Výsledky vyhledávání - "Bidirectional LSTM autoencoder"

Hybrid Cyber Defense Mechanism with PINN for Resilient and Reliable Control against Replay / FDI Attacks in DC Microgrid Systems Autor Machina, Venkata Siva Prasad, Koduru, Sriranga Suprabhath, Madichetty, Sreedhar, Mishra, Sukumar

“…With the growing reliance on DC microgrids (DC MGs) in critical infrastructure, securing them against sophisticated cyberattacks is essential. This study…”
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Compression of Vehicle Trajectories with a Variational Autoencoder Autor Rákos, Olivér, Aradi, Szilárd, Bécsi, Tamás, Szalay, Zsolt

“…The perception and prediction of the surrounding vehicles’ trajectories play a significant role in designing safe and optimal control strategies for connected…”
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A Bidirectional Long Short-Term Memory Autoencoder Transformer for Remaining Useful Life Estimation Autor Fan, Zhengyang, Li, Wanru, Chang, Kuo-Chu

“…Estimating the remaining useful life (RUL) of aircraft engines holds a pivotal role in enhancing safety, optimizing operations, and promoting sustainability,…”
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Unsupervised Machine Learning Methods for Anomaly Detection in Network Packets Autor Park, Hyoseong, Shin, Dongil, Park, Chulgyun, Jang, Jisoo, Shin, Dongkyoo

“… In this study, we propose an IDS model based on the Long Short-Term Memory Autoencoder (LSTM-AE), specifically a Convolutional Neural Network Bidirectional LSTM Autoencoder…”
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Text Mining and Unsupervised Deep Learning for Intrusion Detection in Smart-Grid Communication Networks Autor Azar, Joseph, Al Saleh, Mohammed, Couturier, Raphaël, Noura, Hassan

“… This work proposes an anomaly detection method for high-dimensional sequences based on a bidirectional LSTM autoencoder…”
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Chakra Sheild: AI Powered Insider threat detection system Autor Tarare, Manas, Mataghare, Ojas, Zod, Aaryan, Nathile, Mrunal, Vairagade, Khushboo

“… The system employs a Bidirectional LSTM Autoencoder to learn normal behavioral patterns and detect anomalies via reconstruction error, enhanced by an Isolation Forest for reducing false positives…”
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Urban Road Traffic Accident Severity Prediction Using a Hybrid LSTM-Random Forest Model with Hybrid Attention (LRHHA) Autor Ling, Tao, Mababa, Jonilo C.

“…) for predicting the severity of urban road traffic accidents. Our model first uses a bidirectional LSTM autoencoder to extract deep nonlinear features from time-series accident data…”
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