Abstract: A method and an electronic device for handling a material are provided. The method may include: obtaining a picking task for a target material at a target warehouse; using, in response to determining that a target person arrives at an exit location of the target warehouse, material information of a material held by the target person as target material information, the material information including a material identifier and a material quantity; and comparing material information of a to-be-picked material in the picking task with the target material information, and subtracting the material information of the to-be-picked material from stock information of the target warehouse if a comparison result is that the material information and the target material information are consistent.

Abstract: Imaging devices and processing techniques based on imaging information along the depth direction in an optical coherent tomography (OCT) image are disclosed to enhance observed morphological features. The methods and systems obtain different OCT images of the target object under different reference path lengths, process the different OCT images to obtain a derivative with respect to the signal path length of image information of the different images, and processing the derivative to extract improved image information of the target object. The derivatives may be in a form related to a gradient value of the normalized OCT image intensity, or an attenuation coefficient.

Abstract: Imaging devices and processing techniques based on imaging information along the depth direction in an optical coherent tomography (OCT) image are disclosed to enhance observed morphological features. The methods and systems obtain different OCT images of the target object under different reference path lengths, process the different OCT images to obtain a derivative with respect to the signal path length of image information of the different images, and processing the derivative to extract improved image information of the target object. The derivatives may be in a form related to a gradient value of the normalized OCT image intensity, or an attenuation coefficient.

Abstract: A method of training a neural net includes receiving a plurality of sets of data, each set representative of a plurality of inputs to the neural net and a resulting at least one output from the neural net and calculating a plurality of network weights for the neural network based on the received plurality of sets of data. Calculating the plurality of network weights including attributing greater weight in the calculation to at least one set of the plurality of sets of data than at least one other set of the plurality of sets of data.

Abstract: A method to add expression level fuzzy rule processing capability to existing crisp rule engine without requiring extensive modifications to such rule engine. A system and computer implemented method receive a data set at a crisp rule engine. If the data set involves a fuzzy rule, a callback to a fuzzy rule broker is done. The fuzzy rule broker selects one of multiple tailored processing modules for processing the fuzzy rule. The result of processing the fuzzy rule may be converted to a crisp result and returned to the crisp rule engine.

Abstract: A method for hierarchical visualization of multi-dimensional data is provided. A first dimension-reduction process is applied to a multi-dimensional data set to obtain a first visualization. A subset of the multi-dimensional data set associated with a selected region of the dimension-reduced first visualization is selected. A second dimension-reduction process is applied to the selected subset of the multi-dimensional data set to obtain at least one additional visualization.

Abstract: An apparatus and method for processing mixed data for a selected task is provided. An input transformation module converts mixed data into converted data. A functional mapping module processes the converted data to provide a functional output for the selected task. The selected task may be one or a combination of a variety of possible tasks, including search, recall, prediction, classification, etc. For example, the selected task may be for data mining, database search, targeted marketing, computer virus detection, etc.

Abstract: A method to add expression level fuzzy rule processing capability to existing crisp rule engine without requiring extensive modifications to such rule engine. A system and computer implemented method receive a data set at a crisp rule engine. If the data set involves a fuzzy rule, a callback to a fuzzy rule broker is done. The fuzzy rule broker selects one of multiple tailored processing modules for processing the fuzzy rule. The result of processing the fuzzy rule may be converted to a crisp result and returned to the crisp rule engine.

Abstract: A method for automated feature selection is provided. One or more initial sets of features are generated and evaluated to determine quality scores for the feature sets. Selected ones of the feature sets are (i) chosen according to the quality scores and modified to generate a generation of modified feature sets, (ii) the modified feature sets are evaluated to determine quality scores for the modified feature sets, and (i) and (ii) are repeated until a modified feature set is satisfactory.

Abstract: A system and method for handling duration values includes associating a duration value with a start datetime to allow precise duration handling. Thus, a uniform time line approach may be used to handle duration arithmetic, comparisons, or other computations for duration values. For example, the uniform time line approach may characterize a duration value as a difference between two endpoints of a timeline segment having a length equal to the duration value. As a result, duration operations can be carried out with precision and accuracy using simple calendar calculations.

Abstract: Method of incrementally forming and adaptively updating a neural net model are provided. A function approximation node is incrementally added to the neural net model. Function parameters for the function approximation node are determined and function parameters of other nodes in the neural network model are updated, by using the function parameters of the other nodes prior to addition of the function approximation node to the neural network model.

Abstract: A model maintenance method is provided. If accuracy of prediction by a current model through consultation with new data is determined to be below a predetermined threshold, a compound model is formed by supplementing the current model with a local net trained with the new data.

Abstract: Methods for feature selection based on hierarchical local-region analysis of feature relationships in a data set are provided.

Abstract: A method for feature selection based on hierarchical local-region analysis of feature characteristics in a data set of mixed data type is provided. A data space associated with a mixed-type data set is partitioned into a hierarchy of plural local regions. A relationship metric (for example, a similarity correlation metric) is used to evaluate for each local region a relationship measure between input features and a target. One or more relevant features is identified, by using the relationship measure for each local region.

Abstract: A method of training a neural net includes receiving a plurality of sets of data, each set representative of a plurality of inputs to the neural net and a resulting at least one output from the neural net and calculating a plurality of network weights for the neural network based on the received plurality of sets of data. Calculating the plurality of network weights including attributing greater weight in the calculation to at least one set of the plurality of sets of data than at least one other set of the plurality of sets of data.

Abstract: An adaptive learning method for automated maintenance of a neural net model is provided. The neural net model is trained with an initial set of training data. Partial products of the trained model are stored. When new training data are available, the trained model is updated by using the stored partial products and the new training data to compute weights for the updated model.

Abstract: A method for plan generation is provided. One or more initial plans are generated and evaluated to determine quality scores for the plans. One or more of the plans is selected according to the quality scores and modified to generate modified plans. The modified plans are evaluated to determine updated quality scores for the modified plans. Selection, modification and evaluation of modified plans are repeated until one of the modified plans is satisfactory.

Abstract: A method for feature selection based on hierarchical local-region analysis of feature characteristics in a data set of mixed data type is provided. A data space associated with a mixed-type data set is partitioned into a hierarchy of plural local regions. A relationship metric (for example, a similarity correlation metric) is used to evaluate for each local region a relationship measure between input features and a target. One or more relevant features is identified, by using the relationship measure for each local region.

Abstract: The subject system provides reduced-dimension mapping of pattern data. Mapping is applied through conventional single-hidden-layer feed-forward neural network with non-linear neurons. According to one aspect of the present invention, the system functions to equalize and orthogonalize lower dimensional output signals by reducing the covariance matrix of the output signals to the form of a diagonal matrix or constant times the identity matrix. The present invention allows for visualization of large bodies of complex multidimensional data in a relatively “topologically correct” low-dimension approximation, to reduce randomness associated with other methods of similar purposes, and to keep the mapping computationally efficient at the same time.

Abstract: A method for automated feature selection is provided. One or more initial sets of features are generated and evaluated to determine quality scores for the feature sets. Selected ones of the feature sets are (i) chosen according to the quality scores and modified to generate a generation of modified feature sets, (ii) the modified feature sets are evaluated to determine quality scores for the modified feature sets, and (i) and (ii) are repeated until a modified feature set is satisfactory.