Abstract: The present disclosure relates to a wind farm layout and yaw control method, and an electronic device. The wind farm layout and yaw control method comprises the following steps: acquiring wind farm data, importing the wind farm data into a WFSim model for simulation, obtaining original power data of the wind farm, counting and recording modifiable wind farm layout and yaw parameters, adjusting variable parameters to be changed, using the WFSim model for simulation to obtain an optimal parameter range of different variables and combining the optimal parameters to obtain an optimal working condition, and using the WFSim model for simulation to obtain optimal power data. The wind farm layout and yaw control method according to the present disclosure is based on optimizing the power output of the wind farm, so that it is very convenient to acquired information.

Abstract: A cryogenic laser shock device and method are provided. The device includes a cooling box, a transition chamber, a laser shock chamber, and a master control system. In the method, a sample is cooled by the cooling box, a temperature of the sample is measured in real time by a cryogenic probe, gas in the cooling box is pumped out after the temperature of the sample is stabilized to a set temperature, the cooling box is moved to the laser shock chamber in a vacuum state, and a cryogenic laser shock is implemented through cooperation of a mechanical arm and a three-dimensional motion platform.

Abstract: There is described a deep learning supervised regression based model including methods and systems for facial attribute prediction and use thereof. An example of use is an augmented and/or virtual reality interface to provide a modified image responsive to facial attribute predictions determined from the image. Facial effects matching facial attributes are selected to be applied in the interface.

Abstract: There is described a deep learning supervised regression based model including methods and systems for facial attribute prediction and use thereof. An example of use is an augmented and/or virtual reality interface to provide a modified image responsive to facial attribute predictions determined from the image. Facial effects matching facial attributes are selected to be applied in the interface.

Abstract: A system and a method for time series forecasting. The method includes: providing input feature vectors corresponding to a plurality of future time steps; performing bi-directional long-short term memory network (BiLSTM) on the input feature vectors to obtain hidden outputs corresponding to the plurality of future time steps; for each future time step: performing temporal convolution on the hidden outputs using a plurality of temporal scales to obtain context features at the plurality of temporal scales, and summating the context features at the plurality of temporal scales using a plurality of weights to obtain multi-scale context features; and converting the multi-scale context features to obtain the time series forecasting corresponding to the future time steps.

Abstract: Provided are systems and methods to perform colour extraction from swatch images and to define new images using extracted colours. Source images may be classified using a deep learning net (e.g. a CNN) to indicate colour representation strength and drive colour extraction. A clustering classifier is trained to use feature vectors extracted by the net. Separately, pixel clustering is useful when extracting the colour. Cluster count can vary according to classification. In another manner, heuristics (with or without classification) are useful when extracting. Resultant clusters are evaluated against a set of (ordered) expected colours to determine a match. Instances of standardized swatch images may be defined from a template swatch image and respective extracted colours using image processing. The extracted colour may be presented in an augmented reality GUI such as a virtual try-on application and applied to a user image such as a selfie using image processing.

Abstract: Provided are systems and methods to perform colour extraction from swatch images and to define new images using extracted colours. Source images may be classified using a deep learning net (e.g. a CNN) to indicate colour representation strength and drive colour extraction. A clustering classifier is trained to use feature vectors extracted by the net. Separately, pixel clustering is useful when extracting the colour. Cluster count can vary according to classification. In another manner, heuristics (with or without classification) are useful when extracting. Resultant clusters are evaluated against a set of (ordered) expected colours to determine a match. Instances of standardized swatch images may be defined from a template swatch image and respective extracted colours using image processing. The extracted colour may be presented in an augmented reality GUI such as a virtual try-on application and applied to a user image such as a selfie using image processing.

Abstract: Systems, methods and techniques provide for acne localization, counting and visualization. An image is processed using a trained model to identify objects. The model may be a deep learning (e.g. convolutional neural) network configured for object classification with a detection focus on small objects. The image may be a frontal or profile facial image, processed end to end. The model identifies and localizes different types of acne. Instances are counted and visualized such as by annotating the source image. An example annotation is an overlay identifying a type and location of each instance. Counts by acne type assist with scoring. A product and/or service may be recommended in response to the identification of the acne (e.g. the type, localization, counting and/or a score).

Abstract: There is described a deep learning supervised regression based model including methods and systems for facial attribute prediction and use thereof. An example of use is an augmented and/or virtual reality interface to provide a modified image responsive to facial attribute predictions determined from the image.

Abstract: Provided are systems and methods to perform colour extraction from swatch images and to define new images using extracted colours. Source images may be classified using a deep learning net (e.g. a CNN) to indicate colour representation strength and drive colour extraction. A clustering classifier is trained to use feature vectors extracted by the net. Separately, pixel clustering is useful when extracting the colour. Cluster count can vary according to classification. In another manner, heuristics (with or without classification) are useful when extracting. Resultant clusters are evaluated against a set of (ordered) expected colours to determine a match. Instances of standardized swatch images may be defined from a template swatch image and respective extracted colours using image processing. The extracted colour may be presented in an augmented reality GUI such as a virtual try-on application and applied to a user image such as a selfie using image processing.

Abstract: A system and a method for time series forecasting. The method includes: providing input feature vectors corresponding to a plurality of future time steps; performing bi-directional long-short term memory network (BiLSTM) on the input feature vectors to obtain hidden outputs corresponding to the plurality of future time steps; for each future time step: performing temporal convolution on the hidden outputs using a plurality of temporal scales to obtain context features at the plurality of temporal scales, and summating the context features at the plurality of temporal scales using a plurality of weights to obtain multi-scale context features; and converting the multi-scale context features to obtain the time series forecasting corresponding to the future time steps.