Data-Driven Statistical Learning of Temporal Logic Properties

2014 ◽  
pp. 23-37 ◽  
Author(s):  
Ezio Bartocci ◽  
Luca Bortolussi ◽  
Guido Sanguinetti
Keyword(s):  
Statistical Learning ◽  
Temporal Logic ◽  
Data Driven

scholarly journals A data-driven approach to violin making

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sebastian Gonzalez ◽  
Davide Salvi ◽  
Daniel Baeza ◽  
Fabio Antonacci ◽  
Augusto Sarti
Keyword(s):  
Statistical Learning ◽  
Geometric Parameters ◽  
Data Driven ◽  
Vibrational Properties ◽  
Learning Tools ◽  
Predictive Tool ◽  
Complete Control ◽  
Data Driven Approach ◽  
To Come ◽  
Violin Making

AbstractOf all the characteristics of a violin, those that concern its shape are probably the most important ones, as the violin maker has complete control over them. Contemporary violin making, however, is still based more on tradition than understanding, and a definitive scientific study of the specific relations that exist between shape and vibrational properties is yet to come and sorely missed. In this article, using standard statistical learning tools, we show that the modal frequencies of violin tops can, in fact, be predicted from geometric parameters, and that artificial intelligence can be successfully applied to traditional violin making. We also study how modal frequencies vary with the thicknesses of the plate (a process often referred to as plate tuning) and discuss the complexity of this dependency. Finally, we propose a predictive tool for plate tuning, which takes into account material and geometric parameters.

Multicopter Fault Detection and Identification via Data-Driven Statistical Learning Methods

AIAA Journal ◽  
2021 ◽  
pp. 1-16
Author(s):  
Airin Dutta ◽  
Michael E. McKay ◽  
Fotis Kopsaftopoulos ◽  
Farhan Gandhi
Keyword(s):  
Fault Detection ◽  
Statistical Learning ◽  
Data Driven ◽  
Learning Methods ◽  
Detection And Identification ◽  
Fault Detection And Identification

scholarly journals On an unsupervised method for parameter selection for the elastic net

2021 ◽  
Vol 4 (6) ◽  
pp. 1-36
Author(s):  
Zeljko Kereta ◽  
◽  
Valeriya Naumova
Keyword(s):  
Statistical Learning ◽  
Selection Criteria ◽  
State Of The Art ◽  
Regularization Parameter ◽  
Parameter Selection ◽  
Elastic Net ◽  
Data Driven ◽  
Regularization Theory ◽  
Automated Algorithm ◽  
Selection For

<abstract><p>Despite recent advances in regularization theory, the issue of parameter selection still remains a challenge for most applications. In a recent work the framework of statistical learning was used to approximate the optimal Tikhonov regularization parameter from noisy data. In this work, we improve their results and extend the analysis to the elastic net regularization. Furthermore, we design a data-driven, automated algorithm for the computation of an approximate regularization parameter. Our analysis combines statistical learning theory with insights from regularization theory. We compare our approach with state-of-the-art parameter selection criteria and show that it has superior accuracy.</p></abstract>

scholarly journals A Data-Driven Approach to Violin Making

2021 ◽  
Author(s):  
Sebastian Gonzalez ◽  
Davide Salvi ◽  
Daniel Baeza ◽  
Fabio Antonacci ◽  
Augusto Sarti
Keyword(s):  
Statistical Learning ◽  
Geometric Parameters ◽  
Data Driven ◽  
Vibrational Properties ◽  
Learning Tools ◽  
Predictive Tool ◽  
Complete Control ◽  
Data Driven Approach ◽  
To Come ◽  
Violin Making

Abstract Of all the characteristics of a violin, those that concern its shape are probably the most important ones, as the violin maker has complete control over them. Contemporary violin making, however, is still based more on tradition than understanding, and a definitive scientific study of the specific relations that exist between shape and vibrational properties is yet to come and sorely missed. In this article, using standard statistical learning tools, we show that the modal frequencies of violin tops can, in fact, be predicted from geometric parameters, and that artificial intelligence can be successfully applied to traditional violin making. We also study how modal frequencies vary with the thicknesses of the plate (a process often referred to as plate tuning) and discuss the complexity of this dependency. Finally, we propose a predictive tool for plate tuning, which takes into account material and geometric parameters.

scholarly journals Multi-objective optimal control for proactive decision making with temporal logic models

2019 ◽  
Vol 38 (12-13) ◽  
pp. 1490-1512 ◽  
Author(s):  
Sandeep P. Chinchali ◽  
Scott C. Livingston ◽  
Mo Chen ◽  
Marco Pavone
Keyword(s):  
Decision Making ◽  
Formal Methods ◽  
Temporal Logic ◽  
Generative Models ◽  
Human Robot Interaction ◽  
Data Driven ◽  
High Dimensional ◽  
Formal Specifications ◽  
Value Iteration ◽  
Logic Models

The operation of today’s robots entails interactions with humans, e.g., in autonomous driving amidst human-driven vehicles. To effectively do so, robots must proactively decode the intent of humans and concurrently leverage this knowledge for safe, cooperative task satisfaction: a problem we refer to as proactive decision making. However, simultaneous intent decoding and robotic control requires reasoning over several possible human behavioral models, resulting in high-dimensional state trajectories. In this paper, we address the proactive decision-making problem using a novel combination of formal methods, control, and data mining techniques. First, we distill high-dimensional state trajectories of human–robot interaction into concise, symbolic behavioral summaries that can be learned from data. Second, we leverage formal methods to model high-level agent goals, safe interaction, and information-seeking behavior with temporal logic formulas. Finally, we design a novel decision-making scheme that maintains a belief distribution over models of human behavior, and proactively plans informative actions. After showing several desirable theoretical properties, we apply our framework to a dataset of humans driving in crowded merging scenarios. For it, temporal logic models are generated and used to synthesize control strategies using tree-based value iteration and deep reinforcement learning. In addition, we illustrate how data-driven models of human responses to informative robot probes, such as from generative models such as conditional variational autoencoders, can be clustered with formal specifications. Results from simulated self-driving car scenarios demonstrate that data-driven strategies enable safe interaction, correct model identification, and significant dimensionality reduction.

Data-driven verification of stochastic linear systems with signal temporal logic constraints

Automatica ◽  
2021 ◽  
Vol 131 ◽  
pp. 109781
Author(s):  
Ali Salamati ◽  
Sadegh Soudjani ◽  
Majid Zamani
Keyword(s):  
Linear Systems ◽  
Temporal Logic ◽  
Data Driven ◽  
Stochastic Linear Systems

scholarly journals Data-Driven Verification under Signal Temporal Logic Constraints

2020 ◽  
Vol 53 (2) ◽  
pp. 69-74
Author(s):  
Ali Salamati ◽  
Sadegh Soudjani ◽  
Majid Zamani
Keyword(s):  
Temporal Logic ◽  
Data Driven
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