Search Results - conventional LSTM autoencoder

Sensor-Driven Surrogate Modeling and Control of Nonlinear Dynamical Systems Using FAE-CAE-LSTM and Deep Reinforcement Learning by Kherad, Mahdi, Moayyedi, Mohammad Kazem, Fotouhi-Ghazvini, Faranak, Vahabi, Maryam, Fotouhi, Hossein

“… This paper presents a sensor-driven, non-intrusive reduced-order modeling (NIROM) framework called FAE-CAE-LSTM, which combines convolutional and fully connected autoencoders with a long short-term memory (LSTM) network…”
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LSTM-based autoencoder models for real-time quality control of wastewater treatment sensor data by Seshan, Siddharth, Vries, Dirk, Immink, Jasper, van der Helm, Alex, Poinapen, Johann

“… long short-term memory (LSTM) autoencoder (AE) models, to reconcile faulty sensor signals in WWTPs as compared to autoregressive integrated moving average (ARIMA) models…”
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Intrusion detection in IoT network using temporal wavelet augmented deep dense and LSTM auto-encoders by Zakariah, Mohammed, Amin, Syed Umar, Alrayes, Fatma S., Alnuaim, Abeer, Khan, Zafar Iqbal

“…). This study proposes a new integration of Deep Dense Autoencoders with Temporal Wavelet-Augmented Deep Dense Autoencoders and LSTM Autoencoders to serve as an effective tool to identify and categorize…”
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Enhanced Emotion Recognition Using a Hybrid Autoencoder-LSTM Model Optimized with a Hybrid ACO-WOA Algorithm for Hyperparameter Tuning by Waiker, Vinod, Ramesh, Janjhyam Venkata Naga, Bala, Kiran, Krishnaiah, V. V. Jaya Rama, Jackulin, T., Muniyandy, Elangovan, Shahin, Osama R.

“… Therefore, this paper proposes an improved method for emotion recognition regarding the Hybrid Autoencoder-Long Short-Term Memory (LSTM…”
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Unsupervised learning-based damage detection of mooring lines in floating bridges by Min, Seongi, Song, Jihun, Kim, Seungjun

“…Floating bridges offer a practical alternative to sea-crossing bridges in regions with deep water and poor seabed conditions. It consists of a superstructure…”
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LSTM-Autoencoder Network for the Detection of Seismic Electric Signals by Xue, Jiyan, Huang, Qinghua, Wu, Sihong, Nagao, Toshiyasu

“… Although conventional techniques have made substantial efforts in improving the SES detection accuracy, the success rates of SES detection at certain stations are still less satisfactory due…”
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GCN-Based LSTM Autoencoder with Self-Attention for Bearing Fault Diagnosis by Lee, Daehee, Choo, Hyunseung, Jeong, Jongpil

“… Conventional AI models (convolutional neural networks (CNNs), long short-term memory (LSTM), support vector machine (SVM…”
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Stacked LSTM Sequence-to-Sequence Autoencoder with Feature Selection for Daily Solar Radiation Prediction: A Review and New Modeling Results by Ghimire, Sujan, Deo, Ravinesh C., Wang, Hua, Al-Musaylh, Mohanad S., Casillas-Pérez, David, Salcedo-Sanz, Sancho

“…) feature selection to select model parameters. Features are employed as potential inputs for Long Short-Term Memory and a seq2seq SAELSTM autoencoder Deep Learning (DL…”
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Dual-attention LSTM autoencoder for fault detection in industrial complex dynamic processes by Zeng, Lei, Jin, Qiwen, Lin, Zhiming, Zheng, Chenghang, Wu, Yingchun, Wu, Xuecheng, Gao, Xiang

“…) for fault detection in dynamic processes. Long short-term memory (LSTM) and autoencoder (AE) are combined into a special encoder-decoder LSTM architecture to learn both dynamic features and deep representations of variables in an unsupervised manner…”
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An LSTM-autoencoder based online side channel monitoring approach for cyber-physical attack detection in additive manufacturing by Shi, Zhangyue, Mamun, Abdullah Al, Kan, Chen, Tian, Wenmeng, Liu, Chenang

“…Additive manufacturing (AM) has gained increasing popularity in a large variety of mission-critical fields, such as aerospace, medical, and transportation. The…”
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LSTM-AE based condition monitoring for reciprocating air compressors considering on/off characteristics by Kim, Myeong-Joon, Cho, Hyun-Jik, Kang, Chul-Goo

“…Long short-term memory-autoencoder (LSTM-AE) models are commonly used to detect anomalies in time-series data…”
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CNC 가공 공정에서의 Attention-LSTM Autoencoder 기반 이상 탐지 모델 개발 by 윤여준, 김혜인, 남은석

“… Experimental results showed significant improvements over conventional Autoencoder and LSTM-Autoencoder models…”
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A convolutional autoencoder-based approach with batch normalization for energy disaggregation by Chen, Huan, Wang, Yue-Hsien, Fan, Chun-Hung

“…) autoencoder and uses advanced training techniques, particularly the batch normalization (BN) and hill climbing (HC) algorithm to solve the NILM problem…”
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Enhancing Robustness of OFDM Systems Using LSTM‐Based Autoencoders by P, Rajarajan, Sahaai, Madona B.

“… But maintaining dependable signaling is still difficult, especially when the signal‐to‐noise ratio (SNR) is low. In order to increase the dependability of OFDM systems, this study presents an enhanced LSTM…”
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Detecting failed tethers in submerged floating tunnels using an LSTM autoencoder and DNN algorithms by Min, Seongi, Jeong, Kiwon, Kim, Seungjun

“… The proposed method employs two different artificial neural network algorithms. First, the long short-term memory (LSTM…”
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Developing a novel Temporal Air-quality Risk Index using LSTM autoencoder: A case study with South Korean air quality data by Park, Hyerim, Sohn, Wonho, Kang, Eunjin, Im, Jungho, Lee, Junghye

“… Conventional index calculations often focus on the single most hazardous pollutant or ignore the combined and cumulative effects of multiple pollutants…”
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A survey on anomaly detection for technical systems using LSTM networks by Lindemann, Benjamin, Maschler, Benjamin, Sahlab, Nada, Weyrich, Michael

“…•Focusing on practical application of neural network-based detection algorithms.•LSTM-based approaches allow dynamic and time-variant anomaly detection…”
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A deep LSTM autoencoder-based framework for predictive maintenance of a proton radiotherapy delivery system by Dou, Tai, Clasie, Benjamin, Depauw, Nicolas, Shen, Tim, Brett, Robert, Lu, Hsiao-Ming, Flanz, Jacob B., Jee, Kyung-Wook

“… A novel PdM framework consisting of long short-term memory-based autoencoder (LSTM-AE) modeling of the proton PBS delivery system and a Mahalanobis…”
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An Approach towards Forecasting Time Series Air Pollution Data Using LSTM-based Auto-Encoders by Shakir, Mohamed, Kumaran, U., Rakesh, Dr.N.

“… Recently, Long-Short Term Memory (LSTM) and its variant is getting popular attention related to the prediction of Air Quality Index (AQI…”
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Wind turbine generator early fault diagnosis using LSTM-based stacked denoising autoencoder network and stacking algorithm by Yan, Junshuai, Liu, Yongqian, Meng, Hang, Li, Li, Ren, Xiaoying

“… Specifically, a multivariate spatiotemporal condition monitoring model (LSDAE) was established by combining the LSTM and SDAE networks, which can detect generator early anomalies through real-time monitoring the reconstruction residual…”
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