1,721,011 research outputs found
Bayesian neural network modeling and hierarchical MPC for a tendon-driven surgical robot with uncertainty minimization
Full text linkIn order to guarantee precision and safety in robotic surgery, accurate models of the robot and proper control strategies are needed. Bayesian Neural Networks (BNN) are capable of learning complex models and provide information about the uncertainties of the learned system. Model Predictive Control (MPC) is a reliable control strategy to ensure optimality and satisfaction of safety constraints. In this work we propose the use of BNN to build the highly nonlinear kinematic and dynamic models of a tendon-driven surgical robot, and exploit the information about the epistemic uncertainties by means of a Hierarchical MPC (Hi-MPC) control strategy. Simulation and real world experiments show that the method is capable of ensuring accurate tip positioning, while satisfying imposed safety bounds on the kinematics and dynamics of the robot
Combining piecewise linear regression and a granular computing framework for financial time series classification
No full textFinance is a very broad field where the uncertainty plays a central role and every financial operator have to deal with it. In this paper we propose a new method for a trend prediction on financial time series combining a Linear Piecewise Regression with a granular computing framework. A set of parameters control the behavior of the whole system, thus making their fine tuning a critical optimization task. To this aim in this paper we employ an evolutionary optimization algorithm to tackle this crucial phase. We tested our system on both synthetic benchmarking data and on real financial time series. Our tests show very good classification results on benchmarking data. Results on real data, although not completely satisfactory, are encouraging, suggesting further developments
Information granules filtering for inexact sequential pattern mining by evolutionary computation
No full textNowadays, the wide development of techniques to communicate and store information of all kinds has raised the need to find new methods to analyze and interpret big quantities of data. One of the most important problems in sequential data analysis is frequent pattern mining, that consists in finding frequent subsequences (patterns) in a sequence database in order to highlight and to extract interesting knowledge from the data at hand. Usually real-world data is affected by several noise sources and this makes the analysis more challenging, so that approximate pattern matching methods are required. A common procedure employed to identify recurrent patterns in noisy data is based on clustering algorithms relying on some edit distance between subsequences. When facing inexact mining problems, this plain approach can produce many spurious patterns due to multiple pattern matchings on the same sequence excerpt. In this paper we present a method to overcome this drawback by applying an optimization-based filter that identifies the most descriptive patterns among those found by the clustering process, able to return clusters more compact and easily interpretable. We evaluate the mining system's performances using synthetic data with variable amounts of noise, showing that the algorithm performs well in synthesizing retrieved patterns with acceptable information loss
Noise sensitivity of an information granules filtering procedure by genetic optimization for inexact sequential pattern mining
No full textOne of the most essential challenges in Data Mining and Knowledge Discovery is the development of effective tools able to find regularities in data. In order to highlight and to extract interesting knowledge from the data at hand, a key problem is frequent pattern mining, i.e. to discover frequent substructures hidden in the available data. In many interesting application fields, data are often represented and stored as sequences over time or space of generic objects. Due to the presence of noise and uncertainties in data, searching for frequent subsequences must employ approximate matching techniques, such as edit distances. A common procedure to identify recurrent patterns in noisy data is based on clustering algorithms relying on some edit distance between subsequences. However, this plain approach can produce many spurious patterns due to multiple pattern matchings on close positions in the same sequence excerpt. In this paper, we present a method to overcome this drawback by applying an optimization-based step lter that identifies the most descriptive patterns among those found by the clustering process, and allows to return more compact and easily interpretable clusters. We evaluate the mining systems performances on synthetic data in two separate cases, corresponding respectively to two different (simulated) sources of noise. In both cases, our method performs well in retrieving the original patterns with acceptable information loss.One of the most essential challenges in Data Mining and Knowledge Discovery is the development of effective tools able to find regularities in data. In order to highlight and to extract interesting knowledge from the data at hand, a key problem is frequent pattern mining, i.e. to discover frequent substructures hidden in the available data. In many interesting application fields, data are often represented and stored as sequences over time or space of generic objects. Due to the presence of noise and uncertainties in data, searching for frequent subsequences must employ approximate matching techniques, such as edit distances. A common procedure to identify recurrent patterns in noisy data is based on clustering algorithms relying on some edit distance between subsequences. However, this plain approach can produce many spurious patterns due to multiple pattern matchings on close positions in the same sequence excerpt. In this paper, we present a method to overcome this drawback by applying an optimization-based step lter that identifies the most descriptive patterns among those found by the clustering process, and allows to return more compact and easily interpretable clusters. We evaluate the mining systems performances on synthetic data in two separate cases, corresponding respectively to two different (simulated) sources of noise. In both cases, our method performs well in retrieving the original patterns with acceptable information loss
Exploiting Robot Redundancy for Online Learning and Control
Full text linkAccurate trajectory tracking in the task space is crit- ical in many robotics applications. Model-based robot controllers are able to ensure very good tracking but lose effectiveness in the presence of model uncertainties. On the other hand, online learning-based control laws can handle poor dynamic modeling, as long as prediction errors are kept small and decrease over time. However, in the case of redundant robots directly controlled in the task space, this condition is not usually met. We present an online learning-based control framework that exploits robot redundancy so as to increase the overall performance and shorten the learning transient. The validity of the proposed approach is shown through a comparative study conducted in simulation on a KUKA LWR4+ robot
On-Line Learning for Planning and Control of Underactuated Robots With Uncertain Dynamics
Full text linkWe present an iterative approach for planning and controlling motions of underactuated robots with uncertain dynamics. At its core, there is a learning process which estimates the perturbations induced by the model uncertainty on the active and passive degrees of freedom. The generic iteration of the algorithm makes use of the learned data in both the planning phase, which is based on optimization, and the control phase, where partial feedback linearization of the active dofs is performed on the model updated on-line. The performance of the proposed approach is shown by comparative simulations and experiments on a Pendubot executing various types of swing-up maneuvers. Very few iterations are typically needed to generate dynamically feasible trajectories and the tracking control that guarantees their accurate execution, even in the presence of large model uncertainties
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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