17results about How to "Improve initial performance" patented technology

The invention discloses a robot servo parameter self-adaption method and system based on gravity compensation. The method comprises the steps that before the mechanical arm is installed, instruction debugging is conducted on each joint under different loads, the rotating speed of the joints and the rotating speed of a motor are obtained, control parameters are adjusted and calibrated, and finally the calibration result is stored as joint feature information; modeling a mechanical structure, and calculating dynamic parameters of the mass, the mass center and the motor deflection angle of an arm lever; in the operation process, the load is monitored, and a gravity compensation value is calculated in combination with attitude information; uniformly distributing the compensation value to a control period through a feedforward data channel, and applying the compensation value to a motor and a load as a compensation amount of a torque ring instruction; and the servo system dynamically adjusts the control parameter according to the gravity compensation value and the setting parameter. By implementing the method provided by the embodiment of the invention, the defects in parameter setting and dynamic adaptability of the existing technical scheme can be overcome, and the robustness and performance of the servo system in a variable environment can be improved.

The embodiment of the invention relates to the technical field of batteries, and provides a preparation method of a lithium ion battery, which comprises the following steps: winding or laminating a positive plate, a negative plate and a diaphragm, and putting into a shell to form an initial battery cell; in the step of the first liquid injection process, a first electrolyte is injected into the initial battery cell, and the first electrolyte comprises the following components: 0.5 wt%-2wt% of vinylene carbonate, 5wt%-15wt% of lithium salt and a non-aqueous organic solvent; performing a formation process on the initial battery cell; in the step of the second liquid injection process, a second electrolyte is injected into the initial battery cell, and the second electrolyte comprises the following components: 5wt%-20wt% of vinylene carbonate, 5wt%-15wt% of lithium salt, 0.005 wt%-30wt% of an infiltration stabilizer and a non-aqueous organic solvent; wherein the first electrolyte or the second electrolyte further comprises at least one of 1, 3-propane sultone or fluoroethylene carbonate, so that the lithium ion battery is at least favorably kept to have high energy efficiency and long cycle life under the condition of low liquid injection amount.

The invention relates to a hyperspectral image blind super-resolution reconstruction method based on meta-transfer learning, which effectively transfers spatial knowledge of a natural image domain to a hyperspectral image domain by utilizing a pre-training model of a large number of natural images, and improves the initial performance of the model. Besides, a meta-transfer learning strategy is adopted, so that the model can quickly adapt to different degradation forms, and the robustness and generalization ability in practical application are enhanced. Meanwhile, through the knowledge edge distillation compression model, the parameter quantity and the calculation overhead of the network are reduced, and the efficiency of the model is improved. And the expression capability of the model on image details is enhanced through the transmission of the multi-stage attention feature map. And finally, the model is finely adjusted by using the block recursion characteristic, so that the precision of high-resolution reconstruction is further improved, and the edges and details of the generated image are clearer.

The embodiment of the present application relates to the field of battery technology and provides a method for preparing a lithium-ion battery, comprising: winding or laminating a positive electrode sheet, a negative electrode sheet and a separator and then placing them into a shell to form an initial battery cell; in a first liquid injection process, injecting a first electrolyte into the initial battery cell, the components of the first electrolyte including: 0.5wt%~2wt% of vinylene carbonate, 5wt%~15wt% of lithium salt and a non-aqueous organic solvent; performing a formation process on the initial battery cell; in a second liquid injection process, injecting a second electrolyte into the initial battery cell, the components of the second electrolyte including: 5wt%~20wt% of vinylene carbonate, 5wt%~15wt% of lithium salt, 0.005wt%~30wt% of a wetting stabilizer and a non-aqueous organic solvent; wherein, the first electrolyte or the second electrolyte further includes: at least one of 1,3-propane sultone or fluoroethylene carbonate, which is at least conducive to maintaining the lithium-ion battery with high energy efficiency and long cycle life under the condition of low liquid injection amount.

The application relates to a kind of distributed unmanned aerial vehicle ad hoc network routing methods based on deep reinforcement learning, comprising;According to Markov decision process, the deep reinforcement learning architecture of unmanned aerial vehicle communication network is built;Running Dijkstra algorithm sends original data packet from source node to destination node and generates original training data according to the routing process of original data packet to pre-train the deep reinforcement learning architecture;The coordinates of the destination node D of target data packet are input, the next hop node B of current node A is obtained using the pre-trained deep reinforcement learning architecture, and target training data is generated, and the deep reinforcement learning architecture is retrained according to the original training data and target training data;Next hop node B is used as starting node, until the next hop node is the destination node, the routing of target data packet is completed, and the application can enhance the robustness of network and improve the life of unmanned aerial vehicle communication network.

The invention discloses a block copolymer compatibilizer containing a semiconductor polymer and an elastomer fragment, and preparation, separation and application thereof. The structural formula of the compatilizer is as shown in formula 1. The preparation method comprises the following steps: synthesizing a semiconductor conjugated polymer by utilizing a polycondensation reaction mechanism, carrying out end capping on the semiconductor conjugated polymer through a metal coupling reaction (Stille reaction or Suzuki reaction), and polymerizing with an elastomer flexible segment to prepare a segmented copolymer compatilizer containing semiconductor and elastomer segments at the same time. On the basis of a'compatibility 'strategy, organic unification of electrical properties and mechanical properties of the flexible stretchable electronic material can be realized, and the prepared block copolymer'compatilizer' can effectively link a conjugated polymer and an elastomer polymer through a similar intermiscible principle to prepare a composite active layer with efficient charge transport and excellent tensile properties. And a flexible and stretchable field effect transistor is constructed. # imgabs0 #

The invention relates to the technical field of software radio transmission, and discloses a USRP modulation signal identification method based on migration deep learning, a wireless signal is transmitted through a transmitting end, after a USRP receiving end receives the signal, the signal is received and stored through a signal acquisition program, and the USRP modulation signal identification method based on migration deep learning is realized. Providing a required signal training set and a signal test set for a CNN model for modulation signal identification, wherein the signal training set comprises a pre-training data set and a transfer learning training data set; pre-training a convolutional neural network model for modulation signal identification through the generated pre-training data set; pre-training a convolutional neural network model for modulation signal identification through the generated transfer learning training data set; deploying the trained transfer learning convolutional neural network model to a signal test set, and verifying a modulation signal identification result; according to the invention, the modulation signal identification problem in wireless communication is solved.

The present invention discloses a polysulfone-ZrO2 composite diaphragm, a preparation method and an application thereof. By adding one or more lamellar hydrophilic inorganic substances, the granular ZrO2 is interpenetrated to achieve efficient dispersion, inhibiting large-area agglomeration of nano-sized ZrO2 particles, making their distribution uniform, thereby improving the mechanical properties of the diaphragm without sacrificing hydrophilicity. The method is simple and efficient to operate. The prepared polysulfone-ZrO2 composite diaphragm has good dispersibility, relatively uniform pore size and distribution, excellent mechanical properties, low membrane surface resistance and strong gas barrier properties, and has good application prospects in alkaline water electrolysis devices.

This invention proposes a method for the coordinated control of argon flow rate and micro-positive pressure in an electroslag remelting furnace, comprising: acquiring oxygen content and micro-positive pressure signals within the furnace; predicting future furnace conditions using a deep belief network model; offline optimizing the initial parameters of a fuzzy PID controller using a particle swarm optimization algorithm; online adaptively adjusting the controller parameters based on real-time signals and prediction results; and finally, outputting coordinated control commands for argon flow rate and dust collector valve opening. This invention achieves proactive control by predicting future furnace conditions using a deep belief network model, overcoming the problem of large system lag; offline parameter optimization of the fuzzy PID controller using a particle swarm optimization algorithm provides it with excellent initial performance adaptable to different operating conditions; adaptive adjustment of controller parameters through online fuzzy inference enables the system to be robust to dynamic processes; and finally, precise and stable coordinated control of oxygen content and micro-positive pressure within the furnace is achieved by calculating the coordinated argon flow rate and dust collector valve opening commands.

This invention proposes a water level detection system and method based on a pre-trained model, comprising: an image acquisition unit, a data preprocessing unit, a pre-trained model storage and retrieval unit, a data comparison and local data generation unit, a manual annotation and screening unit, and a fine-tuning training and model update unit. Through a two-stage model optimization process of "pre-training and local fine-tuning," combined with intelligent deviation analysis and a closed-loop iteration mechanism, it overcomes the pain points of traditional water level detection such as low efficiency, poor model generalization, and uncontrollable data quality. It achieves a high-precision, highly adaptable, and low-maintenance water level monitoring solution with significant industry application value.

This invention discloses a highly bioavailable ticagrelor solid dispersion, its formulation, preparation method, and application. The solid dispersion is prepared by hot melt extrusion of ticagrelor and a specific ternary carrier system, wherein the ternary carrier system is from Soluplus. ® The formulation consists of copovidone and hydroxypropyl methylcellulose. This solid dispersion is further combined with disintegrants, fillers, and lubricants to form a pharmaceutical composition. This invention utilizes the synergistic effect of a ternary carrier and hot-melt extrusion technology to ensure ticagrelor exists stably in an amorphous form, significantly improving drug dissolution and oral bioavailability. Experiments show that the formulation exhibits a dissolution rate ≥85% in pH 6.8 phosphate buffer after 30 min, and a relative bioavailability in beagle dogs reaching 150%–250% of commercially available tablets. It also demonstrates excellent stability, with no crystallization observed in a 6-month accelerated assay, indicating significant clinical value and market potential.

The invention belongs to the technical field of electric meter measurement detection, and particularly relates to an electric energy meter aging process temperature and measurement accuracy anomaly detection method and system, and the method comprises the steps: obtaining source field data and target field data; performing field adaptive pre-training on the feature extractor and the error regression device according to a self-defined global loss function to obtain a pre-training model; generating a personalized fine tuning factor for the target ammeter according to the complexity, reflected by the target domain data, of the environment where the target ammeter is located; and on the basis of the personalized fine tuning factor, performing online fine tuning on the pre-training model deployed on the target ammeter to obtain a real error prediction value of the ammeter to be detected, and judging whether the aging process temperature or the metering accuracy of the ammeter is abnormal or not according to the real error prediction value. According to the invention, domain deviation can be eliminated from the pre-training stage, and adaptive online fine tuning is realized, so that the accuracy of metering error correction in the full life cycle of the electric meter is improved.

The application belongs to the technical field of power load prediction and the field of network flow prediction, discloses a power flow prediction method based on neural network and boosting, combines a GRU-Xgboost module, a residual adjustment module and a missing value reconstruction, carries out decomposition reconstruction and adjustment on a complex network flow time sequence, proposes a new network flow time sequence prediction model, considers time correlation of time sequence data, and improves prediction accuracy.For the difference of the power network, according to the service characteristics of the power, the characteristic factors influencing the flow model and the effective processing of the nonlinear complex characteristics and unknown noise of the flow sequence are found, so that the flow prediction model for the power network is established, and the prediction information is provided for the relevant departments to make decisions.

The application relates to the field of magnet materials, and particularly discloses a neodymium-iron-boron material and a preparation method and application thereof. The preparation method of the neodymium-iron-boron material comprises the following steps: S1, respectively melting and pouring main phase material and auxiliary phase material to obtain main phase rapid solidification tablets and auxiliary phase rapid solidification tablets; S2, respectively performing hydrogen breaking treatment on the main phase rapid solidification tablets and the auxiliary phase rapid solidification tablets to obtain main phase coarse powder and auxiliary phase coarse powder, and respectively performing airflow grinding to obtain main phase fine powder and auxiliary phase fine powder; S3, performing pressing on a mixture containing the main phase fine powder and the auxiliary phase fine powder to obtain a blank, and performing sintering to obtain a neodymium-iron-boron material matrix; and S4, performing diffusion treatment on the neodymium-iron-boron material matrix by using a diffusion source; the obtained neodymium-iron-boron material has excellent remanence and coercive force, and good stability of magnetic performance.

The invention discloses a bonding type UV viscosity-reducing adhesive tape as well as a preparation method and application thereof in wafer cutting, and the preparation method specifically comprises the following steps: (1) adding a thermal initiator into a photoinitiator monomer and an acrylate monomer, and carrying out free radical copolymerization reaction to obtain a matrix adhesive solution; (2) adding a photoactive monomer and an active tin reagent into the matrix glue solution, and carrying out grafting reaction to obtain a UV peelable glue solution; (3) coating the UV peelable glue solution on the surface of a base material layer to form a UV peelable glue layer; and (4) covering a release film on the surface of the UV peelable adhesive layer to obtain the bonding type UV viscosity-reducing adhesive tape. The bonding type UV viscosity-reducing adhesive tape prepared by the invention has the following characteristics: the initial peel strength is high, and a protected material can be firmly fixed; after UV irradiation, the peeling strength is obviously reduced, and the material is easy to peel off from the surface of the material; and the whole process is simple, and has the remarkable advantages of no pollution, no adhesive residue and the like.

The present application relates to the technical field of automatic driving, in particular to a depth map completion method based on double-branch gradual fusion, comprising: processing color images and laser radar sparse depth images of the same target scene through a double-branch depth completion network based on mixed attention fusion and gradual refinement; training the double-branch depth completion network based on mixed attention fusion and gradual refinement using a training set in a depth completion dataset, obtaining network weight parameters after each round of training, and verifying the network weight parameters through a verification set, selecting the network weight parameters with the smallest error as the optimal network weight parameters; and performing depth completion on the double-branch depth completion network using a test set and the optimal network weight parameters to obtain a target dense depth image. The present application optimizes feature processing by extracting, fusing and refining the features of sparse depth images and color images, and finally obtains a dense depth image with rich details and accurate depth values.

The application discloses a method for preparing SnO2 thin film in iodine vapor environment and application thereof, and belongs to the technical field of battery device preparation. The preparation method comprises the following steps: (1) dissolving SnCl2 in isopropyl alcohol and stirring in air to obtain a SnO2 precursor solution; (2) performing ultraviolet ozone treatment on a substrate, and rotating and coating the SnCl2 precursor solution on the obtained substrate; after the spin coating, the substrate is placed in a closed iodine vapor environment for standing, and heating annealing treatment is performed to obtain the SnO2 thin film. On the basis of the solution preparation method, the SnO2 thin film is innovatively obtained by using iodine vapor oxidation and low-temperature heating, the method for preparing the SnO2 thin film is simple, and the preparation cost is relatively low; the prepared perovskite thin film will not be decomposed, the service life of the device can be prolonged on the basis that the efficiency of the perovskite battery device is kept close to the initial performance of the control group.