Search Results - convolutional neural network-autoencoder ((architecture OR architektura) OR architektury)

Recreating Fingerprint Images by Convolutional Neural Network Autoencoder Architecture by Saponara, Sergio, Elhanashi, Abdussalam, Zheng, Qinghe

“… In this work, a convolutional neural network autoencoder has been used to reconstruct fingerprint images…”
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Plantar Pressure-Based Gait Recognition with and Without Carried Object by Convolutional Neural Network-Autoencoder Architecture by Wu, Chin-Cheng, Tsai, Cheng-Wei, Wu, Fei-En, Chiang, Chi-Hsuan, Chiou, Jin-Chern

“… To improve the disadvantage, we proposed a convolutional neural network autoencoder (CNN-AE) architecture for user classification based on plantar pressure gait recognition…”
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Multivariate time-series cyberattack detection in the distributed secondary control of AC microgrids with convolutional neural network autoencoder ensemble by Roshanzadeh, Behshad, Choi, Jeewon, Bidram, Ali, Martínez-Ramón, Manel

“… An autoencoder is a neural network architecture, where the model is trained to reconstruct its input in an unsupervised manner…”
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A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders by Essien, Aniekan, Giannetti, Cinzia

“… The model comprises a deep convolutional…”
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NEURAL NETWORK AUTOENCODER MODEL FOR FORMING REDUCED VECTOR CHARACTERISTICS OF ECG SIGNALS by Mnevec, Anton, Ivanushkina, Natalia

“…The paper considers the actual problem of improving neural network models for the classification of cardiovascular pathologies by compressing the information contained in electrocardiographic (ECG) signals…”
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Detection of Bleeding Events in Electronic Health Record Notes Using Convolutional Neural Network Models Enhanced With Recurrent Neural Network Autoencoders: Deep Learning Approach by Li, Rumeng, Hu, Baotian, Liu, Feifan, Liu, Weisong, Cunningham, Francesca, McManus, David D, Yu, Hong

“…Bleeding events are common and critical and may cause significant morbidity and mortality. High incidences of bleeding events are associated with…”
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Multitask-Guided Deep Clustering With Boundary Adaptation by Zhang, Xiaobo, Wang, Tao, Zhao, Xiaole, Wen, Dengmin, Zhai, Donghai

“… In this study, a multitask-guided deep clustering (DC) with boundary adaptation (MTDC-BA) based on a convolutional neural network autoencoder (CNN-AE) is proposed…”
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Unsupervised electric motor fault detection by using deep autoencoders by Principi, Emanuele, Rossetti, Damiano, Squartini, Stefano, Piazza, Francesco

“… Deep neural networks have been successfully employed for this task, but, up to the authors &#x02BC knowledge, they have never been used in an unsupervised scenario…”
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Deep convolutional autoencoders for the time–space reconstruction of liquid rocket engine flames by Zapata Usandivaras, José F., Bauerheim, Michael, Cuenot, Bénédicte, Urbano, Annafederica

“…The integration of high-fidelity numerical simulations into the rocket engine design-cycle promises to cut costs in an ever more competitive launch market…”
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A Deep-learning Anomaly-detection Method to Identify Gamma-Ray Bursts in the Ratemeters of the AGILE Anticoincidence System by Parmiggiani, N., Bulgarelli, A., Ursi, A., Macaluso, A., Di Piano, A., Fioretti, V., Aboudan, A., Baroncelli, L., Addis, A., Tavani, M., Pittori, C.

“… The model is implemented with a convolutional neural network autoencoder…”
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Cutting-Edge Convolutional Neural Network Autoencoders for Anomaly Detection in ECG Signals: Advancements in Early Cardiac Diagnosis by Kaushik, Pratham, Sharma, Pooja

“…The current research deals with the complex domain of ECG signal processing and classification using convolutional neural network auto-encoders…”
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Recent advances in deep learning models: a systematic literature review by Malhotra, Ruchika, Singh, Priya

“… Convolutional Neural Network, Recurrent Neural Network, Long Short Term Memory, Generative Adversarial Network, Autoencoder and Transformer Neural Network…”
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A systematic review of rolling bearing fault diagnoses based on deep learning and transfer learning: Taxonomy, overview, application, open challenges, weaknesses and recommendations by Hakim, Mohammed, Omran, Abdoulhdi A. Borhana, Ahmed, Ali Najah, Al-Waily, Muhannad, Abdellatif, Abdallah

“… The most widely used DL algorithms for detecting bearing faults include Convolutional Neural Network, Recurrent neural network, Autoencoder, and Generative Adversarial Network…”
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Approximating Human-Level 3D Visual Inferences With Deep Neural Networks by O’Connell, Thomas P., Bonnen, Tyler, Friedman, Yoni, Tewari, Ayush, Sitzmann, Vincent, Tenenbaum, Joshua B., Kanwisher, Nancy

“… Next, we construct a set of candidate 3D-aware DNNs including 3D neural field (Light Field Network), autoencoder, and convolutional architectures…”
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An intelligent fault detection and diagnosis monitoring system for reactor operational resilience: Unknown fault detection by Mendoza, Mario, Tsvetkov, Pavel V.

“…Multiple advanced reactor designs envision deployment scenarios that feature reactor operations with significantly reduced operating staff compared to present…”
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Joint autoencoder-regressor deep neural network for remaining useful life prediction by İnce, Kürşat, Genc, Yakup

“…•We introduce a joint autoencoder and regressor architecture for remaining useful life prediction, and demonstrate the effectiveness of this model on two prognostics benchmark…”
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Vehicle Detection From UAV Imagery With Deep Learning: A Review by Bouguettaya, Abdelmalek, Zarzour, Hafed, Kechida, Ahmed, Taberkit, Amine Mohammed

“…Vehicle detection from unmanned aerial vehicle (UAV) imagery is one of the most important tasks in a large number of computer vision-based applications. This…”
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Innovative Noise Extraction and Denoising in Low-Dose CT Using a Supervised Deep Learning Framework by Zhang, Wei, Salmi, Abderrahmane, Yang, Chifu, Jiang, Feng

“…Low-dose computed tomography (LDCT) imaging is a critical tool in medical diagnostics due to its reduced radiation exposure. However, this reduction often…”
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Application of computer vision and deep learning for flame monitoring and combustion anomaly detection by Abdurakipov, S, Butakov, E

“… We have developed a deep neural network autoencoder, which is a combination of convolutional layers, fully-connected layers and upsampling layers…”
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Recent advances and future research directions in deep learning as applied to geochemical mapping by Xu, Ying, Zuo, Renguang, Chen, Zhiyi, Shi, Zixian, Kreuzer, Oliver P.

“… (i.e., from 2019 to 2025), namely deep belief network, recurrent neural network, convolutional neural network, autoencoder, and generative adversarial network…”
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