Clustering for Different Scales of Measurement - the Gap-Ratio Weighted K-means Algorithm

This paper describes a method for clustering data that are spread out over large regions and which dimensions are on different scales of measurement. Such an algorithm was developed to implement a robotics application consisting in sorting and storing objects in an unsupervised way. The toy dataset used to validate such application consists of Lego bricks of different shapes and colors. The uncontrolled lighting conditions together with the use of RGB color features, respectively involve data with a large spread and different levels of measurement between data dimensions. To overcome the combination of these two characteristics in the data, we have developed a new weighted K-means algorithm, called gap-ratio K-means, which consists in weighting each dimension of the feature space before running the K-means algorithm. The weight associated with a feature is proportional to the ratio of the biggest gap between two consecutive data points, and the average of all the other gaps. This method is compared with two other variants of K-means on the Lego bricks clustering problem as well as two other common classification datasets.Comment: 13 pages, 6 figures, 2 tables. This paper is under the review process for AIAP 201

A simple and generic CAD/CAM approach for AFM probe-based machining

Atomic Force Microscopy (AFM) probe-based machining allows surface structuring at the nano-scale via the mechanical modification of material. This results from the direct contact between the tip of an AFM probe and the surface of a sample. Given that AFM instruments are primarily developed for obtaining high-resolution topography information of inspected specimen, raster scanning typically defines the trajectory followed by the tip of an AFM probe. Although most AFM manufacturers provide software modules to perform user-defined tip displacement operations, such additional solutions can be limited with respect to 1) the range of tip motions that can be designed, 2) the level of automation when defining tip displacement strategies and 3) the portability for easily transferring trajectories data between different AFM instruments. In this context, this research presents a feasibility study, which aims to demonstrate the applicability of a simple and generic CAD/CAM approach when implementing AFM probe-based nano-machining for producing two-dimensional (2D) features with a commercial AFM instrument

On Algebraic Approach for MSD Parametric Estimation

This article address the identification problem of the natural frequency and the damping ratio of a second order continuous system where the input is a sinusoidal signal. An algebra based approach for identifying parameters of a Mass Spring Damper (MSD) system is proposed and compared to the Kalman-Bucy filter. The proposed estimator uses the algebraic parametric method in the frequency domain yielding exact formula, when placed in the time domain to identify the unknown parameters. We focus on finding the optimal sinusoidal exciting trajectory which allow to minimize the variance of the identification algorithms. We show that the variance of the estimators issued from the algebraic identification method introduced by Fliess and Sira-Ramirez is less sensitive to the input frequency than the ones obtained by the classical recursive Kalman-Bucy filter. Unlike conventional estimation approach, where the knowledge of the statistical properties of the noise is required, algebraic method is deterministic and non-asymptotic. We show that we don't need to know the variance of the noise so as to perform these algebraic estimators. Moreover, as they are non-asymptotic, we give numerical results where we show that they can be used directly for online estimations without any special setting.International audienceThis article address the identification problem of the natural frequency and the damping ratio of a second order continuous system where the input is a sinusoidal signal. An algebra based approach for identifying parameters of a Mass Spring Damper (MSD) system is proposed and compared to the Kalman-Bucy filter. The proposed estimator uses the algebraic parametric method in the frequency domain yielding exact formula, when placed in the time domain to identify the unknown parameters. We focus on finding the optimal sinusoidal exciting trajectory which allow to minimize the variance of the identification algorithms. We show that the variance of the estimators issued from the algebraic identification method introduced by Fliess and Sira-Ramirez is less sensitive to the input frequency than the ones obtained by the classical recursive Kalman-Bucy filter. Unlike conventional estimation approach, where the knowledge of the statistical properties of the noise is required, algebraic method is deterministic and non-asymptotic. We show that we don't need to know the variance of the noise so as to perform these algebraic estimators. Moreover, as they are non-asymptotic, we give numerical results where we show that they can be used directly for online estimations without any special setting

A simple and generic CAD/CAM approach for AFM probe-based machining

Atomic Force Microscopy (AFM) probe-based machining allows surface structuring at the nano-scale via the mechanical modification of material. This results from the direct contact between the tip of an AFM probe and the surface of a sample. Given that AFM instruments are primarily developed for obtaining high-resolution topography information of inspected specimen, raster scanning typically defines the trajectory followed by the tip of an AFM probe. Although most AFM manufacturers provide software modules to perform user-defined tip displacement operations, such additional solutions can be limited with respect to 1) the range of tip motions that can be designed, 2) the level of automation when defining tip displacement strategies and 3) the portability for easily transferring trajectories data between different AFM instruments. In this context, this research presents a feasibility study, which aims to demonstrate the applicability of a simple and generic CAD/CAM approach when implementing AFM probe-based nano-machining for producing two-dimensional (2D) features with a commercial AFM instrument

Water transfer and crack regimes in nano-colloidal gels

International audienceDirect observations of the surface and shape of model nano-colloidal gels associated with measurements of the spatial distribution of water content during drying show that air starts to significantly penetrate the sample when the material stops shrinking. We show that whether the material fractures or not during desiccation, as air penetrates the porous body, the water saturation decreases but remains almost homogeneous throughout the sample. This air-invasion is at the origin of another type of fracture due to capillary effects; these results provide a new insight in the liquid dynamics at the nano-scale. PACS number(s): 47.56.+r, 68.03.Fg, 81.40.N

On Algebraic Approach for MSD Parametric Estimation

This article address the identification problem of the natural frequency and the damping ratio of a second order continuous system where the input is a sinusoidal signal. An algebra based approach for identifying parameters of a Mass Spring Damper (MSD) system is proposed and compared to the Kalman-Bucy filter. The proposed estimator uses the algebraic parametric method in the frequency domain yielding exact formula, when placed in the time domain to identify the unknown parameters. We focus on finding the optimal sinusoidal exciting trajectory which allow to minimize the variance of the identification algorithms. We show that the variance of the estimators issued from the algebraic identification method introduced by Fliess and Sira-Ramirez is less sensitive to the input frequency than the ones obtained by the classical recursive Kalman-Bucy filter. Unlike conventional estimation approach, where the knowledge of the statistical properties of the noise is required, algebraic method is deterministic and non-asymptotic. We show that we don't need to know the variance of the noise so as to perform these algebraic estimators. Moreover, as they are non-asymptotic, we give numerical results where we show that they can be used directly for online estimations without any special setting

Easy grasping location learning from one-shot demonstration

In this paper, we propose a fast learner grasping pipeline able to grasp objects at a specific location few minutes after being taught by an operator. Our motivation is to ease reconfiguration of robot according to a specific task, without any CAD model, nor existing database, nor simulator. We build a CNN pipeline which performs a semantic segmentation of object, and recognizes authorized and prohibited grasping location shown during demonstration. For that we have simplified the input space, created a data augmentation process and proposed a light CNN architecture allowing learning in less than 5 minutes. Validation on a real 7-DOF robot shown good performances (70 to 100% depending on the object), with only a one-shoot operator’s demonstration. Performances remain good when grasping similar unseen objects, and with several objects in the robot’s workspace using few demonstrations. A video highlighting the main aspects can be found at https://www.youtube.com/watch?v=rYCIk6njBo