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Anomaly detection is the process of identifying patterns that move differently from normal in a certain order. This process is considered one of the necessary measures for the safety of intelligent production systems. This study proposes a real-time anomaly detection system capable of using and analyzing data in smart production systems consisting...
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... This data is vital for grid operators to monitor and optimize grid performance. By analyzing data from IEDs, operators can pinpoint inefficiencies, predict potential issues, and enhance energy flow for improved reliability and cost-effectiveness [7]. IEDs also facilitate the integration of renewable energy sources by managing their variability to ensure grid stability. ...
... The objective value of the flow is given as flF(u) and the neighbor is offered as NiF(v) , the dimension issue is presented as j and the relation among the new position in the flow is given in Eq. (7). ...
The integration of Information and Communication Technologies (ICT) into conventional power grids has given rise to smart grids, which oversee electrical power distribution, generation, and utilization. Despite their benefits, smart grids face communication challenges due to various abnormalities. Detecting these anomalies is crucial for identifying power outages, energy theft, equipment failures, structural faults, power consumption irregularities, and cyber-attacks. While power systems handle natural disturbances adeptly, identifying anomalies caused by cyber-attacks remains complex. This paper introduces an intelligent Deep Learning (DL) approach for smart grid anomaly detection. Data is initially collected from standard sources such as smart meters, weather stations, and user behavior records. Optimal weighted feature selection is performed using the Modified Flow Direction Algorithm (MFDA) before inputting the selected features into an "Adaptive Residual Recurrent Neural Network with Dilated Gated Recurrent Unit (ARRNN-DGRU)" for anomaly identification. Simulation results confirm the model's superior performance, demonstrating a higher detection rate compared to existing methods and enhancing the robustness of the smart grid system.
Incorporating Cu and Zn into Mg as a biomaterial offers a unique opportunity to exploit their antibacterial performance and biodegradability. The main challenge in this area is understanding the ratio and effects of these elements. To achieve this, the present work, based on two separate studies, aims to develop a regression model and apply machine learning (ML) to predict the wear behaviors using the effects of Cu and Zn elements doped into Mg matrix at low ratios on wear and micro and nanostructure properties (Grain size, density, hardness, Crystallite Size, microstrain, dislocation density). The wear behavior of the samples was investigated under 5–20 N loads at a constant sliding speed of 42 mm/s. Auto Sklearn library was used to generate training models that accurately predict the wear loss, friction coefficient, and specific wear rate values. The model showed satisfactory explanatory power and reliability in predicting the volume loss target. It also exhibited remarkable capability in predicting the friction coefficient and specific wear rate targets. The results of sample wear tests (MgZn2 under 15 N) conducted to generate data not included in the dataset showed a high degree of agreement with the ML results. Sensitivity analyses confirmed that Load, Environment, Hardness, and Grain Size are the most influential factors in predicting wear behavior, further validating the model’s reliability and interpretability.
With increasing security threats in public spaces, automatic detection of dangerous weapons and fire accidents in surveillance videos is an important capability for pre-emptive safety measures. Anomaly detection in computer vision has become an important research problem with applications in surveillance, industrial inspection, and medical imaging. Recent advances in deep learning provide powerful techniques for analysing visual data and identifying irregularities. The proposed system uses edge detection to isolate potential hazards in video frames, followed by a convolutional neural networks (CNN) classifier and other techniques to identify anomalies such as weapons, fire, and violence in the cropped image patches, and thus provides a survey of deep learning techniques for anomaly detection in CCTV surveillance videos. A key advantage of deep learning in video analysis is its ability to learn feature representations directly from visual data and CNN can capture spatial patterns, while recurrent networks can model temporal dynamics in video. Techniques including pre-trained models such as feature extractors, combining convolutional and recurrent architectures, generating region proposals to focus on anomalous areas, and training models on labelled anomaly data are used. However, challenges remain, including limited training data, difficulty modelling all normal patterns, and reducing false alarm rates. Deep CNNs are well-suited for weapon detection across images and videos, capturing visual features associated with firearms and violent intent. The experimental results validate the proposed anomaly detection system, demonstrating real-time efficiency and impressive accuracy in tasks like violence (91%), weapon (98%), and fire (95%) detection.
The integration of Information and Communication Technologies (ICT) into the conventional power grid defines a smart grid, overseeing electrical power distribution, generation, and utilization. Despite its benefits, the smart grid encounters communication challenges due to various abnormalities. Detecting these anomalies is crucial for identifying power outages, energy theft, equipment failure, structural faults, power consumption irregularities, and cyber-attacks. While power systems adeptly handle natural disturbances, discerning cyber-attack-induced anomalies proves complex. This paper introduces an intelligent deep learning approach for smart grid anomaly detection. Initially, data is collected from standard smart meter, weather, and user behavior sources. Optimal weighted feature selection, utilizing the Modified Flow Direction Algorithm (MFDA), precedes inputting selected features into the "Adaptive Residual Recurrent Neural Network with Dilated Gated Recurrent Unit (ARRNN-DGRU)" for anomaly identification. Simulation results affirm the model's superior performance, with a heightened detection rate compared to existing methods, bolstering the smart grid system's robustness.
