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194 results on '"Duan, Zhansheng"'

7. Automatic Analog Pointer Instruments Reading Using Panel Detection and Landmark Localization Networks

Author

Yang, Keshan, Duan, Zhansheng, Fang, Kai, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Sun, Fuchun, editor, Liu, Huaping, editor, and Hu, Dewen, editor

Published
2019
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17. Introduction

Author

Kong, Xiangyu, Hu, Changhua, Duan, Zhansheng, Kong, Xiangyu, Hu, Changhua, and Duan, Zhansheng

Published
2017
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18. GSGS: A Computational Framework to Reconstruct Signaling Pathways from Gene Sets

Author

Acharya, Lipi, Judeh, Thair, Duan, Zhansheng, Rabbat, Michael, and Zhu, Dongxiao

Subjects
Quantitative Biology - Quantitative Methods
Abstract

We propose a novel two-stage Gene Set Gibbs Sampling (GSGS) framework, to reverse engineer signaling pathways from gene sets inferred from molecular profiling data. We hypothesize that signaling pathways are structurally an ensemble of overlapping linear signal transduction events which we encode as Information Flow Gene Sets (IFGS's). We infer pathways from gene sets corresponding to these events subjected to a random permutation of genes within each set. In Stage I, we use a source separation algorithm to derive unordered and overlapping IFGS's from molecular profiling data, allowing cross talk among IFGS's. In Stage II, we develop a Gibbs sampling like algorithm, Gene Set Gibbs Sampler, to reconstruct signaling pathways from the latent IFGS's derived in Stage I. The novelty of this framework lies in the seamless integration of the two stages and the hypothesis of IFGS's as the basic building blocks for signal pathways. In the proof-of-concept studies, our approach is shown to outperform the existing Bayesian network approaches using both continuous and discrete data generated from benchmark networks in the DREAM initiative. We perform a comprehensive sensitivity analysis to assess the robustness of the approach. Finally, we implement the GSGS framework to reconstruct signaling pathways in breast cancer cells.

Published
2011

20. Nonlinear Filtering for Emission Source Tracking Using Biased RSS Measurements

Author

Li, Xianqing, Duan, Zhansheng, Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kotenko, Igor, Series editor, Liu, Ting, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Sun, Fuchun, editor, Liu, Huaping, editor, and Hu, Dewen, editor

Published
2017
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23. Distributed Fusion of Multiple Model Estimators Using Minimum Forward Kullback–Leibler Divergence Sum

Author

Wei, Zheng, Duan, Zhansheng, and Hanebeck, Uwe D.

Abstract

The problem of distributed fusion of Gaussian mixture models (GMMs) provided by the local multiple model (MM) estimators is addressed in this article. Taking GMMs instead of combined Gaussian assumed probability density functions (pdfs) as the output of local MM estimators can retain more detailed (or internal) information about local estimations, but the accompanying challenge is to perform the fusion of GMMs. For this problem, a distributed fusion framework of GMMs under the minimum forward Kullback–Leibler (KL) divergence sum criterion is proposed first. Then, because the KL divergence between GMMs is not analytically tractable, two suboptimal distributed fusion algorithms are further developed within this framework. These two fusion algorithms all have closed forms. Numerical examples verify their effectiveness in terms of both computational efficiency and estimation accuracy.

Published
2024
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48. Adaptive turn rate estimation using range rate measurements

Author

Yuan, Xianghui, Han, Chongzhao, Duan, Zhansheng, and Lei, Ming

Subjects
Monte Carlo method -- Usage, Coordinate measuring machines -- Usage, Coordinate measuring machines -- Models, Aerospace and defense industries, Business, Computers, Electronics, Electronics and electrical industries
Abstract

The coordinated turn (CT) model is often used to track maneuvering target which performs CT motion. The key point to the successful use of this model is to determine the turn rate parameter. A new method to estimate the turn rate by using radar range-rate measurements is presented. First, four possible turn rates can be achieved with the range-rate measurements. Second, the minimum turn rate and its opposite value are chosen to be the possible turn rates. Finally, an interacting multiple model (IMM) algorithm with one constant velocity (CV) model and two CT models is designed to track the maneuvering target which performs uniform and ct motions. Monte-Carlo simulation results show that this algorithm is not only better than equivalent noise approach in tracking performance, but also better than the conventional IMM algorithm with the adaptive turn rate model. Further simulations show that the algorithm is robust with the accuracy of the range-rate measurements.

Published
2006

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