Abstract: Provided is an overhead travelling crane assembly including an overhead rail and a hoist unit realized to travel along the rail, characterized by a parking lock mounted to the hoist unit, which parking lock includes a brake assembly which, in its default position, prevents movement of the hoist unit along the overhead rail; and a release means realized to release the brake assembly to allow movement of the hoist unit along the overhead rail. Also provided is a method of operating such an overhead travelling crane assembly; and a wind turbine including such an overhead travelling crane assembly in the nacelle.

Abstract: Provided is an overhead travelling crane assembly including an overhead rail and a hoist unit realized to travel along the rail, characterized by a parking lock mounted to the hoist unit, which parking lock includes a brake assembly which, in its default position, prevents movement of the hoist unit along the overhead rail; and a release means realized to release the brake assembly to allow movement of the hoist uni -along the overhead rail. Also provided is a method of operating such an overhead travelling crane assembly; and a wind turbine including such an overhead travelling crane assembly in the nacelle.

Abstract: Approaches for classifying training samples with minimal error in a neural network using a low complexity neural network classifier, are described. In one example, for the neural network, an upper bound on the Vapnik-Chervonenkis (VC) dimension is determined. Thereafter, an empirical error function corresponding to the neural network is determined. A modified error function based on the upper bound on the VC dimension and the empirical error function is generated, and used for training the neural network.

Abstract: Systems and methods for classifying binary data based training data having a predefined sample size is obtained. The training data is composed of separable binary datasets. An exact bound on Vapnik-Chervonenkis (VC) dimension of a classifier for the training data is determined. The exact bound is based one or more variables defining the hyperplane. The exact bound may be minimized for generating a classifier for predicting one class to which a given data sample of the training data belongs.

Abstract: The present invention relates to techniques for determining unknown concentration of constituents of any known mixture. The said techniques comprising: obtaining a plurality of chromatograms relating to known concentration of known mixtures and at least one chromatogram relating to unknown concentration of the known mixture; converting each of the chromatograms into signal vectors; condensing the dimensions of each of the signal vectors for obtaining low dimensional signal vectors; processing the low dimensional signal vectors representing the chromatograms relating to known concentrations to obtain output values; and processing the at least one low dimensional signal vector representing the chromatogram relating to unknown concentration by utilizing the obtained output values for determining the unknown concentration of each of the constitutes of the known mixture.

Abstract: Approaches for classifying training samples with minimal error in a neural network using a low complexity neural network classifier, are described. In one example, for the neural network, an upper bound on the Vapnik-Chervonenkis (VC) dimension is determined. Thereafter, an empirical error function corresponding to the neural network is determined. A modified error function based on the upper bound on the VC dimension and the empirical error function is generated, and used for training the neural network.

Abstract: The present invention relates to techniques for determining unknown concentration of constituents of any known mixture. The said techniques comprising: obtaining a plurality of chromatograms relating to known concentration of known mixtures and at least one chromatogram relating to unknown concentration of the known mixture; converting each of the chromatograms into signal vectors; condensing the dimensions of each of the signal vectors for obtaining low dimensional signal vectors; processing the low dimensional signal vectors representing the chromatograms relating to known concentrations to obtain output values; and processing the at least one low dimensional signal vector representing the chromatogram relating to unknown concentration by utilizing the obtained output values for determining the unknown concentration of each of the constitutes of the known mixture.

Abstract: Systems and methods for classifying test data based on maximum margin classifier are described. In one implementation, the method includes obtaining training data having a predefined sample size, wherein the training data is composed of separable data-sets. For the training data, a Vapnik-Chervonenkis (VC) dimension for the training data is determined. For the VC dimension, an exact bound is subsequently determined. The exact bound may be minimized for obtaining the minimum VC classifier for predicting at least one class to which samples of the training data belong.

Abstract: A process for preparation of (R)-aromatic ?-hydroxy ketones of formula (I), said process occurring in strain(s) of yeast expressing a recombinant pyruvate decarboxylase, having cysteine residues at positions 221 and 222 of PDC1, an isoenzyme of said pyruvate decarboxylase, substituted with glutamate and alanine respectively such that the said mutation being in the regulatory site of pyruvate decarboxylase, selectively favours carboligation reaction over decarboxylation.

Abstract: Techniques for assessing pronunciation abilities of a user are provided. The techniques include recording a sentence spoken by a user, performing a classification of the spoken sentence, wherein the classification is performed with respect to at least one N-ordered class, and wherein the spoken sentence is represented by a set of at least one acoustic feature extracted from the spoken sentence, and determining a score based on the classification, wherein the score is used to determine an optimal set of at least one question to assess pronunciation ability of the user without human intervention.

Abstract: Techniques for assessing pronunciation abilities of a user are provided. The techniques include recording a sentence spoken by a user, performing a classification of the spoken sentence, wherein the classification is performed with respect to at least one N-ordered class, and wherein the spoken sentence is represented by a set of at least one acoustic feature extracted from the spoken sentence, and determining a score based on the classification, wherein the score is used to determine an optimal set of at least one question to assess pronunciation ability of the user without human intervention.

Abstract: In accordance with an aspect of the present invention, a method of data object transformation is disclosed. The method includes receiving a message from a communications line, the message including one or more data objects of a first object type, wherein the message is in a first communications format, converting the message from the first communications format to a second communications format, converting the one or more data objects from the first object type to a second object type, wherein the one or more data objects are converted using a first set of one or more transformation classes, each of the one or more transformation classes generated using mapping rules, and transmitting the converted one or more second object type data objects to an application.

Abstract: The invention relates to an analog to digital converter computing all the bits in parallel or sequentially, without using decoding logic having an analog input and a digital output, wherein given the analog signal x, each bit can be computed by applying a formula containing a non linear periodic function which may be sine shaped or pulse shaped.

Abstract: The invention relates to an analog to digital converter computing all the bits in parallel or sequentially, without using decoding logic having an analog input and a digital output, wherein given the analog signal x, each bit can be computed by applying a formula containing a non linear periodic function which may be sine shaped or pulse shaped.

Abstract: An electrical discharge machine with automatically replaceable electrodes. The machine includes an electrode holder with a clamp operable to alternately receive and release an electrode, an electrode cartridge containing a plurality of stacked electrodes and a shuttle movable toward and away from the electrode holder. When the shuttle is moved toward the electrode holder, it removes an electrode from the cartridge and moves it into alignment with the electrode holder.