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Reactive dividing wall column for hydrolysis of methyl acetate:Design and control

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Chinese Journal of Chemical Engineering 2016年第10期24卷 1360-1368页

作者： Lumin Li Lanyi Sun Delian Yang Wang Zhong Yi Zhu Yuanyu Tian State Key laboratory of Heavy Oil processing China University of PetroleumQingdao 266580China Synopsys.inc. 690 East Middlefield RoadMountain ViewCA 94043USA

Reactive distillation and dividing wall column distillation are two kinds of effective separation technologies,and their integrated configuration,reactive dividing wall column(RDWC),presents attractive *** this study,the rigorous simulation of RDWC for methyl acetate hydrolysis is performed,and sensitivity analysis is conducted to obtain the minimum reboiler *** a comparison is made between the conventional process and RDWC process,and it shows that 20.1% energy savings can be achieved by RDWC *** addition,the dynamic characteristic of RDWC is studied and an effective control strategy is *** simple PI control scheme with three temperature loops can obtain reasonable control performance and maintain products at desired *** is proved that this RDWC process is an energy efficiency alternative with good controllability.

关键词： Reactive dividing wall column Methyl acetate Hydrolysis Design Control

ixed Cadzow filtering method in fractional Fourier domain

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Applied Geophysics 2018年第2期15卷 271-279,363,364页

作者： Cao Zhong-Lin Cao Jun-Xing Wu Fu-Rong He Guang-Ming Zhou Qiang Wu Yu-Lin Chengdu University of Technology Chengdu Sichuan 610059 China Research and Development Center BGP Inc. CNPC Zhuozhou Hebei 072750 China mountain Geophysical Technology Test Center CNPC Chengdu Sichuan 610213 China

Conventional frequency domain method used in random noise attenuation singular value decomposition （SVD） filtering processing causes bending event damage. To mitigate this problem, we present a mixed Cadzow filtering method based on fractional Fourier transform to suppress random noise in 3D seismic data. First, the seismic data is transformed to the time-frequency plane via the fractional Fourier transform. Second, based on the Eigenimage filtering method and Cadzow filtering method, the mixed high-dimensional Hankel matrix is built; then, SVD is performed. Finally, random noise is eliminated effectively by reducing the rank of the matrix. The theoretical model and real applications of the mixed filtering method in a region of Sichuan show that our method can not only suppress noise effectively but also preserve the frequency and phase of effective signals quite well and significantly improve the signal-to-noise ratio of 3D post-stack seismic data.

关键词： 3D random noise Cadzow filtering method fractional Fourier transforms

Deep Learning Based Target Tracking and Classification for Infrared Videos Using Compressive Measurements

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Journal of Signal and Information processing 2019年第4期10卷 167-199页

作者： Chiman Kwan Bryan Chou Jonathan Yang Trac Tran Applied Research LLC Rockville MD USA Google Inc. Mountain View CA USA Department of Electrical and Computer Engineering The Johns Hopkins University Baltimore MD USA

Although compressive measurements save data storage and bandwidth usage, they are difficult to be used directly for target tracking and classification without pixel reconstruction. This is because the Gaussian random matrix destroys the target location information in the original video frames. This paper summarizes our research effort on target tracking and classification directly in the compressive measurement domain. We focus on one particular type of compressive measurement using pixel subsampling. That is, original pixels in video frames are randomly subsampled. Even in such a special compressive sensing setting, conventional trackers do not work in a satisfactory manner. We propose a deep learning approach that integrates YOLO (You Only Look Once) and ResNet (residual network) for multiple target tracking and classification. YOLO is used for multiple target tracking and ResNet is for target classification. Extensive experiments using short wave infrared (SWIR), mid-wave infrared (MWIR), and long-wave infrared (LWIR) videos demonstrated the efficacy of the proposed approach even though the training data are very scarce.

关键词： Target Tracking Classification Compressive Sensing SWIR MWIR LWIR YOLO ResNet Infrared Videos

Target Tracking and Classification Using Compressive Measurements of MWIR and LWIR Coded Aperture Cameras

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Journal of Signal and Information processing 2019年第3期10卷 73-95页

作者： Chiman Kwan Bryan Chou Jonathan Yang Akshay Rangamani Trac Tran Jack Zhang Ralph Etienne-Cummings Applied Research LLC Rockville Maryland USA Google Inc. Mountain View California USA Department of Electrical and Computer Engineering the Johns Hopkins University Baltimore USA Department of Electrical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA

Pixel-wise Code Exposure (PCE) camera is one type of compressive sensing camera that has low power consumption and high compression ratio. Moreover, a PCE camera can control individual pixel exposure time that can enable high dynamic range. Conventional approaches of using PCE camera involve a time consuming and lossy process to reconstruct the original frames and then use those frames for target tracking and classification. In this paper, we present a deep learning approach that directly performs target tracking and classification in the compressive measurement domain without any frame reconstruction. Our approach has two parts: tracking and classification. The tracking has been done using YOLO (You Only Look Once) and the classification is achieved using Residual Network (ResNet). Extensive experiments using mid-wave infrared (MWIR) and long-wave infrared (LWIR) videos demonstrated the efficacy of our proposed approach.

关键词： Target Tracking Classification Compressive Sensing MWIR LWIR YOLO ResNet Infrared Videos

Digital staining in optical microscopy using deep learning-a review

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PhotoniX 2023年第1期4卷 64-95页

作者： Lucas Kreiss Shaowei Jiang Xiang Li Shiqi Xu Kevin C.Zhou Kyung Chul Lee Alexander Mühlberg Kanghyun Kim Amey Chaware Michael Ando Laura Barisoni Seung Ah Lee Guoan Zheng Kyle J.Lafata Oliver Friedrich Roarke Horstmeyer Department of Biomedical Engineering Duke UniversityDurhamNC 27708USA Institute of Medical Biotechnology Friedrich-Alexander University(FAU)ErlangenGermany Department of Biomedical Engineering University of ConnecticutMansfieldConnecticutUSA Department of Radiation Physics Duke UniversityDurhamNC 27708USA Department of Electrical Engineering&Computer Sciences University of CaliforniaBerkeleyCAUSA School of Electrical&Electronic Engineering Yonsei UniversitySeoul 03722Republic of Korea Google Inc.Mountain ViewCA 94043USA Department of Pathology Duke UniversityDurhamNC 27708USA

Until recently,conventional biochemical staining had the undisputed status as well-established benchmark for most biomedical problems related to clinical diagnostics,fundamental research and *** this role as gold-standard,staining protocols face several challenges,such as a need for extensive,manual processing of samples,substantial time delays,altered tissue homeostasis,limited choice of contrast agents,2D imaging instead of 3D tomography and many ***-free optical technologies,on the other hand,do not rely on exogenous and artificial markers,by exploiting intrinsic optical contrast mechanisms,where the specificity is typically less obvious to the human *** the past few years,digital staining has emerged as a promising concept to use modern deep learning for the translation from optical contrast to established biochemical contrast of actual *** this review article,we provide an in-depth analysis of the current state-of-the-art in this field,suggest methods of good practice,identify pitfalls and challenges and postulate promising advances towards potential future implementations and applications.

关键词： Deep learning Digital staining Optical microscopy Virtual staining In-silica Pseudo-H&E Virtual fluorescence Generative models Image translation