Local-Diffusion-based Distributed SMC-PHD Filtering Using Sensors with Limited Sensing Range Tiancheng Li ; Víctor Elvira ; Hongqi Fan ; Juan M. Corchado IEEE Sensors Journal Abstract: We investigate the problem of distributed multitarget tracking by using a set of spatially dispersed, collaborative sensors with limited sensing range (LSR), where each sensor runs a sequential Monte Carlo-probability hypothesis density filter and exchanges relevant posterior information with its neighbors. The key challenge stems from the LSR of neighbor sensors whose fields of view (FoVs) are partially/non-overlapped and therefore they may observe different targets at the same time. With regard to the local common FoVs among neighbor sensors, the proposed distributed fusion scheme, called local diffusion, performs one iteration of neighbor communication per filtering step in either of two means. One is given by immediate particle exchange, in which a reject-control operation is devised to reduce the number of communicating particles. The other is done by converting the particle distribution to Gaussian functions for parametric information exchange and fusion. The performance of both approaches has been experimentally investigated via simulation for different LSR situations and compared with cutting-edge approaches. T. Li, V. Elvira, H. Fan and J. M. Corchado, Local-Diffusion-Based Distributed SMC-PHD Filtering Using Sensors With Limited Sensing Range, in IEEE Sensors Journal , vol. 19, no. 4, pp. 1580-1589, 15 Feb.15, 2019. doi: 10.1109/JSEN.2018.2882084 @ARTICLE{Li2019local, author={T. Li and V. Elvira and H. Fan and J. M. Corchado}, journal={IEEE Sensors Journal}, title={Local-Diffusion-Based Distributed \\cal{SMC-PHD} Filtering Using Sensors With Limited Sensing Range}, year={2019}, volume={19}, number={4}, pages={1580-1589}, doi={10.1109/JSEN.2018.2882084}, ISSN={1530-437X}, month={Feb.},}
The induced stress in Li-ion batteries electrode(LIBs) will be discussed in this article, which focus mainly on the modeling and simulating. Two papers from JPS: "Zhang et.al, JPS, 2012, 220-227" and "Haftbaradaran et.al, JPS, 2011, 361-370" are analysed to demonstrate the latest development of this problem. The internal stress will increase the density of defects in batteries, which as a result will affect the capacity and life span of them. The poor cyclic performance of battery could be modeled by strongly coupled diffusion-stress model.Haftbaradaran et.al has developed it to deal with the highly nonlinear behavior of diffusion process in high solute concentration.The validity ofcontinuumhas been proved by comparing with simulation results by Molecular dynamics, one of thecomparisonis demonstrated below, the discrete point are results obtained by MD method while the line represent calculation of thecontinuummodel. Four points concerning the nonlinear behavior of diffusion behavior has been discussed in the work by Haftbaradaran et.al byeliminating their effects one by one in new comparisons between the theoretical and simulating results. In the work of Zhang et.al, more attentions has been paid to the layered structure of electrode in LIBs. Their model has been adapted to discuss the symmetry of of electrode plate, conditions bilayer electrode plate and the effects of charging conditions. The role of current collector in relation with the electrode has been discussed and it has been concluded that the materials of current collector should be as thin as possible and the elastic modulus should be smaller to enhance a much lower stress in the electrode. That is to say, the diffusion induced stress could be well modeled nowadays to predict the performance of new materials in battery electrode. From my perspective, the applicable of models in different kind of materials should be evaluated because the structure and and chemical properties vary between different kind of electrode materials.
最近一段时间一直在整理与social network analysis and modeling，information diffusion相关的论文。虽然关于social media上的研究工作开展没几年，但是参与这方面研究的专家以及这方面的工作确实非常的多的，很多complex network方面的大牛也参合了进来，比如像 Duncan Watts (Yahoo! Research), J.Kleinberg (Cornell Univ.), B.A.Huberman (HP lab), Albert L Barabasi (Northeastern Univ.), L.A.Adamic (Michigan Univ.) J.Leskovec (Stanford Univ.)等。这篇博文主要总结了一下近三年来L.A.Adamic关于social networks主要工作，主要集中在link prediction方向。 1) I rate you. You rate me. Should we do so publicly? (paper) Are online social networks accurate? OSNs是一个数据的宝库，社交网络的特性加强了某些功能（如推荐），如何获得真实的rating。 trust and friendship的量化问题 trust and friendship truthful ratings reliable? gender effect, age, geography Conclusion: Rating friends is tricky,rating enimies is trickier. 2) Implicit Structure (link prediction) Microscale Dynamics What we need to track? timings underlying networks Challenges Root may be unknown Multiple possible paths Uncrawled space, alternate media (email, voice) No links 3) Meme spread in networks info changes as it propagates through a network multi-step diffusion is responsible for change the change can be model as a simple urn process the characteristics of nodes and their position in the network correspond to different amount of change 4) Social networks influence and are influenced by information exchange Computational social science can contribute to understanding of social phenomena homophily information diffusion social influence knowledge exchange trust reciprocity 其中，本人对其两个工作比较感兴趣。 一、different kinds of info popularity profiles （见Fig.1, Fig.2） 二、meme tracing （见Fig.3） Fig.1 different kinds of info popularity profiles Fig.2 different kinds of info popularity profiles Fig.3 meme example
This is my first blog article. It is my research note. About the derivation of Diffusion Coefficient. Because in my school computer lab, there is no chinese input. So have to use english. I hope my work can do help others. Eistein ever derivated the coefficient. By Dr.Jaynes use Bayesian method to do the same thing, it looks much concise. If someone is of interest in this, please see the attachment. Derivation of Diffusion Coefficient By Dr.Jaynes
标签: DIffusion
