A method and system for virtual environment-based interfaces applied to selected objects from video directs a system's focus of attention to an image within a first video stream and identifies an object in the image by applying a trained neural network. In response to a communication from a user comprising language and/or images describing a virtual environment, a second trained neural network is applied to generate a second video stream that embodies the identified object within a virtual environment that is in accordance with the user-described virtual environment. The second video stream is then delivered to the user. The system's focus of attention and/or generation of the virtual environment may be informed by user preferences that are inferred from user behaviors.

A method for optimally selecting a flood-control operation scheme based on a temporal convolutional network. The method includes evaluating the flood-control operation schemes in a group of reservoirs; a time-sequence evaluating indicator matrix combining the comprehensive evaluation indicators and the time sequence, which serves as an input of the temporal convolutional network, is constructed to calculate comprehensive scores for training samples of the flood-control operation schemes based on a fuzzy set theory and an improved entropy weight method; a structure of the temporal convolutional network is determined; the temporal convolutional network is trained by adopting a loss function combining a mean square error and a Nash efficiency coefficient; and the time-sequence evaluating indicator matrix for the flood-control operation schemes is input into the temporal convolutional network to obtain the comprehensive evaluation values for the schemes, and an optimal comprehensive evaluation value is taken as an optimal flood-control operation scheme.

An architecture for predicting and modeling geological characteristics of a reservoir includes one or more neural networks, a static modeling module, a dynamic modeling module, and a fuzzy inference engine to provide recommendations for drilling a wellbore. The neural networks receive log data for coordinates along a well trajectory, and determine a geophysical relationship for a property of a subterranean formation as a function of distance vectors between the coordinates along the well trajectory and one or more sets of randomly generated coordinates. The static modeling module generates three-dimensional static models of a volume of interest based on predicted properties of formations residing therein from the neural networks. The dynamic modeling module determines connectivity values between clusters of formations based on nodal connectivity of neighboring clusters, assigns pressure values across the volume of interest, and generates a three-dimensional dynamic model for the volume of interest based on the pressure values.

This document describes techniques and systems for fuzzy labeling of low-level electromagnetic sensor data. Sensor data in the form of an energy spectrum is obtained and the points within an estimated geographic boundary of a scatterer represented by the smear is labeled with a value of one. The remaining points of the energy spectrum are labeled with values between zero and one with the values decreasing the further away each respective remaining point is from the geographic boundary. The fuzzy labeling process may harness more in-depth information available from the distribution of the energy in the energy spectrum. A model can be trained to efficiently label an energy spectrum map in this manner. This may result in lower computational costs than other labeling methods. Additionally, false detections by the sensor may be reduced resulting in more accurate detection and tracking of objects.

This document describes techniques and systems for fuzzy labeling of low-level electromagnetic sensor data. Sensor data in the form of an energy spectrum is obtained and the points within an estimated geographic boundary of a scatterer represented by the smear is labeled with a value of one. The remaining points of the energy spectrum are labeled with values between zero and one with the values decreasing the further away each respective remaining point is from the geographic boundary. The fuzzy labeling process may harness more in-depth information available from the distribution of the energy in the energy spectrum. A model can be trained to efficiently label an energy spectrum map in this manner. This may result in lower computational costs than other labeling methods. Additionally, false detections by the sensor may be reduced resulting in more accurate detection and tracking of objects.

Systems and methods for predicting forward market pricing for renewable energy credit based on human behavioral data are disclosed. An example transaction-enabling system may include a forward market circuit to access a forward energy credit market and a market forecasting circuit to automatically generate a forecast for a forward market price of an energy credit in the forward energy credit market where the forecast is based at least in part on a human behavior information collected from at least one human behavioral data source. The example system may further include wherein the energy credit includes a renewable energy credit associated with a renewable energy system, and a smart contract circuit to perform at least one of selling the renewable energy credit or purchasing the renewable energy credit on the forward energy credit market in response to the forecasted forward market price of the energy credit.

Implementations described and claimed herein provide systems and methods for developing a reservoir. In one implementation, a static model of the reservoir is received. The static model has one or more clusters of rock types. A reservoir graph is generated from the static model. The reservoir graph represents each of the one or more clusters as a vertex. A graph connectivity of the reservoir graph is defined through a nodal connectivity of neighboring vertices. Pressure values are propagated across three-dimensional space of the reservoir graph using the connectivity. A dynamic model of the reservoir is generated using the pressure values and fluid saturation values.

Implementations described and claimed herein provide systems and methods for developing a reservoir. In one implementation, a static model of the reservoir is received. The static model has one or more clusters of rock types. A reservoir graph is generated from the static model. The reservoir graph represents each of the one or more clusters as a vertex. A graph connectivity of the reservoir graph is defined through a nodal connectivity of neighboring vertices. Pressure values are propagated across three-dimensional space of the reservoir graph using the connectivity. A dynamic model of the reservoir is generated using the pressure values and fluid saturation values.

The present invention relates to a grabbing detection method based on an RP-ResNet, which method belongs to the field of computer vision, and in particular relates to recognition and positioning of a grabbing point of a mechanical arm. The method comprises: inputting a target object image; pre-processing data; performing data processing by means of an RP-ResNet model; and finally, generating a grabbing block diagram of a grabbing target. On the basis of a model ResNet 50, a region proposal network is used in the 30th layer of a network, fuzzy positioning is performed on the position of a grabbing point, feature information of high and low layers is fully fused to strengthen the utilization of information of low layers, and an SENet structure is added to the 40th layer of the network, thereby further increasing the detection accuracy of a grabbing point. By means of a grabbing detection framework based on ResNet-50, a residual network, a region proposal idea and SENet are combined, such that it is ensured that rapid target detection is realized, and the accuracy rate of target detection is further improved.

The present invention relates to a grabbing detection method based on an RP-ResNet, which method belongs to the field of computer vision, and in particular relates to recognition and positioning of a grabbing point of a mechanical arm. The method comprises: inputting a target object image; pre-processing data; performing data processing by means of an RP-ResNet model; and finally, generating a grabbing block diagram of a grabbing target. On the basis of a model ResNet 50, a region proposal network is used in the 30th layer of a network, fuzzy positioning is performed on the position of a grabbing point, feature information of high and low layers is fully fused to strengthen the utilization of information of low layers, and an SENet structure is added to the 40th layer of the network, thereby further increasing the detection accuracy of a grabbing point. By means of a grabbing detection framework based on ResNet-50, a residual network, a region proposal idea and SENet are combined, such that it is ensured that rapid target detection is realized, and the accuracy rate of target detection is further improved.