scholarly journals Simulated Data Generation through Algorithmic Force Coefficient Estimation for AI-Based Robotic Projectile Launch Modeling

2021 ◽  
Author(s):  
Sajiv Shah ◽  
Ayaan Haque ◽  
Fei Liu
Keyword(s):  
Simulated Data ◽  
Data Generation ◽  
Force Coefficient ◽  
Coefficient Estimation

scholarly journals VEHICLE LOCALIZATION BY LIDAR POINT CORRELATION IMPROVED BY CHANGE DETECTION

2016 ◽  
Vol XLI-B1 ◽  
pp. 703-710 ◽  
Author(s):  
A. Schlichting ◽  
C. Brenner
Keyword(s):  
Change Detection ◽  
Reference Data ◽  
Correlation Method ◽  
Simulated Data ◽  
Detection Algorithm ◽  
Training Data ◽  
Image Correlation ◽  
Data Generation ◽  
Vehicle Localization ◽  
Dynamic Objects

LiDAR sensors are proven sensors for accurate vehicle localization. Instead of detecting and matching features in the LiDAR data, we want to use the entire information provided by the scanners. As dynamic objects, like cars, pedestrians or even construction sites could lead to wrong localization results, we use a change detection algorithm to detect these objects in the reference data. If an object occurs in a certain number of measurements at the same position, we mark it and every containing point as static. In the next step, we merge the data of the single measurement epochs to one reference dataset, whereby we only use static points. Further, we also use a classification algorithm to detect trees. <br><br> For the online localization of the vehicle, we use simulated data of a vertical aligned automotive LiDAR sensor. As we only want to use static objects in this case as well, we use a random forest classifier to detect dynamic scan points online. Since the automotive data is derived from the LiDAR Mobile Mapping System, we are able to use the labelled objects from the reference data generation step to create the training data and further to detect dynamic objects online. The localization then can be done by a point to image correlation method using only static objects. We achieved a localization standard deviation of about 5 cm (position) and 0.06° (heading), and were able to successfully localize the vehicle in about 93 % of the cases along a trajectory of 13 km in Hannover, Germany.

scholarly journals Variational Model-based Policy Optimization

2021 ◽  
Author(s):  
Yinlam Chow ◽  
Brandon Cui ◽  
Moonkyung Ryu ◽  
Mohammad Ghavamzadeh
Keyword(s):  
Objective Function ◽  
Simulated Data ◽  
Parameter Tuning ◽  
Variational Model ◽  
Continuous Control ◽  
Data Generation ◽  
Model Based ◽  
Model Free ◽  
And Performance ◽  
Policy Optimization

Model-based reinforcement learning (RL) algorithms allow us to combine model-generated data with those collected from interaction with the real system in order to alleviate the data efficiency problem in RL. However, designing such algorithms is often challenging because the bias in simulated data may overshadow the ease of data generation. A potential solution to this challenge is to jointly learn and improve model and policy using a universal objective function. In this paper, we leverage the connection between RL and probabilistic inference, and formulate such an objective function as a variational lower-bound of a log-likelihood. This allows us to use expectation maximization (EM) and iteratively fix a baseline policy and learn a variational distribution, consisting of a model and a policy (E-step), followed by improving the baseline policy given the learned variational distribution (M-step). We propose model-based and model-free policy iteration (actor-critic) style algorithms for the E-step and show how the variational distribution learned by them can be used to optimize the M-step in a fully model-based fashion. Our experiments on a number of continuous control tasks show that our model-based (E-step) algorithm, called variational model-based policy optimization (VMBPO), is more sample-efficient and robust to hyper-parameter tuning than its model-free (E-step) counterpart. Using the same control tasks, we also compare VMBPO with several state-of-the-art model-based and model-free RL algorithms and show its sample efficiency and performance.

scholarly journals Single-Cell Transcriptome Profiling Simulation Reveals the Impact of Sequencing Parameters and Algorithms on Clustering

Life ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 716
Author(s):  
Yunhe Liu ◽  
Aoshen Wu ◽  
Xueqing Peng ◽  
Xiaona Liu ◽  
Gang Liu ◽  
...  
Keyword(s):  
Clustering Algorithms ◽  
Simulated Data ◽  
Ground Truth ◽  
Real Data ◽  
Transcriptome Profiling ◽  
Actual Data ◽  
Generation Process ◽  
Data Generation ◽  
The Matrix ◽  
The Impact

Despite the scRNA-seq analytic algorithms developed, their performance for cell clustering cannot be quantified due to the unknown “true” clusters. Referencing the transcriptomic heterogeneity of cell clusters, a “true” mRNA number matrix of cell individuals was defined as ground truth. Based on the matrix and the actual data generation procedure, a simulation program (SSCRNA) for raw data was developed. Subsequently, the consistency between simulated data and real data was evaluated. Furthermore, the impact of sequencing depth and algorithms for analyses on cluster accuracy was quantified. As a result, the simulation result was highly consistent with that of the actual data. Among the clustering algorithms, the Gaussian normalization method was the more recommended. As for the clustering algorithms, the K-means clustering method was more stable than K-means plus Louvain clustering. In conclusion, the scRNA simulation algorithm developed restores the actual data generation process, discovers the impact of parameters on classification, compares the normalization/clustering algorithms, and provides novel insight into scRNA analyses.

scholarly journals Time delay lens modelling challenge

2021 ◽  
Vol 503 (1) ◽  
pp. 1096-1123 ◽  
Author(s):  
X Ding ◽  
T Treu ◽  
S Birrer ◽  
G C-F Chen ◽  
J Coles ◽  
...  
Keyword(s):  
Time Delay ◽  
Computing Time ◽  
Percent Level ◽  
Simulated Data ◽  
Hubble Constant ◽  
Data Sets ◽  
Data Generation ◽  
Systematic Uncertainties ◽  
Modelling Methodology ◽  
Numerical Precision

ABSTRACT In recent years, breakthroughs in methods and data have enabled gravitational time delays to emerge as a very powerful tool to measure the Hubble constant H0. However, published state-of-the-art analyses require of order 1 yr of expert investigator time and up to a million hours of computing time per system. Furthermore, as precision improves, it is crucial to identify and mitigate systematic uncertainties. With this time delay lens modelling challenge, we aim to assess the level of precision and accuracy of the modelling techniques that are currently fast enough to handle of order 50 lenses, via the blind analysis of simulated data sets. The results in Rungs 1 and 2 show that methods that use only the point source positions tend to have lower precision ($10\!-\!20{{\ \rm per\ cent}}$) while remaining accurate. In Rung 2, the methods that exploit the full information of the imaging and kinematic data sets can recover H0 within the target accuracy (|A| &lt; 2 per cent) and precision (&lt;6 per cent per system), even in the presence of a poorly known point spread function and complex source morphology. A post-unblinding analysis of Rung 3 showed the numerical precision of the ray-traced cosmological simulations to be insufficient to test lens modelling methodology at the percent level, making the results difficult to interpret. A new challenge with improved simulations is needed to make further progress in the investigation of systematic uncertainties. For completeness, we present the Rung 3 results in an appendix and use them to discuss various approaches to mitigating against similar subtle data generation effects in future blind challenges.

scholarly journals Built on sand: the shaky foundations of simulating single-cell RNA sequencing data

2021 ◽  
Author(s):  
Helena L Crowell ◽  
Sarah X Morillo Leonardo ◽  
Charlotte Soneson ◽  
Mark D Robinson
Keyword(s):  
Quality Control ◽  
Single Cell ◽  
Rna Sequencing ◽  
Simulated Data ◽  
Quality Standard ◽  
Data Generation ◽  
Clustering Methods ◽  
Sequencing Data ◽  
Study Results ◽  
Single Cell Rna Sequencing

With the emergence of hundreds of single-cell RNA-sequencing (scRNA-seq) datasets, the number of computational tools to analyse aspects of the generated data has grown rapidly. As a result, there is a recurring need to demonstrate whether newly developed methods are truly performant - on their own as well as in comparison to existing tools. Benchmark studies aim to consolidate the space of available methods for a given task, and often use simulated data that provide a ground truth for evaluations. Thus, demanding a high quality standard for synthetically generated data is critical to make simulation study results credible and transferable to real data. Here, we evaluated methods for synthetic scRNA-seq data generation in their ability to mimic experimental data. Besides comparing gene- and cell-level quality control summaries in both one- and two-dimensional settings, we further quantified these at the batch- and cluster-level. Secondly, we investigate the effect of simulators on clustering and batch correction method comparisons, and, thirdly, which and to what extent quality control summaries can capture reference-simulation similarity. Our results suggest that most simulators are unable to accommodate complex designs without introducing artificial effects; they yield over-optimistic performance of integration, and potentially unreliable ranking of clustering methods; and, it is generally unknown which summaries are important to ensure effective simulation-based method comparisons.

scholarly journals VEHICLE LOCALIZATION BY LIDAR POINT CORRELATION IMPROVED BY CHANGE DETECTION

2016 ◽  
Vol XLI-B1 ◽  
pp. 703-710 ◽  
Author(s):  
A. Schlichting ◽  
C. Brenner
Keyword(s):  
Change Detection ◽  
Reference Data ◽  
Correlation Method ◽  
Simulated Data ◽  
Detection Algorithm ◽  
Training Data ◽  
Image Correlation ◽  
Data Generation ◽  
Vehicle Localization ◽  
Dynamic Objects

LiDAR sensors are proven sensors for accurate vehicle localization. Instead of detecting and matching features in the LiDAR data, we want to use the entire information provided by the scanners. As dynamic objects, like cars, pedestrians or even construction sites could lead to wrong localization results, we use a change detection algorithm to detect these objects in the reference data. If an object occurs in a certain number of measurements at the same position, we mark it and every containing point as static. In the next step, we merge the data of the single measurement epochs to one reference dataset, whereby we only use static points. Further, we also use a classification algorithm to detect trees. &lt;br&gt;&lt;br&gt; For the online localization of the vehicle, we use simulated data of a vertical aligned automotive LiDAR sensor. As we only want to use static objects in this case as well, we use a random forest classifier to detect dynamic scan points online. Since the automotive data is derived from the LiDAR Mobile Mapping System, we are able to use the labelled objects from the reference data generation step to create the training data and further to detect dynamic objects online. The localization then can be done by a point to image correlation method using only static objects. We achieved a localization standard deviation of about 5 cm (position) and 0.06° (heading), and were able to successfully localize the vehicle in about 93 % of the cases along a trajectory of 13 km in Hannover, Germany.

Estimating and Controlling Overlap in Gaussian Mixtures for Clustering Methods Evaluation

2020 ◽  
Vol 28 (02) ◽  
pp. 183-211
Author(s):  
Radhwane Gherbaoui ◽  
Mohammed Ouali ◽  
Nacéra Benamrane
Keyword(s):  
Ad Hoc ◽  
Simulated Data ◽  
Gaussian Mixtures ◽  
Data Generation ◽  
Clustering Methods ◽  
Artificial Data ◽  
Major Drawback ◽  
Automatic Estimation ◽  
Overlap Rate ◽  
Test Scenarios

The ad hoc nature of the clustering methods makes simulated data paramount in assessing the performance of clustering methods. Real datasets could be used in the evaluation of clustering methods with the major drawback of missing the assessment of many test scenarios. In this paper, we propose a formal quantification of component overlap. This quantification is derived from a set of theorems which allow us to derive an automatic method for artificial data generation. We also derive a method to estimate parameters of existing models and to evaluate the results of other approaches. Automatic estimation of the overlap rate can also be used as an unsupervised learning approach in data mining to determine the parameters of mixture models from actual observations.

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