNR of 16.4 dB. ECG R-peaks are visually identified, making this architecture suitable for heart rate (HR), and heart rate variability (HRV) estimations in long-term dry-electrode single-lead ECG monitoring applications. © 2024 IEEE.

关键词： Table lookup

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Intrinsic Entropy: A Novel Adaptive Method for Measuring the Instantaneous Complexity of Time Series

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IEEE Signal Processing Letters 2023年 30卷 160-164页

作者： Huang, Po-Hsun Hsiao, Tzu-Chien Hsinchu300 Taiwan Nycu Department of Computer Science College of Cs and Institute of Biomedical Engineering College of Electrical and Computer Engineering Hsinchu300 Taiwan

The determination of appropriate parameters and an appropriate window size in most entropy-based measurements of time-series complexity is a challenging problem. Inappropriate settings can lead to the loss of intrinsic information within a time series. Therefore, two parameter-free methods, namely the intrinsic entropy (IE) and ensemble IE (eIE) methods, are proposed in this paper. The eIE method requires two parameters, which can be easily determined through an orthogonality test. The proposed approaches can measure instantaneous complexity;thus, they do not require a predetermined window size. White noise and three other varieties of colored noise were used to test the stability of the proposed methods, and five types of synthetic signals and logistic maps were applied for measuring instantaneous complexity and regularity. The results revealed that the IE and eIE methods exhibit satisfactory stability. Both methods provide point-by-point entropy measures for time series. The eIE method is useful for measuring the complexity of frequency and amplitude modulation. Furthermore, the periodicity of time series can be detected using the two proposed methods. © 1994-2012 IEEE.

关键词： Empirical mode decomposition

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State-of-the-Art in Cyber-Physical Security for Dynamic Wireless Charging of Electric Vehicles 50

State-of-the-Art in Cyber-Physical Security for Dynamic Wire...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Yazdanipour, Samaneh Oruganti, V.S.R. Varaprasad Son, Jeonggi Arani, Mohammadreza F.M. Williamson, Sheldon S. Toronto Metropolitan University Department of Electrical Computer and Biomedical Engineering Toronto Canada Ontario Tech University Department of Electrical Computer and Software Engineering Oshawa Canada

ISBN: (纸本)9781665464543

Dynamic Wireless Charging systems for electric vehicles offers a revolutionary approach to EV charging, enabling vehicles to charge while in motion and overcoming the limitations of stationary wireless charging. Despite their efficiency and convenience, dynamic wireless charging systems introduce complex security vulnerabilities that must be addressed to ensure system reliability and safety. This paper provides a comprehensive review of the cyber-physical security challenges associated with electric vehicle dynamic wireless charging systems, examining potential threats such as cyber attacks and physical tampering. It identifies specific vulnerabilities in communication protocols, hardware, and software, and discusses various mitigation strategies such as encryption and physical layer security enhancements. Furthermore, the paper highlights recent advancements in security technologies and outlines future research directions to enhance the resilience of dynamic wireless charging systems. By addressing these security challenges, stakeholders can foster a safer environment for the deployment of dynamic wireless charging, thereby promoting the broader adoption of electric vehicles and advancing sustainable transportation solutions. © 2024 IEEE.

关键词： State of charge

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Microstrip Patch Antenna with an Inverted T-Type Notch in the Partial Ground for Breast Cancer Detections

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computer Modeling in engineering & Sciences 2024年第2期138卷 1301-1322页

作者： Nure Alam Chowdhury Lulu Wang Md Shazzadul Islam Linxia Gu Mehmet Kaya Department of Biomedical Engineering and Science Florida Institute of TechnologyMelbourneFL 32901USA Biomedical Device Innovation Center Shenzhen Technology UniversityShenzhen518118China Department of Electrical and Computer Engineering International Islamic University MalaysiaJalan GombakSelangor53100Malaysia

This study designs a microstrip patch antenna with an inverted T-type notch in the partial ground to detect tumorcells inside the human *** size of the current antenna is small enough(18mm×21mm×1.6mm)todistribute around the breast *** operating frequency has been observed from6–14GHzwith a minimumreturn loss of−61.18 dB and themaximumgain of current proposed antenna is 5.8 dBiwhich is flexiblewith respectto the size of *** the distribution of eight antennas around the breast phantom,the return loss curveswere observed in the presence and absence of tumor cells inside the breast phantom,and these observations showa sharp difference between the presence and absence of tumor *** simulated results show that this proposedantenna is suitable for early detection of cancerous cells inside the breast.

关键词： Antenna microwave wideband cancer breast phantom tumor detection

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Correntropy-Based Data Selective Adaptive Filtering

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IEEE Transactions on Circuits and Systems I: Regular Papers 2024年第2期71卷 754-766页

作者： Chien, Ying-Ren Wu, Sheng-Teng Tsao, Hen-Wai Diniz, Paulo S.R. Adaptive and Autonomous Communication Laboratory Department of Electrical Engineering National Ilan University Yilan City260007 Taiwan College of Electrical Engineering and Computer Science Graduate Institute of Communication Engineering National Taiwan University Taipei10617 Taiwan Program of Electrical Engineering Department of Electronics and Computer Engineering COPPE/Poli Universidade Federal do Rio de Janeiro Rio de Janeiro68504 Brazil

Data selection can be used in conjunction with adaptive filtering algorithms to avoid unnecessary weight updating and thereby reduce computational overhead. This paper presents a novel correntropy-based data selection method as an alternative to conventional data selection mechanisms based on squared error values. We developed a variable correntropy sensing algorithm to maximize the instantaneous correntropy function for the Gaussian kernel function to mitigate the impact of impulse noise and other forms of noise that can be disregarded in data selection. The proposed data selection mechanism can be implemented with any adaptive filtering algorithm. In simulations, the proposed method (implemented with the least mean squared algorithm) outperformed comparable error-based data selection schemes in terms of hit rate and miss rate, and the resulting weight updating ratio was close to the expected weight updating ratio. © 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

关键词： Adaptive filters

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Semi‑supervised contour‑driven broad learning system for autonomous segmentation of concealed prohibited baggage items

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 1-18页

作者： Divya Velayudhan Abdelfatah Ahmed Taimur Hassan Muhammad Owais Neha Gour Mohammed Bennamoun Ernesto Damiani Naoufel Werghi Department of Electrical Engineering and Computer Sciences Center for Cyber‑Physical SystemsKhalifa University of Science and TechnologyAbu Dhabi 127788United Arab Emirates Department of Electrical Computer and Biomedical EngineeringAbu Dhabi UniversityAbu Dhabi 59911United Arab Emirates Department of Computer Science and Software Engineering the University of Western AustraliaPerthWA 6009Australia

With the exponential rise in global air traffic,ensuring swift passenger processing while countering potential security threats has become a paramount concern for aviation *** X-ray baggage monitoring is now standard,manual screening has several limitations,including the propensity for errors,and raises concerns about passenger *** address these drawbacks,researchers have leveraged recent advances in deep learning to design threatsegmentation ***,these models require extensive training data and labour-intensive dense pixelwise annotations and are finetuned separately for each dataset to account for inter-dataset ***,this study proposes a semi-supervised contour-driven broad learning system(BLS)for X-ray baggage security threat instance segmentation referred to as *** research methodology involved enhancing representation learning and achieving faster training capability to tackle severe occlusion and class imbalance using a single training routine with limited baggage *** proposed framework was trained with minimal supervision using resource-efficient image-level labels to localize illegal items in multi-vendor baggage *** specifically,the framework generated candidate region segments from the input X-ray scans based on local intensity transition cues,effectively identifying concealed prohibited items without entire baggage *** multi-convolutional BLS exploits the rich complementary features extracted from these region segments to predict object categories,including threat and benign *** contours corresponding to the region segments predicted as threats were then utilized to yield the segmentation *** proposed C-BLX system was thoroughly evaluated on three highly imbalanced public datasets and surpassed other competitive approaches in baggage-threat segmentation,yielding 90.04%,78.92%,and 59.44%in terms of mIoU on GDXray,SIXray,and Compass-XP,***,the lim

关键词： Baggage X-ray imagery Broad learning systems Threat detection Threat segmentation

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A learning-based approach to fault detection and fault-tolerant control of permanent magnet DC motors

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Journal of engineering and Applied Science 2023年第1期70卷 109页

作者： Sardashti, Abolghasem Nazari, Jamal Department of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran Faculty of Electrical Biomedical and Mechatronics Engineering Qazvin Branch Islamic Azad University Qazvin Iran

In the context of Industry 4.0, which prioritizes intelligent and efficient solutions for industrial systems, this paper introduces an innovative methodology for fault detection and fault-tolerant control of DC motors. Leveraging the capabilities of machine learning and reinforcement learning, our approach aims to achieve optimal performance while maintaining a low computational burden. At the heart of our strategy lies a reinforcement learning-enhanced proportional-integral controller meticulously designed for precise positioning of DC motors. Through extensive comparative analysis, we establish the superiority of this controller in terms of precision, efficiency, and user accessibility when compared to traditional techniques. To ensure robust fault detection, we synergize a model-based observer with Mahalanobis distance-based outlier analysis, creating a swift and accurate diagnostic method for sensor faults. In cases of sensor malfunctions, an internal model-based control strategy comes into play, enabling the system to uphold its effectiveness despite disruptions. The effectiveness of our proposed methods is vividly demonstrated through simulations in the MATLAB environment, utilizing a DC motor subjected to sensor failures. The results unequivocally highlight the advantages of our approach, showcasing improved precision, faster operation, cost-effectiveness, and streamlined simplicity. As such, our approach finds suitability for industrial applications. In our quest to strike a delicate balance between performance and complexity, our techniques are purposefully crafted to provide intelligent yet pragmatic solutions that promote reliability, safety, and sustainability. This paper contributes to the evolving landscape of intelligent industrial solutions by offering a comprehensive framework that optimizes performance while minimizing complexity and costs. In doing so, we lay the foundation for a more efficient and resilient industrial ecosystem. © 2023, Faculty o

关键词： Fault detection

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