42results about How to "Reduce the difficulty of optimization" patented technology

The invention discloses a weak supervision fine-grained image recognition method based on a visual self-attention mechanism. The method involves a student-model module, a teacher-model module and a classification-model module. The student-model and the teacher-model are combined through a Teacher-Student loop feedback mechanism based on Pairwise Approach sorting learning so as to form a self-attention region recommendation network, so that the relation between discriminative region positioning and fine-grained feature learning is enhanced, the discriminative region in the fine-grained image can still be accurately detected under the condition of lacking a target bounding box and a part marking point, and the recognition accuracy is promoted to be remarkably improved; meanwhile, a convolution layer is shared by the three modules, namely, the student-model, the teacher-model and the class-model, so that the model storage space is effectively compressed, the calculation cost is reduced, the method meets the real-time recognition task requirement, and the method is suitable for a large-scale real scene; and besides, a dynamic weight distribution mechanism is adopted in multi-task jointlearning to reduce the amount of artificially set hyper-parameters and enhance the robustness of the model, finally, the whole model is trained and learned in an end-to-end single-stage mode, and thenetwork optimization difficulty is reduced.

The invention discloses a voice quality optimization method comprising the following steps: counting poor-quality cells; comprehensively analyzing the cause of each poor-quality cell based on a variety of data sources; clustering the causes of the poor-quality cells; and merging the poor-quality cells applicable to similar solutions based on the cause cluster, and carrying out batch-processing for the same cause. The invention further discloses a voice quality optimization device. By adopting the technical scheme of the invention, the problem that there is no systematic analysis means for traditional poor-quality problem processing and the efficiency is low is solved, poor-quality cells with voice quality problems in a mobile communication network can be counted out, the causes of the problems can be analyzed quickly and positioned accurately, a wireless network can be optimized quickly, and the voice quality of the mobile network is improved.

The invention discloses a combined X-band five-bit phase shifter based on active and inactive structures, and mainly solves the problems of large gain error and low phase shift precision in the priorart. The phase shifter comprises filters (1), an active balun (3) and switches (4); multiple filters and switches are respectively provided and are alternately connected; the last switch is connectedwith an input end of the active balun; an output end of the active balun is connected with an orthogonal signal generator (2) and is used for generating positive and negative in-phase signals and positive and negative orthogonal signals; and an output end of the orthogonal signal generator is connected with a one-in-four circuit (5), and is used for selecting one path of signals from four paths ofsignals, namely the positive and negative in-phase signals and the positive and negative orthogonal signals to output, thereby implementing transformation of the signals among four quadrants. The phase shifter has the advantages of small gain error and high phase shift precision, and can be used in a radio-frequency integrated circuit in which a high-precision phase shifter is needed by a radio-frequency microwave phased array receiver.

The invention discloses a self-shielding gradient coil of a nuclear magnetic resonance spectrometer and a design method thereof. The invention is characterized in that, the self-shielding gradient coil comprises a main coil and a shielding coil which is coaxially arranged on the outer side of the main coil. The windings of the main coil are composed of a plurality of stages of main coil upper windings and main coil lower windings which are symmetrically arranged up and down. The centers of winding wire troughs, the circle radiuses of winding coils and turns per coil, corresponding to the maincoil upper windings and the main coil lower windings, are symmetrically arranged up and down in the one-to-one mode. The windings of the main coil are wound in the winding wire troughs and are arranged in a single-layer winding mode in the axial direction. Each stage of main coil winding corresponds to an independent driving current channel. According to the self-shielding gradient coil and the design method thereof, the series single-channel circuit design is changed into the matrix multi-channel circuit design, and the coil structure is simplified. The optimization difficulty of a target field is reduced. The probability that the actually generated gradient field is deformed and the linearity is not pure due to the winding translation and the interlayer dislocation is reduced.

The invention provides a method for optimizing a boiler based on a least square support vector machine (LSSVM) combustion model. The method comprises the following steps of: acquiring an initial training sample set; acquiring LSSVM model parameters and a first characteristic matrix; acquiring a sample closest to a check sample from the training sample set as a reference sample, and performing row and column exchange on the first characteristic matrix to obtain a second characteristic matrix; calculating the inverse of the second characteristic matrix; acquiring partitioning parameters of the second characteristic matrix; acquiring a third characteristic matrix of an LSSVM model; calculating the inverse of the third characteristic matrix; acquiring the corrected LSSVM model parameters; assigning the third characteristic matrix to the first characteristic matrix, and assigning the inverse of the third characteristic matrix to the inverse of the first characteristic matrix; acquiring the combustion efficiency of the boiler by using the corrected LSSVM model; and performing combustion optimal control on a corrected combustion model. The invention also provides a device for optimizing the boiler based on the LSSVM combustion model. By the method and the device, the optimization difficulty of the combustion of the boiler in a power station can be reduced, and the real-time performance of optimization is improved.

The invention provides a cell apoptosis algorithm-based dynamic recovery system and method of a power distribution network. The cell apoptosis algorithm-based dynamic recovery system comprises an information acquisition agent module, a load agent module, a repair team agent module, a data processing agent module, a restorability estimation agent module, a candidate restoring tree agent module, a synergic agent module and a control center agent module. According to the cell physiological property, the network reconstitution and repair problem is decomposed to a plurality of continuous steps, the optimization difficulty of each step is reduced, and the problem of optimization of a traditional method for many times to easily cause dimension difficulty is solved; according to a restorability estimation value, a recovery mode of a candidate restoring tree at each time frame is determined, a recovery target which is the more important to the stage is determined according to each mode, and the problems of optimizing complexity of the recovery process and long recovery time caused by multi-target optimizing are simplified during the recovery process; and fault recovery and repair are coordinated to consider a recovery scheme, the recovery efficiency is improved, the economic loss is reduced, and the cell apoptosis algorithm-based dynamic recovery system is applicable to the problem oflarge-area power failure of a multi-fault large-power grid system.

The invention discloses a tramcar timing energy-saving operation optimization method and a tramcar timing energy-saving operation optimization system based on a double-layer algorithm. The tramcar timing energy-saving operation optimization method comprises the following steps that a tramcar kinematics model, a vehicle-mounted super capacitor bank model and an energy consumption calculation modelare established; a speed curve is combined and generated according to the speed command given value; tramcar single-interval operation optimization is carried out based on a multi-objective genetic algorithm, and a Pareto leading edge curve about time-energy consumption is generated; whole-line timing energy-saving operation optimization of the tramcar is carried out based on a dynamic programmingalgorithm, and the total operation energy consumption is reduced on the basis that the total operation time of the whole line is not changed. According to the method, the running energy consumption of the train section can be effectively reduced, the optimal configuration of the train running timetable can be realized, and the train running cost is reduced.

The invention discloses an aircraft trajectory control method fused with a Gaussian process. The aircraft trajectory control method comprises the following three parts: three-dimensional data collection, Gaussian process modeling and model prediction control. According to the method, model predictive control is used as a master control algorithm, meanwhile, a Gaussian process is fused to predict the process of the next moment, the model predictive control fused with the Gaussian process is combined with the motion speed track of the four-rotor aircraft, and the method has certain high efficiency, accuracy and real-time performance. And the trajectory tracking error of the aircraft can be effectively reduced. According to the aircraft trajectory model predictive control method, the optimization difficulty in the model predictive control process is reduced by sampling the data set in the training sample, and the stability of model predictive control is improved.

The invention provides ahorizon automatic interpretation method based on minimum seismic waveform unit classification. The horizon automatic interpretation method includes the steps that 1, seismic data of a target interval are subjected to continuous enhancement pretreatment; 2, coarse-mesh horizon interpretation is carried out on target horizon; 3, a preliminary interpretation result obtained inthe step 2 is used as a control point of horizon time, and top and bottom time windows containing a plurality of complete seismic waveforms are obtained by shifting upwards and downwards; 4, seismicdata in the time windows are divided into minimum waveform units; 5, characteristic values of the minimum seismic waveform units are extracted; 6, a minimum seismic waveform unit classification modelof a random forest is constructed; and 7, a horizon automatic interpretation result of the whole area is optimized. According to the horizon automatic interpretation method, the difficulty of optimizing a horizon automatic recognition algorithm is lowered, meanwhile professional constraints are imposed, the reliability of the horizon automatic interpretation result is effectively improved, and good application prospects are achieved.

The invention discloses a graph optimization method. The graph optimization method comprises the following steps: providing a to-be-optimized graph, wherein the to-be-optimized graph comprises a firstgraph and a second graph and the first graph and the second graph are different in size; carrying out OPC processing on a first graph with low adjustability; performing OPC processing on the second graph; and meanwhile, adaptively adjusting the first graph. Thus, the width of the final first graph is greater than the minimum size of the mask on the basis of ensuring that the final first graph isrelatively accurate, the condition that the width of the final first graph is close to the minimum size of the mask is avoided, and the graph optimization difficulty is also reduced. Therefore, the quality of the mask plate can be improved through the mask preparation.

The invention discloses a neural network model optimization method based on class expansion learning. The method specifically comprises the following steps: obtaining a data set which is used for training and contains various types of samples, and defining an algorithm target; extracting features of each type of pictures in the data set by using a universal model, and evaluating the error-prone degree of each type according to the distribution condition of each type of features; adding several types of data with the highest error-prone degree into a training pool, and optimizing the neural network by using the data in the training pool; after the optimization is completed, adding the remaining data with the highest error-prone degree into a training pool, expanding the categories in the training pool, and further optimizing the neural network by using the training pool on the basis of the neural network obtained by the last training; and continuously performing class expansion on the training pool until the whole data set enters the training pool to obtain a final optimized neural network model. The method is suitable for neural network model optimization based on multi-class datasets in supervised learning, and has good effect and robustness for various complex conditions.

The invention discloses a global optimization preprocessing method for intelligent manufacturing production scheduling. The global optimization preprocessing method comprises the following steps: pre-solving a mixed integer linear programming problem, and reducing a search domain for the mixed integer nonlinear programming problem; the pre-solving of the mixed integer linear programming problem comprising the following steps: (1) performing variable selection based on a Fourier-Motzkin elimination method; (2) performing a redundancy constraint elimination method based on a Fourier-Motzkin elimination method; and (3) pre-solving a linear programming problem based on a Fourier-Motzkin elimination method. According to the global optimization preprocessing method for intelligent manufacturingproduction scheduling, aiming at solving the problem of solving a large-scale engineering decision optimization problem in practice, two types of model preprocessing methods with universality are adopted, so that the solving performance is effectively improved, the method can be directly applied to engineering decision problems with complex structures, and the real-time performance and the qualityof complex production scheduling decisions are greatly improved.

The invention provides an optical fiber probe and a variable-focus optical fiber OCT device based on an adjustable mode field area. The OCT device comprises a broadband light source, a laser light source, a first coupler, a reference arm light path structure, a sample arm light path structure and a spectral analysis component; the source light beam is divided into a first light beam and a second light beam by a coupler I; the sample arm light path structure adopts an optical fiber probe as a sample arm; the first light beam enters the reference arm light path structure, and the second light beam is coupled with pump light emitted by the laser light source and enters an optical fiber probe of the sample arm light path structure, so that the focus of the optical fiber OCT device is adjustable; and the spectral analysis component is used for receiving and analyzing the first light beam reflected back from the reference arm light path structure and the second light beam reflected back fromthe sample arm light path structure. The variable-focus optical fiber OCT device adopts an all-fiber probe structure as a sample arm, is small and exquisite, is easy to package, and has the characteristic of adjustable focus; and meanwhile, the variable-focus optical fiber OCT device can be used for endoscopy, and achieves high-resolution imaging beyond the depth-of-field range.

The invention provides a method and a system for establishing a cluster service. The method for establishing the cluster service comprises following steps: group calling initiating BSS receives a voice group calling request sent by an initiator and sends a service request message carrying supported voice coding-decoding types to a mobile switching center (MSC) according to the voice group calling request; the MSC receives a first group calling establishing request message sent by the initiator after the service of the MSC and the initiator is connected, wherein the first group calling establishing request message carries the voice coding-decoding types supported by the initiator and the group calling receiving BSS information; the MSC selects a first voice coding-decoding type for the use of the group calling initiating BSS through the group calling initiating BSS according to the service request message and the first group calling establishing request message; the MSC selects a second voice coding-decoding type for the use of the group calling receiving BSS through the group calling BSS according to the first voice coding-decoding type; group calling receiving BSS assigns the selected results to corresponding users. According to the technical scheme, by means of the IP-based A interface, voice group calling service is carried out with increased voice service quality and reduced network cost.

The invention provides a fuel rod shape optimization method based on a Bezier curve. The method comprises the following steps: 1, building an x-y coordinate system through employing a central point ofa cross section of a fuel rod as an origin of coordinates, carrying out the shape optimization of a quarter of the outer contour of the fuel rod in a first quadrant, and setting a target function; 2,fitting a quarter of the outer contour of the fuel rod by adopting a quadratic Bezier curve to obtain three Bezier curves and seven control point coordinates, and taking the seven control point coordinates as design variables; 3, formulating constraint conditions according to the original size data of the outer contour of the quarter of the fuel rod; 4, calculating the perimeter of the outer contour of the quarter of the fuel rod and the area defined in the first quadrant; and step 5, according to the objective function, the design variable and the constraint condition, writing an optimization program in Matlab, then performing operation to obtain an optimization result of the outer contour of the quarter of the fuel rod, and drawing according to the optimization result of the outer contour of the quarter of the fuel rod to obtain the optimized overall outer contour of the fuel rod.