Abstract: Provided are a method for tracking carbon flow of a power system, a device and a medium. The method includes: acquiring related data of carbon flow tracking, where the related data include an injected active power sum column matrix of generator sets of all substations to be tracked, an injected carbon flow rate sum column matrix of generator sets of all substations to be tracked, an output power distribution matrix of a branch formed between substations to be tracked, and a power flow proportion distribution matrix of the branch formed between substations to be tracked; acquiring a carbon potential matrix EN of each substation to be tracked; and acquiring a carbon flow rate of each substation to be tracked, a carbon flow rate of the branch, and a carbon flow rate of an output load on the basis of the carbon potential matrix of each substation to be tracked.

Abstract: This disclosure describes one or more implementations of an alpha matting system that utilizes a deep learning model to generate alpha mattes for digital images utilizing an alpha-range classifier function. More specifically, in various implementations, the alpha matting system builds and utilizes an object mask neural network having a decoder that includes an alpha-range classifier to determine classification probabilities for pixels of a digital image with respect to multiple alpha-range classifications. In addition, the alpha matting system can utilize a refinement model to generate the alpha matte from the pixel classification probabilities with respect to the multiple alpha-range classifications.

Abstract: The present invention provides a radar target detection method based on estimation before detection (EBD), which comprises: obtaining pre-detect targets (PDTs) based on conventional pulse-Doppler processing and pre-detection; estimating ranges and speeds of PDTs, i.e., performing parameter EBD; establishing a dimension-reduction observation model of a received signal based on PDTs and parameter thereof; reconstructing a target vector in the dimension-reduction observation model based on a sparse recovery algorithm; and designing a generalized likelihood ratio detector based on the reconstruction result for target detection. The method of the present invention can significantly reduce the radar signal processing loss, and the target detector used in the method has the constant false alarm rate (CFAR) property, so that the weak target detection performance can be greatly improved.

Abstract: An integrated base station antenna comprises a passive antenna that includes a front radome, a matching dielectric layer and a rear radome; and an active antenna mounted on the back of the passive antenna. A distance between the rear radome of the passive antenna and the matching dielectric layer is a first distance, and the distance between the active antenna and the rear radome of the passive antenna is a second distance, where the first distance is selected as 0.25+n/2 times the equivalent wavelength, where n is a positive integer, and the second distance is selected as 0.25+N/2 times the equivalent wavelength, where N is a natural number. The equivalent wavelength is within the range of 0.8 to 1.2 times of the wavelength corresponding to a center frequency of an operating frequency band of the radiating elements in the active antenna.

Abstract: The present invention belongs to animal experiments field, disclosed a single factor modeling-constraint method and device for mice chronic fatigue syndrome, the modeling-constraint method is specifically using mineral water bottle to restraint mice; the mineral water bottle is divided into upper, middle and lower parts; the body of mineral water bottle is uniformly drilled for mice to breathe; the inner of the mineral water bottle is blocked by a cardboard to reduce the move space of mice; cover the mice through the divided bottle; one end of the opening is tightly attach to the wall, the other end is tightly attach to the mice feeding box, and keeping 30 minutes to restraint. The present invention utilizes mineral water bottle to make three simple restraint tube, not only saves the costs of experiment, but also turns waste into treasure, which is a simple operation, and increases the possibility of the experiment.

Abstract: Automatic partitioning of a machine learning model may be performed for training across multiple processing devices. A training job for a machine learning model may specify a number of partitions for a machine learning model. An optimization parameter may be determined for the machine learning model. Different partitions of the machine learning model to be trained across multiple processing devices may be determined based on the specified number of partitions and the optimization parameter. A schedule for executing the training job may be generated according to the respective partitions of the machine learning model. The training job may be executed according to the schedule.

Abstract: The present invention belongs to animal experiments field, disclosed a single factor modeling-constraint method and device for mice chronic fatigue syndrome, the modeling-constraint method is specifically using mineral water bottle to restraint mice; the mineral water bottle is divided into upper, middle and lower parts; the body of mineral water bottle is uniformly drilled for mice to breathe; the inner of the mineral water bottle is blocked by a cardboard to reduce the move space of mice; cover the mice through the divided bottle; one end of the opening is tightly attach to the wall, the other end is tightly attach to the mice feeding box, and keeping 30 minutes to restraint. The present invention utilizes mineral water bottle to make three simple restraint tube, not only saves the costs of experiment, but also turns waste into treasure, which is a simple operation, and increases the possibility of the experiment.

Abstract: This data classification device comprises an acquisition unit, a data classification unit, an output unit, and an input unit. The acquisition unit acquires data to be classified as input data. The data classification unit uses a classification model to estimate the classification of the input data. The output unit outputs display data displaying: an image divided into a plurality of groups based on a group division standard based on the confidence level when the classification model estimates the classification of the input data; and an image for changing the group division standard. The input unit acquires the data for changing the group division standard as an input result. Moreover, the output unit outputs display data in which the classified data is divided into the plurality of groups based on the group division standard indicated by the input result.

Abstract: In a prediction device, an acquisition means acquires input data. A prediction means performs prediction based on elements included in the input data using a prediction model and generates a prediction result. A display control means generates a first display screen indicating the prediction result, based on the input data and the prediction result. Here, the first display screen includes a graph showing the prediction result generated for each unit time period over a predetermined time period, and a value of each element of the input data acquired for each unit time period. Also, the first display screen displays the value of each element of the input data in such a manner that an element used for the prediction and an element not used for the prediction are distinguished from each other.

Abstract: The present invention discloses a distributed optimization method of regional integrated energy considering different building heating modes, comprising: based on a heating resistance and heat capacity network model, building an RIEDHS optimal scheduling model considering different building heating modes; by a coordination operator, initializing a Lagrange multiplier and global variable information and sending related information to an electricity sub-network and a heating sub-network which perform internal local optimization according to respective sub-problems and return coupling variable information to the coordination operator; and by the coordination operator, receiving the coupling variable information from the electricity sub-network and the heating sub-network, judging whether a convergence condition is met according to the coupling variable information and global variable information: ending the process if so, otherwise updating the Lagrange multiplier and a global variable, and re-executing the loca

Abstract: A robot system for oral cavity and tooth treatment. The system includes a console, a seat device, a robot and a navigation device. The console includes a display device, an operating device and an emergency stop device. The seat device includes a body support portion, an orientation adjustment portion and a head fixing portion. The robot comprises a main controller, a robotic arm, a manipulator. The navigation device performs navigation inside a tooth in a root canal treatment operation according to pre-stored tooth information. The treatment instrument in the manipulator can be replaceable. The seat device can be adjusted such that a patient can face any direction between forward and downward, or lie prostrate.

Abstract: A robot system for oral cavity and tooth treatment. The system includes a console, a seat device, a robot and a navigation device. The console includes a display device, an operating device and an emergency stop device. The seat device includes a body support portion, an orientation adjustment portion and a head fixing portion. The robot comprises a main controller, a robotic arm, a manipulator. The navigation device performs navigation inside a tooth in a root canal treatment operation according to pre-stored tooth information. The treatment instrument in the manipulator can be replaceable. The seat device can be adjusted such that a patient can face any direction between forward and downward, or lie prostrate.