Abstract: Flow battery systems and control methods are disclosed. The flow battery includes components such as positive electrolyte tank, a negative electrolyte tank, a primary stack having a positive porous electrode and a negative porous electrode separated by a membrane, a power bus linked to the electrodes, an electrolyte pump and an auxiliary stack, etc. The power load on a power bus is monitored and as required pumps are activated if the detected power load on the power bus is greater than a first threshold power load. One of the pumps are deactivated if the detected power load on the power bus is less than a second threshold power load which is lower than the first threshold power load.

Abstract: Systems and methods for generating regressors based on data sparsity using a machine learning (ML) model are described. A system is configured to provide a plurality of time series datasets to a recurrent neural network (RNN) of a machine learning (ML) model. The RNN generates one or more outputs associated with one or more time series datasets, and the system provides a first portion and a second portion of the one or more outputs to a regressor layer and a classification layer of the ML model, respectively. The regressor layer generates one or more regressors for the one or more time series datasets, and the classification layer generates one or more classifications associated with the one or more regressors (with each indicating whether an associated regressor is valid). Whether a classification indicates a regressor is valid may be based on time series data sparsity.

Abstract: Power and controller integrated circuits for electric vehicle applications are enabled. For example, a system can comprise a plurality of battery cells, and a plurality of application specific integrated circuits (ASICs) electrically coupled to the plurality of battery cells, wherein one or more ASICs of the plurality of ASICs comprises a respective control ASIC, and wherein the ASICs comprise respective bidirectional direct current to alternating current (DC-AC) converters and charge or discharge the plurality of battery cells.

Abstract: Power and controller integrated circuits for electric vehicle applications are enabled. For example, a system can comprise a plurality of battery cells, and a plurality of application specific integrated circuits (ASICs) electrically coupled to the plurality of battery cells, wherein one or more ASICs of the plurality of ASICs comprises a respective power ASIC, and wherein the ASICs comprise respective bidirectional direct current to alternating current (DC-AC) converters and charge or discharge the plurality of battery cells.

Abstract: This disclosure relates generally to a system and method to identify various products on a plurality of images of various shelves of a retail store to facilitate compliance with respect to planograms. Planogram is a visual plan, which designates the placement of products on shelves and merchandising display fixtures of a retail store. Planograms are used to create consistency between store locations, to provide proper shelf space allocation, to improve visual merchandising appeal, and to create product-pairing suggestions. There are a few assumptions considering one instance per product class is available beforehand and the physical dimension of each product template is available in some suitable unit of length. In case of absence of physical dimension of the products, a context information of the retail store will be used. The context information is that the products of similar shapes or classes are arranged together in the shelves for consumers' convenience.

Abstract: The fine-grained variations in product images are usually due to slight variations in text, size, and color of the package. Both marginal variations in image content and illumination poses an important challenge in product classification. This disclosure relates to a system and method for fine-grained classification of similar-looking products utilizing object-level and part-level information. The system simultaneously captures an object-level and part-level information of the product. The object-level classification score of the product is estimated with the trained RC-Net, a deep supervised convolutional autoencoder. For annotation-free modelling of part-level information of the product the discriminative part-proposal of the product is identified around the BRISK key points. An ordered sequence of the discriminative part-proposals and the product image, encoded using stacked convolutional LSTM network, estimates the part-level classification score.

Abstract: A materials handling vehicle technology monitor receives wirelessly, from a fleet of materials handling vehicles, electronic vehicle records. Each electronic vehicle record includes technology feature data recorded by a controller on an associated materials handling vehicle. Typically, the electronic vehicle record is generated in response to a corresponding technology feature on the materials handling vehicle being operated in a work environment. Moreover, each electronic vehicle record includes an operator identification of an operator of the materials handling vehicle at the time the technology feature data is recorded. The monitor also generates for each operator, an electronic measurement based upon a comparison of expected technology feature usage, e.g., a threshold, compared to the technology feature data in the received electronic vehicle records, which are associated with the corresponding operator.

Abstract: This disclosure relates generally to a system and method to identify various products on a plurality of images of various shelves of a retail store to facilitate compliance with respect to planograms. Planogram is a visual plan, which designates the placement of products on shelves and merchandising display fixtures of a retail store. Planograms are used to create consistency between store locations, to provide proper shelf space allocation, to improve visual merchandising appeal, and to create product-pairing suggestions. There are a few assumptions considering one instance per product class is available beforehand and the physical dimension of each product template is available in some suitable unit of length. In case of absence of physical dimension of the products, a context information of the retail store will be used. The context information is that the products of similar shapes or classes are arranged together in the shelves for consumers' convenience.

Abstract: Object recognition based estimation of planogram compliance provides an expected arrangement of products in shelves. Identifying whether a product is placed in an appropriate location of a shelf is a challenging task due to various real-time parameters associated with image capturing. In the present disclosure, an input image associated with shelf of a retail store is received and a product images are cropped. Further, a set of reference images stored in a database are scaled corresponding to the input image. Further, one or more composite matching scores are calculated based on normalized cross-correlation and shape based feature matching to obtain one or more probable product images associated with a location. Further, a Directed Acyclic Graph (DAG) is constructed based on the one or more composite scores and the one or more probable products. Finally, an optimal matching product image for a particular location is obtained from the DAG.

Abstract: Object recognition based estimation of planogram compliance provides an expected arrangement of products in shelves. Identifying whether a product is placed in an appropriate location of a shelf is a challenging task due to various real-time parameters associated with image capturing. In the present disclosure, an input image associated with shelf of a retail store is received and a product images are cropped. Further, a set of reference images stored in a database are scaled corresponding to the input image. Further, one or more composite matching scores are calculated based on normalized cross-correlation and shape based feature matching to obtain one or more probable product images associated with a location. Further, a Directed Acyclic Graph (DAG) is constructed based on the one or more composite scores and the one or more probable products. Finally, an optimal matching product image for a particular location is obtained from the DAG.