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检索条件"主题词=Autoencoder"
4206 条 记 录,以下是121-130 订阅
排序:
autoencoder-Based Feature Extraction for Identifying Hate Speech Spreaders in Social Media
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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2024年 第4期11卷 4819-4827页
作者: Kumar, Gunjan Singh, Jyoti Prakash Singh, Amit Kumar Natl Inst Technol Patna Dept Comp Sci & Engn Patna 800005 India
Hate speech on social media has become a big problem, making regular users very upset and giving victims depression and suicidal thoughts. Early identification of the user spreading this type of hate speech may be a b... 详细信息
来源: 评论
autoencoder-based nonlinear Bayesian locally weighted regression for soft sensor development
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ISA TRANSACTIONS 2020年 103卷 143-155页
作者: Liu, Kang Shao, Weiming Chen, Guoming China Univ Petr Ctr Offshore Engn & Safety Technol Qingdao 266580 Peoples R China China Univ Petr Coll New Energy Qingdao 266580 Peoples R China
The framework of locally weighted learning (LWL) has established itself as a popular tool for developing nonlinear soft sensors in process industries. For LWL-based soft sensors, the key factor for achieving high perf... 详细信息
来源: 评论
autoencoder Neural Network Based Intelligent Hybrid Beamforming Design for mmWave Massive MIMO Systems
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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2020年 第3期6卷 1019-1030页
作者: Tao, Jiyun Chen, Jienan Xing, Jing Fu, Shengli Xie, Junfei Univ Elect Sci & Technol China Natl Key Lab Sci & Technol Commun Chengdu 611731 Peoples R China Univ North Texas Dept Elect Engn Denton TX 76201 USA San Diego State Univ Dept Elect & Comp Engn San Diego CA 92182 USA
Hybrid beamforming (HB) is a promising technology for the millimeter-wave (mmWave) massive multiple-input-multiple-output (MIMO) system, which supplies high data capacity with low complexity for next-generation commun... 详细信息
来源: 评论
autoencoder node saliency: Selecting relevant latent representations
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PATTERN RECOGNITION 2019年 88卷 643-653页
作者: Fan, Ya Ju Lawrence Livermore Natl Lab Ctr Appl Sci Comp Livermore CA 94551 USA
The autoencoder is an artificial neural network that performs nonlinear dimension reduction and learns hidden representations of unlabeled data. With a linear transfer function it is similar to the principal component... 详细信息
来源: 评论
autoencoder with Fitting Network for Terahertz Wireless Communications:A Deep Learning Approach
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China Communications 2022年 第3期19卷 172-180页
作者: Zhaohui Huang Dongxuan He Jiaxuan Chen Zhaocheng Wang Sheng Chen Beijing National Research Center for Information Science and Technology Department of Electronic EngineeringTsinghua UniversityBeijing 100084China Shenzhen International Graduate School Tsinghua UniveristyShenzhen 518055China School of Electronics and Computer Science University of SouthamptonSouthampton SO171BJU.K.
Terahertz wireless communication has been regarded as an emerging technology to satisfy the ever-increasing demand of ultra-high-speed wireless ***,affected by the imperfections of cheap and energy-efficient Terahertz... 详细信息
来源: 评论
autoencoder-Based End-to-End OTFS System Design With Hardware Impairments
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IEEE WIRELESS COMMUNICATIONS LETTERS 2024年 第8期13卷 2285-2289页
作者: Singh, Amit Sharma, Sanjeev Sharma, Mohit Deka, Kuntal da Costa, Daniel B. Indian Inst Technol BHU Varanasi Varanasi 221005 India Technol Innovat Inst AI & Telecom Dept Abu Dhabi U Arab Emirates Indian Inst Technol Guwahati EEE Dept Gauhati 781039 India King Fahd Univ Petr & Minerals Dept Elect Engn Dhahran 31261 Saudi Arabia
Orthogonal time-frequency space (OTFS) modulation is an innovative waveform which effectively multiplexes information symbols across a delay-Doppler (DD) plane, resulting in a superior performance, particularly in dou... 详细信息
来源: 评论
autoencoder-Based Enhanced Orthogonal Time Frequency Space Modulation
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IEEE COMMUNICATIONS LETTERS 2023年 第10期27卷 2628-2632页
作者: Tek, Yusuf Islam Dogukan, Ali Tugberk Basar, Ertugrul Koc Univ Dept Elect & Elect Engn Commun Res & Innovat Lab CoreLab TR-34450 Istanbul Turkiye
Orthogonal time frequency space (OTFS) is a novel waveform that provides a superior performance in doubly-dispersive channels. Since it spreads information symbols across the entire delay-Doppler plane, OTFS can achie... 详细信息
来源: 评论
autoencoder as a New Method for Maintaining Data Privacy While Analyzing Videos of Patients With Motor Dysfunction: Proof-of-Concept Study
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JOURNAL OF MEDICAL INTERNET RESEARCH 2020年 第5期22卷 e16669页
作者: D'Souza, Marcus Van Munster, Caspar E. P. Dorn, Jonas F. Dorier, Alexis Kamm, Christian P. Steinheimer, Saskia Dahlke, Frank Uitdehaag, Bernard M. J. Kappos, Ludwig Johnson, Matthew Univ Hosp Basel Dept Med Neurol Clin & Policlin Petersgraben 4 Basel 4031 Switzerland Univ Hosp Basel Dept Biomed Neurol Clin & Policlin Petersgraben 4 Basel 4031 Switzerland Univ Hosp Basel Dept Clin Res Neurol Clin & Policlin Petersgraben 4 Basel 4031 Switzerland Univ Basel Petersgraben 4 Basel 4031 Switzerland Univ Amsterdam Multiple Sclerosis Ctr Amsterdam Dept Neurol Med Ctr Amsterdam Netherlands Novartis Pharma AG Basel Switzerland Luzerner Kantonsspital Neuroctr Luzern Switzerland Univ Bern Dept Neurol Inselspital Bern Switzerland Microsoft Res Cambridge England
Background: In chronic neurological diseases, especially in multiple sclerosis (MS), clinical assessment of motor dysfunction is crucial to monitor the disease in patients. Traditional scales are not sensitive enough ... 详细信息
来源: 评论
autoencoder-based anomaly root cause analysis for wind turbines
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Energy and AI 2021年 第2期4卷 57-65页
作者: Cyriana M.A.Roelofs Marc-Alexander Lutz Stefan Faulstich Stephan Vogt Fraunhofer IEE Konigstor 59Kassel 34119Germany Intelligent Embedded Systems Universitat KasselWilhelmshoher Allee 67Kassel 34121Germany
A popular method to detect anomalous behaviour or specific failures in wind turbine sensor data uses a specific type of neural network called an *** models have proven to be very successful in detecting such deviation... 详细信息
来源: 评论
autoencoder Model Using Edge Enhancement to Detect Communities in Complex Networks
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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2023年 第2期48卷 1303-1314页
作者: Chaudhary, Laxmi Singh, Buddha Jawaharlal Nehru Univ New Delhi 110067 India
Community structure is the utmost significant characteristics in complex networks. Numerous algorithms of community detection have been developed so far. Some methods consider only the lower-order framework, i.e. node... 详细信息
来源: 评论