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Journal articles on the topic 'Particle-based method'

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Author: Grafiati

Published: 14 March 2023

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1

Fair, Rebecca, Xiaohu Guo, and Tao Cui. "Particle sorting for the projection based particle method." Engineering Analysis with Boundary Elements 109 (December 2019): 199–208. http://dx.doi.org/10.1016/j.enganabound.2019.07.022.

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2

SÖDERSTEN, Axel, Takuya MATSUNAGA, Seiichi KOSHIZUKA, Tomoyuki HOSAKA, and Eiji ISHII. "Adaptive resizing-based multi-resolution particle method." Mechanical Engineering Journal 9, no. 1 (2022): 21–00309. http://dx.doi.org/10.1299/mej.21-00309.

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3

Qian, Chen, and Keiichiro Yasuda. "Particle Swarm Optimization Based Global Descent Method." IEEJ Transactions on Electrical and Electronic Engineering 4, no. 6 (November 2009): 731–33. http://dx.doi.org/10.1002/tee.20472.

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4

Liu, Ce, Baotong Li, Qingfang Liu, Jun Hong, and Kaitai Li. "A novel implicit meshless particle method: NURBS-based particle hydrodynamics (NBPH)." Computer Methods in Applied Mechanics and Engineering 406 (March 2023): 115895. http://dx.doi.org/10.1016/j.cma.2023.115895.

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5

Tabrizi-Zarringhabaei, Saman, Reza Goli Ejlali, Mikaeil Yousefzadeh Fard, and Sayyedjavad Sayyedfattahi. "AN IMAGE-BASED METHOD TO DETERMINE THE PARTICLE SIZE DISTRIBUTION (PSD) OF FINE-GRAINED SOIL." Rudarsko-geološko-naftni zbornik 34, no. 3 (2019): 81–88. http://dx.doi.org/10.17794/rgn.2019.3.9.

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6

Shuan-Jun Song, Shuan-Jun Song, Cheng-Hong Qiu Shuan-Jun Song, Long-Guang Peng Cheng-Hong Qiu, and Sheng Hu Long-Guang Peng. "An Assembly Line Multi-Station Assembly Sequence Planning Method Based on Particle Swarm Optimization Algorithm." 電腦學刊 33, no. 1 (February 2022): 115–25. http://dx.doi.org/10.53106/199115992022023301011.

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<p>Aiming at the problem that the existing assembly sequence planning methods are difficult to meet the multi-station assembly requirements of assembly line, an assembly sequence planning method of assembly line considering the constraints of station sequence and station capability is proposed. The multi-station assembly sequence model is established to describe the allocation scheme and assembly sequence of parts. The conditions and generating rules of feasible assembly sequence are given. The assembly time variance of each station is used as the fitness function, and the particle swarm optimization (PSO) algorithm is designed. Taking an engineering vehicle assembly as an example, the optimal integration solution of multi-station assembly sequence and job assignment is obtained by using this algorithm, and the validity of the model is verified.</p> <p> </p>

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7

Zhang, Jingxiu, Zhiwei Zhang, and Longfei Hou. "Detection of Particle Concentration and Particle Size Based on Aerodynamic Particle Size Spectrometer." Advances in Multimedia 2022 (August 24, 2022): 1–10. http://dx.doi.org/10.1155/2022/4152186.

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In order to solve the problem of the continuous online monitoring of the fixed pollution source emission particles, on the basis of the advantages and disadvantages of the comprehensive analysis of the extinction method, the β-ray absorption method, the micro-oscillating balance method, the charge transfer method, and the light scattering method, the particle size distribution and concentration optical detection system based on aerodynamic particle size spectrometer is designed according to the Mie light scattering principle. A high-performance aerodynamic particle size spectrometer is designed using a single-wavelength method to measure the concentration of particulate matter, and the equipment and the detection system are used in the continuous monitoring of particulate matter emitted by thermal power plants and compared with the ES632 equipment. Through the system test, the results show that compared with the ES632 equipment performance, the lowest concentration measured by the design of aerodynamic particle size spectrometer equipment can reach 0.1 mg/m3 with a measurement accuracy of 0.01 mg/m3. Compared with the ES632 equipment, the measurement concentration value is 8% higher, which meets the test requirements.

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8

Tian, Tian. "Detecting Particle Clusters in Particle-Fluid Systems by a Density Based Method." Communications in Computational Physics 26, no. 5 (June 2019): 1617–30. http://dx.doi.org/10.4208/cicp.2019.js60.09.

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9

QIU Jiatao, 邱家涛, 李玉山 LI Yushan, 王彩玲 WANG Cailing, and 刘洋 LIU Yang. "An Integral Histogrambased Particle Filtering Tracking Method." ACTA PHOTONICA SINICA 40, no. 11 (2011): 1761–66. http://dx.doi.org/10.3788/gzxb20114011.1761.

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10

WANG, Na. "Folded Airbag Simulation Based on Corpuscular Particle Method." Journal of Mechanical Engineering 51, no. 12 (2015): 120. http://dx.doi.org/10.3901/jme.2015.12.120.

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11

Sadhu, Smita, Shovan Bhaumik, Arnaud Doucet, and T. K. Ghoshal. "Particle-method-based formulation of risk-sensitive filter." Signal Processing 89, no. 3 (March 2009): 314–19. http://dx.doi.org/10.1016/j.sigpro.2008.09.006.

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12

Chang, YuanZhang, Kai Bao, Jian Zhu, and EnHua Wu. "A particle-based method for granular flow simulation." Science China Information Sciences 55, no. 5 (March 16, 2012): 1062–72. http://dx.doi.org/10.1007/s11432-012-4564-0.

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13

Sun, Guoqiang, Jihong Zheng, Gang Zheng, and Songlin Zhuang. "Small particle detection method based on laser feedback." Frontiers of Optoelectronics in China 1, no. 3-4 (December 2008): 241–46. http://dx.doi.org/10.1007/s12200-008-0073-6.

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14

Zou, Ling. "Simulating Liquid Dynamics by a Particle-Based Method." Applied Mechanics and Materials 380-384 (August 2013): 1121–24. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1121.

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There is much research focusing on natural phenomena simulation in virtual reality and computer graphics. Liquid is chosen as our research object, because it is one of the most common natural phenomena. A particle-based modeling method for dynamic liquid simulation is presented in this paper. In our approach, accurate solutions for the Navier-Stokes equations are first accomplished in an Euler-based grid at each time step. This returns a velocity field calculated based on the pressure solved from a converted Poisson equation. Finally, particle movements are advected through this velocity field in order to simulate the dynamics of fluid volume. Experiment shows that visual effect which can satisfy users requirement is achieved by this method. This application has promising potentials in the areas of movie making, computer games, virtual construction and virtual simulation in medicine, etc.

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15

Srinivasan, Gowri, Elizabeth Keating, John David Moulton, Zora V. Dash, and Bruce A. Robinson. "Convolution-based particle tracking method for transient flow." Computational Geosciences 16, no. 3 (December 7, 2011): 551–63. http://dx.doi.org/10.1007/s10596-011-9265-z.

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16

Ding, Tianxiang, Xuyan Hou, Man Li, Guangyu Cao, Jixuan Liu, Xianlin Zeng, and Zongquan Deng. "Investigation on Computing Method of Martian Dust Fluid Based on the Energy Dissipation Method." International Journal of Aerospace Engineering 2020 (May 23, 2020): 1–13. http://dx.doi.org/10.1155/2020/2370385.

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In this paper, an initiative Martian dust fluid simulating research based on the energy dissipation method was developed to simulate the deposition process of Martian dust fluid which was caused by surface adhesion between particles and Martian rovers. Firstly, an energy dissipation model of particles based on the Discrete Element Method (DEM) was established because of the characteristics of Martian dust particles such as tiny size and viscoelasticity. This model is based on the existing DMT model to analyze the collision deposition of dust fluid particles, including particle-spacecraft collision and particle-particle collision. Secondly, this paper analyzed the characteristics of particles after their first collision, then, established the stochastic model of critical wind speed for the particle deposition process. Finally, a series of simulations of the Martian dust fluid particle deposition process were done based on DEM-CFD. The results verified the accuracy of the energy dissipation model and the stochastic model, which could also verify the feasibility and effectiveness of the computing method of Martian dust fluid based on the DEM-CFD technology.

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17

Razaq, Zeina Hassan. "Speech Scrambling based on Particle Swarm Optimisation." Webology 19, no. 1 (December 24, 2021): 70–82. http://dx.doi.org/10.14704/web/v19i1/web19005.

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Securing any communication system where important data may be transmitted through the channel is a very crucial issue. One of the good solutions in providing security for the speech is to use speech scrambling techniques. The chaotic system used in security has properties that make it a good choice for scrambling speech signal and the optimisation algorithm can provide a perfect performance when used to enhance the hybrid of more than one method. In this paper, we suggest a system that uses an optimisation method, namely, particle swarm optimisation. The evaluation measures prove that the output of the optimisation method has better performance among the methods used in the comparison, including chaotic maps and hybrid chaotic maps.

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18

TANG, Xian-lun, La-mei LI, and Bo-jie JIANG. "Mobile robot SLAM method based on multi-agent particle swarm optimized particle filter." Journal of China Universities of Posts and Telecommunications 21, no. 6 (December 2014): 78–86. http://dx.doi.org/10.1016/s1005-8885(14)60348-4.

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19

Sanchez, J. J., and P. W. Randles. "A quasi-static dual particle method for solids based on dual particle dynamics." International Journal for Numerical Methods in Engineering 94, no. 2 (January 31, 2013): 183–203. http://dx.doi.org/10.1002/nme.4451.

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20

SUSAKI, Yasunori, Ichiro KIMURA, Akikazu KAGA, and Yasuaki KUROE. "Particle Image Velocimetry Based on Gradient-Based Method Using Neural Networks." Journal of the Visualization Society of Japan 19, Supplement2 (1999): 163–66. http://dx.doi.org/10.3154/jvs.19.supplement2_163.

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21

HAVANGI, R., M. TESHNEHLAB, M. A. NEKOUI, and H. D. TAGHIRAD. "A NOVEL PARTICLE FILTER BASED SLAM." International Journal of Humanoid Robotics 10, no. 03 (September 2013): 1350018. http://dx.doi.org/10.1142/s0219843613500187.

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In this paper, a new approach to SLAM is proposed that is based on particle filter and soft computing techniques. In this approach, the robot pose is estimated based on unscented marginal particle filter (UMPF) and the static map is considered as parameters that are updated using soft computing. Significant improvement in the proposed method is observed in terms of accuracy of estimation and consistency compared to conventional methods. A number of simulations and experiments are presented to evaluate the algorithm's performance compared to conventional approaches.

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22

Chen, Xianjie, Zhuoliang Xie, and Guoru Nai. "GB decomposition method based on standard particle swarm algorithm." IOP Conference Series: Earth and Environmental Science 660, no. 1 (February 1, 2021): 012078. http://dx.doi.org/10.1088/1755-1315/660/1/012078.

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23

Wang, Shi-yuan, and Jiu-chao Feng. "A Method of Chaotic Communication Based on Particle Filter." Journal of Electronics & Information Technology 30, no. 1 (February 18, 2011): 89–92. http://dx.doi.org/10.3724/sp.j.1146.2006.00847.

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24

ZHOU, Hang, and Junyong YE. "Stratified sampling particle filter algorithm based on clustering method." Journal of Computer Applications 33, no. 1 (September 22, 2013): 69–71. http://dx.doi.org/10.3724/sp.j.1087.2013.00069.

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25

ZHAI Zi-yu, 翟子羽, and 叶美盈 YE Mei-ying. "A Coating Design Method Based on Particle Swarm Optimization." ACTA PHOTONICA SINICA 40, no. 9 (2011): 1338–41. http://dx.doi.org/10.3788/gzxb20114009.1338.

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26

LIU Xian-yun, 刘宪云, 方佳怡 Fang Jia-yi, 李磊 LI Lei, and 元春梅 YUAN Chun-mei. "Aerosol Detection Method Based on Wide-Range Particle Spectrometer." ACTA PHOTONICA SINICA 45, no. 5 (2016): 501002. http://dx.doi.org/10.3788/gzxb20164505.0501002.

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27

Zhu Hong, Zhang Hai, Tang Gaodi, and Li Zhongyun. "Method against radar’s deception jamming based on particle filter." High Power Laser and Particle Beams 26, no. 11 (2014): 113201. http://dx.doi.org/10.3788/hplpb20142611.113201.

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28

Li, Hong-wei, Jun Wang, and Hai-tao Wang. "A New Particle Filter Based on Differential Evolution Method." Journal of Electronics & Information Technology 33, no. 7 (August 3, 2011): 1639–43. http://dx.doi.org/10.3724/sp.j.1146.2010.01212.

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29

Lambrou, Eleftherios, and Leonidas N. Gergidis. "A particle digitization-based computational method for continuum percolation." Physica A: Statistical Mechanics and its Applications 590 (March 2022): 126738. http://dx.doi.org/10.1016/j.physa.2021.126738.

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30

Yang, Chao, Gang Meng, Hui Guo, Yansong Wang, and Lingli Sun. "A hybrid wave superposition method based on particle filter." Applied Acoustics 194 (June 2022): 108806. http://dx.doi.org/10.1016/j.apacoust.2022.108806.

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31

Xia, Hong, Qingyi Dong, Hui Gao, Yanping Chen, and ZhongMin Wang. "Service Partition Method Based on Particle Swarm Fuzzy Clustering." Wireless Communications and Mobile Computing 2021 (December 8, 2021): 1–12. http://dx.doi.org/10.1155/2021/7225552.

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It is difficult to accurately classify a service into specific service clusters for the multirelationships between services. To solve this problem, this paper proposes a service partition method based on particle swarm fuzzy clustering, which can effectively consider multirelationships between services by using a fuzzy clustering algorithm. Firstly, the algorithm for automatically determining the number of clusters is to determine the number of service clusters based on the density of the service core point. Secondly, the fuzzy c -means combined with particle swarm optimization algorithm to find the optimal cluster center of the service. Finally, the fuzzy clustering algorithm uses the improved Gram-cosine similarity to obtain the final results. Extensive experiments on real web service data show that our method is better than mainstream clustering algorithms in accuracy.

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32

Dong, Yong, Binghu Huang, Fuyuan Wang, and Yawen He. "Research on ocean current visualization method based on particle." Journal of Physics: Conference Series 2006, no. 1 (August 1, 2021): 012038. http://dx.doi.org/10.1088/1742-6596/2006/1/012038.

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33

Wang, Chao, and Ke Luo. "Rough Clustering Method Based on Particle Swarm Optimization Algorithm." Applied Mechanics and Materials 325-326 (June 2013): 1632–36. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.1632.

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As a relatively novel clustering approach, Particle Swarm Optimization (PSO) prevents k-means algorithm from falling into local optimum effectively, and has made relatively notable successes in clustering, however, using Hard C-Means algorithm when randomly obtaining initial clustering centers is required in most existing PSOs, while no definite limit existing in these samples actually. Based on this, we utilized an improved PSO; along with effective processing methods on boundary objects of Rough Set Theory, we proposed a new rough clustering algorithm based on PSO. It can adjust the upper and lower approximations weighting factors dynamically, and coordinate the proportions of upper and lower approximations in different generations as well. Finally, we compared it with several common clustering methods using Iris dataset of UCI. It turned out that the algorithm has higher accuracy and stability, along with better comprehensive performance.

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34

GUO Si-qiu, 郭巳秋, 许廷发 XU Ting-fa, 王洪庆 WANG Hong-qing, 张一舟 ZHANG Yi-zhou, and 申子宜 SHEN Zi-yi. "Object tracking method based on improved particle swarm optimization." Chinese Journal of Optics and Applied Optics 7, no. 5 (2014): 759–67. http://dx.doi.org/10.3788/co.20140705.0759.

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35

Nakamura, Yukinori, Kazushi Higashiyama, Kentaro Hirata, and Kunihisa Okano. "A Particle Filter-based Estimation Method Considering Communication Delay." IEEJ Transactions on Electronics, Information and Systems 139, no. 4 (April 1, 2019): 454–59. http://dx.doi.org/10.1541/ieejeiss.139.454.

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36

Duan, Ping, Jia Li, and Hai Yang Lv. "Variation function fitting method based on particle swarm optimization." MATEC Web of Conferences 44 (2016): 02091. http://dx.doi.org/10.1051/matecconf/20164402091.

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37

TANAKA, Satoyuki, Syota Sadamoto, and Shigenobu Okazawa. "409 Solid/Structural Analysis using Galerkin-Based Particle Method." Proceedings of The Computational Mechanics Conference 2008.21 (2008): 185–86. http://dx.doi.org/10.1299/jsmecmd.2008.21.185.

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38

MANABE, Masatoshi, Takayuki YAMADA, Shinji NISHIWAKI, and Kazuhiro IZUI. "2111 Level Set-Based Topology Optimization Using Particle Method." Proceedings of The Computational Mechanics Conference 2009.22 (2009): 402–3. http://dx.doi.org/10.1299/jsmecmd.2009.22.402.

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39

Sánchez-Escobar, Juan Jaime, Liliana Ibeth Barbosa-Santillán, Javier Vargas-Ubera, and Félix Aguilar-Valdés. "Inversion method based on stochastic optimization for particle sizing." Applied Optics 55, no. 22 (July 21, 2016): 5806. http://dx.doi.org/10.1364/ao.55.005806.

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40

Kante, Normunds, Mykola Kryshchuk, and Jurijs Lavendels. "Charged Particle Location Modeling Based Experiment Plan Acquisition Method." Procedia Computer Science 104 (2017): 592–97. http://dx.doi.org/10.1016/j.procs.2017.01.177.

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41

Wang, Zheng, Cuiqiong Mo, and Huanyao Dai. "Target Detection Method Before Tracking based on Particle Filter." Procedia Computer Science 107 (2017): 141–47. http://dx.doi.org/10.1016/j.procs.2017.03.070.

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42

Salpavaara, Timo, Matti Järveläinen, Sari Seppälä, Teemu Yli-Hallila, Jarmo Verho, Matti Vilkko, Jukka Lekkala, and Erkki Levänen. "Passive resonance sensor based method for monitoring particle suspensions." Sensors and Actuators B: Chemical 219 (November 2015): 324–30. http://dx.doi.org/10.1016/j.snb.2015.04.121.

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43

Wu, Yu-jia, Yan Cao, and Qiang-feng Wang. "Assembly sequence planning method based on particle swarm algorithm." Cluster Computing 22, S1 (November 10, 2017): 835–46. http://dx.doi.org/10.1007/s10586-017-1331-4.

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44

Wang, Yuh-Rau, Wei-Hung Lin, and Ling Yang. "An intelligent watermarking method based on particle swarm optimization." Expert Systems with Applications 38, no. 7 (July 2011): 8024–29. http://dx.doi.org/10.1016/j.eswa.2010.12.129.

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45

Liang, Xiaolei, Wenfeng Li, Yu Zhang, and MengChu Zhou. "An adaptive particle swarm optimization method based on clustering." Soft Computing 19, no. 2 (April 17, 2014): 431–48. http://dx.doi.org/10.1007/s00500-014-1262-4.

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46

Hon, Sean Y., Shingyu Leung, and Hongkai Zhao. "A cell based particle method for modeling dynamic interfaces." Journal of Computational Physics 272 (September 2014): 279–306. http://dx.doi.org/10.1016/j.jcp.2014.04.032.

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47

Chu, Chengbing, and Shidong Yang. "A Particle Filter Based Reference Fingerprinting Map Recalibration Method." IEEE Access 7 (2019): 111813–27. http://dx.doi.org/10.1109/access.2019.2931992.

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48

Gang, Sheng Yan, and Fei Tong. "Network Routing Optimization based on Mixed Particle Method Research." International Journal of Future Generation Communication and Networking 8, no. 4 (August 31, 2015): 239–50. http://dx.doi.org/10.14257/ijfgcn.2015.8.4.24.

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49

Miled, Mohamed, Antoine Gagne, and Mohamad Sawan. "Hybrid Modeling Method for a DEP Based Particle Manipulation." Sensors 13, no. 2 (January 30, 2013): 1730–53. http://dx.doi.org/10.3390/s130201730.

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50

Zhang, Huang, Qianfeng Liu, Benke Qin, and Hanliang Bo. "Simulating particle collision process based on Monte Carlo method." Journal of Nuclear Science and Technology 52, no. 11 (January 21, 2015): 1393–401. http://dx.doi.org/10.1080/00223131.2014.1003152.

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