scholarly journals Data-driven deep density estimation

2021 ◽  
Author(s):  
Patrik Puchert ◽  
Pedro Hermosilla ◽  
Tobias Ritschel ◽  
Timo Ropinski
Keyword(s):  
Data Analysis ◽  
Density Estimation ◽  
Population Data ◽  
Training Data ◽  
Data Driven ◽  
Discrete Observations ◽  
Efficient Manner ◽  
Continuous Models ◽  
3D Scans ◽  
Spatial Locations

AbstractDensity estimation plays a crucial role in many data analysis tasks, as it infers a continuous probability density function (PDF) from discrete samples. Thus, it is used in tasks as diverse as analyzing population data, spatial locations in 2D sensor readings, or reconstructing scenes from 3D scans. In this paper, we introduce a learned, data-driven deep density estimation (DDE) to infer PDFs in an accurate and efficient manner, while being independent of domain dimensionality or sample size. Furthermore, we do not require access to the original PDF during estimation, neither in parametric form, nor as priors, or in the form of many samples. This is enabled by training an unstructured convolutional neural network on an infinite stream of synthetic PDFs, as unbound amounts of synthetic training data generalize better across a deck of natural PDFs than any natural finite training data will do. Thus, we hope that our publicly available DDE method will be beneficial in many areas of data analysis, where continuous models are to be estimated from discrete observations.

scholarly journals Obey validity limits of data-driven models through topological data analysis and one-class classification

2021 ◽  
Author(s):  
Artur M. Schweidtmann ◽  
Jana M. Weber ◽  
Christian Wende ◽  
Linus Netze ◽  
Alexander Mitsos
Keyword(s):  
Data Analysis ◽  
Process Optimization ◽  
Persistent Homology ◽  
Training Data ◽  
Data Driven ◽  
Topological Data Analysis ◽  
Support Vector ◽  
Reduced Space ◽  
One Class Classification ◽  
Topological Data

AbstractData-driven models are becoming increasingly popular in engineering, on their own or in combination with mechanistic models. Commonly, the trained models are subsequently used in model-based optimization of design and/or operation of processes. Thus, it is critical to ensure that data-driven models are not evaluated outside their validity domain during process optimization. We propose a method to learn this validity domain and encode it as constraints in process optimization. We first perform a topological data analysis using persistent homology identifying potential holes or separated clusters in the training data. In case clusters or holes are identified, we train a one-class classifier, i.e., a one-class support vector machine, on the training data domain and encode it as constraints in the subsequent process optimization. Otherwise, we construct the convex hull of the data and encode it as constraints. We finally perform deterministic global process optimization with the data-driven models subject to their respective validity constraints. To ensure computational tractability, we develop a reduced-space formulation for trained one-class support vector machines and show that our formulation outperforms common full-space formulations by a factor of over 3000, making it a viable tool for engineering applications. The method is ready-to-use and available open-source as part of our MeLOn toolbox (https://git.rwth-aachen.de/avt.svt/public/MeLOn).

scholarly journals A Data-Driven Surrogate Approach for the Temporal Stability Forecasting of Vegetation Covered Dikes

Water ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 107
Author(s):  
Elahe Jamalinia ◽  
Faraz S. Tehrani ◽  
Susan C. Steele-Dunne ◽  
Philip J. Vardon
Keyword(s):  
Numerical Simulation ◽  
Water Flux ◽  
Temporal Stability ◽  
Synthetic Data ◽  
Climatic Conditions ◽  
Training Data ◽  
Data Driven ◽  
Data Set ◽  
Surface Cracking ◽  
Real Time Analysis

Climatic conditions and vegetation cover influence water flux in a dike, and potentially the dike stability. A comprehensive numerical simulation is computationally too expensive to be used for the near real-time analysis of a dike network. Therefore, this study investigates a random forest (RF) regressor to build a data-driven surrogate for a numerical model to forecast the temporal macro-stability of dikes. To that end, daily inputs and outputs of a ten-year coupled numerical simulation of an idealised dike (2009–2019) are used to create a synthetic data set, comprising features that can be observed from a dike surface, with the calculated factor of safety (FoS) as the target variable. The data set before 2018 is split into training and testing sets to build and train the RF. The predicted FoS is strongly correlated with the numerical FoS for data that belong to the test set (before 2018). However, the trained model shows lower performance for data in the evaluation set (after 2018) if further surface cracking occurs. This proof-of-concept shows that a data-driven surrogate can be used to determine dike stability for conditions similar to the training data, which could be used to identify vulnerable locations in a dike network for further examination.

A novel fMRI group data analysis method based on data-driven reference extracting from group subjects

2015 ◽  
Vol 122 (3) ◽  
pp. 362-371 ◽  
Author(s):  
Yuhu Shi ◽  
Weiming Zeng ◽  
Nizhuan Wang ◽  
Dongtailang Chen
Keyword(s):  
Data Analysis ◽  
Data Driven ◽  
Analysis Method ◽  
Data Analysis Method ◽  
Group Data

A Copula-Based Sampling Method for Data-Driven Prognostics and Health Management

2013 ◽  
Author(s):  
Zhimin Xi ◽  
Rong Jing ◽  
Pingfeng Wang ◽  
Chao Hu
Keyword(s):  
Sampling Method ◽  
Failure Time ◽  
Health Management ◽  
Remaining Useful Life ◽  
Training Data ◽  
Data Driven ◽  
Prognostics And Health Management ◽  
Statistical Relationship ◽  
Cooling Fans ◽  
On Line

This paper develops a Copula-based sampling method for data-driven prognostics and health management (PHM). The principal idea is to first build statistical relationship between failure time and the time realizations at specified degradation levels on the basis of off-line training data sets, then identify possible failure times for on-line testing units based on the constructed statistical model and available on-line testing data. Specifically, three technical components are proposed to implement the methodology. First of all, a generic health index system is proposed to represent the health degradation of engineering systems. Next, a Copula-based modeling is proposed to build statistical relationship between failure time and the time realizations at specified degradation levels. Finally, a sampling approach is proposed to estimate the failure time and remaining useful life (RUL) of on-line testing units. Two case studies, including a bearing system in electric cooling fans and a 2008 IEEE PHM challenge problem, are employed to demonstrate the effectiveness of the proposed methodology.

Imitation of Demonstrations Using Bayesian Filtering With Nonparametric Data-Driven Models

2017 ◽  
Vol 140 (3) ◽  
Author(s):  
Nurali Virani ◽  
Devesh K. Jha ◽  
Zhenyuan Yuan ◽  
Ishana Shekhawat ◽  
Asok Ray
Keyword(s):  
Particle Filtering ◽  
Dynamic Models ◽  
Training Data ◽  
Data Driven ◽  
Conditional Density ◽  
Bayesian Filtering ◽  
State Dependent ◽  
State Evolution ◽  
And Control ◽  
Nonparametric Kernel

This paper addresses the problem of learning dynamic models of hybrid systems from demonstrations and then the problem of imitation of those demonstrations by using Bayesian filtering. A linear programming-based approach is used to develop nonparametric kernel-based conditional density estimation technique to infer accurate and concise dynamic models of system evolution from data. The training data for these models have been acquired from demonstrations by teleoperation. The trained data-driven models for mode-dependent state evolution and state-dependent mode evolution are then used online for imitation of demonstrated tasks via particle filtering. The results of simulation and experimental validation with a hexapod robot are reported to establish generalization of the proposed learning and control algorithms.

Data-driven method for training data selection for deep learning

2021 ◽  
Author(s):  
C. Lacombe ◽  
I. Hammoud ◽  
J. Messud ◽  
H. Peng ◽  
T. Lesieur ◽  
...  
Keyword(s):  
Deep Learning ◽  
Training Data ◽  
Data Selection ◽  
Data Driven ◽  
Selection For ◽  
Training Data Selection

scholarly journals A framework for data-driven design in a product innovation process: data analysis and visualisation for model-based decision making

2020 ◽  
Vol 24 (1) ◽  
pp. 68
Author(s):  
Rob Vingerhoeds ◽  
Claude Baron ◽  
Alessandro Bertoni ◽  
Xin Yi ◽  
Phillippe Esteban
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Product Innovation ◽  
Innovation Process ◽  
Data Driven ◽  
Process Data ◽  
Model Based

scholarly journals Boosting Intelligent Data Analysis in Smart Sensors by Integrating Knowledge and Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6168
Author(s):  
Piotr Łuczak ◽  
Przemysław Kucharski ◽  
Tomasz Jaworski ◽  
Izabela Perenc ◽  
Krzysztof Ślot ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Layer Structure ◽  
Recognition Performance ◽  
Conjunctive Normal Form ◽  
Hybrid Structure ◽  
Training Data ◽  
Intelligent Data Analysis ◽  
Feed Forward Neural Network ◽  
Sensor Devices

The presented paper proposes a hybrid neural architecture that enables intelligent data analysis efficacy to be boosted in smart sensor devices, which are typically resource-constrained and application-specific. The postulated concept integrates prior knowledge with learning from examples, thus allowing sensor devices to be used for the successful execution of machine learning even when the volume of training data is highly limited, using compact underlying hardware. The proposed architecture comprises two interacting functional modules arranged in a homogeneous, multiple-layer architecture. The first module, referred to as the knowledge sub-network, implements knowledge in the Conjunctive Normal Form through a three-layer structure composed of novel types of learnable units, called L-neurons. In contrast, the second module is a fully-connected conventional three-layer, feed-forward neural network, and it is referred to as a conventional neural sub-network. We show that the proposed hybrid structure successfully combines knowledge and learning, providing high recognition performance even for very limited training datasets, while also benefiting from an abundance of data, as it occurs for purely neural structures. In addition, since the proposed L-neurons can learn (through classical backpropagation), we show that the architecture is also capable of repairing its knowledge.

scholarly journals Hubungan antara Promosi dengan Pengambilan Keputusan Peserta Didik untuk Mengikuti Bimbingan Belajar di BBC

2018 ◽  
Vol 1 (1) ◽  
pp. 121
Author(s):  
Suci Junianti ◽  
Jamaris Jamaris ◽  
Vevi Sunarti
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Data Collection ◽  
Random Sampling ◽  
Sampling Technique ◽  
Population Data ◽  
Entire Population ◽  
Stratified Random Sampling ◽  
Data Collection Tool ◽  
Guidance Study

This study aims to see whether there is a relationship between promotion and decision making learners in following the tutoring in BBC Limbanang Kecamatan Suliki Kabupaten Lima Puluh Kota. This type of research is correlational. The population in this study is the citizens learn BBC learning guidance. Sampling technique in this research is stratified random sampling from entire population. Data collection techniques used are questionnaires, while the data collection tool questionnaire. Technique of data analysis by using formula of percentage and product moment. From the results of the study showed that: promotion done by the guidance of learning in BBC less good so that decision to follow guidance study in BBC considered less good also by learners.

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