Abstract: An optimization method for turbines of a thermal power unit based on sparse big data mining in the technical field of turbine optimization. The optimization method comprises: acquiring historical operating data of a unit; selecting a set of controllable operating parameters highly correlated with the heat rate of turbines as optimized parameters; constructing a new sparse data-oriented pattern-growth association rule mining algorithm based on binary matrixes and the hyperlink technique; proposing a load balancing strategy based on matrix operation on an Apache Spark platform, and parallelly implementing the global calculation-balanced pattern-growth association rule mining algorithm; and discretizing the historical operating data by clustering, parallelly mining the discretized historical operating data to obtain an association rule, and performing de-discretization to obtain target values of the optimized parameters of the turbines under boundary conditions.

Abstract: A preparation method of perovskite film based on semicarbazide hydrochloride (SEM-HCl) additive and a preparation method of photovoltaic device are provided, a perovskite optimized crystallization strategy is provided, a tin-based perovskite thin film is prepared based on a solution method, and a purpose of regulating and controlling perovskite crystallization is achieved by means of a cross-linking effect of special groups in SEM-HCl and perovskite components. Defects of crystals of the prepared perovskite film are reduced, the non-radiative recombination in a transmission process of carriers is suppressed, and a photoelectric conversion efficiency and a photovoltaic device stability are remarkably improved.

Abstract: The present application discloses a task scheduling method based on an improved particle swarm optimization algorithm, which includes: obtaining task data to be scheduled, encoding particles according to the task data; iterating the particles by a particle swarm optimization algorithm; in response to that the particle swarm optimization algorithm does not fall into a local optimal solution, outputting a scheduling scheme; and in response to that the particle swarm optimization algorithm falls into the local optimal solution, outputting the scheduling scheme by fusing the particle swarm optimization algorithm with a cuckoo search algorithm. The present application introduces a cuckoo search algorithm when the particle swarm optimization algorithm falls into a local optimal solution, solving the dilemma of the particle swarm optimization algorithm falling into a local optimal solution, while improving the global search capability of the algorithm.

Abstract: The present disclosure discloses an Internet-of-Things-oriented machine learning container image download system and a method. The Internet-of-Things-oriented machine learning container image download system includes a master node and a plurality of computing nodes; the master node is configured to store and convert a machine learning model, and build a machine learning container image from the format-converted machine learning model; and issue an image download instruction to each of the computing nodes after image information of the machine learning container image is completely built; and each of the computing nodes is configured to receive the image download instruction, download the machine learning container image, and start a machine learning container; and receive data collected by Internet-of-Things devices, and return a data processing result to the Internet-of-Things devices.

Abstract: An image defogging method based on dark channel prior is provided, including: acquiring a dark channel image and a light channel image of a haze weather image according to a haze weather image; selecting pixel values in the light channel image and calculating an atmospheric light value A; obtaining a transmittance image based on the light channel image and the atmospheric light value A; obtaining a restored image of defogging based on the transmittance image and the atmospheric light value A. By using the pixel values of the light channel to distinguish whether pixel points comply with the dark channel prior, calculating the transmittance of image areas that comply with the dark channel prior and those that do not, so as to avoid the situation where the image defogging effect is not good due to inaccurate transmittance estimation, making the defogging of images containing high brightness areas more clear.

Abstract: An audio visual haptic signal reconstruction method includes first utilizing a large-scale audio-visual database stored in a central cloud to learn knowledge, and transferring same to an edge node; then combining, by means of the edge node, a received audio-visual signal with knowledge in the central cloud, and fully mining semantic correlation and consistency between modals; and finally fusing the semantic features of the obtained audio and video signals and inputting the semantic features to a haptic generation network, thereby realizing the reconstruction of the haptic signal. The method effectively solves the problems that the number of audio and video signals of a multi-modal dataset is insufficient, and semantic tags cannot be added to all the audio-visual signals in a training dataset by means of manual annotation. Also, the semantic association between heterogeneous data of different modals are better mined, and the heterogeneity gap between modals are eliminated.

Abstract: Disclosed is a multi-scale fusion defogging method based on a stacked hourglass network, including inputting a foggy image into a preset image defogging network; and outputting a fogless image after the foggy image is processed by the image defogging network. The image defogging network includes a 7×7 convolutional layer, a stacked hourglass module, a feature fusion, a multi-scale jump connection module, a 1×1 convolutional layer, a 3×3 convolutional layer, a hierarchical attention distillation module, the 3×3 convolutional layer and the 1×1 convolutional layer connected sequentially.

Abstract: A source-grid-load-storage networked collaborative frequency control method is disclosed, which comprises acquiring total active power ?P to be regulated during a secondary frequency regulation process of a power grid; performing frequency regulation by source-grid-load-storage of the power distribution system, allocating power regulation capacities, and determining whether the desired total active power is met after the frequency regulation; if the desired total active power is met, determining whether power of power generation units is out of limit; if the power of the power generation units is not out of limit, keeping active power of the power distribution system in balance to complete frequency regulation of the power grid; if the power of the power generation units is out of limit, correcting the power regulation capacities of the power generation units of the source-grid-load-storage and compensating a power difference to keep the active power of the power distribution system in balance.

Abstract: A common control channel-free communication method and system featuring integrated autonomous link establishment and frequency conversion, belonging to the technical field of wireless communication is provided. The method comprises: autonomously establishing, by a caller communication node a two-way communication link with a responder communication node through a calling and responding mechanism; and monitoring, by the caller communication node, operating frequency points through a real-time interference detection mechanism, and autonomously updating transmitted signal feature parameters of the caller communication node at certain frequency conversion slots, so that the responder communication node can update transmitted signal feature parameters in the responder communication node after receiving the updated transmitted signal feature parameters.

Abstract: A high-gain and low-RCS (radar cross section) broadband circularly polarized metasurface antenna based on a novel sequential-rotation feeding network includes three layers of dielectric substrates and five metal layers as well as three resistors, which are from top to bottom: a first metal layer, a first dielectric substrate, a second metal layer, a second dielectric substrate, a third metal layer, a fourth metal layer, a third dielectric substrate and a fifth metal layer. The first three metal layers are all metasurface arrays composed of 10*10 metal patches; the fourth metal layer and the third metal layer define a resonant cavity by means of a distance therebetween; the fourth metal layer is provided with four slits having rotational symmetry; and the fifth metal layer is a hybrid feeding network composed of microstrip lines and including three equal power dividers and three resistors.

Abstract: A method for small object detection in drone scene based on deep learning is provided, which includes: inputting images captured by a drone into a pre-trained generator based on an Unet network structure to output normal-light images; inputting the normal-light images into a object detection backbone network to output a plurality of multidimensional matrix feature maps, wherein the object detection backbone network integrates a channel attention mechanism and a spatial attention mechanism based on convolutional block Self-Block, and a 7*7 large convolutional kernel is used; inputting the plurality of multidimensional matrix feature maps into a BiFPN-S module of a feature pyramid for feature fusion, so as to output a plurality of corresponding feature maps for predicting objects of different sizes.

Abstract: A networked control method for primary frequency regulation of a new energy power station based on a source-grid-load-storage networked cloud decision control system platform comprises: determining, according to historical operating data of a new energy power station, primary frequency regulation predictive values of power generation units of the new energy power station; determining, according to the primary frequency regulation predictive values, optimal control sequences of inverters of the power generation units at different times based on a pre-established inverter active power model, wherein the optimal control sequences comprise multiple control quantities of active power of the inverters; marking the optimal control sequences at the different times with time scales, sending the optimal control sequences to executing devices of the power generation units, receiving the optimal control sequences, and determining whether to store or not store the optimal control sequences; and determining the control qua

Abstract: The disclosure provides a method for detecting and defending a Distributed Denial of Service attack in an SDN environment. The method includes: building data messages acquired as feature messages by a proxy module; sending the feature messages to a pre-built detection model to obtain a detection result; making a decision instruction based on the detection result; and performing control operations by the proxy module based on the decision instruction.

Abstract: The present disclosure discloses an image segmentation method combined with superpixel and multi-scale hierarchical feature recognition. This method is based on a convolutional neural network model taking multi-scale hierarchical features extracted from a Gaussian pyramid of an image as a recognition basis, and then being connected with a multilayer perceptron to achieve the recognition of each pixel in the image, moreover, this method is used tier performing superpixel segmentation on the image and is combined with a method for improving superpxiel in combination with LBP texture features to segment an original image so that an obtained superpixel block is more fitted to edges of targets, then, the original image is merged according to a mean value of a color, and finally, recognition of each target in the image is achieved.

Abstract: The present disclosure discloses a multi-task joint computing unloading and resource allocation method based on D2D (Device-to-Device) communication. For solving the problems that computing resources of a local terminal cannot complete all computing tasks on time when there are multiple computing tasks with delay requirements on a local terminal, the method introduces a computing unloading mechanism to reduce the delay and the overhead of the local terminal itself. The method is based on a D2D communication technology. In the scenario where mobile terminals are densely distributed, the local terminal can unload the computing tasks at the same time to several surrounding idle terminals for processing. According to the method, a total overhead objective function is established in consideration of the task delay, energy consumption, and unloading fee.

Abstract: A method for selecting a content forwarding node in a vehicle ad-hoc network on the basis of service type, comprising the following steps: calculating a bandwidth occupation proportion factor according to the type of requested content information and a bandwidth occupation situation of a neighboring vehicle which can perform forwarding in a one-hop transmission range; defining a virtual arrival time of a candidate forwarding vehicle to an initial request vehicle according to the distance between the neighboring vehicle and the initial request vehicle and a driving speed of the neighboring vehicle; calculating a forwarding node impact factor according to the bandwidth occupation proportion factor and the virtual arrival time; and selecting a forwarding node according to the forwarding node impact factor.

Abstract: A multi-timescale voltage regulation method based on source-grid-load-storage multi-terminal collaboration of a power distribution network is disclosed, which comprises: establishing, based on a Petri network, a multi-mode switching control model based on voltage security event trigger to realize effective control of a global voltage; establishing multi-objective optimization taking into account a source-storage-load regulation cost and a network transmission loss to realize collaborative and dynamic control of controllable resources of a source terminal, a load terminal and a storage terminal in each operating mode; and establishing a source-storage-load multi-terminal collaboration-based distributed voltage control model based on voltage security event trigger over a short timescale by taking into account the problems of voltage magnitude being out of limit and voltage leap, and solving online an optimal control sequence of the source terminal, the load terminal and the storage terminal in a receding horizo

Abstract: An active feedback control method for a quantum communication system based on machine learning is disclosed. In the transmission process of a quantum key distribution system, the present invention uses a pre-trained double-layer LSTM network to predict, according to a real-time ambient temperature, humidity and laser light intensity fluctuation, as well as voltage changes in the past moment, a zero-phase voltage value of a phase modulator at a receiving end at the next moment, and updates the network at a fixed time interval, so that the LSTM network can accurately predict for a long time, ensuring that the quantum key distribution system operates stably and efficiently for a long time. The present invention greatly improves the transmission efficiency of the quantum key distribution system by method of active prediction and feedback control.

Abstract: Disclosed is a joint recommendation and cache optimization method based on a collaboration of multiple base stations, each base station in a target area randomly caching contents stored to the cloud, when each base station in the target area receives a to-be-responded content request, constructing the recommendation optimization sub-model, the access optimization sub-model and the cache optimization sub-model, then constructing and solving the optimization problem to obtain the target base station, optimal cached contents and the recommendation list that correspond to the to-be-responded content request. Through the technical solution of the present application, the total time delay of the system is minimized, and by jointly optimizing the recommendation decision, access decision and cache decision, the delay can be significantly reduced with a lower cache capacity, and more efficient for users, good experience.

Abstract: A heavy-load and high-precision transmission mechanism applicable to sheet-metal bending equipment, which includes pressing arms, connecting rods, hinged supports, pressing arm lifting assemblies and pressing arm lifting driving assemblies. The pressing arms are symmetrically arranged on a top part of a rack. A front end part of each pressing arm facing an upper cross beam is hinged with the cross beam through the connecting rod. An intermediate part of each pressing arm or a rear end part of each pressing arm away from the cross beam is hinged on the rack through the hinged support. The rear end part or the intermediate part of each pressing arm is provided with a set of pressing arm lifting assemblies. Each set of pressing arm lifting assemblies is connected with a set of pressing arm lifting driving assemblies. Each set of pressing arm lifting driving assembly includes an all-electric servo motor.