Disney Patente

In some embodiments, a method receives a plurality of metric values for quality of service based on delivering content in a content delivery system. Respective metric values into a fuzzy value in a plurality of possible fuzzy values where a plurality of fuzzy values is determined. The method determines a plurality of fuzzy rules where a fuzzy rule is associated with a fuzzy value. Respective conditions for fuzzy rules are applied to the plurality of fuzzy values to determine fuzzy rules that have conditions met. A fuzzy rule applies when respective conditions for the fuzzy rule are met by one or more fuzzy values in the plurality of fuzzy values. A fuzzy output is determined from fuzzy values that are associated with the fuzzy rules that have conditions met. A unified quality score is calculated from the fuzzy output. The method outputs the unified quality score.

Methods and systems for a high-resolution virtual vehicle presence sensor are discussed herein. For example, the virtual sensor system may include a plurality of sensors analyzing a known path and configured to determine path-based vehicle data corresponding to a path-based vehicle, wherein each sensor of the plurality of sensors is positionally independent of each other sensor in the plurality of sensors. The virtual sensor system may further include a processing element configured to receive the path-based vehicle data from the plurality of sensors, calculate infilling kinematic data of the path-based vehicle at positions between the plurality of sensors based on the path-based vehicle data and known path information corresponding to the known path, and generate a visualization of the infilling kinematic data, the path-based vehicle data, and the known path information. The virtual sensor system may further include a display configured to display the visualization.

A system includes a hardware processor and a memory storing software code and a user preferences database including at least one of an aversion of a first user or a preference of the first user. The hardware processor is configured to execute the software code to receive, from an attraction providing a user experience, data describing a characteristic of the user experience, compare, using the data and the user preferences database, the characteristic of the user experience with the aversion and/or the preference of the first user, and determine, based on comparing, that the characteristic of the user experience corresponds to the aversion and/or the preference of the first user. The hardware processor is further configured to execute the software code to alert the first user in response to determining that the characteristic corresponds to the aversion and/or the preference of the first user.

In some embodiments, a method determines an instance of content to be delivered to a client device. The instance of content includes a plurality of segments that is associated with a plurality of profiles. A target metric and a bandwidth distribution are determined. The method determines a performance of the target metric based on the bandwidth distribution and a model for selecting a top profile for the plurality of segments. The model includes a set of conditions. The method solves the model based on the performance of the target metric where solving the model provides a solution that meets the set of conditions and outputs the top profile for each segment. The top profile for each segment for delivery to the client device. The top profile for each segment is used to remove profiles when requesting segments in the plurality of segments during playback of the instance of content.

In some embodiments, a method determines an instance of content to be delivered to a client device. The content includes segments that are associated with a plurality of profiles. A target metric and a bandwidth distribution are determined. The method determines a plurality of top profile decisions for the plurality of segments where a top profile decision lists a top profile that can be selected for respective segments in the plurality of segments. A performance of the target metric is determined for the plurality of top profile decisions based on the bandwidth distribution. The method selects a top profile decision from the top profile decisions based on the performance of the target metric for the top profile decisions. The top profile decision is used to determine a target quality. The target quality is used to select top profiles for requesting segments in the segments during playback of the content.

A system for predicting and mitigating an effect of environmental conditions, the system includes: a user device including a processor and a memory configured to: determine the environmental conditions for a location within an environment; analyze the environmental conditions in light of individual-specific criterion to determine a predicted impact on an individual within the location during the determined environmental conditions; and generate a mitigation response based on the predicted impact.

In some embodiments, a method receives an image and encodes the image into a latent representation in a latent space. A quantization process is performed on the latent representation to generate a quantized latent representation. The quantization process is based on a uniform noise. The method transmits the quantized latent representation to a receiver. An inverse quantization process is performed to generate a reconstructed latent representation via a diffusion model that performs a denoising process for a number of iterations based on a time step t to remove noise from the reconstructed latent representation. The diffusion model is trained to perform denoising using the uniform noise.

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

A method of training a robotic device includes: parameterizing, via a processing element, an input to the robotic device. The parameterizing comprises defining a range of values of the input. The method further includes generating, via the processing element, a plurality of samples of the parameterized input from within the range of values; training a control policy, via the processing element. The training includes: providing the plurality of samples to the control policy, wherein the control policy is adapted to operate an actuator of the robotic device, and generating, via the processing element, a policy action using the control policy; transmitting the policy action to a robotic model, wherein the robotic model includes a physical model of the robotic device. The method further includes deploying the one or more trained control policies to an on-board controller for the robotic device.

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …