Tactile-based Manipulation of Deformable Objects with Dynamic Center of Mass

International audienceTactile sensing feedback provides feasible solutions to robotic dexterous manipulation tasks. In this paper, we present a novel tactile-based framework for detecting/correcting slips and regulating grasping forces while manipulating de-formable objects with the dynamic center of mass. This framework consists of a tangential force based slip detection method and a deformation prevention approach relying on weight estimation. Moreover, we propose a new strategy for manipulating deformable heavy objects. Objects with different stiffnesses, surface textures, and centers of mass are tested in experiments. Results show that proposed approaches are capable of handling objects with uncertainties in their characteristics, and also robust to external disturbances

Estimation of Semi-Varying Coefficient Models with Nonstationary Regressors

We study a semi-varying coefficient model where the regressors are generated by the multivariate unit root I(1) processes. The influence of the explanatory vectors on the response variable satisfies the semiparametric partially linear structure with the nonlinear component being functional coefficients. A semiparametric estimation methodology with the first-stage local polynomial smoothing is applied to estimate both the constant coefficients in the linear component and the functional coefficients in the nonlinear component. The asymptotic distribution theory for the proposed semiparametric estimators is established under some mild conditions, from which both the parametric and nonparametric estimators are shown to enjoy the well-known super-consistency property. Furthermore, a simulation study is conducted to investigate the finite sample performance of the developed methodology and results

Microwave-Assisted Synthesis of Co/CoOx Supported on Earth-Abundant Coal-Derived Carbon for Electrocatalysis of Oxygen Evolution

The evident demand for hydrogen as the ultimate energy fuel for posterity calls for the development of low-cost, efficient and stable electrocatalysts for water splitting. Herein, we report the synthesis of Co/CoOx supported on coal-derived N-doped carbon via a simple microwave-assisted method and demonstrate its application as an efficient catalyst for the oxygen evolution reaction (OER). With the optimal amount of cobalt introduced into the N-doped coal-derived, the developed catalyst achieved overpotentials of 0.370 and 0.429 V during water oxidation at current densities of 1 mA cm(-2) and 10 mA cm(-2), respectively. There was no noticeable loss in the activity of the catalyst during continuous galvanostatic polarization at a current density of 10 mA cm(-2) for a test period of 66 h. The synergistic interaction of the Co/CoOx moieties with the pyridinic and pyrollic nitrogen functional groups in the N-doped carbon, as well with the other heteroatoms species in the pristine coal favored enhancement of the OER electrocatalytic performance. (C) The Author(s) 2019. Published by ECS

SES: Sentiment Elicitation System for Social Media Data

Abstract—Social Media is becoming major and popu-lar technological platform that allows users discussing and sharing information. Information is generated and man-aged through either computer or mobile devices by one person and consumed by many other persons. Most of these user generated content are textual information, as So-cial Networks(Facebook, LinkedIn), Microblogging(Twitter), blogs(Blogspot, Wordpress). Looking for valuable nuggets of knowledge, such as capturing and summarizing sentiments from these huge amount of data could help users make informed decisions. In this paper, we develop a sentiment identification system called SES which implements three dif-ferent sentiment identification algorithms. We augment basic compositional semantic rules in the first algorithm. In the second algorithm, we think sentiment should not be simply classified as positive, negative, and objective but a continuous score to reflect sentiment degree. All word scores are calculated based on a large volume of customer reviews. Due to the special characteristics of social media texts, we propose a third algorithm which takes emoticons, negation word posi-tion, and domain-specific words into account. Furthermore, a machine learning model is employed on features derived from outputs of three algorithms. We conduct our experiments on user comments from Facebook and tweets from twitter. The results show that utilizing Random Forest will acquire a better accuracy than decision tree, neural network, and logistic regression. We also propose a flexible way to represent document sentiment based on sentiments of each sentence contained. SES is available online. Keywords-Social media, sentiment, rule, machine learning I