NSTL国家科技图书文献中心

1. Dense capsule stacked auto-encoder model based DDoS attack detection and hybrid optimal bandwidth allocation with routing in VANET environment NSTL国家科技图书文献中心

Murali Krishna Tanat... | Manimaran Ponnusamy - 《Vehicular Communications》 - 2025,52(Apr.) - 100888.1～100888.14 - 共14页

摘要： Capsule Stacked* Auto Encoder (DCSAE) network is* | hybridization of the Capsule Network with a Stacked Auto | Encoder. Moreover, the Improved Fire Hawks Optimization | The vehicle ad hoc network, or VANET, is a | fantastic tool for smart transport since it improves

关键词： Artificial neuron | Search space | Back-propagation | Dynamic networks | Global best solution | Exploitation and exploration | Spiral model

2. Energy-efficient coverage in wireless sensor networks based on stacked contractive auto encoder with Manta Ray foraging optimisation algorithm NSTL国家科技图书文献中心

S. Muthukumarasamy | B.R. Tapas Bapu - 《International journal of mobile communications》 - 2025,25(1) - 110～128 - 共19页

摘要： in WSN based on stacked contractive auto encoder | In this manuscript, energy-efficient coverage | with Manta Ray foraging optimisation algorithm is | proposed. Initially, IF-RFKM clustering approach is | proposed for dividing the ROI as several clusters and

关键词： Manta Ray foraging optimisation | region of interest | stacked contractive auto encoder | SCAE | wireless sensor network

3. Improving the Accuracy of Community Detection in Social Network Through a Hybrid Method NSTL国家科技图书文献中心

Mahsa Nooribakhsh | Marta Fernandez-Dieg...... - 《Social Networks Analysis and Mining,Part II》 - International Conference on Advances in Social Networks Analysis and Mining - 2025, - 117～126 - 共10页

摘要： stacked auto-encoder (SAE) for dimensionality reduction | The inherent complexity of social networks in | terms of topological properties requires sophisticated | methodologies to detect communities or clusters. Community | detection in social networks is essential for

关键词： Community detection | Social networks | Stacked auto-encoder | Shuffled frog leaping algorithm

4. Domain Adaptive Coding Transfer Diagnosis Method and Its Application in Fault Diagnosis NSTL国家科技图书文献中心

Jiantao Lu | Zhilin Xiao ... - 《Proceedings of the TEPEN International Workshop on Fault Diagnostic and Prognostic,Volume 2》 - TEPEN International Workshop on Fault Diagnostics and Prognostics - 2025, - 271～281 - 共11页

摘要： domain adaptive auto-encoder* clustering module (DAACM* | (ASTNN). Firstly, in DAACM, a hybrid optimized stacked | It still remains a significant challenge for | intelligent fault diagnosis of rolling bearings with | unlabeled samples, and some transfer diagnosis methods

关键词： Intelligent fault diagnosis | Stacked auto encoder | Transfer learning | Attention mechanism | Domain adaptive

5. An Optimized Sequence for Sparse Channel Estimation in a 5G MIMO System NSTL国家科技图书文献中心

Chanchal Soni | Namit Gupta - 《International Journal of Electronics》 - 2025,112(1/3) - 411～433 - 共23页

摘要： estimator with an enhanced stacked* auto encoder (D-ESAE* | ABSTRACT Recently, the massive Multiple-Input | Multiple-Output (MIMO) system has been integrated with | machine learning approaches to realise automatic channel | state information detection. These methods need high

关键词： Massive MIMO-GFDM | sensing matrix | minimum mean square error | Seagull optimization algorithm and additive white gaussian noise

6. Advancing sea level anomaly modeling in the black sea with LSTM Auto-Encoders: A novel approach NSTL国家科技图书文献中心

Yavuzdogan, A. | Kayikci, E. Tanir - 《Ocean modelling》 - 2025,193 - 共17页

摘要： novel approach using an LSTM Auto-Encoder* model* | effectively. We compared LSTM Auto-Encoder* model performance* | results demonstrate that the LSTM Auto- Encoder* model* | highlight the potential of the LSTM Auto-Encoder* model as* | with that of a Stacked* LSTM network, which learns*

关键词： Sea level | Black sea | LSTM | LSTM Auto-Encoder | Anomaly modeling | Deep learning

7. Building electrical load forecasting with occupancy data based on wireless sensing NSTL国家科技图书文献中心

Liu C. | Xu Z.... - 《Applied energy》 - 2025,380(Feb.15) - 1.1～1.18 - 共18页

摘要： building, an improved stacked* sparse auto-encoder (ISSAE* | © 2024 Elsevier LtdBuilding electrical load | forecasting, as a necessary foundation for building energy | management, is of great significance for building energy | efficiency and sustainable urban development. However, the

关键词： Building electrical load forecasting | Data fusion | Occupancy information | Random vector functional link network | Wireless signal sensing

8. A tool wear monitoring method based on data-driven and physical output NSTL国家科技图书文献中心

Yiyuan Qin | Xianli Liu ... - 《Robotics and Computer Integrated Manufacturing》 - 2025,91(Feb.) - 102820.1～102820.15 - 共15页 - 被引量：1

摘要： fused and downscaled using Stacked Sparse Auto-Encoder | In the process of metal cutting, realizing | effective monitoring of tool wear is of great significance | to ensure the quality of parts machining. To | address the tool wear monitoring (TWM) problem, a tool

关键词： Data-driven | Guidance | Physical model | Staged | Tool wear monitoring

9. Bearing condition monitoring via an unsupervised and enhanced stacked auto-encoder NSTL国家科技图书文献中心

Xu, Fan | Hao, Zhenyu ... - 《Journal of the Brazilian Society of Mechanical Sciences and Engineering》 - 2024,46(6) - 367～ - 共12页

摘要： unsupervised stacked* auto-encoder without an output label* | scatterplot (Lowess). That is, a stacked* auto-encoder adds a* | auto-encoder (though only with an output layer) to | , such as stacked* auto-encoders, stacked de-noising* | decoders at several hidden layers within a stacked* noise*

关键词： Deep learning | Health indicator | Bearing | Stacked auto-encoder | Local weighted regression | Smoothing scatterplot | PERFORMANCE DEGRADATION ASSESSMENT | FAULT-DIAGNOSIS | ROTATING MACHINERY | SPECTRAL KURTOSIS...

10. Radar signal detection under low SNR using stacked auto-encoder and time-frequency domain features NSTL国家科技图书文献中心

Yuan Huang | Tao Liu ... - 《Third International Conference on Algorithms, Microchips, and Network Applications (AMNA 2024): 8-10 March 2024.Jinan, China》 - International Conference on Algorithms, Microchips, and Network Applications - 2024, - 131711A.1～131711A.6 - 共6页

摘要： on stacked* auto-encoder (SAE) and time-frequency* | To improve radar signal detection accuracy of | traditional methods under low SNR, a detection method based | domain features is proposed. The time-domain features | , frequency-domain features and joint time-frequency domain

摘要： Capsule Stacked* Auto Encoder (DCSAE) network is* | hybridization of the Capsule Network with a Stacked Auto | Encoder. Moreover, the Improved Fire Hawks Optimization | The vehicle ad hoc network, or VANET, is a | fantastic tool for smart transport since it improves

关键词： Artificial neuron | Search space | Back-propagation | Dynamic networks | Global best solution | Exploitation and exploration | Spiral model

摘要： in WSN based on stacked contractive auto encoder | In this manuscript, energy-efficient coverage | with Manta Ray foraging optimisation algorithm is | proposed. Initially, IF-RFKM clustering approach is | proposed for dividing the ROI as several clusters and

关键词： Manta Ray foraging optimisation | region of interest | stacked contractive auto encoder | SCAE | wireless sensor network

摘要： stacked auto-encoder (SAE) for dimensionality reduction | The inherent complexity of social networks in | terms of topological properties requires sophisticated | methodologies to detect communities or clusters. Community | detection in social networks is essential for

关键词： Community detection | Social networks | Stacked auto-encoder | Shuffled frog leaping algorithm

摘要： domain adaptive auto-encoder* clustering module (DAACM* | (ASTNN). Firstly, in DAACM, a hybrid optimized stacked | It still remains a significant challenge for | intelligent fault diagnosis of rolling bearings with | unlabeled samples, and some transfer diagnosis methods

关键词： Intelligent fault diagnosis | Stacked auto encoder | Transfer learning | Attention mechanism | Domain adaptive

摘要： estimator with an enhanced stacked* auto encoder (D-ESAE* | ABSTRACT Recently, the massive Multiple-Input | Multiple-Output (MIMO) system has been integrated with | machine learning approaches to realise automatic channel | state information detection. These methods need high

关键词： Massive MIMO-GFDM | sensing matrix | minimum mean square error | Seagull optimization algorithm and additive white gaussian noise

摘要： novel approach using an LSTM Auto-Encoder* model* | effectively. We compared LSTM Auto-Encoder* model performance* | results demonstrate that the LSTM Auto- Encoder* model* | highlight the potential of the LSTM Auto-Encoder* model as* | with that of a Stacked* LSTM network, which learns*

关键词： Sea level | Black sea | LSTM | LSTM Auto-Encoder | Anomaly modeling | Deep learning

摘要： building, an improved stacked* sparse auto-encoder (ISSAE* | © 2024 Elsevier LtdBuilding electrical load | forecasting, as a necessary foundation for building energy | management, is of great significance for building energy | efficiency and sustainable urban development. However, the

关键词： Building electrical load forecasting | Data fusion | Occupancy information | Random vector functional link network | Wireless signal sensing

摘要： fused and downscaled using Stacked Sparse Auto-Encoder | In the process of metal cutting, realizing | effective monitoring of tool wear is of great significance | to ensure the quality of parts machining. To | address the tool wear monitoring (TWM) problem, a tool

关键词： Data-driven | Guidance | Physical model | Staged | Tool wear monitoring

关键词： Deep learning | Health indicator | Bearing | Stacked auto-encoder | Local weighted regression | Smoothing scatterplot | PERFORMANCE DEGRADATION ASSESSMENT | FAULT-DIAGNOSIS | ROTATING MACHINERY | SPECTRAL KURTOSIS...

摘要： on stacked* auto-encoder (SAE) and time-frequency* | To improve radar signal detection accuracy of | traditional methods under low SNR, a detection method based | domain features is proposed. The time-domain features | , frequency-domain features and joint time-frequency domain

关键词： radar signal detection | deep learning | time-frequency domain features | feature extraction

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： Capsule Stacked Auto Encoder (DCSAE) network is | hybridization of the Capsule Network with a Stacked Auto | Encoder. Moreover, the Improved Fire Hawks Optimization | The vehicle ad hoc network, or VANET, is a | fantastic tool for smart transport since it improves

关键词： Artificial neuron | Search space | Back-propagation | Dynamic networks | Global best solution | Exploitation and exploration | Spiral model

摘要： in WSN based on stacked contractive auto encoder | In this manuscript, energy-efficient coverage | with Manta Ray foraging optimisation algorithm is | proposed. Initially, IF-RFKM clustering approach is | proposed for dividing the ROI as several clusters and

关键词： Manta Ray foraging optimisation | region of interest | stacked contractive auto encoder | SCAE | wireless sensor network

摘要： stacked auto-encoder (SAE) for dimensionality reduction | The inherent complexity of social networks in | terms of topological properties requires sophisticated | methodologies to detect communities or clusters. Community | detection in social networks is essential for

关键词： Community detection | Social networks | Stacked auto-encoder | Shuffled frog leaping algorithm

摘要： domain adaptive auto-encoder clustering module (DAACM | (ASTNN). Firstly, in DAACM, a hybrid optimized stacked | It still remains a significant challenge for | intelligent fault diagnosis of rolling bearings with | unlabeled samples, and some transfer diagnosis methods

关键词： Intelligent fault diagnosis | Stacked auto encoder | Transfer learning | Attention mechanism | Domain adaptive

摘要： estimator with an enhanced stacked auto encoder (D-ESAE | ABSTRACT Recently, the massive Multiple-Input | Multiple-Output (MIMO) system has been integrated with | machine learning approaches to realise automatic channel | state information detection. These methods need high

关键词： Massive MIMO-GFDM | sensing matrix | minimum mean square error | Seagull optimization algorithm and additive white gaussian noise

摘要： novel approach using an LSTM Auto-Encoder model | effectively. We compared LSTM Auto-Encoder model performance | results demonstrate that the LSTM Auto- Encoder model | highlight the potential of the LSTM Auto-Encoder model as | with that of a Stacked LSTM network, which learns

关键词： Sea level | Black sea | LSTM | LSTM Auto-Encoder | Anomaly modeling | Deep learning

摘要： building, an improved stacked sparse auto-encoder (ISSAE | © 2024 Elsevier LtdBuilding electrical load | forecasting, as a necessary foundation for building energy | management, is of great significance for building energy | efficiency and sustainable urban development. However, the

关键词： Building electrical load forecasting | Data fusion | Occupancy information | Random vector functional link network | Wireless signal sensing

摘要： fused and downscaled using Stacked Sparse Auto-Encoder | In the process of metal cutting, realizing | effective monitoring of tool wear is of great significance | to ensure the quality of parts machining. To | address the tool wear monitoring (TWM) problem, a tool

关键词： Data-driven | Guidance | Physical model | Staged | Tool wear monitoring

摘要： unsupervised stacked auto-encoder without an output label | scatterplot (Lowess). That is, a stacked auto-encoder adds a | auto-encoder (though only with an output layer) to | , such as stacked auto-encoders, stacked de-noising | decoders at several hidden layers within a stacked noise

关键词： Deep learning | Health indicator | Bearing | Stacked auto-encoder | Local weighted regression | Smoothing scatterplot | PERFORMANCE DEGRADATION ASSESSMENT | FAULT-DIAGNOSIS | ROTATING MACHINERY | SPECTRAL KURTOSIS...

摘要： on stacked auto-encoder (SAE) and time-frequency | To improve radar signal detection accuracy of | traditional methods under low SNR, a detection method based | domain features is proposed. The time-domain features | , frequency-domain features and joint time-frequency domain

关键词： radar signal detection | deep learning | time-frequency domain features | feature extraction

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： Capsule Stacked* Auto Encoder (DCSAE) network is* | hybridization of the Capsule Network with a Stacked Auto | Encoder. Moreover, the Improved Fire Hawks Optimization | The vehicle ad hoc network, or VANET, is a | fantastic tool for smart transport since it improves

摘要： domain adaptive auto-encoder* clustering module (DAACM* | (ASTNN). Firstly, in DAACM, a hybrid optimized stacked | It still remains a significant challenge for | intelligent fault diagnosis of rolling bearings with | unlabeled samples, and some transfer diagnosis methods

摘要： estimator with an enhanced stacked* auto encoder (D-ESAE* | ABSTRACT Recently, the massive Multiple-Input | Multiple-Output (MIMO) system has been integrated with | machine learning approaches to realise automatic channel | state information detection. These methods need high

摘要： novel approach using an LSTM Auto-Encoder* model* | effectively. We compared LSTM Auto-Encoder* model performance* | results demonstrate that the LSTM Auto- Encoder* model* | highlight the potential of the LSTM Auto-Encoder* model as* | with that of a Stacked* LSTM network, which learns*

摘要： building, an improved stacked* sparse auto-encoder (ISSAE* | © 2024 Elsevier LtdBuilding electrical load | forecasting, as a necessary foundation for building energy | management, is of great significance for building energy | efficiency and sustainable urban development. However, the

摘要： on stacked* auto-encoder (SAE) and time-frequency* | To improve radar signal detection accuracy of | traditional methods under low SNR, a detection method based | domain features is proposed. The time-domain features | , frequency-domain features and joint time-frequency domain