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52results about How to "Maximizing similarity" patented technology

Trailer hitch

A trailer hitch facilitating proper alignment and connection to the hitch coupler of a trailer. The trailer hitch includes a tow bar assembly and a throat having spaced parallel top and floor panels generally equal in spacing to a vertical thickness of the tow bar and rearwardly diverging side panels. The front of the throat is connected or connectable to an elongated tubular extension or receiver and is sized to be slidably carried within the throat assembly. A slide bar is carried between the tow bar and the throat and includes a stop which limits the distance the tow bar may be extended rearwardly from the throat and, when in the fully or a partially extended position, is movable side-to-side to facilitate proper coupling alignment between the hitch ball and a hitch coupling of a trailer.
Owner:PALMER VAN BRADFORD

Method for registering and merging medical image data

The present invention relates to a method for the registration and superimposition of image data when taking serial radiographs in medical imaging, wherein a plurality of image data sets for a region of a patient (17) that is being investigated are constructed at time intervals using an imaging system (1) and are referenced with a first image data set for the region that is being investigated that was constructed previously using said imaging system (1). In the above method, a location system (2) is used during the production of serial radiographs constantly, or at least at a respective proximity in time to the construction of individual data sets, to determine a current spatial position of the region being investigated in a reference system that is firmly connected to the imaging system (1), whereby in the construction of the first image data set, a first spatial position of the region that is being investigated is recorded. In the construction of some or all further image data sets, the respective current spatial position of the region that is being investigated is determined and an image content of each first image data set is geometrically adapted on the basis of the difference between the first and the current spatial position, such that compensation is made for a different spatial position of the region that is being investigated. The geometrically adapted first image data set or an image data set derived therefrom, or an image data set that is positionally connected thereto by registration is then displayed superimposed with the respective further image data set.
Owner:SIEMENS HEALTHCARE GMBH

Method and apparatus for generating training data for human face recognition, device and computer storage medium

The present disclosure provides a method and apparatus for generating training data for human face recognition, a device and a computer storage medium, wherein the method comprises: inputting accessory-not-worn face images into a generative network, to obtain accessory-worn face images; using the accessory-worn face images as second training data for building the human face recognition model; wherein the generative network is a generative network in a generative adversarial network obtained by pre-training with first training data, the first training data including the accessory-not-worn face images and accessory-worn face images corresponding to a same user identifier. In the present disclosure, the accessory-worn face images obtained in a data augmentation manner greatly expand the amount of training data for building the human face recognition model, and thereby improve the recognition accuracy of the accessory-worn face images.
Owner:BAIDU ONLINE NETWORK TECH (BEIJIBG) CO LTD

Method and Apparatus of Training Acoustic Feature Extracting Model, Device and Computer Storage Medium

A method and apparatus of training an acoustic feature extracting model, a device and a computer storage medium. The method comprises: considering a first acoustic feature extracted respectively from speech data corresponding to user identifiers as training data; training an initial model based on a deep neural network based on a criterion of a minimum classification error, until a preset first stop condition is reached; using a triplet loss layer to replace a Softmax layer in the initial model to constitute an acoustic feature extracting model, and continuing to train the acoustic feature extracting model until a preset second stop condition is reached, the acoustic feature extracting model being used to output a second acoustic feature of the speech data; wherein the triplet loss layer is used to maximize similarity between the second acoustic features of the same user, and minimize similarity between the second acoustic features of different users.
Owner:BAIDU ONLINE NETWORK TECH (BEIJIBG) CO LTD

Employing user input to facilitate inferential sound recognition based on patterns of sound primitives

ActiveUS20160379666A1Mitigates distortionMaximize similarityElectrophonic musical instrumentsSpeech analysisSystem definitionFeature detection
The disclosed embodiments provide a system that generates sound primitives to facilitate sound recognition. First, the system performs a feature-detection operation on sound samples to detect a set of sound features, wherein each sound feature comprises a measurable characteristic of a window of consecutive sound samples. Next, the system creates feature vectors from coefficients generated by the feature-detection operation, wherein each feature vector comprises a set of coefficients for sound features detected in a window. The system then performs a clustering operation on the feature vectors to produce feature-vector clusters, wherein each feature-vector cluster comprises a set of feature vectors that are proximate to each other in a feature-vector space that contains the feature vectors. After the clustering operation, the system defines a set of sound primitives, wherein each sound primitive is associated with a feature-vector cluster. Finally, the system associates semantic labels with the set of sound primitives.
Owner:ANALOG DEVICES INC

Personal travel health vulnerability navigator

InactiveUS20170351832A1Minimizes vulnerabilityImprove travelMedical simulationData processing applicationsLevel dataRisk vulnerability
Individual health vulnerability is assessed by obtaining health risk prevalence level data containing health risk prevalence levels for one or more health risks over a given geographical area. The health risk prevalence level data to generate for each health risk a prevalence level forecast as a function of time and location. A health risk prevalence level map is generated for the given geographical area illustrating current and future health risk prevalence levels for each health risk at a plurality of locations within the given geographical area. Personal health status data are obtained for a given individual along with a proposed travel itinerary f covering at least a portion of the geographical area over a given time duration. The personal health status data, travel itinerary and map generate a personal health risk vulnerability model containing a quantification of vulnerability to the one or more health risks resulting from the travel itinerary.
Owner:IBM CORP

Method for registering and merging medical image data

The present invention relates to a method for the registration and superimposition of image data when taking serial radiographs in medical imaging, wherein a plurality of image data sets for a region of a patient (17) that is being investigated are constructed at time intervals using an imaging system (1) and are referenced with a first image data set for the region that is being investigated that was constructed previously using said imaging system (1). In the above method, a location system (2) is used during the production of serial radiographs constantly, or at least at a respective proximity in time to the construction of individual data sets, to determine a current spatial position of the region being investigated in a reference system that is firmly connected to the imaging system (1), whereby in the construction of the first image data set, a first spatial position of the region that is being investigated is recorded. In the construction of some or all further image data sets, the respective current spatial position of the region that is being investigated is determined and an image content of each first image data set is geometrically adapted on the basis of the difference between the first and the current spatial position, such that compensation is made for a different spatial position of the region that is being investigated. The geometrically adapted first image data set or an image data set derived therefrom, or an image data set that is positionally connected thereto by registration is then displayed superimposed with the respective further image data set.
Owner:SIEMENS HEALTHCARE GMBH

Personal travel health vulnerability navigator

InactiveUS20170351834A1Minimize health vulnerability of individualIncrease the itineraryMedical simulationData processing applicationsHealth riskLevel data
Individual health vulnerability is assessed by obtaining health risk prevalence level data containing health risk prevalence levels for one or more health risks over a given geographical area. The health risk prevalence level data to generate for each health risk a prevalence level forecast as a function of time and location. A health risk prevalence level map is generated for the given geographical area illustrating current and future health risk prevalence levels for each health risk at a plurality of locations within the given geographical area. Personal health status data are obtained for a given individual along with a proposed travel itinerary f covering at least a portion of the geographical area over a given time duration. The personal health status data, travel itinerary and map generate a personal health risk vulnerability model containing a quantification of vulnerability to the one or more health risks resulting from the travel itinerary.
Owner:IBM CORP

Personal travel health vulnerability navigator

InactiveUS20170351831A1Minimize health vulnerability of individualIncrease the itineraryData processing applicationsHealth-index calculationHealth riskLevel data
Individual health vulnerability is assessed by obtaining health risk prevalence level data containing health risk prevalence levels for one or more health risks over a given geographical area. The health risk prevalence level data to generate for each health risk a prevalence level forecast as a function of time and location. A health risk prevalence level map is generated for the given geographical area illustrating current and future health risk prevalence levels for each health risk at a plurality of locations within the given geographical area. Personal health status data are obtained for a given individual along with a proposed travel itinerary f covering at least a portion of the geographical area over a given time duration. The personal health status data, travel itinerary and map generate a personal health risk vulnerability model containing a quantification of vulnerability to the one or more health risks resulting from the travel itinerary.
Owner:IBM CORP
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