scholarly journals Estimating hotspots using a Gaussian mixture model from large-scale taxi GPS trace data

2019 ◽  
Vol 1 (2) ◽  
pp. 145-153
Author(s):  
Jin-jun Tang ◽  
Jin Hu ◽  
Yi-wei Wang ◽  
He-lai Huang ◽  
Yin-hai Wang
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Large Scale ◽  
Spatial Information ◽  
Spatial Clustering ◽  
Gaussian Mixture ◽  
Traffic Information ◽  
Trace Data ◽  
Real Trace ◽  
Two Parameters

Abstract The data collected from taxi vehicles using the global positioning system (GPS) traces provides abundant temporal-spatial information, as well as information on the activity of drivers. Using taxi vehicles as mobile sensors in road networks to collect traffic information is an important emerging approach in efforts to relieve congestion. In this paper, we present a hybrid model for estimating driving paths using a density-based spatial clustering of applications with noise (DBSCAN) algorithm and a Gaussian mixture model (GMM). The first step in our approach is to extract the locations from pick-up and drop-off records (PDR) in taxi GPS equipment. Second, the locations are classified into different clusters using DBSCAN. Two parameters (density threshold and radius) are optimized using real trace data recorded from 1100 drivers. A GMM is also utilized to estimate a significant number of locations; the parameters of the GMM are optimized using an expectation-maximum (EM) likelihood algorithm. Finally, applications are used to test the effectiveness of the proposed model. In these applications, locations distributed in two regions (a residential district and a railway station) are clustered and estimated automatically.

Reduced Degrees of Freedom Gaussian Mixture Model Fitting for Large Scale History Matching Problems

2019 ◽  
Author(s):  
Guohua Gao ◽  
Hao Jiang ◽  
Chaohui Chen ◽  
Jeroen C. Vink ◽  
Yaakoub El Khamra ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
History Matching ◽  
Degrees Of Freedom ◽  
Large Scale ◽  
Model Fitting ◽  
Gaussian Mixture ◽  
Matching Problems

scholarly journals Automated segmentation of intraretinal cystoid macular edema based on Gaussian mixture model

2019 ◽  
Vol 13 (01) ◽  
pp. 1950020
Author(s):  
Jinghong Wu ◽  
Sijie Niu ◽  
Qiang Chen ◽  
Wen Fan ◽  
Songtao Yuan ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Level Set ◽  
Spatial Information ◽  
High Sensitivity ◽  
Gaussian Mixture ◽  
Automated Segmentation ◽  
Potential Fluid ◽  
Sd Oct

We introduce a method based on Gaussian mixture model (GMM) clustering and level-set to automatically detect intraretina fluid on diabetic retinopathy (DR) from spectral domain optical coherence tomography (SD-OCT) images in this paper. First, each B-scan is segmented using GMM clustering. The original clustering results are refined using location and thickness information. Then, the spatial information among every consecutive five B-scans is used to search potential fluid. Finally, the improved level-set method is used to obtain the accurate boundaries. The high sensitivity and accuracy demonstrated here show its potential for detection of fluid.

Traffic Image Segmentation Based on Gaussian Mixture Model with Spatial Information and Sampling

2013 ◽  
Vol 380-384 ◽  
pp. 3702-3705
Author(s):  
Xiao Na Zhang ◽  
Ming Yao ◽  
Feng Zhu ◽  
Jie Ni
Keyword(s):  
Image Segmentation ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Spatial Information ◽  
Gaussian Mixture ◽  
Sampling Theorem ◽  
Information Function ◽  
Cluster Number ◽  
Minimum Sample ◽  
Image Pixels

The application of classical gaussian mixture model to image segmentation has highly computer complexiton and have not taking into account spatial information except intensity values. A image segmentation based on Gaussian mixture model with sampling and spatially information is proposed in order to solve this problem. First, a spatial information function is defined as the neighbour information weighted class probabilities of very pixels; Secondly, the sampling theorem is given in this paper,and the size of the minimum sample has been derived according to the smallest cluster and cluster number; Finally, image pixels are sampled based on the size of the minimum sample to estimate the parameter of model , which are classifed to different clusters according to bayesian rules. The experimental results show the effectiveness of the algorithm.

scholarly journals PoSTcode: Probabilistic image-based spatial transcriptomics decoder

2021 ◽  
Author(s):  
Milana Gataric ◽  
Jun Sung Park ◽  
Tong Li ◽  
Vasy Vaskivskyi ◽  
Jessica Svedlund ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Large Scale ◽  
Probabilistic Method ◽  
Gaussian Mixture ◽  
Full Potential ◽  
Correlated Noise ◽  
New Approach ◽  
Tuning Parameters

Realising the full potential of novel image-based spatial transcriptomic (IST) technologies requires robust and accurate algorithms for decoding the hundreds of thousand fluorescent signals each derived from single molecules of mRNA. In this paper, we introduce PoSTcode, a probabilistic method for transcript decoding from cyclic multi-channel images, whose effectiveness is demonstrated on multiple large-scale datasets generated using different versions of the in situ sequencing protocols. PoSTcode is based on a re-parametrised matrix-variate Gaussian mixture model designed to account for correlated noise across fluorescence channels and imaging cycles. PoSTcode is shown to recover up to 50% more confidently decoded molecules while simultaneously decreasing transcript mislabeling when compared to existing decoding techniques. In addition, we demonstrate its increased stability to various types of noise and tuning parameters, which makes this new approach reliable and easy to use in practice. Lastly, we show that PoSTcode produces fewer doublet signals compared to a pixel-based decoding algorithm.

Reduced-Degrees-of-Freedom Gaussian-Mixture-Model Fitting for Large-Scale History-Matching Problems

SPE Journal ◽  
2019 ◽  
Vol 25 (01) ◽  
pp. 037-055
Author(s):  
Guohua Gao ◽  
Hao Jiang ◽  
Chaohui Chen ◽  
Jeroen C. Vink ◽  
Yaakoub El Khamra ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
History Matching ◽  
Degrees Of Freedom ◽  
Large Scale ◽  
Computational Cost ◽  
Gaussian Mixture ◽  
New Method ◽  
Matching Problem ◽  
Matching Problems

Summary It has been demonstrated that the Gaussian-mixture-model (GMM) fitting method can construct a GMM that more accurately approximates the posterior probability density function (PDF) by conditioning reservoir models to production data. However, the number of degrees of freedom (DOFs) for all unknown GMM parameters might become huge for large-scale history-matching problems. A new formulation of GMM fitting with a reduced number of DOFs is proposed in this paper to save memory use and reduce computational cost. The performance of the new method is benchmarked against other methods using test problems with different numbers of uncertain parameters. The new method performs more efficiently than the full-rank GMM fitting formulation, reducing the memory use and computational cost by a factor of 5 to 10. Although it is less efficient than the simple GMM approximation dependent on local linearization (L-GMM), it achieves much higher accuracy, reducing the error by a factor of 20 to 600. Finally, the new method together with the parallelized acceptance/rejection (A/R) algorithm is applied to a synthetic history-matching problem for demonstration.

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