Abstract: A method and apparatus for optimizing operation simulation of a data center are disclosed, and the method includes: constructing a data center simulation model; acquiring a state data set and an action data set of the data center; inputting the state data set and the action data set into a first state prediction model to obtain a next state data set; judging whether the next state data set meets a state safe judgment condition; if so, inputting the state data set and the action data set into a second state prediction model to obtain a next state data set; optimizing network parameters of a reinforcement learning algorithm; and determining an action data set corresponding to a real-time state data set of the data center by using the trained reinforcement learning algorithm.

Abstract: A high-resolution phase detection method and system based on a plane grating laser interferometer. The method uses a dual-frequency interferometer to measure the displacement, and the measurement signal processing comprises an integral part and a decimal portion, a phase equation set of a displacement measurement signal is constructed according to a measurement optical path principle of a heterodyne plane grating laser interferometer; a non-linear equation set for which the unknowns are instantaneous phase, interval phase and signal amplitude is established; and the equation sets above are solved by using the least squares method, so as to realize phase discrimination, thereby realizing precise displacement measurement. The method can solve the problems in the traditional time measurement-based phase detection technology, such as low measurement accuracy, and failing to satisfy small measuring range measurement.

Abstract: A data-driven wind farm frequency control method based on dynamic mode decomposition. The method enables a low-dimension nonlinear dynamic feature of a wind power system to perform global capturing in a high-dimension space through a state transition matrix given by a Koopman operator theory, thus fewer data samples are necessary while control requirements are satisfied with respect to a model fitting accuracy. Meanwhile, a pure linear feature of a control model also provides a favorable foundation for fast on-line dynamic response, thereby satisfying response accuracy and speed requirements simultaneously in an actual control step.

Abstract: A multi-stage micro-decomposition swirl burner with an ammonia-doped fuel, and a low NOx control method are provided. A natural gas central pipe, a primary air pipe and a burner housing are nested from inside to outside in sequence to form a primary air duct and an over-fire air channel which are isolated from each other. Natural gas is ejected from the natural gas central pipe, and swirl vanes are arranged in the primary air duct, such that the primary air can be mixed with the central natural gas in a swirl state, and then a diffusion flame is formed under the action of an ignition device. Therefore, a pilot flame with sustainable combustion is formed at the center of an injection end of the burner.

Abstract: The present application provides a day-ahead scheduling method and apparatus for a power system, an electronic device, and a storage medium, where the method includes: obtaining power system prediction parameters in a to-be-scheduled time period, where the power system prediction parameters include thermal power unit parameters, renewable energy station parameters, load parameters, energy storage station (ESS) parameters, node parameters, a transmission line parameter and other power gird parameters; constructing a day-ahead scheduling optimization model and generating multiple renewable energy power scenarios based on the power system prediction parameters; solving the day-ahead scheduling optimization model to obtain day-ahead scheduling results; verifying the day-ahead scheduling results based on the power system prediction parameters and the multiple renewable energy power scenarios; and in case that the verifying the day-ahead scheduling results successes, outputting the day-ahead scheduling results.

Abstract: The present disclosure discloses an organic electroluminescent device and a preparation method thereof. The device includes a light-emitting layer, the light-emitting layer includes a host material, an auxiliary host material and a fluorescent dye; the host material is an exciplex prepared by mixing an electron donor material and an electron acceptor material, the auxiliary host material is a thermally activated delayed fluorescence material, a singlet energy level and a triplet energy level of the exciplex are higher than the single energy level and triplet energy level of the auxiliary host material. The above organic electroluminescent device can promote the reverse intersystem crossing of the host material and the auxiliary host material from the triplet excitons to the singlet excitons, enhance the Foster energy transfer, reduce the triplet exciton quenching, therefore the efficiency roll-off of the device is small and the external quantum efficiency is high.

Abstract: This application discloses a planar inverted F antenna pair and an electronic device, and relates to the field of antenna technologies. The planar inverted F antenna pair includes a dielectric substrate, a ground metal plane, and a radiation unit, where the ground metal plane is arranged on a side of the dielectric substrate, two ends of the radiation unit are respectively connected to a first feed portion and a second feed portion, the radiation unit is connected to the ground metal plane through a ground metal sheet, the ground metal sheet is located between the first feed portion and the second feed portion, distances from the first feed portion and the second feed portion to the ground metal sheet are not equal, the ground metal plane is provided with a slot, and two ends of the slot are located on two sides of the ground metal sheet.

Abstract: A data denoising method and a related device are provided. According to the method, an artificial intelligence technology may be used to perform denoising on data, and any target denoising operation in at least one denoising operation performed on noisy data includes: generating, based on first prediction information and second prediction information, distribution information corresponding to the target denoising operation, where the first prediction information indicates predicted noise between second noisy data and clean data, the second prediction information indicates a square of the predicted noise between the second noisy data and the clean data or indicates a square of a predicted distance between the first prediction information and actual noise, and the actual noise includes actual noise between the second noisy data and the clean data; and sampling denoised data in distribution space to which the distribution information points.

Abstract: A computing apparatus and a robustness processing method thereof. The robustness processing method includes: based on model parameters of a target algorithm model, obtaining a mapping relationship between the model parameters and the first computing memristor array; based on an influence factor that determines a critical weight device, determining a way to obtain a weight criticality of the plurality of memristor devices from the influence factor; obtaining an input set of the algorithm model, and determining a criticality value for each of the plurality of memristor devices according to the way; determining a critical weight device among the plurality of memristor devices according to the criticality value for each of the plurality of memristor devices; and based on the critical weight device, performing an optimization processing on the first processing unit.

Abstract: The present application provides an electrolytic solution for rechargeable magnesium batteries, a method for preparing the same, and a rechargeable magnesium battery, wherein the electrolytic solution comprises an electrolyte salt and a non-aqueous solvent, the electrolyte salt comprising [MgxM2x-1Py][Mg(ORF)3Qz], in which x represents an integer between 1 and 6, y represents an integer between 1 and 6, z represents an integer between 0 and 6, M represents a monovalent anion, and each RF independently represents partially fluorinated or perfluorinated C1-C6 aliphatic hydrocarbon group, or partially fluorinated or perfluorinated C6-C12 aromatic hydrocarbon group, and P and Q represent an ligand. The electrolytic solution provided by the present application has excellent electrochemical properties, and at the same time the interfacial stability between the electrolytic solution and the negative electrode is also high.

Abstract: A method for multi-time scale reactive voltage control based on reinforcement learning in a power distribution network is provided, which relates to the field of power system operation and control. The method includes: constituting an optimization model for multi-time scale reactive voltage control in a power distribution network based on a reactive voltage control object of a slow discrete device and a reactive voltage control object of a fast continuous device in the power distribution network; constructing a hierarchical interaction training framework based on a two-layer Markov decision process based on the model; setting a slow agent for the slow discrete device and setting a fast agent for the fast continuous device; and deciding action values of the controlled devices by each agent based on measurement information inputted, so as to realize the multi-time scale reactive voltage control while the slow agent and the fast agent perform continuous online learning.

Abstract: A method for aggregating a group of EVs based on EV flexibility includes: obtaining a prediction result on a fast charging demand of each EV based on a preset model; determining a distribution area of each substation involved in an EV activity range as an aggregation area; establishing an aggregation model based on the EV fast charging demand, in which the aggregation model includes energy balance equations and various constraints; obtaining an EV fast charging load power curve and an EV energy curve of each aggregation area based on the aggregation model; determining upper and lower limits of multiple EV fast charging load power curves as a flexibility range for the EV fast charging load power, and determining upper and lower limits of multiple EV energy curves as a flexibility range for the EV energy; and aggregating a group of EVs based on the above flexibility ranges.

Abstract: This application discloses a radio frequency transceiver, and a communication device. A radio frequency amplification circuit includes two transmission paths disposed between a radio frequency input end and a radio frequency output end. The two transmission paths include a first transmission path and a second transmission path. The first transmission path includes a first amplifier, a first coil, a second coil, and a second amplifier that are sequentially coupled. The second transmission path includes a third amplifier, a third coil, a fourth coil, and a fourth amplifier that are sequentially coupled. Any two coils of the first coil, the second coil, the third coil, and the fourth coil are magnetically coupled. The first amplifier and the second amplifier is adjustable, or The third amplifier and the fourth amplifier is adjustable.

Abstract: The present application discloses an image processing method and image processing apparatus for recognizing a target object.

Abstract: Provided is a femtosecond laser system for processing a micro-hole array, comprising: a femtosecond laser, a half-wave plate, a polarizer, a concave lens, a convex lens, a diaphragm, a mechanical shutter, a phase-type spatial light modulator, a first plano-convex lens, a reflecting mirror, a second plano-convex lens, a dichroic mirror, a camera, a processing objective lens, a six-axis translation stage and a transmissive white light source.

Abstract: The present application provides a biological active peptide from seawater pearl, the sequence of which is Ile-Pro-Ser-Thr-Thr-Pro-Phe-Pro-Ser-Thr-Thr-Val-Ala-Thr-Thr-Thr-Met, and the name of which is SCOL polypeptide. The biological active peptide can improve an ACE2 enzyme activity to 3.76 times, and can be applied in preparation of a drug improving an ACE2 activity, treating hypertension, anti-heart failure, anti-tissue fibrosis, anti-inflammation, and type 2 diabetes mellitus and the complication thereof, and relieving neuropathic pain. The biological active peptide can be specifically bound to the ACE2, effectively inhibits the 65% binding between novel coronavirus S protein and the ACE2, can be applied in preparation of an anti-coronavirus infection drug as an inhibitor, and specifically applied in preparation of anti-novel coronavirus drug.

Abstract: At least one embodiment of the present disclosure provides a data processing method based on a memristor array and an electronic apparatus. The data processing method includes: acquiring a plurality of first analog signals; setting the memristor array, and writing data of a parameter matrix corresponding to the data processing into the memristor array; and inputting the plurality of first analog signals into a plurality of column signal input terminals of the set memristor array, respectively, controlling operation of the memristor array to perform the data processing on the plurality of first analog signals, and obtaining a plurality of second analog signals after performing the data processing at a plurality of row signal output terminals of the memristor array, respectively.

Abstract: Provided are a method and an apparatus for enhancing the security of a quantum key distribution network. The quantum key distribution network includes a first node, a second node, and at least one relay node, by means of which the first node and the second node implement a first stage of quantum key distribution; the first node and the second node share a first key pool, which includes at least one key; and the method is executed on either the first node or the second node. The method includes: acquiring a first key obtained after the first stage of quantum key distribution; determining the seed key from the first key pool; generating a first random string by applying, based on the seed key, a first algorithm predetermined with a correspondent node, the first random string having a length equal to that of the first key; and acquiring a second key by performing a preset first bit operation on the first key and the first random string.

Abstract: The present application relates to a service decision method and a service decision device. The method includes: receiving a task request sent by a terminal, the task request including a terminal identifier, terminal location information and task information of the terminal; determining that the terminal is currently in an overlapping coverage area, generating a decision-making instruction according to the task request and a target decision network, and sending the decision-making instruction to the terminal according to the terminal identifier, the decision-making instruction being used to indicate whether a target unmanned aerial vehicle server provides to the terminal a service corresponding to the task request, and the target decision-making instruction being used by the terminal to select one server from among the target unmanned aerial vehicle server and the other unmanned aerial vehicle servers to provide the service. Using the present method can improve resource utilization.

Abstract: A method and system for power grid optimization control based on a linear time-varying model is provided. The system model is first estimated offline using historical data combined with a piecewise linear regression method that supports a vector regression model. For online applications, a time-varying linear model prediction control framework based on a piecewise linear model coordinates the optimization control of both fast and slow regulating devices in the power grid. This approach does not require precise system model parameters, instead learning the power grid model from historical data. The method optimizes voltage distribution, reduces operating costs, and addresses issues like bad data and collinearity in the historical data, improving the voltage quality and enabling optimal operation even with incomplete models.