Fast Modeling and Identification of Robot Dynamics Using the Lasso

2013 ◽  
Author(s):  
Cong Wang ◽  
Xiaowen Yu ◽  
Masayoshi Tomizuka
Keyword(s):  
Machine Learning ◽  
Promising Result ◽  
Data Driven ◽  
Robot Dynamics ◽  
Learning Approach ◽  
Penalized Least Squares ◽  
Least Squares Regression ◽  
Direct Drive ◽  
L1 Norm ◽  
Machine Learning Approach

This paper presents an approach for fast modeling and identification of robot dynamics. By using a data-driven machine learning approach, the process is simplified considerably from the conventional analytical method. Regressor selection using the Lasso (l1-norm penalized least squares regression) is used. The method is explained with a simple example of a two-link direct-drive robot. Further demonstration is given by applying the method to a three-link belt-driven robot. Promising result has been demonstrated.

scholarly journals Clustering suicides: A data-driven, exploratory machine learning approach

2019 ◽  
Vol 62 ◽  
pp. 15-19 ◽  
Author(s):  
Birgit Ludwig ◽  
Daniel König ◽  
Nestor D. Kapusta ◽  
Victor Blüml ◽  
Georg Dorffner ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cluster Analysis ◽  
Analysis Data ◽  
The Other ◽  
Data Driven ◽  
Learning Approach ◽  
Suicide Methods ◽  
Machine Learning Approach ◽  
The One ◽  
Methods Of Suicide

Abstract Methods of suicide have received considerable attention in suicide research. The common approach to differentiate methods of suicide is the classification into “violent” versus “non-violent” method. Interestingly, since the proposition of this dichotomous differentiation, no further efforts have been made to question the validity of such a classification of suicides. This study aimed to challenge the traditional separation into “violent” and “non-violent” suicides by generating a cluster analysis with a data-driven, machine learning approach. In a retrospective analysis, data on all officially confirmed suicides (N = 77,894) in Austria between 1970 and 2016 were assessed. Based on a defined distance metric between distributions of suicides over age group and month of the year, a standard hierarchical clustering method was performed with the five most frequent suicide methods. In cluster analysis, poisoning emerged as distinct from all other methods – both in the entire sample as well as in the male subsample. Violent suicides could be further divided into sub-clusters: hanging, shooting, and drowning on the one hand and jumping on the other hand. In the female sample, two different clusters were revealed – hanging and drowning on the one hand and jumping, poisoning, and shooting on the other. Our data-driven results in this large epidemiological study confirmed the traditional dichotomization of suicide methods into “violent” and “non-violent” methods, but on closer inspection “violent methods” can be further divided into sub-clusters and a different cluster pattern could be identified for women, requiring further research to support these refined suicide phenotypes.

Deep Optimization of Catalyst Layer Composition via Data-Driven Machine Learning Approach

2020 ◽  
Author(s):  
Bowen Wang ◽  
Biao Xie ◽  
Jin Xuan ◽  
Wen Gu ◽  
Dezong Zhao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Catalyst Layer ◽  
Data Driven ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Layer Composition

scholarly journals A Machine Learning Approach to the Residential Relocation Distance of Households Living in the Seoul Metropolitan Region

2018 ◽  
Author(s):  
Changhyo Yi ◽  
Kijung Kim
Keyword(s):  
Machine Learning ◽  
Residential Mobility ◽  
Ordinary Least Squares ◽  
Metropolitan Region ◽  
Learning Approach ◽  
Least Squares Regression ◽  
Mobility Patterns ◽  
Residential Relocation ◽  
Machine Learning Approach ◽  
Seoul Metropolitan Region

This study aimed to ascertain the applicability of a machine learning approach to the description of residential mobility patterns of households in the Seoul metropolitan region (SMR). The spatial range and temporal scope of the empirical study were set to 2015 to review the most recent residential mobility patterns in the SMR. The analysis data used in this study involve the microdata of Internal Migration Statistics provided by the Microdata Integrated Service of Statistics Korea. We analysed the residential relocation distance of households in the SMR by using machine learning techniques such as ordinary least squares regression and decision tree regression. The results of this study showed that a decision tree model can be more advantageous than ordinary least squares regression in terms of the explanatory power and estimation of moving distance. A large number of residential movements are mainly related to the accessibility to employment markets and some household characteristics. The shortest movements occur when households with two or more members move into densely populated districts. In contrast, job-based residential movements have relatively longer distance. Furthermore, we derived knowledge on residential relocation distance, which can provide significant information on the urban management of metropolitan residential districts and the construction of reasonable housing policies.

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