Abstract: Disclosed a method for predicting permeability of a multi-mineral phase digital core based on deep learning. In the present disclosure, a three-dimensional digital core is constructed and a pore structure is randomly generated; after a plurality of multi-mineral digital core images is acquired by performing image segmentation on the three-dimensional digital core, permeability corresponding to each of the multi-mineral digital core images is acquired by simulation using multi-physics field simulation software and a multi-mineral digital core data set is constructed based on the plurality of multi-mineral digital core images and the permeability corresponding to each of the multi-mineral digital core images; an SE-ResNet18 convolutional neural network is constructed and trained with the multi-mineral digital core data set; and an image of a multi-mineral core to be predicted is input into the trained SE-ResNet18 convolutional neural network to determine the permeability of the multi-mineral core.

Abstract: A data processing method is provided. In the method, a historical walk vertex adjacent to a target walk vertex is determined. An edge transition probability between the target walk vertex and each of a set of next possible vertexes in a first out-neighbor set is determined according to first out-edge information. A to-be-reached vertex of the set of next possible vertexes in the first out-neighbor set is determined according to the edge transition probabilities. Second out-edge information corresponding to the target walk vertex is generated based on the first out-neighbor set. Walking from the target walk vertex to the to-be-reached vertex is performed. The second out-edge information is transmitted to the to-be-reached vertex. Further, a random walk sequence corresponding to the target walk vertex is generated based on a walk quantity corresponding to the target walk vertex reaching a preset threshold for walk steps.

Abstract: A data processing method is provided. In the method, a historical walk vertex adjacent to a target walk vertex is determined. An edge transition probability between the target walk vertex and each of a set of next possible vertexes in a first out-neighbor set is determined according to first out-edge information. A to-be-reached vertex of the set of next possible vertexes in the first out-neighbor set is determined according to the edge transition probabilities. Second out-edge information corresponding to the target walk vertex is generated based on the first out-neighbor set. Walking from the target walk vertex to the to-be-reached vertex is performed. The second out-edge information is transmitted to the to-be-reached vertex. Further, a random walk sequence corresponding to the target walk vertex is generated based on a walk quantity corresponding to the target walk vertex reaching a preset threshold for walk steps.

Abstract: Embodiments of this disclosure include a data processing method. In the method, a training sample set that includes a plurality of graph computing task training samples is obtained. At least one performance indicator feature of each of the graph computing task training samples is extracted. The at least one performance indicator feature includes one or more of a graph data feature, a graph processing platform feature, a graph algorithm feature, and a machine hardware feature. A target performance prediction model is generated based on a mapping relationship between actual execution times of the graph computing task training samples and the performance indicator features. According to at least one performance indicator feature of an inputted graph computing task test sample, a predicted execution time of the graph computing task test sample is output based on the target performance prediction model.

Abstract: An apparatus for driving a light emitting diode (LED). A rising edge of a pulse width modulation (PWM) signal is first sensed. Upon sensing the rising edge, a threshold pulse (TP) signal is initiated that has a configured width started when the rising edge is sensed, an LED current with an amplitude at a previously set level is generated, and starting to charge a capacitor which yields a voltage Vcap. Subsequently, a falling edge of either the PWM signal or the TP signal is detected. Upon detecting the failing edge, the circuit stops charging the capacitor, samples, after a first delay from the detected falling edge, the voltage Vcap, and adjusts a level of the amplitude of the LED current based on the sampled voltage Vcap. When the falling edges of both the PWM and TP signal are detected, the LED current is terminated.

Abstract: An apparatus for driving a light emitting diode (LED). A rising edge of a pulse width modulation (PWM) signal is first sensed. Upon sensing the rising edge, a threshold pulse (TP) signal is initiated that has a configured width started when the rising edge is sensed, an LED current with an amplitude at a previously set level is generated, and starting to charge a capacitor which yields a voltage Vcap. Subsequently, a falling edge of either the PWM signal or the TP signal is detected. Upon detecting the failing edge, the circuit stops charging the capacitor, samples, after a first delay from the detected falling edge, the voltage Vcap, and adjusts a level of the amplitude of the LED current based on the sampled voltage Vcap. When the falling edges of both the PWM and TP signal are detected, the LED current is terminated.