Video abnormality behavior identification plays a pivotal role in improving the safety and security of surveillance systems by identifying unusual events within video streams. However, existing methods face challenges...
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The problem of imbalanced data classification learning has received much *** classification algorithms are susceptible to data skew to favor majority samples and ignore minority *** weighted minority oversampling tech...
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The problem of imbalanced data classification learning has received much *** classification algorithms are susceptible to data skew to favor majority samples and ignore minority *** weighted minority oversampling technique(MWMOTE)is an effective approach to solve this problem,however,it may suffer from the shortcomings of inadequate noise filtering and synthesizing the same samples as the original minority *** this end,we propose an improved MWMOTE method named joint sample position based noise filtering and mean shift clustering(SPMSC)to solve these ***,in order to effectively eliminate the effect of noisy samples,SPMsC uses a new noise filtering mechanism to determine whether a minority sample is noisy or not based on its position and distribution relative to the majority *** that MWMOTE may generate duplicate samples,we then employ the mean shift algorithm to cluster minority samples to reduce synthetic replicate ***,data cleaning is performed on the processed data to further eliminate class *** on extensive benchmark datasets demonstrate the effectiveness of SPMsC compared with other sampling methods.
Age-related Macular Degeneration (AMD) is the most common eye disease that causes visual impairment in elder people. Prevalently, AMD is detected by Spectral Domain Optical Coherence Tomography (SD-OCT) for diagnosis ...
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In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh env...
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In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.
Dexterous robot manipulation has shone in complex industrial scenarios, where multiple manipulators, or fingers, cooperate to grasp and manipulate objects. When encountering multi-objective optimization with system co...
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Dexterous robot manipulation has shone in complex industrial scenarios, where multiple manipulators, or fingers, cooperate to grasp and manipulate objects. When encountering multi-objective optimization with system constraints in such scenarios, model predictive control(MPC) has demonstrated exceptional performance in complex multi-robot manipulation tasks involving multi-objective optimization with system constraints. However, in such scenarios, the substantial computational load required to solve the optimal control problem(OCP) at each triggering instant can lead to significant delays between state sampling and control application, hindering real-time performance. To address these challenges, this paper introduces a novel robust tube-based smooth MPC approach for two fundamental manipulation tasks: reaching a given target and tracking a reference trajectory. By predicting the successor state as the initial condition for imminent OCP solving, we can solve the forthcoming OCP ahead of time, alleviating delay effects. Additionally,we establish an upper bound for linearizing the original nonlinear system, reducing OCP complexity and enhancing response speed. Grounded in tube-based MPC theory, the recursive feasibility and closed-loop stability amidst constraints and disturbances are ensured. Empirical validation is provided through two numerical simulations and two real-world dexterous robot manipulation tasks, which shows that the seamless control input by our methods can effectively enhance the solving efficiency and control performance when compared to conventional time-triggered MPC strategies.
Biometrics systems utilizing hand geometry, fingerprint, iris, face, palm print, voice, gesture, and palm print have been utilised for authentication purposes. Through these templates, the face template is suggested a...
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Federated Learning(FL),as an emergent paradigm in privacy-preserving machine learning,has garnered significant interest from scholars and engineers across both academic and industrial *** its innovative approach to mo...
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Federated Learning(FL),as an emergent paradigm in privacy-preserving machine learning,has garnered significant interest from scholars and engineers across both academic and industrial *** its innovative approach to model training across distributed networks,FL has its vulnerabilities;the centralized server-client architecture introduces risks of single-point ***,the integrity of the global model—a cornerstone of FL—is susceptible to compromise through poisoning attacks by malicious *** attacks and the potential for privacy leakage via inference starkly undermine FL’s foundational privacy and security *** these reasons,some participants unwilling use their private data to train a model,which is a bottleneck in the development and industrialization of federated *** technology,characterized by its decentralized ledger system,offers a compelling solution to these *** inherently prevents single-point failures and,through its incentive mechanisms,motivates participants to contribute computing ***,blockchain-based FL(BCFL)emerges as a natural progression to address FL’s *** study begins with concise introductions to federated learning and blockchain technologies,followed by a formal analysis of the specific problems that FL *** discusses the challenges of combining the two technologies and presents an overview of the latest cryptographic solutions that prevent privacy leakage during communication and incentives in *** addition,this research examines the use of BCFL in various fields,such as the Internet of Things and the Internet of ***,it assesses the effectiveness of these solutions.
Multi-exposure image fusion (MEF) involves combining images captured at different exposure levels to create a single, well-exposed fused image. MEF has a wide range of applications, including low light, low contrast, ...
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The present study advances object detection and tracking techniques by proposing a novel model combining Automated Image Annotation with Inception v2-based Faster RCNN (AIA-IFRCNN). The research methodology utilizes t...
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Deep learning methods have played a prominent role in the development of computer visualization in recent years. Hyperspectral imaging (HSI) is a popular analytical technique based on spectroscopy and visible imaging ...
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