A Spinor Method of Solution of Manipulators’ Inverse Kinematics Problem with Rotational Pairs

A new method and algorithm of solution of multijoint manipulators with rotational pairs inverse kinematic problem was created. The method is based on the principally new approach-spinor representation of the spatial generalized rotations. One of the advantages of the method is that it allows elaborating formulation of technological tasks for manipulators in terms of Cartesian coordinates instead of traditional angles’ terms. Besides the method allows using only one (absolute) coordinate system and does not require a set of different (relative) coordinate systems. It provides easy, reliable and efficient way of solution of inverse kinematics problem of multijoints manipulators with rotational pairs.spinors, rotations, Euler’s angles, basic representations, kinematics inverse problem, orthogonal transformations

Simple Relations between Elements of the Three-Dimensional Orthogonal Matrix of the Basic Representation and Euler and RPY’ Angles

In this Article transformation angles of the methods of RPY (Roll, Pitch and Yaw) and Euler are found using Spinor method [Milnikov A.A., Prangishvili A.I., Rodonaia I.D. (2005) ], which The method is based on the principally new approach-spinor representation of the spatial generalized rotations. And provides easy, reliable and efficient way of solution of inverse kinematics problem of multijoints spherical manipulators.RPY’ angles, Euler’s angles, spinors, rotations, and kinematics inverse problem,

Pauli Matrices and the Theory of Representations of the Group of Rotations

It is shown that Pauli Matrixes can be derived from irreducible rotation group representations of the weight =1/2, which in turn based on the system of infinitesimal (elementary) spatial rotations. The last permits to substantiate why Pauli matrixes can be so sufficiently used for modeling of physical rotations.Pauli matrices, Group rotations, spinor transformation, group of rotation

Resolving Lexical Ambiguity in Tensor Regression Models of Meaning

This paper provides a method for improving tensor-based compositional distributional models of meaning by the addition of an explicit disambiguation step prior to composition. In contrast with previous research where this hypothesis has been successfully tested against relatively simple compositional models, in our work we use a robust model trained with linear regression. The results we get in two experiments show the superiority of the prior disambiguation method and suggest that the effectiveness of this approach is model-independent

Consideration of Easygard Post Design on the Base of Advances Concurrent Engineering Tool

The essence of CE is the integration of product design and process planning into one common activity. Concurrent design helps improve the quality of early design decisions and has a tremendous impact on life-cycle cost of the product. It is important to decide at an early stage in design which type of assembly method is likely to be adopted, based on the method yielding the lowest costs. This section allows the designer to decide, from the values of basic product and company parameters (production volume, number of parts, etc.) which assembly method is likely to be the most economic. The purchase of special-purpose automation equipment (high-speed automatic assembly) would almost certainly provide excellent return on investment. Somewhere between these extremes is a range of annual production volumes for which robot assembly might be the best economic choice if the assembly were appropriately designed. However, changing system is not only the way to reduce the production time and cost. It is also possible to reduce time and cost significantly by using software package and designing or redesigning assembly of product appropriately. Paper below represents design optimization possibilities by DFA approach for Easygard post engineering design produced by one of the UK medium size manufacturing company.

A Convolutional Neural Network for Modelling Sentences

The ability to accurately represent sentences is central to language understanding. We describe a convolutional architecture dubbed the Dynamic Convolutional Neural Network (DCNN) that we adopt for the semantic modelling of sentences. The network uses Dynamic k-Max Pooling, a global pooling operation over linear sequences. The network handles input sentences of varying length and induces a feature graph over the sentence that is capable of explicitly capturing short and long-range relations. The network does not rely on a parse tree and is easily applicable to any language. We test the DCNN in four experiments: small scale binary and multi-class sentiment prediction, six-way question classification and Twitter sentiment prediction by distant supervision. The network achieves excellent performance in the first three tasks and a greater than 25% error reduction in the last task with respect to the strongest baseline

Separating Rescission and Restitution

This is the accepted author manuscript. The final version is available via Lexis®Library.This article argues that rescission of a contract and restitution of what passed under it are best viewed as two distinct operations. Similarly, inability to give counter-restitution, lapse of time, and third party rights, should not be bars to rescission; they are best accommodated at the restitution stage