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Статті в журналах з теми "Distributed Data Fusion (DDF)"

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Автор: Grafiati

Опубліковано: 6 вересня 2023

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1

Lu, Zongqing, Su-Lim Tan, and Jit Biswas. "D2F: A Routing Protocol for Distributed Data Fusion in Wireless Sensor Networks." Wireless Personal Communications 70, no. 1 (June 13, 2012): 391–410. http://dx.doi.org/10.1007/s11277-012-0700-9.

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2

Joelson, Anders, and Freyr Gauti Sigmundsson. "Additional operation rates after surgery for degenerative spine diseases: minimum 10 years follow-up of 4705 patients in the national Swedish spine register." BMJ Open 12, no. 12 (December 2022): e067571. http://dx.doi.org/10.1136/bmjopen-2022-067571.

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Анотація:

ObjectivesTo identify rates of additional operation after the index operation for degenerative lumbar spine diseases.DesignRetrospective register study.SettingNational outcome data from Swespine, the National Swedish spine register.ParticipantsA total of 4705 patients who underwent one-level surgery for degenerative disk disease (DDD) or lumbar spinal stenosis (LSS) with or without degenerative spondylolisthesis (DS) between 1 January 2007 and 31 December 2010 were followed from 1 January 2007 to 31 December 2020 to record all cases of additional lumbar spine operations.InterventionsOne-level spinal decompression and/or posterolateral fusion for degenerative spine diseases.Primary outcome measuresNumber of additional operations.ResultsAdditional operations were more common at adjacent levels for patients with LSS with DS treated with decompression and fusion whereas additional operations were more evenly distributed between the index level and the adjacent levels for DDD treated with fusion and LSS with and without DS treated with decompression only. For patients younger than 60 years, treated with decompression and fusion for LSS with DS, the additional operations were evenly distributed between the index level and the adjacent levels.ConclusionsThere are different patterns of additional operations following the index procedure after surgery for degenerative spine diseases. Rigidity across previously mobile segments is not the only important factor in the development of adjacent segment disease (ASD) after spinal fusion, also the underlying disease and age may play parts in ASD development. The findings of this study can be used in the shared decision-making process when surgery is a treatment option for patients with degenerative lumbar spine diseases as the first operation may be the start of a series of additional spinal operations for other degenerative spinal conditions, either at the index level or at other spinal levels.

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3

Peyman, Setoodeh, Khayatian Alireza, and Farjah Ebrahim. "Attitude Estimation By Divided Difference Filter-Based Sensor Fusion." Journal of Navigation 60, no. 1 (December 15, 2006): 119–28. http://dx.doi.org/10.1017/s037346330600405x.

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Strapdown inertial navigation systems (INS) often employ aiding sensors to increase accuracy. Nonlinear filtering algorithms are then needed to fuse the collected data from these aiding sensors with measurements of strapdown rate gyros. Aiding sensors usually have slower dynamics compared to gyros and therefore collect data at lower rates. Thus the system will be unobservable between aiding sensors' sampling instants, and the error covariance, which shows the uncertainty in the estimation, grows during the sampling period. This paper presents a divided difference filter (DDF)-based data fusion algorithm, which utilizes the complementary noise profile of rate gyros and gravimetric inclinometers to extend their limits and achieve more accurate attitude estimates. It is confirmed experimentally that DDF achieves better covariance estimates compared to the extended Kalman filter (EKF) because the uncertainty in the state estimate is taken care of in the DDF polynomial approximation formulation.

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4

Chair, Z., and P. K. Varshney. "Distributed Bayesian hypothesis testing with distributed data fusion." IEEE Transactions on Systems, Man, and Cybernetics 18, no. 5 (1988): 695–99. http://dx.doi.org/10.1109/21.21597.

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5

Xue, Ying Hua, and Jing Li. "Distributed Information Fusion Structure Based on Data Fusion Tree." Advanced Materials Research 225-226 (April 2011): 488–91. http://dx.doi.org/10.4028/www.scientific.net/amr.225-226.488.

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A distributed information fusion structure based on data fusion tree is built to realize precise localization and efficient navigation for the mobile robot. The multi-class, multi-level information from robot and environment is fused using different algorithms in different levels, and make the robot have a deeper understanding to the whole environment. Experiments demonstrate that the new model proposed in the paper can improve the positioning precision of robot greatly, and the search efficiency and success rate are also better than traditional mode.

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6

Hollinger, Geoffrey A., Srinivas Yerramalli, Sanjiv Singh, Urbashi Mitra, and Gaurav S. Sukhatme. "Distributed Data Fusion for Multirobot Search." IEEE Transactions on Robotics 31, no. 1 (February 2015): 55–66. http://dx.doi.org/10.1109/tro.2014.2378411.

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7

Fan, Lingling. "Data fusion-based distributed Prony analysis." Electric Power Systems Research 143 (February 2017): 634–42. http://dx.doi.org/10.1016/j.epsr.2016.10.052.

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8

Mcgrath, Michael, and Yuan Zheng. "Distributed contextual data fusion with ACIPL." IEEE Aerospace and Electronic Systems Magazine 24, no. 8 (August 2009): 31–36. http://dx.doi.org/10.1109/maes.2009.5256385.

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9

Park, Gyu-Dong, and Young-Tae Byun. "Improving the Distributed Data Fusion Ability of the JDL Data Fusion Model." Journal of the Korea Institute of Military Science and Technology 15, no. 2 (April 5, 2012): 147–54. http://dx.doi.org/10.9766/kimst.2012.15.2.147.

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10

Hang, Guo, and Yu Min. "Data fusion in distributed multi-sensor system." Geo-spatial Information Science 7, no. 3 (January 2004): 214–17. http://dx.doi.org/10.1007/bf02826294.

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11

Baek, W., and S. Bommareddy. "Optimal m-ary data fusion with distributed sensors." IEEE Transactions on Aerospace and Electronic Systems 31, no. 3 (July 1995): 1150–52. http://dx.doi.org/10.1109/7.395226.

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12

Demirbas, K. "Distributed sensor data fusion with binary decision trees." IEEE Transactions on Aerospace and Electronic Systems 25, no. 5 (1989): 643–49. http://dx.doi.org/10.1109/7.42081.

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13

Allerton, D. J., and H. Jia. "Distributed data fusion algorithms for inertial network systems." IET Radar, Sonar & Navigation 2, no. 1 (February 1, 2008): 51–62. http://dx.doi.org/10.1049/iet-rsn:20060159.

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14

Klausner, A., A. Tengg, and B. Rinner. "Distributed Multilevel Data Fusion for Networked Embedded Systems." IEEE Journal of Selected Topics in Signal Processing 2, no. 4 (August 2008): 538–55. http://dx.doi.org/10.1109/jstsp.2008.925988.

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15

Regazzoni, Carlo S., and Alessandra Tesei. "Distributed data fusion for real-time crowding estimation." Signal Processing 53, no. 1 (August 1996): 47–63. http://dx.doi.org/10.1016/0165-1684(96)00075-8.

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16

Bakr, Muhammad Abu, and Sukhan Lee. "Distributed Multisensor Data Fusion under Unknown Correlation and Data Inconsistency." Sensors 17, no. 11 (October 27, 2017): 2472. http://dx.doi.org/10.3390/s17112472.

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17

Li, Cheng, and Guangping Zhu. "Underwater multi-sensor Bayesian distributed detection and data fusion." MATEC Web of Conferences 283 (2019): 07014. http://dx.doi.org/10.1051/matecconf/201928307014.

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Анотація:

The relationship of decision rule of sensor for each other is relevant to data fusion, so different topological network of sensors usually results in different performance. This paper considers the parallel and sequential topological data fusion in some detail and applies it to detect underwater signal with three sensors which respectively detects the energy, impulse width and frequency. In this paper, the signal detection model is specified for binary hypotheses testing problem. This paper compares the probabilities of error and Bayesian risk under both topologies corresponding to different value of priori probabilities of two hypotheses. Usually, the parallel architecture of detection and fusion with three sensors as specified in this paper needs to solve eleven nonlinear equations to determine the thresholds of three sensors and fusion rules, as to sequential architecture, five nonlinear equations need to be solved. So, this paper attempts to search numerical solutions for the parallel and sequential architecture of distributed detection and data fusion. Finally, this signal detection problem is simulated.

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18

von der Weth, Axel, Frederik Arbeiter, Dmitry Klimenko, Volker Pasler, and Georg Schlindwein. "Permeation Data Analysis Considering a Nonzero Hydrogen Concentration on the Low Pressure Detector Side for a Purged Permeation Experiment." Defect and Diffusion Forum 391 (February 2019): 18–29. http://dx.doi.org/10.4028/www.scientific.net/ddf.391.18.

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Currently available diffusion constant and Sieverts constant experimental results are based on time dependent permeation experiments. The common principle is an analysis which is expecting that the permeating hydrogen is “transported” from the retentate chamber to the permeate chamber through the connecting membrane, with a vanishing hydrogen partial pressure on the permeate side. But reality shows a different behaviour caused by the fact that a nonzero hydrogen partial pressure in the permeate chamber is necessary for detection purposes. This nonzero pressure is mostly not considered by analysis. This issue is solved (approximatively) numerically by the procedure as described in this paper. This work is rooted in the field of fusion research, where so called purge gas with low partial pressure of tritium is contacting the structural materials (300-550°C) of the fusion reactor (blanket) and of process equipment, where the tritium losses are of interest. The developed algorithms are intended for the evaluation of an experiment termed “Q-PETE” (Q for any hydrogen isotope, PEermeation Transport Experiment), which abstracts the hydrogen transport conditions of the fusion blanket, and where the effect of nonzero hydrogen concentration on the permeate side is relevant. The algorithms are useful for all experiments, where the ratio of hydrogen pressures between retentate and peremeate side are far from infinite.

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19

He, Yue Shun, and Hong Ling Wang. "Performance Assessment for Distributed Information Fusion System Based on Data Mining." Advanced Materials Research 121-122 (June 2010): 534–39. http://dx.doi.org/10.4028/www.scientific.net/amr.121-122.534.

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This paper presents a series of methods based on data mining theory for information fusion system in order to avoid the ubiquitous problems of comprehensive evaluation in the process of information fusion. The methodology is developed by analyzing the significance factor that has impact on fusion performance based on gradual regression method and by analyzing the performance testing results of fusion by group using hierarchy clustering method. Through the analysis of performance testing results for distributed information fusion system, the factors that have effects on fusion performance and the correlation between various influencing factors have been discovered. Moreover, the performance of information fusion system is enhanced by adjusting the disadvantageous factors and improving the design methods.

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20

He, Yue Shun, Jun Zhang, and Jie He. "Distributed Multi-Source Spatial Data Fusion Model Construction and Performance Evaluation." Key Engineering Materials 460-461 (January 2011): 404–8. http://dx.doi.org/10.4028/www.scientific.net/kem.460-461.404.

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This paper mainly analyzed the principle of multi-source spatial data fusion, and expounded the multi-source spatial data fusion of the distributed model structure. The paper considers a distributed multi-sensor information fusion system factors, A performance evaluation model was established which was suitable for distributed multi-sensor information fusion system, It can estimate the system's precision, track quality, filtering quality, and the relevant between Navigation Paths and so on. Meanwhile, we had a lot of experiments by the datum which generated by the simulation test environment, experiments show that this evaluation model is valid.

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21

Xu, Shi Jun, Li Hong, and Yong Hong Hu. "A Distributed Bayesian Fusion Algorithm Research." Advanced Materials Research 181-182 (January 2011): 1006–12. http://dx.doi.org/10.4028/www.scientific.net/amr.181-182.1006.

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In this paper, the signal detection problem when distributed sensors are used a global decision is desired is considered. Local decisions from the sensors are fed to the data fusion center which then yields a global decision based on a fusion rule. Based on The data fusion theories of Bayesian criterion used for a distributed parallel structure, fusion rules at the fusion center、 the decision rules of sensors and the results of the computer simulation for two identical sensors, two different sensors and three identical sensors are presented. The results of the computer simulation show that the performance of the fusion system, compared with the sensor, has been improved. For the case there are three identical sensors in the fusion system, Bayesian risk is reduced by 26.5%, compared with the sensor.

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22

Chang, S. K., G. Costagliola, E. Jungert, and F. Orciuoli. "Querying Distributed Multimedia Databases and Data Sources for Sensor Data Fusion." IEEE Transactions on Multimedia 6, no. 5 (October 2004): 687–702. http://dx.doi.org/10.1109/tmm.2004.834862.

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23

Yuan, Xianghui, Zhansheng Duan, and Chongzhao Han. "Performance Analysis for Distributed Fusion with Different Dimensional Data." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/571572.

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Different sensors or estimators may have different capability to provide data. Some sensors can provide a relatively higher dimensional data, while other sensors can only provide part of them. Some estimators can estimate full dimensional quantity of interest, while others may only estimate part of it due to some constraints. How is such kind of data with different dimensions fused? How do the common part and the uncommon part affect each other during fusion? To answer these questions, a fusion algorithm based on linear minimum mean-square error (LMMSE) estimation is provided in this paper. Then the fusion performance is analyzed, which is the main contribution of this work. The conclusions are as follows. First, the fused common part is not affected by the uncommon part. Second, the fused uncommon part will benefit from the common part through the cross-correlation. Finally, under certain conditions, both the more accurate common part and the stronger correlation can result in more accurate fused uncommon part. The conclusions are all supported by some tracking application examples.

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24

Su, Xin, Kuan Fan, and Wenbo Shi. "Privacy-Preserving Distributed Data Fusion Based on Attribute Protection." IEEE Transactions on Industrial Informatics 15, no. 10 (October 2019): 5765–77. http://dx.doi.org/10.1109/tii.2019.2912175.

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25

Shu, L., M. Chen, J. J. P. C. Rodrigues, and J. Lloret. "Editorial: Distributed intelligence and data fusion for sensor systems." IET Communications 5, no. 12 (August 12, 2011): 1633–36. http://dx.doi.org/10.1049/iet-com.2011.0534.

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26

Uhlmann, Jeffrey K. "Covariance consistency methods for fault-tolerant distributed data fusion." Information Fusion 4, no. 3 (September 2003): 201–15. http://dx.doi.org/10.1016/s1566-2535(03)00036-8.

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27

Zhang, Zheng, Yan Guo, and Guozhi Song. "A Distributed Face Recognition Framework Based on Data Fusion." International Journal of Database Theory and Application 7, no. 4 (August 31, 2014): 87–98. http://dx.doi.org/10.14257/ijdta.2014.7.4.08.

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28

Prosdocimi, Ilaria, Elizabeth J. Stewart, and Gianni Vesuviano. "A depth–duration–frequency analysis for short-duration rainfall events in England and Wales." Hydrology Research 48, no. 6 (May 6, 2017): 1624–38. http://dx.doi.org/10.2166/nh.2017.140.

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Abstract This study presents a depth–duration–frequency (DDF) model, which is applied to the annual maxima of sub-hourly rainfall totals of selected stations in England and Wales. The proposed DDF model follows from the standard assumption that the block maxima are generalised extreme value (GEV) distributed. The model structure is based on empirical features of the observed data and the assumption that, for each site, the distribution of the rainfall maxima of all durations can be characterised by common lower bound and skewness parameters. Some basic relationships between the location and scale parameters of the GEV distributions are enforced to ensure that frequency estimates for different durations are consistent. The derived DDF curves give a good fit to the observed data. The rainfall depths estimated by the proposed model are then compared with the standard DDF models used in the United Kingdom. The proposed model performs well for the shorter return periods for which reliable estimates of the rainfall frequency can be obtained from the observed data, while the standard methods show more variable results. Although the standard methods used no or little sub-hourly data in their calibration, they give fairly reliable estimates for the estimated rainfall depths overall.

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29

Seo, Jiho, Jonghyeok Lee, Jaehyun Park, Hyungju Kim, and Sungjin You. "Distributed Two-Dimensional MUSIC for Joint Range and Angle Estimation with Distributed FMCW MIMO Radars." Sensors 21, no. 22 (November 16, 2021): 7618. http://dx.doi.org/10.3390/s21227618.

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To estimate range and angle information of multiple targets, FMCW MIMO radars have been exploited with 2D MUSIC algorithms. To improve estimation accuracy, received signals from multiple FMCW MIMO radars are collected at the data fusion center and processed coherently, which increases data communication overhead and implementation complexity. To resolve them, we propose the distributed 2D MUSIC algorithm with coordinate transformation, in which 2D MUSIC algorithm is operated with respect to the reference radar’s coordinate at each radar in a distributed way. Rather than forwarding the raw data of received signal to the fusion center, each radar performs 2D MUSIC with its own received signal in the transformed coordinates. Accordingly, the distributed radars do not need to report all their measured signals to the data fusion center, but they forward their local cost function values of 2D MUSIC for the radar image region of interest. The data fusion center can then estimate the range and angle information of targets jointly from the aggregated cost function. By applying the proposed scheme to the experimentally measured data, its performance is verified in the real environment test.

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30

Muley, Abhinav. "Global Data Fusion versus Local Pattern Fusion in Mining Multiple Databases: A Comparative Review." Journal of Computational and Theoretical Nanoscience 17, no. 9 (July 1, 2020): 3844–49. http://dx.doi.org/10.1166/jctn.2020.9046.

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With the emergence of big data, mining distributed databases has become a critical task in the domain of discovery of knowledge from databases. Many of the traditional multiple-database mining methods developed until now have emphasized mining the mono-database, which is a pool of all the local databases merged at a central site; local patterns discovered at local sites are not analyzed in mono-database mining. However, in real-world applications, data collected from multiple databases may be duplicitous and unreliable. Therefore, developing methods to discover reliable, high-quality knowledge from multiple databases is a challenging task when mining multi-sourced data. This paper scrupulously reviews all the existing methods for mining multiple and distributed databases based on global data fusion and local pattern fusion techniques. The research issues and recently developed methods, which involves local pattern analysis in multi-database mining, are also discussed.

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31

Han, Lu Hui, Tao Fa, and Ya Wen Zhao. "PALS and TEM Study on Irradiation Defects of 12Cr-ODS Steels Induced by He/H Ions." Defect and Diffusion Forum 373 (March 2017): 96–99. http://dx.doi.org/10.4028/www.scientific.net/ddf.373.96.

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The purpose of this study is to evaluate the irradiation defects of 12Cr-ODS steels induced by He/H ions, to provide basic understanding concerning development of fusion reactor components. Firstly, single He、H ion implantation and He/H ion co-implantation of 12Cr-ODS steels were performed at room temperature; and then SIMS were used to determine the He/H ion depth; finally, the irradiation induced defects were investigated by PALS and TEM. Characterization of the implanted samples with SIMS shows that He/H ions are mainly distributed at 4-6μm depth, consistent with the SRIM simulation. The PALS results show that the positron lifetime of H ions implanted samples increases slightly with increasing incident ions fluence, while for He and He/H ion implantation it is reversed. In addition, TEM results demonstrate that after irradiation, cavities are created in all samples, and He ion irradiation produce seriously larger damage compared to H ion. The positron lifetime results can be mainly ascribed to the difference of He and H ion interaction with defects.

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32

Ehala, Johannes, Jaanus Kaugerand, Raido Pahtma, Sergei Astapov, Andri Riid, Timo Tomson, Jürgo-Sören Preden, and Leo Mõtus. "Situation awareness via Internet of things and in-network data processing." International Journal of Distributed Sensor Networks 13, no. 1 (January 2017): 155014771668657. http://dx.doi.org/10.1177/1550147716686578.

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Computing on the edge of the Internet of things comprises among other tasks in-sensor signal processing and performing distributed data fusion and aggregation at network nodes. This poses a challenge to distributed sensor networks of low computing power devices that have to do complex fusion, aggregation and signal processing in situ. One of the difficulties lies in ensuring validity of data collected from heterogeneous sources. Ensuring data validity, for example, the temporal and spatial correctness of data, is crucial for correct in-network data fusion and aggregation. The article considers wireless sensor technology in military domain with the aim of improving situation awareness for military operations. Requirements for contemporary intelligence, surveillance and reconnaissance applications are explored and an experimental wireless sensor network, designed to enhance situation awareness to both in-the-field units and remote intelligence operatives, is described. The sensor nodes have the capability to perform in-sensor signal processing and distributed in-network data aggregation and fusion complying with edge computing paradigm. In-network data processing is supported by service-oriented middleware which facilitates run-time sensor discovery and tasking and ad hoc (re)configuration of the network links. The article describes two experiments demonstrating the ability of the wireless sensor network to meet intelligence, surveillance and reconnaissance requirements. The efficiency of distributed data fusion is evaluated and the importance and effect of establishing data validity is shown.

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33

Papatsimpa, Charikleia, and Jean-Paul Linnartz. "Distributed Fusion of Sensor Data in a Constrained Wireless Network." Sensors 19, no. 5 (February 27, 2019): 1006. http://dx.doi.org/10.3390/s19051006.

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Smart buildings with connected lighting and sensors are likely to become one of the first large-scale applications of the Internet of Things (IoT). However, as the number of interconnected IoT devices is expected to rise exponentially, the amount of collected data will be enormous but highly redundant. Devices will be required to pre-process data locally or at least in their vicinity. Thus, local data fusion, subject to constraint communications will become necessary. In that sense, distributed architectures will become increasingly unavoidable. Anticipating this trend, this paper addresses the problem of presence detection in a building as a distributed sensing of a hidden Markov model (DS-HMM) with limitations on the communication. The key idea in our work is the use of a posteriori probabilities or likelihood ratios (LR) as an appropriate “interface” between heterogeneous sensors with different error profiles. We propose an efficient transmission policy, jointly with a fusion algorithm, to merge data from various HMMs running separately on all sensor nodes but with all the models observing the same Markovian process. To test the feasibility of our DS-HMM concept, a simple proof-of-concept prototype was used in a typical office environment. The experimental results show full functionality and validate the benefits. Our proposed scheme achieved high accuracy while reducing the communication requirements. The concept of DS-HMM and a posteriori probabilities as an interface is suitable for many other applications for distributed information fusion in wireless sensor networks.

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34

Guerriero, Marco, Lennart Svensson, and Peter Willett. "Bayesian Data Fusion for Distributed Target Detection in Sensor Networks." IEEE Transactions on Signal Processing 58, no. 6 (June 2010): 3417–21. http://dx.doi.org/10.1109/tsp.2010.2046042.

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35

Duan, Zhansheng, and X. Rong Li. "Lossless Linear Transformation of Sensor Data for Distributed Estimation Fusion." IEEE Transactions on Signal Processing 59, no. 1 (January 2011): 362–72. http://dx.doi.org/10.1109/tsp.2010.2084574.

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36

Wallace, C. J., G. M. West, S. D. J. McArthur, and D. Towle. "Distributed Data and Information Fusion for Nuclear Reactor Condition Monitoring." IEEE Transactions on Nuclear Science 59, no. 1 (February 2012): 182–89. http://dx.doi.org/10.1109/tns.2011.2176959.

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37

He, Hongmei, Zhenhuan Zhu, and Erkki Mäkinen. "Task-oriented distributed data fusion in autonomous wireless sensor networks." Soft Computing 19, no. 8 (August 19, 2014): 2305–19. http://dx.doi.org/10.1007/s00500-014-1421-7.

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38

李, 兰. "Multi-Source Data Fusion Pretreatment System in Distributed Hydrological Models." Journal of Water Resources Research 03, no. 03 (2014): 267–74. http://dx.doi.org/10.12677/jwrr.2014.33033.

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39

He, Shaoming, Hyo-Sang Shin, and Antonios Tsourdos. "Distributed Joint Probabilistic Data Association Filter With Hybrid Fusion Strategy." IEEE Transactions on Instrumentation and Measurement 69, no. 1 (January 2020): 286–300. http://dx.doi.org/10.1109/tim.2019.2894048.

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40

Dai, Hongsheng, Murray Pollock, and Gareth Roberts. "Monte Carlo fusion." Journal of Applied Probability 56, no. 01 (March 2019): 174–91. http://dx.doi.org/10.1017/jpr.2019.12.

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AbstractIn this paper we propose a new theory and methodology to tackle the problem of unifying Monte Carlo samples from distributed densities into a single Monte Carlo draw from the target density. This surprisingly challenging problem arises in many settings (for instance, expert elicitation, multiview learning, distributed ‘big data’ problems, etc.), but to date the framework and methodology proposed in this paper (Monte Carlo fusion) is the first general approach which avoids any form of approximation error in obtaining the unified inference. In this paper we focus on the key theoretical underpinnings of this new methodology, and simple (direct) Monte Carlo interpretations of the theory. There is considerable scope to tailor the theory introduced in this paper to particular application settings (such as the big data setting), construct efficient parallelised schemes, understand the approximation and computational efficiencies of other such unification paradigms, and explore new theoretical and methodological directions.

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41

Zhang, Peng, Shuyu Zhou, Peng Liu, and Mengwei Li. "Distributed Ellipsoidal Intersection Fusion Estimation for Multi-Sensor Complex Systems." Sensors 22, no. 11 (June 6, 2022): 4306. http://dx.doi.org/10.3390/s22114306.

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This paper investigates the problem of distributed ellipsoidal intersection (DEI) fusion estimation for linear time-varying multi-sensor complex systems with unknown input disturbances and measurement data transmission delays. For the problem with external unknown input disturbance signals, a non-informative prior distribution is used to model the problem. A set of independent random variables obeying Bernoulli distribution is also used to describe the situation of measurement data transmission delay caused by network channel congestion, and appropriate buffer areas are added at the link nodes to retrieve the delayed transmission data values. For multi-sensor systems with complex situations, a minimum mean square error (MMSE) local estimator is designed in a Bayesian framework based on the maximum a posteriori (MAP) estimation criterion. In order to deal with the unknown correlations among the local estimators and to select the fusion estimator with lower computational complexity, the fusion estimator is designed using ellipsoidal intersection (EI) fusion technique, and the consistency of the estimator is demonstrated. In this paper, the difference between DEI fusion and distributed covariance intersection (DCI) fusion and centralized fusion estimation is analyzed by a numerical example, and the superiority of the DEI fusion method is demonstrated.

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42

Zhao, Xiukuan, Ruolin Wang, Haichang Gu, Gangbing Song, and Y. L. Mo. "Innovative Data Fusion Enabled Structural Health Monitoring Approach." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/369540.

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Piezoceramic-based active sensing is a useful approach to structural health monitoring. This approach often involves a large number of distributed piezoceramic transducers. It may be confusing to incorporate each sensor data. It is desired to develop an automated health monitoring approach to obtain a comprehensive and accurate health monitoring result by simultaneously interpreting data from all sensors. In this paper, an innovative data fusion enabled structural health monitoring (SHM) approach based on the Dempster-Shafer (D-S) evidence theory is proposed to obtain comprehensive SHM results for a distributed sensor network in a civil infrastructure. Considering that evidence from multiple different information sources (sensor data) has different levels of significance, not all evidence is equivalently effective for the final decision. A weighted fusion damage index (WFDI) is proposed to perform damage identification based on the authors’ recently developed piezoceramic-based smart aggregates. Experimental data of a two-story concrete frame was used to study the effectiveness of the proposed weighted fusion damage index. Analyses show that the proposed weighted fusion damage index can reveal the damage status of different areas of the frame. The results are consistent with the visual inspection of the cracks on the concrete frame.

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43

Tzanettis, Ioannis, Christina-Maria Androna, Anastasios Zafeiropoulos, Eleni Fotopoulou, and Symeon Papavassiliou. "Data Fusion of Observability Signals for Assisting Orchestration of Distributed Applications." Sensors 22, no. 5 (March 7, 2022): 2061. http://dx.doi.org/10.3390/s22052061.

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Nowadays, various frameworks are emerging for supporting distributed tracing techniques over microservices-based distributed applications. The objective is to improve observability and management of operational problems of distributed applications, considering bottlenecks in terms of high latencies in the interaction among the deployed microservices. However, such frameworks provide information that is disjoint from the management information that is usually collected by cloud computing orchestration platforms. There is a need to improve observability by combining such information to easily produce insights related to performance issues and to realize root cause analyses to tackle them. In this paper, we provide a modern observability approach and pilot implementation for tackling data fusion aspects in edge and cloud computing orchestration platforms. We consider the integration of signals made available by various open-source monitoring and observability frameworks, including metrics, logs and distributed tracing mechanisms. The approach is validated in an experimental orchestration environment based on the deployment and stress testing of a proof-of-concept microservices-based application. Helpful results are produced regarding the identification of the main causes of latencies in the various application parts and the better understanding of the behavior of the application under different stressing conditions.

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44

Zlatev, Zlatko, Galina Veres, and Zoheir Sabeur. "Agile Data Fusion and Knowledge Base Architecture for Critical Decision Support." International Journal of Decision Support System Technology 5, no. 2 (April 2013): 1–20. http://dx.doi.org/10.4018/jdsst.2013040101.

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This paper describes the architecture and deployment of a software platform for information fusion, knowledge hosting and critical decision support. The work has been carried out under the TRIDEC project (www.tridec-online.eu), focusing on geo-information fusion and collaborative decision making. Four technologies underpin the architecture: 1) A message oriented middleware, for distributed communications; 2) A leveraged hybrid storage solution, for efficient storage of heterogeneous datasets and semantic knowledge; 3) A generic data fusion container, for dynamic algorithms control; and 4) A single conceptual model and schema, as systems’ semantic meta-model. Deployment for industrial drilling operations is described. Agility is manifested with the ability to integrate data sources from a proprietary domain, dynamically discover new datasets and configure and task fusion algorithms to operate on them, aided by efficient information storage. The platform empowers decision support by enabling dynamic discovery of information and control of the fusion process across geo-distributed locations.

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45

Chen, Xiao Yun, Xian Fu Chen, Shao Quan Zhang, and Wen Bin Zhang. "Classification Moving Vehicle Based on Multisensor Data Using Fusion of Multi-Class SVMs Methods." Advanced Materials Research 945-949 (June 2014): 1978–81. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.1978.

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In this paper, we propose a special Multi-class SVMs (MSVM) data fusion strategy which is applied to classify vehicle based on multiple pavement structural strain time histories. The centralized and distributed fusion strategies are applied to combine information from several data sources. In the centralized strategy, all information from several data sources is centralized and combined to construct an input space. Then a MSVM classifier is trained. In distributed schemes, the individual data sources are processed separately and modeled by using the MSVM. Then new data fusion strategies are used to combine the information from the individual MSVM to acquire the final classification outputs. Two popular Multi-class SVMs algorithms (One-against-all OAA, One-against-one OAO) are used to construct classifier based on aforementioned two fusion strategies, respectively. The results are compared between SVM-based fusion approach and single data source SVM using two MSVM algorithms, respectively. The result shows this SVM-based fusion approach significantly improves the results of classification accuracy and robustness. The proposed Multisensor data fusion methods can also be applied in other fields.

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46

MATSUZAKI, Takashi, Kousuke MARUYAMA, Yasushi OBATA, Kentaro SAKISAKA, and Hiroshi KAMEDA. "Distributed Sensor Data Fusion with Transmission Assignment by Error Covariance Matrix." Transactions of the Society of Instrument and Control Engineers 51, no. 10 (2015): 724–35. http://dx.doi.org/10.9746/sicetr.51.724.

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47

Liu, Baoyu, Xingqun Zhan, and Zheng Zhu. "Multisensor Parallel Largest Ellipsoid Distributed Data Fusion with Unknown Cross-Covariances." Sensors 17, no. 7 (June 29, 2017): 1526. http://dx.doi.org/10.3390/s17071526.

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48

Lakshmi, S., and E. A. Mary Anita. "Ship Position tracking Scheme Based on Distributed Multi-sensor Data Fusion." Indian Journal of Public Health Research & Development 9, no. 3 (2018): 443. http://dx.doi.org/10.5958/0976-5506.2018.00320.0.

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49

Martinerie, F. "Data fusion and tracking using HMMs in a distributed sensor network." IEEE Transactions on Aerospace and Electronic Systems 33, no. 1 (January 1997): 11–28. http://dx.doi.org/10.1109/7.570704.

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50

Liu, Anfeng, Xiao Liu, Tianyi Wei, Laurence T. Yang, Seungmin (Charlie) Rho, and Anand Paul. "Distributed Multi-Representative Re-Fusion Approach for Heterogeneous Sensing Data Collection." ACM Transactions on Embedded Computing Systems 16, no. 3 (July 7, 2017): 1–25. http://dx.doi.org/10.1145/2974021.

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