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Networked Kalman filtering with combined constraints of bandwidth and random delay

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Science China(Information Sciences) 2012年第10期55卷 2343-2357页

作者： ge quanbo 1,2,wen chenglin 1 & XU TingLiang 1 1 School of Automation,Hangzhou Dianzi University,Hangzhou 310018,China 2 State Key Laboratory of Industrial Control Technology,Zhejiang University,Hangzhou 310027,China 1. School of Automation Hangzhou Dianzi University Hangzhou 310018 China2. State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou 310027 China

Kalman filter is a powerful tool in target tracking and self-localization across wireless sensor networks with many *** paper considers the filters design for networked systems with combined constraints of bandwidth and random delay,and proposes a kind of universal networked Kalman estimator for given linear time invariant(LTI) or determinate parameters(DP) systems,which are also called linear and determinate parameters(LDP) systems ***,two modeling ways to treat bit quantization error vector are given and the corresponding LDP systems taken after bit quantization are ***,we introduce an equivalent weighted summation form of the conventional linear Kalman filter,which fully uses two essential characteristics,namely offline computation property from the LDP systems and linear weighted summation property from the linear minimum mean square error ***,by adopting the replacement and innovation compensation operations,a kind of integrated networked estimator,involving two estimation algorithms with different modeling ways to bit quantization error actually,is presented based on the weighted summation filter for random delay *** proposed universal networked estimation algorithms have some outstanding advantages such as extensive application,concise algorithm structure,high estimate accuracy,and good running *** examples are demonstrated to validate the proposed networked estimation algorithms.

关键词： Kalman filter LDP systems LMMSE limited bandwidth random delay

HYBRID FILTER WITH PREDICT-ESTIMATOR AND COMPENSATOR FOR THE LINEAR TIME INVARIANT DELAYED SYSTEM

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Journal of Electronics(China) 2009年第5期26卷 666-672页

作者： wen chenglin ge quanbo Feng Xiaoliang Institute of Information and Control Hangzhou Dianzi University Hangzhou 310018 China

This paper investigates the problem of real-time estimation for one kind of linear time invariant systems which subject to limited communication capacity. The communication limitations include signal transmission delay, the out-of-sequence measurements and data packet dropout, which appear typically in a network environment. The kernel of filter design is equally to formularize the traditional Kalman filter as one linear weighted summation which is composed of the initial state estimate and all sequential sampled measurements. For it can adapt aforementioned information limitations, the linear weighted summation is then decomposed into two stages. One is a predict-estimator composed by all reached measurements, another is one compensator constructed by those time-delayed data. In the network environment, there are obvious differences between the new hybrid filter and those existing delayed Kalman filters. For example, the novel filter can be optimal in the sense of linear minimum mean square error as soon as all measurements available and has the lowest running time than these existing delayed filters. One simulation, including two cases, is utilized to illustrate the design procedures proposed in this paper.

关键词： Kalman filter Signal transmission delay Measurement summation Predict-estimator Compensator

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Targeting TrkB–PSD-95 coupling to mitigate neurological disorders

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Neural Regeneration Research 2025年第3期20卷 715-724页

作者： Xin Yang Yu-wen Alvin Huang John Marshall Department of Molecular Biology Cell Biology and BiochemistryBrown UniversityProvidenceRIUSA Department of Neurology Warren Alpert Medical School of Brown UniversityProvidenceRIUSA Center for Translational Neuroscience Robert J.and Nancy D.Carney Institute for Brain Science and Brown Institute for Translational ScienceBrown UniversityProvidenceRIUSA

Tropomyosin receptor kinase B(TrkB)signaling plays a pivotal role in dendritic growth and dendritic spine formation to promote learning and *** activity-dependent release of brain-derived neurotrophic factor at synapses binds to pre-or postsynaptic TrkB resulting in the strengthening of synapses,reflected by long-term ***,the association of postsynaptic density protein-95 with TrkB enhances phospholipase Cγ-Ca^(2+)/calmodulin-dependent protein kinaseⅡand phosphatidylinositol 3-kinase-mechanistic target of rapamycin signaling required for long-term *** this review,we discuss TrkB-postsynaptic density protein-95 coupling as a promising strategy to magnify brain-derived neurotrophic factor signaling towards the development of novel therapeutics for specific neurological disorders.A reduction of TrkB signaling has been observed in neurodegenerative disorders,such as Alzheimer's disease and Huntington's disease,and enhancement of postsynaptic density protein-95 association with TrkB signaling could mitigate the observed deficiency of neuronal connectivity in schizophrenia and *** with brain-derived neurotrophic factor is problematic,due to poor pharmacokinetics,low brain penetration,and side effects resulting from activation of the p75 neurotrophin receptor or the truncated TrkB.T1 *** TrkB agonists and antibodies that activate TrkB are being intensively investigated,they cannot distinguish the multiple human TrkB splicing isoforms or cell type-specific *** TrkB–postsynaptic density protein-95 coupling provides an alternative approach to specifically boost TrkB signaling at localized synaptic sites versus global stimulation that risks many adverse side effects.

关键词： Angelman syndrome autism brain-derived neurotrophic factor depression neurodegenerative disorder neurodevelopmental disorder postsynaptic density protein-95 synaptic plasticity TrkB

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An Asynchronous Quasi-Cloud/Edge/Client Collaborative Federated Learning Mechanism for Fault Diagnosis

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Chinese Journal of Electronics 2021年第5期30卷 969-977页

作者： MA Xue wen chenglin School of Automation Hangzhou Dianzi University School of Automation Guangdong University of Petrochemical Technology

Although the federated learning method has the ability to balance data and protect data privacy by means of model aggregation, while the existing methods are difficult to achieve the effectiveness of centralized learning under data sharing. The existing federated structure only has a certain degree of confidentiality for data privacy, that is to say, each client can reconstruct a part of the information of other clients based on the model parameters shared between the server and the clients under certain conditions. In order to make the federated learning mechanism more confidential,we breaks the existing mechanism that the parameters between the federated model and the client model are completely shared, and establishes a new asynchronous quasi-cloud/edge/client collaborative federated learning mechanism. We construct a hierarchical multi-level confidential communication network, where the network parameters are shared in a way of quasi-cloud/edge/client coordination without data communication. The cloud and the edges respectively use the sequential Kalman filter algorithm to perform an asynchronous fusion of the network parameters uploaded in their respective fusion centers for the next round of updates; The effectiveness of the proposed algorithm is verified on the data of a type of rotating machinery.

关键词： production engineering computing federated model hierarchical multilevel confidential communication network model aggregation client model sequential Kalman filter Kalman filters client-server systems learning (artificial intelligence) cloud computing centralized learning federated learning method fault diagnosis data privacy

High-Order Extended Kalman Filter Design for a Class of Complex Dynamic Systems with Polynomial Nonlinearities

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Chinese Journal of Electronics 2021年第3期30卷 508-515页

作者： SUN Xiaohui wen chenglin wen Tao School of Automation Hangzhou Dianzi University School of Automation Guangdong University of Petrochemical Technology School of Electronic and Information Engineering Beijing Jiaotong University

A novel High-order extended Kalman Filter(HEKF) is designed for a class of complex dynamic systems with polynomial nonlinearities. The state and measurement models are represented by multidimensional high-order polynomials, respectively. All highorder polynomials in the state model are defined as implicit variables. By combining original variables with implicit variables, the state model is equivalently formulated to be a pseudo-linear form. By modeling dynamic relationship between implicit variables and combining original variables with all implicit variables, a new linear augmented state model is established correspondingly. The measurement model can be equivalently formulated as a linear form. On the basis of the new linear state and linear measurement models, the HEKF is designed and derived in *** results demonstrate the effectiveness of the proposed estimator.

关键词： state estimation implicit variables linear form state model modeling dynamic relationship measurement model nonlinear filters Kalman Filter design polynomials HEKF high-order polynomials combining original variables Kalman filters polynomial nonlinearities measurement models linear state complex dynamic systems pseudolinear form

Maximum Correntropy High-Order Extended Kalman Filter

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Chinese Journal of Electronics 2022年第1期31卷 190-198页

In this paper, a novel maximum correntropy high-order extended Kalman filter(H-MCEKF) is proposed for a class of nonlinear non-Gaussian systems presented by polynomial form. All high-order polynomial terms in the state model are defined as implicit variables and regarded as parameter variables; the original state model is equivalently formulated into a pseudo-linear form with original variables and parameter variables; the dynamic relationship between each implicit variable and all variables is modeled, then an augmented linear state model appears by combing with pseudo-linear state model; similarly, the nonlinear measurement model can be equivalently rewritten into linear form; once again, the statistical characteristics of non-Gaussian modeling error are described by mean value and variance based on their finite samples; combing original measurement model with predicted value regarded as added state measurement, a cost function to solve the state estimation based on maximum correntropy criterion(MCC) is constructed; on the basis of this cost function, the state estimation problem can be equivalently converted into a recursive solution problem in the form of Kalman filter, in which the filter gain matrix is solved by numerical iteration though its fixed-point equation; illustration examples are presented to demonstrate the effectiveness of the new algorithm.

关键词： cost function filter gain matrix numerical iteration matrix algebra state estimation polynomial form nonlinear measurement nonGaussian modeling error Kalman filters nonGaussian systems sampling methods pseudolinear state model recursive solution problem statistical characteristics state measurement polynomials maximum entropy methods implicit variables high-order polynomial terms H-MCEKF nonlinear filters augmented linear state model MCC pseudolinear form parameter variables maximum correntropy high-order extended Kalman filter fixed-point equation iterative methods maximum correntropy criterion finite samples

A Novel Step-by-Step High-Order Extended Kalman Filter Design for a Class of Complex Systems with Multiple Basic Multipliers

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Chinese Journal of Electronics 2021年第2期30卷 313-321页

A novel step-by-step linearization high-order Extended Kalman filter SH-EKF is designed for a class of nonlinear systems composed of linear functions and the product of several separable basic functions. The basic functions in the state and measurement models are defined as latent variables; the state and measurement models are equivalently formulated into pseudo-linear models based on the combination of the original variable and the latent variables; latent variables are regarded as new variables,and a dynamic linear model between each latent variable and other latent variables with original state is established;the measurement model is rewritten into the first-order linear product form between the current state and each latent variable; latent variables are solved by Kalman filter step by step, and a stepwise linearized high-order extended Kalman filter is designed. Illustration examples are presented to demonstrate the effectiveness of the new algorithm.

关键词： step-by-step linearization high-order Extended Kalman filter SH-EKF latent variables first-order linear product form multiple basic multipliers Kalman filter step dynamic linear model nonlinear filters measurement model original variable linear functions Kalman filters separable basic functions measurement models Kalman filter design novel step-by-step high-order pseudolinear models

Dissecting the cell microenvironment of ovarian endometrioma through single-cell RNA sequencing

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Science China Life Sciences 2025年第1期 116-129页

作者： Jiangpeng Wu Siyu Xia wenting Ye Yan Sun Jing Cai Fubing Yu Haiping wen Xiuwei Yi Taikang Li Mingwei Chen Jiayun Chen ge Song Chuanbin Yang Yali Song Jigang Wang Department of Reproductive Medicine Dongguan Maternal and Child Health Care Hospital Department of Urology and Shenzhen Clinical Research Centre for GeriatricsShenzhen People's HospitalSouthern University of Science and Technology State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs Artemisinin Research Centerand Institute of Chinese Materia MedicaChina Academy of Chinese Medical Sciences Reproductive Medicine Center Foshan Women and Children Hospital State Key Laboratory of Antiviral Drugs School of PharmacyHenan University

Ovarian endometrioma（OE）, also known as “chocolate cysts,” is a cystic mass that develops in the ovaries due to endometriosis and is a common gynecological condition characterized by the growth of endometrial tissue outside the uterus, leading to symptoms such as dysmenorrhea, pelvic pain, and infertility. However, the precise molecular and cellular mechanisms driving this pathophysiology remain largely unknown, posing challenges for diagnosis and treatment. Here, we employed integrated single-cell transcriptomic profiling of over 52,000 individual cells from endometrial tissues of OE patients and healthy donors and identified twelve major cell populations. We identified notable alterations in cell type-specific proportions and molecular signatures associated with OE. Notably, the activation of IGFBP5+macrophages with pro-inflammatory properties, NK cell exhaustion, and aberrant proliferation of IQCG+and KLF2+epithelium are key features and may be the potential mechanisms underlying the pathogenesis of OE. Collectively, our data contribute to a better understanding of OE at the single cell level and may pave the way for the development of novel therapeutic strategies.

关键词： ovary high-throughput sequencing transcriptomics gene expression analysis ligand-receptor analysis

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Wave effect of gravitational waves intersected with a microlens field Ⅱ:An adaptive hierarchical tree algorithm and population study

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 241-254页

作者： Xikai Shan Guoliang Li Xuechun Chen wen Zhao Bin Hu Shude Mao Department of Astronomy Tsinghua University Purple Mountain Observatory Chinese Academy of Sciences Institute for Frontier in Astronomy and Astrophysics Beijing Normal University School of Physics and Astronomy Beijing Normal University School of Astronomy and Space Science University of Science and Technology of China CAS Key Laboratory for Researches in Galaxies and Cosmology Department of Astronomy University of Science and Technology of ChinaChinese Academy of Sciences

The gravitational lensing wave effect generated by a microlensing field embedded in a lens galaxy is an inevitable phenomenon in strong lensed gravitational waves （SLGWs）.This effect presents both challenges and opportunities for the detection and application of ***,investigating this wave effect requires computing a complete diffraction integral over each microlens in the *** is extremely time-consuming due to the large number of microlenses （10～3-10～6）.Therefore,simply adding all the microlenses is ***,the complexity of the time delay surface makes the lens plane resolution a crucial factor in controlling numerical *** this paper,we propose a trapezoid approximation-based adaptive hierarchical tree algorithm to meet the challenges of calculation speed and *** find that this algorithm accelerates the calculation by four orders of magnitude compared to the simple adding method and is one order of magnitude faster than the fixed hierarchical tree algorithm proposed for electromagnetic *** importantly,our algorithm ensures controllable numerical errors,increasing confidence in the *** with our previous work （***.66,239511,2023）,this paper addresses all numerical issues,including integral convergence,precision,and computational time1).Finally,we conducted a population study on the microlensing wave effect of SLGWs using this algorithm and found that the microlensing wave effect cannot be ignored,especially for Type II SLGWs （from saddle position of the time delay surface） due to their intrinsic geometric structures and their typical intersection with a denser microlensing ***,more than 33%（11%） of SLGWs have a mismatch larger than 1%（3%） compared to the unlensed ***,we found that the mismatch between signal pairs in a doubly imaged GW is generally larger than 10-3,and 61%（25%） of signal pairs have a mismatch larger than 1%（3%）.Therefo

关键词： gravitational lensing diffractional integral algorithm gravitational wave