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Articles de revues sur le sujet « Probability-based method »

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Auteur : Grafiati
Publié le 14 mars 2023

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1

Tang, Li, Jie-zhong Zou et Wen-sheng Yang. « A numerical method based on probability theory ». Journal of Central South University of Technology 10, no 2 (juin 2003) : 159–61. http://dx.doi.org/10.1007/s11771-003-0060-4.

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Grigoriu, Mircea, et Katerina D. Papoulia. « Effective conductivity by a probability-based local method ». Journal of Applied Physics 98, no 3 (août 2005) : 033706. http://dx.doi.org/10.1063/1.1993775.

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Kruger, D., et W. T. Penzhorn. « Adaptive probability estimation based on IIR filtering method ». Electronics Letters 38, no 25 (2002) : 1659. http://dx.doi.org/10.1049/el:20021111.

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Li, Zhi-Gang, Jun-Gang Zhou et Bo-Ying Liu. « System Reliability Analysis Method Based on Fuzzy Probability ». International Journal of Fuzzy Systems 19, no 6 (8 août 2017) : 1759–67. http://dx.doi.org/10.1007/s40815-017-0363-5.

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Huang, Yingping, Ross McMurran, Gunwant Dhadyalla et R. Peter Jones. « Probability based vehicle fault diagnosis : Bayesian network method ». Journal of Intelligent Manufacturing 19, no 3 (19 janvier 2008) : 301–11. http://dx.doi.org/10.1007/s10845-008-0083-7.

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He, Liangli, Zhenzhou Lu et Kaixuan Feng. « A novel estimation method for failure-probability-based-sensitivity by conditional probability theorem ». Structural and Multidisciplinary Optimization 61, no 4 (21 décembre 2019) : 1589–602. http://dx.doi.org/10.1007/s00158-019-02437-x.

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Zheqi, Zhu, Ren Bo, Zhang Xiaofeng, Zeng Hang, Xue Tao et Chen Qingge. « Neural network-based probability forecasting method of aviation safety ». IOP Conference Series : Materials Science and Engineering 1043, no 3 (1 janvier 2021) : 032063. http://dx.doi.org/10.1088/1757-899x/1043/3/032063.

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Mauriello, Paolo, et Domenico Patella. « A DATA-ADAPTIVE PROBABILITY-BASED FAST ERT INVERSION METHOD ». Progress In Electromagnetics Research 97 (2009) : 275–90. http://dx.doi.org/10.2528/pier09092307.

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Zhao, Yongxiang. « PROBABILITY-BASED CYCLIC STRESS-STRAIN CURVES AND ESTIMATION METHOD ». Chinese Journal of Mechanical Engineering 36, no 08 (2000) : 102. http://dx.doi.org/10.3901/jme.2000.08.102.

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Yang, Xing, Xiaodong Hu et Zhiqing Li. « The conditional risk probability-based seawall height design method ». International Journal of Naval Architecture and Ocean Engineering 7, no 6 (novembre 2015) : 1007–19. http://dx.doi.org/10.1515/ijnaoe-2015-0070.

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Mahmoodi, Mohammad Reza, et Sayed Masoud Sayedi. « A face detection method based on kernel probability map ». Computers & ; Electrical Engineering 46 (août 2015) : 205–16. http://dx.doi.org/10.1016/j.compeleceng.2015.02.005.

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Du, Sen, Yunjia Wang, Meinan Zheng, Dawei Zhou et Yuanping Xia. « Goaf Locating Based on InSAR and Probability Integration Method ». Remote Sensing 11, no 7 (4 avril 2019) : 812. http://dx.doi.org/10.3390/rs11070812.

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Mining goafs can cause many hazards, such as burst water, spontaneous combustion of coal seams, surface collapse, etc. In this paper, a feature-points-based method for the efficient location of mining goafs is proposed. Different interferometric synthetic aperture radar (DInSAR) is used to monitor the subsidence basin caused by mining. Using the principles of the probability integral method (PIM), the inflection points and the boundary points of the basin monitored by DInSAR are determined and used as feature points to locate the goaf. In this paper, the necessity of locating goafs and the traditional methods used for this task are discussed first. Then, the results of verifying the proposed method by both a simulation experiment and real data experiment are presented. Six RADARSAT-2 images from 13th October 2015 to 5th March 2016 were used to acquire the subsidence basin caused by the 15235 working faces of the Jiulong mining area. The average relative errors of the simulation experiment and real data experiment were about 6.43% and 12.59%, respectively. The average absolute errors of the simulation experiment and real data experiment were about 28 m and 38 m, respectively. In the final part of this paper, the error sources are discussed to illustrate the factors that can affect the location result.
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陈, 帅. « Acoustic Wave Leakage Monitoring Method Based on Sequential Probability ». Journal of Sensor Technology and Application 08, no 03 (2020) : 107–14. http://dx.doi.org/10.12677/jsta.2020.83012.

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Li, Shipeng, Xinyu Yang, Rui Zhao, Yuqing Liu, Xue Zhou et Libiao Zhang. « An Indoor Positioning Method Based on RSSI Probability Distribution ». IOP Conference Series : Materials Science and Engineering 490 (10 avril 2019) : 042054. http://dx.doi.org/10.1088/1757-899x/490/4/042054.

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Wang, Kunpeng, Andrei Rineiski, Werner Maschek, Hongchun Wu et Liangzhi Cao. « Neutron transport model based on the transmission probability method ». Energy Conversion and Management 72 (août 2013) : 33–38. http://dx.doi.org/10.1016/j.enconman.2013.02.020.

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Li, Jichao, Tao Wang et Qingxue Shang. « Probability‐based seismic reliability assessment method for substation systems ». Earthquake Engineering & ; Structural Dynamics 48, no 3 (27 décembre 2018) : 328–46. http://dx.doi.org/10.1002/eqe.3138.

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Xiao, Jie, Yinlun Huang et Helen H. Lou. « A probability distribution estimation based method for dynamic optimization ». AIChE Journal 53, no 7 (2007) : 1805–16. http://dx.doi.org/10.1002/aic.11209.

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Wang, Dan, et Jie Li. « An efficient load effect combination method based on probability density evolution method ». Structural Safety 97 (juillet 2022) : 102217. http://dx.doi.org/10.1016/j.strusafe.2022.102217.

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SEECHARAN, TURUNA S., et GORDON J. SAVAGE. « PROBABILITY-BASED DESIGN OPTIMIZATION OF DYNAMIC SYSTEMS ». International Journal of Reliability, Quality and Safety Engineering 19, no 01 (février 2012) : 1250001. http://dx.doi.org/10.1142/s0218539312500015.

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The mechanistic model of a dynamic system is often so complex that it is not conducive to probability-based design optimization. This is so because the common method to evaluate probability is the Monte Carlo method which requires thousands of lifetime simulations to provide probability distributions. This paper presents a methodology that (1) replaces the implicit mechanistic model with a simple explicit model, and (2), transforms the dynamic, probabilistic, problem into a time invariant probability problem. Probabilities may be evaluated by any convenient method, although the first-order reliability method is particularly attractive because of its speed and accuracy. A part of the methodology invokes design of computer experiments and approximating functions. Training sets of the design variables are selected, a few computer experiments are run to produce a matrix of corresponding responses at discrete times, and then the matrix is replaced with a vector of so-called metamodels. Responses at an arbitrary design set and at any time are easily calculated and then used to formulate common, time-invariant, performance measures. Design variables are treated as random variables and limit-state functions are formed in standard normal probability space. Probability-based design is now straightforward and optimization determines the best set of distribution parameters. Systems reliability methods may be invoked for multiple competing performance measures. Further, singular value decomposition may be used to reduce greatly the number of metamodels needed by transforming the response matrix into two smaller matrices: One containing the design variable-specific information and the other the time-specific information. An error analysis is presented. A case study of a servo-control mechanism shows the new methodology provides controllable accuracy and a substantial time reduction when compared to the traditional mechanistic model with Monte Carlo sampling.
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Chen, Yan, Vitaliy Marchenko et Robert F. Rogers. « Joint Probability-Based Neuronal Spike Train Classification ». Computational and Mathematical Methods in Medicine 10, no 3 (2009) : 229–39. http://dx.doi.org/10.1080/17486700802448615.

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Neuronal spike trains are used by the nervous system to encode and transmit information. Euclidean distance-based methods (EDBMs) have been applied to quantify the similarity between temporally-discretized spike trains and model responses. In this study, using the same discretization procedure, we developed and applied a joint probability-based method (JPBM) to classify individual spike trains of slowly adapting pulmonary stretch receptors (SARs). The activity of individual SARs was recorded in anaesthetized, paralysed adult male rabbits, which were artificially-ventilated at constant rate and one of three different volumes. Two-thirds of the responses to the 600 stimuli presented at each volume were used to construct three response models (one for each stimulus volume) consisting of a series of time bins, each with spike probabilities. The remaining one-third of the responses where used as test responses to be classified into one of the three model responses. This was done by computing the joint probability of observing the same series of events (spikes or no spikes, dictated by the test response) in a given model and determining which probability of the three was highest. The JPBM generally produced better classification accuracy than the EDBM, and both performed well above chance. Both methods were similarly affected by variations in discretization parameters, response epoch duration, and two different response alignment strategies. Increasing bin widths increased classification accuracy, which also improved with increased observation time, but primarily during periods of increasing lung inflation. Thus, the JPBM is a simple and effective method performing spike train classification.
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Gao, Haitao, Peng Xu, Jin Tao, Shihui Huang, Rugang Wang et Quan Zhou. « Voltage Flicker Detection Based on Probability Resampling ». Energies 13, no 13 (30 juin 2020) : 3350. http://dx.doi.org/10.3390/en13133350.

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Digital flicker detection devices need to store a large amount of evaluation data during measurement process, which leads to high requirements for hardware resources and algorithm execution efficiency. In this paper, a digital flicker detection method based on probability resampling is studied. In particular, before statistical evaluation, probability resampling is applied to screen the instantaneous flicker visual sensitivity data to compress redundant data. Additionally, the effectiveness of the method was numerically simulated and experimentally tested. The results show that the proposed method can accurately measure the voltage flicker value and can effectively compress the redundant evaluation data to be evaluated and has significant advantages in releasing hardware storage space, in improving algorithm execution efficiency and real-time performance, and in reducing processor workload. This method provides an engineering application reference for designing digital flicker detectors, especially for the software upgrade of traditional power quality testing equipment.
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BALÁZS, FERENC, et SÁNDOR IMRE. « QUANTUM COMPUTATION BASED PROBABILITY DENSITY FUNCTION ESTIMATION ». International Journal of Quantum Information 03, no 01 (mars 2005) : 93–98. http://dx.doi.org/10.1142/s0219749905000578.

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Signal processing techniques will lean on blind methods in the near future, where no redundant, resource allocating information will be transmitted through the channel. To achieve a proper decision, however, it is essential to know at least the probability density function (PDF), which to estimate is classically a time consumpting and/or less accurate hard task that may make decisions to fail. This paper describes the design of a quantum assisted PDF estimation method also by way of an example, which promises to achieve the exact PDF by proper setting of parameters in a very rapid way.
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YUAN, Xiukai. « Reliability Measure Based on Failure Probability Function and Its Solution by Conditional Probability Simulation Method ». Journal of Mechanical Engineering 48, no 08 (2012) : 144. http://dx.doi.org/10.3901/jme.2012.08.144.

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Chen, Junlan, Ke Wang et Zhoubing Xiong. « Collision probability prediction algorithm for cooperative overtaking based on TTC and conflict probability estimation method ». International Journal of Vehicle Design 77, no 4 (2018) : 195. http://dx.doi.org/10.1504/ijvd.2018.099709.

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Xiong, Zhoubing, Ke Wang et Junlan Chen. « Collision probability prediction algorithm for cooperative overtaking based on TTC and conflict probability estimation method ». International Journal of Vehicle Design 77, no 4 (2018) : 195. http://dx.doi.org/10.1504/ijvd.2018.10021448.

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Clark, Stephen, et David Weir. « Class-Based Probability Estimation Using a Semantic Hierarchy ». Computational Linguistics 28, no 2 (juin 2002) : 187–206. http://dx.doi.org/10.1162/089120102760173643.

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This article concerns the estimation of a particular kind of probability, namely, the probability of a noun sense appearing as a particular argument of a predicate. In order to overcome the accompanying sparse-data problem, the proposal here is to define the probabilities in terms of senses from a semantic hierarchy and exploit the fact that the senses can be grouped into classes consisting of semantically similar senses. There is a particular focus on the problem of how to determine a suitable class for a given sense, or, alternatively, how to determine a suitable level of generalization in the hierarchy. A procedure is developed that uses a chi-square test to determine a suitable level of generalization. In order to test the performance of the estimation method, a pseudo-disambiguation task is used, together with two alternative estimation methods. Each method uses a different generalization procedure; the first alternative uses the minimum description length principle, and the second uses Resnik's measure of selectional preference. In addition, the performance of our method is investigated using both the standard Pearson chi-square statistic and the log-likelihood chi-square statistic.
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Jiang, Ping, Yunyan Xing, Xiang Jia et Bo Guo. « Weibull Failure Probability Estimation Based on Zero-Failure Data ». Mathematical Problems in Engineering 2015 (2015) : 1–8. http://dx.doi.org/10.1155/2015/681232.

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Reliability testing is often carried out with small sample sizes and short duration because of increasing costs and the restriction of development time. Therefore, for highly reliable products, zero-failure data are often collected in such tests, which could not be used to evaluate reliability by traditional methods. To cope with this problem, the match distribution curve method was proposed by some researchers. The key step needed to exercise this method is to estimate the failure probability, which has yet to be solved in the case of the Weibull distribution. This paper presents a method to estimate the intervals of failure probability for the Weibull distribution by using the concavity or convexity and property of the distribution function. Furthermore, to use the method in practice, this paper proposes using the approximate value of the shape parameter determined by either engineering experience or by hypothesis testing through apvalue. The estimation of the failure probability is thus calculated using a Bayesian approach. A numerical example is presented to validate the effectiveness and robustness of the method.
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Xu, Tingting, Hui Zhang et Boquan Li. « Pythagorean fuzzy TOPSIS method based on 2-tuple probability weight ». Journal of Intelligent & ; Fuzzy Systems 40, no 5 (22 avril 2021) : 9113–26. http://dx.doi.org/10.3233/jifs-201533.

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In this paper, the concept of 2-tuple probability weight is presented, and on this basis, the technique for order preference by similarity to ideal solution (TOPSIS) method in Pythagorean fuzzy environment is given. First, the definition of 2-tuple probability weight is put forward, and two examples are provided to illustrate that 2-tuple probability weight can effectively prevent the loss of information. Second, the notion of real-value 2-tuple is defined for any two real numbers, and some basic operations, operation properties, and sorting functions are introduced. Finally, a 2-tuple probability weight Euclidean distance is provided, a new Pythagorean fuzzy TOPSIS method is further proposed, and the flexibility and effectiveness of the proposed methods are illustrated by an example and two comparative analyses.
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Wang, Yong Hui, Jian Wei Zhou et Bing Wen. « Subsidence Prediction under Thick Alluvium Based on Probability Integration Method ». Applied Mechanics and Materials 448-453 (octobre 2013) : 3808–13. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.3808.

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The surface subsidence resulting from coal mining can lead to terrible land damage, thus studying the distribution of surface subsidence under thick alluvium is of crucial significance.Focusing on the example of Henghe coalmine in Jining, Shandong province, this paperestablished the prediction model for main section and arbitrary points in mining areas based on the probability integration method, by which the size of subsidence, tilt, curvature, horizontal movement and deformation were predicted after the exploitation of coalbeds 2# and 3#. And the result, which is basically consistent with the actual situation, shows that subsidence areais 78 hm2 and the settlement reaches 9987mm. The thick alluvium covered the mining area accountsfor the greater surface subsidence coefficient, subsidence value, horizontal movement coefficient,horizontal movement, subsidence area and smaller deviation of inflection point.
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Jia, Qinglan, et Chunyu Liu. « Intelligent Scheduling Method of Port Vehicles Based on Probability Information ». Journal of Coastal Research 103, sp1 (23 juin 2020) : 726. http://dx.doi.org/10.2112/si103-149.1.

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Fan, Chao-dong, Hong-lin Ouyang et Ying-jie Zhang. « Small Probability Strategy Based Otsu Thresholding Method for Image Segmentation ». Journal of Electronics & ; Information Technology 35, no 9 (26 février 2014) : 2081–87. http://dx.doi.org/10.3724/sp.j.1146.2012.01598.

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Liu, Zhang Jun, et Ya Kong. « Fuzzy Probability Method-Based Risk Assessment of International Engineering Project ». Advanced Materials Research 368-373 (octobre 2011) : 3180–83. http://dx.doi.org/10.4028/www.scientific.net/amr.368-373.3180.

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In the present paper, the fuzzy probability method is applied to assess the risk of international engineering project. Firstly, According to the risk from the whole cycle of international engineering project, The influential factors of the project bidding risk assessment are analyzed. The risk of international engineering project is divided into four parts, such as national risk, industrial risk, involved parties risk and project’s risk, and an assessment index system of international project risk is constructed. Secondly, based on the fuzzy probability method, a new model of the risk assessment for international engineering project is developed. In order to assess index weighted value scientifically, the analytical hierarchy process (AHP) is introduced into the fuzzy weighted values. Finally, the validity and rationality of the risk evaluation for international engineering project is verified through the risk instance of a project.
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LIU, Dan. « Feature Selection Method Based on Overlapped Probability of Intersection Area ». Chinese Journal of Mechanical Engineering 45, no 02 (2009) : 114. http://dx.doi.org/10.3901/jme.2009.02.114.

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Vicente, E., A. Mateos et A. Jiménez-Martín. « A Betting- and Lottery-Based Method for Fuzzy Probability Elicitation ». International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 27, no 01 (février 2019) : 121–44. http://dx.doi.org/10.1142/s0218488519500065.

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It is very common to use linguistic term scales, whose terms are previously associated with different fuzzy numbers to assign probabilities to events in decision-making processes. However, the rigidity of such scales generates bias in the probability elicitation process and does not allow experts to adequately express their probabilistic judgments. We propose a betting and lottery-based method for eliciting a fuzzy number from the expert that represents his/her probabilistic judgments for a given event, along with a quality measure of the probabilistic judgments based on precision and consistency measures. We also enact a simulation process to analyze possible biases in the proposed fuzzy probability elicitation method.
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Kompaneets, B. S., et D. A. Boyarkov. « Electric Grids Technical Evaluation Method based on their Failure Probability ». IOP Conference Series : Materials Science and Engineering 791 (9 avril 2020) : 012030. http://dx.doi.org/10.1088/1757-899x/791/1/012030.

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Deng, Zhan, et Jianyu Wang. « A novel decision probability transformation method based on belief interval ». Knowledge-Based Systems 208 (novembre 2020) : 106427. http://dx.doi.org/10.1016/j.knosys.2020.106427.

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M.N. Fu, Alan, et Hong Yan. « A new probabilistic relaxation method based on probability space partition ». Pattern Recognition 30, no 11 (novembre 1997) : 1905–17. http://dx.doi.org/10.1016/s0031-3203(97)00009-5.

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Su, Cheng, Zhijian Huang et Jianhua Xian. « A modified response spectrum method based on uniform probability spectrum ». Bulletin of Earthquake Engineering 17, no 2 (27 septembre 2018) : 657–80. http://dx.doi.org/10.1007/s10518-018-0485-7.

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Sun, Bo, Meng-Meng Li, Bao-Peng Liao, Xi Yang, Yi-Tong Cao, Bo-Feng Cui, Qiang Feng, Yi Ren et De-Zhen Yang. « Time-variant reliability modeling based on hybrid non-probability method ». Archive of Applied Mechanics 90, no 2 (27 septembre 2019) : 209–19. http://dx.doi.org/10.1007/s00419-019-01605-1.

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Su, Yong-hua, Ming-hua Zhao et Yue-ying Zhang. « Numerical method of slope failure probability based on Bishop model ». Journal of Central South University of Technology 15, no 1 (février 2008) : 100–105. http://dx.doi.org/10.1007/s11771-008-0020-0.

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Bera, Titas, M. Seetharama Bhat et Debasish Ghose. « Analysis of Obstacle based Probabilistic RoadMap Method using Geometric Probability ». IFAC Proceedings Volumes 47, no 1 (2014) : 462–69. http://dx.doi.org/10.3182/20140313-3-in-3024.00245.

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Chiu, Chao-LiN, et Yen-Chang Chen. « An efficient method of discharge estimation based on probability concept ». Journal of Hydraulic Research 41, no 6 (novembre 2003) : 589–96. http://dx.doi.org/10.1080/00221680309506891.

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Dang, Thuat-Cong, Thien-Phu Le et Pascal Ray. « Seismic fragility curves based on the probability density evolution method ». Vietnam Journal of Mechanics 39, no 2 (21 juin 2017) : 177–89. http://dx.doi.org/10.15625/0866-7136/10208.

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A seismic fragility curve that shows the probability of failure of a structure in function of a seismic intensity, for example peak ground acceleration (PGA), is a powerful tool for the evaluation of the seismic vulnerability of the structures in nuclear engineering and civil engineering. The common assumption of existing approaches is that the fragility curve is a cumulative probability log-normal function. In this paper, we propose a new technique for construction of seismic fragility curves by numerical simulation using the Probability Density Evolution Method (PDEM). From the joint probability density function between structural response and random variables of a system and/or excitations, seismic fragility curves can be derived without the log-normal assumption. The validation of the proposed technique is performed on two numerical examples.
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Jaspersen, Johannes G., et Gilberto Montibeller. « Probability Elicitation Under Severe Time Pressure : A Rank-Based Method ». Risk Analysis 35, no 7 (7 avril 2015) : 1317–35. http://dx.doi.org/10.1111/risa.12357.

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Yin, Zili, et Wei Zhang. « Fault Diagnosis Method of Fault Indicator Based on Maximum Probability ». IOP Conference Series : Materials Science and Engineering 199 (mai 2017) : 012068. http://dx.doi.org/10.1088/1757-899x/199/1/012068.

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Li, Jing, Jinglai Li et Dongbin Xiu. « An efficient surrogate-based method for computing rare failure probability ». Journal of Computational Physics 230, no 24 (octobre 2011) : 8683–97. http://dx.doi.org/10.1016/j.jcp.2011.08.008.

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Cai, Chang-Qing, et Jian-Wen Shen. « A probability-consistent method based on practical ground surface motion ». Acta Seismologica Sinica 11, no 5 (septembre 1998) : 565–72. http://dx.doi.org/10.1007/s11589-998-0071-1.

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梁, 祖鹏. « One Dependence Conditional Probability Encoding Method Based on Attribute Weighting ». Operations Research and Fuzziology 13, no 01 (2023) : 74–87. http://dx.doi.org/10.12677/orf.2023.131009.

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Li, Junwei, Aoxiang Zhao et Huanyu Liu. « A Decision Probability Transformation Method Based on the Neural Network ». Entropy 24, no 11 (11 novembre 2022) : 1638. http://dx.doi.org/10.3390/e24111638.

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When the Dempster–Shafer evidence theory is applied to the field of information fusion, how to reasonably transform the basic probability assignment (BPA) into probability to improve decision-making efficiency has been a key challenge. To address this challenge, this paper proposes an efficient probability transformation method based on neural network to achieve the transformation from the BPA to the probabilistic decision. First, a neural network is constructed based on the BPA of propositions in the mass function. Next, the average information content and the interval information content are used to quantify the information contained in each proposition subset and combined to construct the weighting function with parameter r. Then, the BPA of the input layer and the bias units are allocated to the proposition subset in each hidden layer according to the weight factors until the probability of each single-element proposition with the variable is output. Finally, the parameter r and the optimal transform results are obtained under the premise of maximizing the probabilistic information content. The proposed method satisfies the consistency of the upper and lower boundaries of each proposition. Extensive examples and a practical application show that, compared with the other methods, the proposed method not only has higher applicability, but also has lower uncertainty regarding the transformation result information.
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Wu, Tianli, Hao Xia, Shuo Liu et Yanyou Qiao. « Probability-Based Indoor Positioning Algorithm Using iBeacons ». Sensors 19, no 23 (28 novembre 2019) : 5226. http://dx.doi.org/10.3390/s19235226.

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High-precision indoor positioning is important for modern society. This paper proposes a way to achieve high positioning accuracy and obtain a trajectory close to the actual path in a common application scenario by smartphone without the use of a complicated algorithm. In the actual positioning process, a stable signal source can reduce the signal interference caused by environments. Bluetooth low energy has its own advantages in indoor positioning because it can be seen as a more stable signal source. In this study, we used smartphones to record the changing Bluetooth signals and used a basic nearest neighbor, weight centroid, and probability-based method, which we called an advanced weighted centroid method, to obtain position coordinates and the motion trajectory during the experiment. We used a weight centroid method based on least squares to solve the overdetermined problem. This can also be used to calculate the initial position of the advanced weight centroid. The advanced weighted centroid method introduced a Gaussian distribution to model the distribution of the signal. Translating a deterministic problem into a fuzzy probability problem aligns more with positioning facts and can achieve better results. Experimental results showed that the root-mean-square error (RMSE) of the dynamic positioning result obtained through the probabilistic method was within 1 m and had a more consistent trajectory. Moreover, the impact of the number of iBeacons on the positioning accuracy has been discussed, and a reference for iBeacon placement has been provided. In addition, an experiment was also conducted on the effect of signal transmission frequency on accuracy.
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