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High-speed multimode fiber imaging system based on conditional generative adversarial network

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Chinese Optics Letters 2021年第8期19卷 1-5页

作者： Zhenming Yu Zhenyu Ju Xinlei Zhang Ziyi Meng Feifei Yin Kun Xu State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and TelecommunicationsBeijing 100876China

The multimode fiber(MMF)has great potential to transmit high-resolution images with less invasive methods in endoscopy due to its large number of spatial modes and small core ***,spatial modes crosstalk will inevitably occur in MMFs,which makes the received images become speckles.A conditional generative adversarial network(GAN)composed of a generator and a discriminator was utilized to reconstruct the received *** conduct an MMF imaging experimental system of transmitting over 1 m MMF with a 50μm *** with the conventional method of U-net,this conditional GAN could reconstruct images with fewer training datasets to achieve the same performance and shows higher feature extraction capability.

关键词： fiber optics imaging imaging systems deep learning conditional generative adversarial network

Deep conditional generative adversarial network for History Matching with Complex Geologies

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Deep Conditional Generative Adversarial Network for History ...

Toward Improved Accuracy in Quasi-Static Elastography Using Deep Learning

第40届中国控制会议

作者： Yunxue Lv Xiaopeng Ma Baodi Liu Jiaxing Li Kai Zhang Weifeng Liu College of Oceanography and Space Informatics China University of Petroleum School of Petroleum Engineering China University of Petroleum College of Control Science and Engineering China University of Petroleum

History matching is an important part of reservoir numerical simulation research and the basis for predicting oilfield development *** present,integrated smoother and ensemble Kalman filter methods are the mainstream methods of history ***,these data assimilation algorithms need to be based on Gaussian assumptions and cannot guarantee the geological authenticity of the modified ***,to achieve end-to-end processing of dynamic data to modify the reservoir model,while ensuring that the distribution of data does not need to be restricted by Gaussian distribution,this paper proposes the use of the deep conditional generative adversarial network（CGAN） method for automatic reservoir history matching for the first *** mapping relationship between static geological reservoir parameters and dynamic production data is found through the training generative network,which reduces the uncertainty of the reservoir model obtained by automatic history matching and explains parallel offline *** numerical simulation results show that the prediction results on the 2D two-phase flow model are consistent with the results of the proposed deep conditional generative adversarial network method.

关键词： Deep Learning conditional generative adversarial network History Matching Inversion

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 911-935页

作者： Yue Mei Jianwei Deng Dongmei Zhao Changjiang Xiao Tianhang Wang Li Dong Xuefeng Zhu Department of Engineering Mechanics State Key Laboratory of Structural Analysis for Industrial EquipmentDalian University of TechnologyDalian116023China International Research Center for Computational Mechanics Dalian University of TechnologyDalian116023China DUT-BSU Joint Institute Dalian University of TechnologyDalian116023China Department of Automotive Engineering Tongji UniversityShanghai201804China *** SeattleWA98109USA School of Automotive Engineering Dalian University of TechnologyDalian116023China

Elastography is a non-invasive medical imaging technique to map the spatial variation of elastic properties of soft *** quality of reconstruction results in elastography is highly sensitive to the noise induced by imaging measurements and *** address this issue,we propose a deep learning(DL)model based on conditional generative adversarial networks(cGANs)to improve the quality of nonhomogeneous shear modulus *** train this model,we generated a synthetic displacement field with finite element simulation under known nonhomogeneous shear modulus *** the simulated and experimental displacement fields are used to validate the proposed *** reconstructed results demonstrate that the DL model with synthetic training data is able to improve the quality of the reconstruction compared with the well-established optimization ***,we emphasize that our DL model is only trained on synthetic *** might provide a way to alleviate the challenge of obtaining clinical or experimental data in ***,this work addresses several fatal issues in applying the DL technique into elastography,and the proposed method has shown great potential in improving the accuracy of the disease diagnosis in clinical medicine.

关键词： Nonhomogeneous elastic property distribution reconstruction deep learning finite element method inverse problem elastography conditional generative adversarial network

Fast prediction of indoor airflow distribution inspired by synthetic image generation artificial intelligence

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Building Simulation 2023年第7期16卷 1219-1238页

作者： Cary A.Faulkner Dominik S.Jankowski John E.Castellini Jr Wangda Zuo Philipp Epple Michael D.Sohn Ali Taleb Zadeh Kasgari Walid Saad Department of Mechanical Engineering University of Colorado BoulderBoulderCOUSA HySON Institut SonnebergGermany Department of Architectural Engineering Pennsylvania State UniversityUniversity ParkPAUSA Department of Mechanical Engineering Coburg University of Applied SciencesCoburgGermany Energy Analysis and Environmental Impacts Division Lawrence Berkeley National LaboratoryBerkeleyCAUSA Department of Electrical and Computer Engineering Virginia TechArlingtonVAUSA

Prediction of indoor airflow distribution often relies on high-fidelity,computationally intensive computational fluid dynamics(CFD)*** intelligence(Al)models trained by CFD data can be used for fast and accurate prediction of indoor airflow,but current methods have limitations,such as only predicting limited outputs rather than the entire flow ***,conventional Al models are not always designed to predict different outputs based on a continuous input range,and instead make predictions for one or a few discrete *** work addresses these gaps using a conditional generative adversarial network(CGAN)model approach,which is inspired by current state-of-the-art Al for synthetic image *** create a new Boundary Condition CGAN(BC-CGAN)model by extending the original CGAN model to generate 2D airflow distribution images based on a continuous input parameter,such as a boundary ***,we design a novel feature-driven algorithm to strategically generate training data,with the goal of minimizing the amount of computationally expensive data while ensuring training quality of the Al *** BC-CGAN model is evaluated for two benchmark airflow cases:an isothermal lid-driven cavity flow and a non-isothermal mixed convection flow with a heated *** also investigate the performance of the BC-CGAN models when training is stopped based on different levels of validation error *** results show that the trained BC-CGAN model can predict the 2D distribution of velocity and temperature with less than 5%relative error and up to about 75,ooo times faster when compared to reference CFD *** proposed feature-driven algorithm shows potential for reducing the amount of data and epochs required to train the Al models while maintaining prediction accuracy,particularly when the flow changes non-linearlywith respectto an input.

关键词： artificial intelligence indoor airflow conditional generative adversarial network computational fluid dynamics

AUTOSIM:Automated Urban Traffic Operation Simulation via Meta-Learning

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IEEE/CAA Journal of Automatica Sinica 2023年第9期10卷 1871-1881页

作者： Yuanqi Qin Wen Hua Junchen Jin Jun Ge Xingyuan Dai Lingxi Li Xiao Wang Fei-Yue Wang Zhejiang Lab Hangzhou 310030China ECARX Technology Co. Ltd.Shanghai 201811China IEEE Zhejiang Supcon Information Company Ltd. Hangzhou 310052China the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Indiana University-Purdue University Indianapolis(IUPUI) IndianapolisIndiana 46202 USA School of Artificial Intelligence Anhui UniversityHefei 230039China

Online traffic simulation that feeds from online information to simulate vehicle movement in real-time has recently seen substantial advancement in the development of intelligent transportation systems and urban traffic *** has been a challenging problem due to three aspects:1)The diversity of traffic patterns due to heterogeneous layouts of urban intersections;2)The nature of complex spatiotemporal correlations;3)The requirement of dynamically adjusting the parameters of traffic models in a real-time *** cater to these challenges,this paper proposes an online traffic simulation framework called automated urban traffic operation simulation via meta-learning(AUTOSIM).In particular,simulation models with various intersection layouts are automatically generated using an open-source simulation tool based on static traffic geometry *** a meta-learning technique,AUTOSIM enables an automated learning process for dynamic model settings of traffic scenarios featured with different spatiotemporal ***,AUTOSIM is capable of adapting traffic model parameters according to dynamic traffic information in real-time by using a *** computational experiments,we demonstrate the effectiveness of the meta-learningbased framework that is capable of providing reliable supports to real-time traffic simulation and dynamic traffic operations.

关键词： conditional generative adversarial network signalized urban networks short-term link speed prediction

adversarial Training-Aided Time-Varying Channel Prediction for TDD/FDD Systems

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China Communications 2023年第6期20卷 100-115页

作者： Zhen Zhang Yuxiang Zhang Jianhua Zhang Feifei Gao State Key Lab of networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China Institute for Artificial Intelligence Tsinghua UniversityBeijing 100084China

In this paper, a time-varying channel prediction method based on conditional generative adversarial network(CPcGAN) is proposed for time division duplexing/frequency division duplexing(TDD/FDD) systems. CPc GAN utilizes a discriminator to calculate the divergence between the predicted downlink channel state information(CSI) and the real sample distributions under a conditional constraint that is previous uplink CSI. The generator of CPcGAN learns the function relationship between the conditional constraint and the predicted downlink CSI and reduces the divergence between predicted CSI and real *** capability of CPcGAN fitting data distribution can capture the time-varying and multipath characteristics of the channel well. Considering the propagation characteristics of real channel, we further develop a channel prediction error indicator to determine whether the generator reaches the best state. Simulations show that the CPcGAN can obtain higher prediction accuracy and lower system bit error rate than the existing methods under the same user speeds.

关键词： channel prediction time-varying channel conditional generative adversarial network multipath channel deep learning

Fluo-Fluo translation based on deep learning

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Chinese Optics Letters 2022年第3期20卷 82-88页

作者： Zhengfen Jiang Boyi Li Tho N.H.T.Tran Jiehui Jiang Xin Liu Dean Ta School of Communication&Information Engineering Shanghai UniversityShanghai 200A44China Academy for Engineering&Technology Fudan UniversityShanghai 200433China State Key Laboratory of Medical Neurobiology Fudan UniversityShanghai 200433China Center for Biomedical Engineering Fudan UniversityShanghai 200433China

Fluorescence microscopy technology uses fluorescent dyes to provide highly specific visualization of cell components,which plays an important role in understanding the subcellular ***,fluorescence microscopy has some limitations such as the risk of non-specific cross labeling in multi-labeled fluorescent staining and limited number of fluo-rescence labels due to spectral *** paper proposes a deep learning-based fluorescence to fluorescence[Flu0-Fluo]translation method,which uses a conditional generative adversarial network to predict a fluorescence image from another fluorescence image and further realizes the multi-label fluorescent *** cell types used include human motor neurons,human breast cancer cells,rat cortical neurons,and rat *** effectiveness of the method is verified by successfully generating virtual fluorescence images highly similar to the true fluorescence *** study shows that a deep neural network can implement Fluo-Fluo translation and describe the localization relationship between subcellular structures labeled with different fluorescent *** proposed Fluo-Fluo method can avoid non-specific cross labeling in multi-label fluorescence staining and is free from spectral *** theory,an unlimited number of fluorescence images can be predicted from a single fluorescence image to characterize cells.

关键词： deep learning conditional generative adversarial network fluorescence image image translation