Suchergebnisse - gated recurrent convolutional autoencoder

Joint damage detection of structures with noisy data by an effective deep learning framework using autoencoder-convolutional gated recurrent unit von Truong, Tam T., Lee, Jaehong, Nguyen-Thoi, T.

“… In order to deal with this issue, this paper proposes a new deep learning (DL) framework using an autoencoder-convolutional gated recurrent unit (A-CGRU …”
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Anomaly detection using convolutional autoencoder with residual gated recurrent unit and weak supervision for photovoltaic thermal heat pump system von John, Lukudu Samuel, Yoon, Sungmin, Li, Jiteng, Wang, Peng

“… Utilizing Convolutional Autoencoders with Residual Bi-Directional Gated Recurrent Unit bottleneck offers an advanced approach for detecting anomalies in heat pumps …”
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IoT-based prediction model for aquaponic fish pond water quality using multiscale feature fusion with convolutional autoencoder and GRU networks von Sundararajan, Suma Christal Mary, Shankar, Yamini Bhavani, Selvam, Sinthia Panneer, Manogaran, Nalini, Seerangan, Koteeswaran, Natesan, Deepa, Selvarajan, Shitharth

“… The Internet of Things (IoT)-based smart solutions have been developed to predict water quality and they are becoming an increasingly important means of …”
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Data-Driven OTFS Channel Estimation Based on Gated Recurrent Convolutional Autoencoder von Chen, Junshen, Yuan, Qihao, Zhang, Shiyao, Liu, Chang

“… To tackle this problem, in this paper, we propose a novel gated recurrent convolutional autoencoder (GRCAE …”
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An Anomaly Detection Method for Smart Grid Based on Convolutional Autoencoder and Gated Recurrent Unit von Hu, Xiangdong, Xing, Xin

“… An anomaly detection method for smart grid based on convolutional autoencoders (CAE) and gated recurrent units …”
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A deep hybrid CSAE-GRU framework with two-stage balancing for automatic epileptic seizure detection using EEG-derived features von Xiang, Fei, Liu, Mingyue, Chen, Wenna, Zheng, Shaojie, Zhang, Jincan, Du, Ganqin

“… Epilepsy is a neurological disorder characterized by abnormal neuronal discharges in the brain, posing a persistent challenge in clinical diagnosis. This study …”
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MTCR-AE: A Multiscale Temporal Convolutional Recurrent Autoencoder for unsupervised malicious network traffic detection von Ahmed, Mukhtar, Chen, Jinfu, Akpaku, Ernest, Sosu, Rexford Nii Ayitey

“… This paper introduces the Multiscale Temporal Convolutional Recurrent Autoencoder (MTCR-AE), an innovative framework designed to detect malicious network traffic by leveraging Multiscale Temporal Convolutional Networks …”
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Electricity theft detection for energy optimization using deep learning models von Pamir, Javaid, Nadeem, Javed, Muhammad Umar, Houran, Mohamad Abou, Almasoud, Abdullah M., Imran, Muhammad

“… The rapid increase in nontechnical loss (NTL) has become a principal concern for distribution system operators (DSOs) over the years. Electricity theft makes …”
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Novel stacked hybrid autoencoder for imputing LISA data gaps von Mao, Ruiting, Lee, Jeong Eun, Edwards, Matthew C.

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SRCAE-STCBiGRU: a fused deep learning model for remaining useful life prediction of rolling bearings von Deng, Linfeng, Yan, Xinhui, Li, Wei

“… for the convolutional neural networks. To solve the problem, this paper proposes a RUL prediction framework based on stacked residual convolutional autoencoder and spatio-temporal convolutional bidirectional gated recurrent unit …”
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Integrating Abnormal Gait Detection with Activities of Daily Living Monitoring in Ambient Assisted Living: A 3D Vision Approach von Diraco, Giovanni, Manni, Andrea, Leone, Alessandro

“… Gait analysis plays a crucial role in detecting and monitoring various neurological and musculoskeletal disorders early. This paper presents a comprehensive …”
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Enhancing wind power prediction with self-attentive variational autoencoders: A comparative study von Harrou, Fouzi, Dairi, Abdelkader, Dorbane, Abdelhakim, Sun, Ying

“… The method incorporates a Variational Autoencoder (VAE) with a self-attention mechanism applied in both the encoder and decoder …”
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Estimating daily global solar radiation using deep learning von Hamdaouy, Hamid, Benghoulam, El Mahjoub, Chaibi, Mohamed, Berrada, Mohamed, Hmaidi, Abdellah El

“… The proposed models include a Variational Autoencoder (VAE) and three novel hybrid architectures …”
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CNN-LSTM-AM approach for outdoor wireless optical communication systems von Abdelsattar, Montaser, Amer, Eman S., Ziedan, Hamdy A., Salama, Wessam M.

“… By combining Convolutional Neural Networks (CNNs), Generative Adversarial Network (GAN), Gated Recurrent Unit (GRU …”
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LSTENet: Cement productivity prediction using a self-attention spatio-temporal variational autoencoder von Shi, Guangsi, Pan, Shirui, Zou, Ruiping

“… In the advent of the Industry 4.0 paradigm, intelligent manufacturing has gained prominence with the integration of advanced Artificial Intelligence (AI) …”
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History matching of petroleum reservoirs using deep neural networks von Alguliyev, Rasim, Aliguliyev, Ramiz, Imamverdiyev, Yadigar, Sukhostat, Lyudmila

“… Our model is based on a convolutional variational autoencoder using AlexNet and bi-directional gated recurrent units …”
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Power Consumption Anomaly Detection of Smart Grid Based on CAE-GRU von Yang, Jing, Song, Qiang, Hu, Lei, Xin, Minyong, Xiao, Renxin

“… Deep learning technologies offer new opportunities in this field. This paper proposes a deep learning approach based on Convolutional Autoencoders (CAEs …”
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Hybrid Time-Series Framework for Daily-Based PM2.5 Forecasting von Chiang, Pei-Wen, Horng, Shi-Jinn

“… : the autoencoder, the dilated convolutional neural network, and the gated recurrent unit. The experimental dataset with 76 monitoring stations from the Taiwan …”
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A novel denoising autoencoder hybrid network for remaining useful life estimation of lithium‐ion batteries von Xia, Wei, Xu, Jinli, Liu, Baolei, Duan, Huiyun

“… ). This architecture integrates a stacked convolutional neural network with subsequent layers of bidirectional gated recurrent units within an encoder–decoder framework …”
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Intrusion Detection System Using Hybrid Model of Denoising Autoencoder and Ladder Variational Autoencoder von Navyakala, SriramVenkata, Rathinam, Rajesh

“… The deep learning based approach for intrusion detection was introduced on reconstructing network traffic dataset using 3 autoencoder architectures and classification using Convolutional Neural Networks (CNN …”
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