21 results about "Computer learning" patented technology

The invention provides a smart agriculture fusion system based on Internet of Things and digital twinning, and relates to the technical field of data acquisition, and the system comprises an Internet of Things sensing network which is used for collecting data of soil humidity, soil oxygen, environmental parameters, meteorological information and crop growth states; the big data processing unit is used for receiving the data acquired by the sensor and feeding back the processed data to the digital twinborn model; establishing a digital twinborn model, and constructing the digital twinborn model of the farmland by using the real-time data and historical data of the farmland through a computer learning method and a deep learning algorithm; the control and decision-making unit is used for making farmland management strategies and decisions based on the analysis and prediction results of the digital twinborn model; a user remotely monitors and manages the operation state of the farmland through a terminal, and through the Internet of Things and the digital twinning technology, informatization and intelligentization of the whole chain of agricultural production are achieved, and the efficiency and quality of agricultural production are improved.

The invention discloses a data processing method and device based on a convolutional neural network, equipment and a medium, and relates to the field of computer learning, and the method comprises the steps: obtaining to-be-processed data in a preset memory, and alternately caching the to-be-processed data into a preset cache block according to a preset data caching rule; determining a processing task corresponding to the to-be-processed data, and allocating a corresponding processing unit to the to-be-processed data according to the processing task; processing the to-be-processed data according to a preset neural network model based on the processing unit to obtain corresponding processing unit output data; the preset neural network model is a neural network model obtained based on depth separable convolution. By alternately using the two cache modules, continuous processing of data streams is achieved, meanwhile, the processing units are allocated according to different calculation tasks, resource allocation can be dynamically adjusted according to task requirements, software algorithm processing and hardware structure optimization are combined, and the data calculation efficiency is improved.

A computer implemented method for blood flow imaging including: obtaining CT or MRI image data comprising a plurality of corresponding images capturing at least a portion of one or both an increase phase and a decline phase of a contrast agent in a cardiovasculature of interest; extracting at least one image feature from the CT or MRI image data; providing the at least one image feature and an associated at least one non-image feature to a machine learning model to generate a predicted value of area under a time-enhancement curve of the contrast agent within the cardiovasculature of interest, the machine learning model trained with training inputs of the at least one image feature with the at least one non-image feature, and associated with an area under a time-enhancement curve value as ground truth; converting the predicted value of area under the time-enhancement curve to a time rate of change of contrast agent concentration in the cardiovasculature of interest; determining a blood flow characteristic in the cardiovasculature of interest based on a ratio of mass of the contrast agent in the cardiovasculature of interest to the time rate of change of contrast agent concentration in the cardiovasculature of interest. Systems for blood flow imaging are also described.

The present invention discloses a laser obstacle removal remote control system for remote obstacle removal of overhead lines, which relates to the field of laser obstacle removal technology, in order to solve the problem of inaccurate obstacle acquisition by laser obstacle removal equipment. The integration and optimization of steps such as image processing, dynamic adjustment and batch normalization of the present invention can significantly improve the processing efficiency of image data, so that the laser obstacle removal equipment can respond and identify obstacles more quickly, and the obstacle features in the optimized image data are trained and calculated with the obstacle model using a computer learning algorithm, which can greatly improve the system's adaptability and robustness. By continuously learning new obstacle features, the system can gradually improve its recognition performance, calculate the characteristics and operation mode of the obstacle through a particle filtering method, and adjust the direction of the laser beam in real time according to the calculation results, thereby ensuring accurate tracking of the obstacle position.

Embodiments of the present invention are directed to systems, devices, and methods for displaying and securing articles of merchandise. In one example, a merchandise security system includes a plurality of articles of merchandise on display in a retail store and at least one machine learning camera configured to capture images of one or more of the articles of merchandise for detecting information regarding the articles of merchandise, making predictions based on such information, and providing notifications based on such information.

Techniques are disclosed relating to automating authentication decisions for a multi-factor authentication scheme based on computer learning. In disclosed embodiments, a mobile device receives a first request corresponding to a factor in a first multi-factor authentication procedure. Based on user input approving or denying the first request, the mobile device sends a response to the first request and stores values of multiple parameters associated with the first request. The mobile device receives a second request corresponding to a factor in a second multi-factor authentication procedure where the second request is for authentication for a different account than the first request. The mobile device automatically generates an approval response to the second request based on performing a computer learning process on inputs that include values of multiple parameters for the second request and the stored values of the multiple parameters associated with the first request. The approval response is automatically generated and sent without receiving user input to automate the second request.

An Intelligent Adaptive Language Computer Learning Device

A colorimetric compliance control device includes a electronic communication device, a non-transitory computer readable medium, an electronic controller. The electronic communication device is programmed to electronically receive colorimetric information regarding a vehicle component. The non-transitory computer readable medium electronically stores a non-transitory computer learning model using the colorimetric information acquired by the detector as a first input. The non-transitory computer learning model generates a colorimetric value as an output value related to the colorimetric information. The electronic controller is programmed to compare the output value with a predetermined exemplary value. The electronic controller is programmed to determine when the output value deviates from the predetermined exemplary value.

Systems, methods, and data sets for automatically and precisely generating highlight or summary videos of content are presented herein. For example, in one or more embodiments, videos of sports events can be refined or condensed into highlights, which greatly benefits sports media, broadcasters, video creators or commentators, or other short video creators in terms of cost reduction, fast and large-scale generation, and saving tedious engineering time. Embodiments of the framework can also be used or adapted to better promote sports teams, players, and / or games, and to produce stories that celebrate the spirit of sports or its players. It should be noted that when presented in the context of sports, the method can be used for videos including other content and events.

The utility model relates to a multifunctional one-chip microcomputer learning board, comprising a control chip U1, a power supply module, an AD conversion module, a motor module and an LED display module, the AD conversion module, the motor module and the LED display module are all connected with the control chip U1, and the power supply module is respectively connected with the control chip U1, the AD conversion module, the motor module and the LED display module. The single-chip microcomputer training device can be used for single-chip microcomputer learning, can also be used for practical operation training and examination of single-chip microcomputer modules of electricians and technicians, and is small in area, small in occupied space, convenient to carry and store, low in cost and high in cost performance.

The application belongs to the field of computational theoretical chemistry, and discloses a method for rapidly predicting natural chemical connection reaction rate by using a machine learning model.The method comprises the following steps: firstly, synthesizing a series of peptide thioesters containing different thioester groups to construct a peptide thioester library; then, determining the natural chemical connection reaction rates of different peptide thioesters as sample labels; subsequently, analyzing the influence of structure on the reaction rate of the peptide thioester through quantum structure optimization to obtain the feature descriptor of each sample from the calculation simulation; finally, based on the feature descriptor and the sample label, a formula of structure-function relationship is summarized by using a computer learning algorithm, so that the influence of the structure of the peptide thioester on the natural chemical connection reaction rate can be rapidly predicted.The method greatly improves the work efficiency, the result is accurate, the reproducibility is good, and the method is convenient to popularize and apply.

1. Name of the design product: Computer desk. 2. Use of the design product: A computer desk for placing computers and for learning and working. 3. Design key points of the design product: Lies in the shape. 4. Picture or photograph that best shows the design key points: Perspective view.

The present invention proposes an improved method based on computer learning that utilizes a combination of a graph convolutional network and natural language processing (NLP) to process a knowledge graph to effectively organize event and service data. The proposed method includes training learnable parameters of a graphical convolutional network (GCN), and deploying the GCN to update a knowledge graph for label estimation. A synthetic question and answer (QA) workflow pipeline process based on the generated knowledge graph may be used for downstream processes to generate suggestions that represent the most effective response to the event estimated by the machine learning model.

1. Name of the product subject to this design: Toy (Computer Learning Machine 1). 2. Purpose of this design: for children to play with. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

Embodiments of the present invention are directed to systems, devices, and methods for displaying and securing articles of merchandise. In one example, a merchandise security system includes a plurality of articles of merchandise on display in a retail store and at least one machine learning camera configured to capture images of one or more of the articles of merchandise for detecting information regarding the articles of merchandise, making predictions based on such information, and providing notifications based on such information.

The invention discloses a hotlinking early warning method based on big data streaming media, and the method comprises the following steps: real-time data monitoring: an ETL data collection module which comprises the steps: carrying out the classified collection and induction of eight types of data; data preprocessing and classification: a data computer learning module; an intelligent early warning mechanism: a basic data module; an early warning output module; in conclusion, compared with the prior art, existing hotlinking processing mainly focuses on shielding and rarely has early warning, the patent focuses on business production data, server resources and possible problems in machine learning regression algorithm estimation, early warning is focuses on, and the system has higher practicability and timeliness; compared with the prior art, the main creativity is that various scene data are generated in the watching process of a video APP, server resource occupation conditions and Spark real-time calculation are combined, possible problems are estimated by reasonably using a machine learning regression algorithm, risk information is positioned, and multidirectional combination is timely and efficient.

1. Name of the product in this design: Toy (Computer Learning Machine 2). 2. Purpose of this design: for children to play with. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

The invention discloses a laser remote obstacle clearance and monitoring system, which relates to the technical field of intelligent detection and control, and comprises the steps of acquiring image data of an obstacle clearance area, performing training calculation on obstacle characteristics in optimized image data by utilizing a computer learning algorithm, and confirming the position of an obstacle through similarity comparison. Marking the position data as obstacle position data; aiming according to the position data of the obstacle, calculating state information of the obstacle through a particle filtering method, adjusting the direction of the laser beam in real time, and aiming and tracking the position of the obstacle; laser parameters are adjusted according to the obstacle aiming position data in combination with the obstacle characteristics and the operation distance, laser beams are emitted to instantly heat and evaporate the surface of the target object, and the obstacle is removed; in the obstacle clearing process, the obstacle clearing area is continuously monitored, the obstacle clearing effect is fed back to the remote control center, the obstacle clearing strategy is adjusted in time, and the problems of insufficient accuracy and weak scene adaptability in the existing obstacle clearing technology are solved.

Techniques are disclosed relating to automating authentication decisions for a multi- factor authentication scheme based on computer learning. In disclosed embodiments, a mobile device receives a first request corresponding to a factor in a first multi-factor authentication procedure. Based on user input approving or denying the first request, the mobile device sends a response to the first request and stores values of multiple parameters associated with the first request. The mobile device receives a second request corresponding to a factor in a second multi-factor authentication procedure where the second request is for authentication for a different account than the first request. The mobile device automatically generates an approval response to the second request based on performing a computer learning process on inputs that include values of multiple parameters for the second request and the stored values of the multiple parameters associated with the first request. The approval response is automatically generated and sent without receiving user input to automate the second request.

1. Name of the product subject to this design: Toy (Computer Learning Machine). 2. Purpose of this design: for children to play with. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

To efficiently search for mediators. [Solution] A mediator search method is provided, which involves a computer learning training data that associates experimental values ​​of the electrical properties of a solution containing a mediator and cells with the mediator; estimating the electrical properties of each of a plurality of mediators not included in the training data based on the results of the learning; and adding the experimental values ​​of the electrical properties of at least some of the plurality of mediators whose electrical properties have been estimated, along with the mediators themselves, to the training data, repeating this process until the experimental values ​​of the electrical properties meet a criterion, thereby searching for a mediator whose experimental values ​​meet the criterion.