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The invention discloses an unsupervised cross-domain pedestrian re-identification method based on clustering, and belongs to the field of computer vision. The method specifically comprises the following steps: (1) establishing a neural network for pre-training; (2) inputting the labeled data in the source domain into a pre-training network, and performing supervised pre-training; (3) building a double-flow mutual learning network framework for fine adjustment; (4) training a double-flow mutual learning framework, and outputting a pedestrian re-identification model suitable for a target domain.Unsupervised cross-domain pedestrian re-identification is realized by utilizing a labeled source domain data set, a label-free target domain data set and a mutual learning network framework; according to the method, a self-attention mechanism and global joint pooling operation are introduced, a new loss function, namely joint flexible optimization loss, is provided, and a clustering method more suitable for open set data is selected, so that the model performance is obviously improved.

The invention discloses a method and system of thermal infrared remote sensing image super-resolution reconstruction based on a MAP algorithm, the method and system comprises the following steps: obtaining a segment of sequence thermal infrared band remote sensing images, wherein the sequence images comprise at least two frames of images; completing rectification by using the automatic extraction and matching based on a high precision automatic rectification method of angular point characteristics; achieving the super-resolution reconstruction of the sequence images by using the MAP algorithm, and putting forward an adaptive selection method of an edge penalty function threshold; carrying out application-oriented quality evaluation on target resolution images after reconstruction. According to the method and system of the thermal infrared remote sensing image super-resolution reconstruction based on the MAP algorithm, the high precision automatic rectification among images can be achieved, parameter threshold values can be selected adaptively, the interference of human factors can be reduced, the super-resolution reconstruction of real time can be achieved, and therefore the problems that thermal infrared remote sensing image resolution is low, a reconstruction method cannot achieve automation and is influenced by human factors seriously, speed is not high enough, reconstruction quality cannot be objectively and authentically evaluated in the prior art are solved.

The invention discloses a compressor fault diagnosis method based on XGBoost feature extraction. The method comprises the following steps: firstly, according to fault data and a fault type, customizing a loss function of an XGBoost algorithm, and iteratively constructing a fault split tree; secondly, extracting leaf node position index vectors of the samples in the fault tree, carrying out featurecoding reconstruction, and acqiirng intelligent representation of hidden fault information; and then, based on the representation matrix, respectively establishing fault prediction models by using anSVM (Support Vector Machine) algorithm and a neural network algorithm to realize prediction and diagnosis of multiple fault modes. The method has the characteristics that hidden fault feature information in the data can be fully mined, so that the fault diagnosis and prediction precision is higher.

The invention discloses a method for recognizing audio based on spectrogram significance test. The method is characterized by comprising the following steps: 1, acquiring spectrograms of different sound sources, and extracting characteristics to obtain a basic characteristics set; 2, obtaining a significance map by the GBVS algorithm, and extracting a main map by the main map separating method; 3, extracting a hierarchy correlation map; 4, acquiring a PCA characteristics map; 5, building GCNN sound source models of different sound sources; 6, recognizing the sound sources of which the spectrograms are to be tested according to the GCNN sound source models. With the adoption of the method, the characteristic information of unknown audio type under a complex environment can be effectively represented, and meanwhile, the audio can be quickly and automatically recognized.

The invention relates to a distribution network topology error identification algorithm based on AMI measurement information, which is characterized by comprising the following steps: S1, calculatingthe voltage Upc of a coupling node to which each load belongs, and obtaining a coupling node voltage sample space to which the loads belong; S2, calculating the current I<L> of a branch to which eachload belongs, and obtaining a branch current sample space to which the loads belong; S3, calculating the voltage correlation coefficient and the current correlation coefficient of the loads accordingto the voltage and current sample spaces obtained in S1 and S2; and S4, completing topology calibration and correction. The running time required for identification is reduced. The quick action of topology identification is ensured, and the reliability and effectiveness of topology identification are ensured.

The invention provides a high-precision control method of motor position servo system; the implementation of the system comprises the following steps: step 1, establishing a motor position servo system model; step 2, designing a high-precision motor controller based on limited time interference estimation; and step 3, adjusting parameters of the high-precision motor control law based on limited time interference estimation, so as to make the system satisfy the control performance index. The provided high-precision control method of motor position servo system establishes the motor position servo system model aiming at the features of the motor position servo system, and designs the high-precision motor controller based on the limited time interference estimation, so as to estimate and compensate unmodeled interference of the system at real time; the total disturbance of the system can be well estimated by adjusting the parameters of the control law, thereby being effectively solving the uncertain nonlinear problem of the motor servo system; the integral robust item ensures the overall stability of the system.

The invention discloses a high-order volume Kalman filtering and neural network-based state estimation method. The method is realized through the following steps of: firstly establishing a state space model for a nonlinear system by utilizing a neural network; combining a weight of the neural network and a state variable of the system to serve as a new state variable; and updating the new state in real time by adoption of high-order volume Kalman filtering, so as to achieve the real approach, to a nonlinear system model, of the neural network and the accurate estimation of a state value. Through experimental simulation, the effectiveness of the method is proved.

The invention provides a method for determining an exposure moment of star sensor data of a satellite. The method comprises the following steps that: (1) a hardware counter on a spaceborne computer is configured to be a pulse generator through software, PPS pulses are generated at regular time and sent to the star sensor to be used as synchronization pulse signals; (2) countdown of a pulse generator is periodically processed, and an interval between a PPS pulse emission moment and a current moment is calculated based on an obtained count value; and (3) the star sensor records time datation of an exposure moment of current data relative to current PPS pulse time, the time datation is transmitted as a part of data to the spaceborne computer, the spaceborne computer respectively reads star time at the current moment and a count value of the pulse generator when using the star sensor data for attitude determination, and the exposure moment of the star sensor data can be accurately obtained according to the datation of the star sensor data and the number of PPS pulses. With the method provided by the invention, the exposure moment of the data of the star sensor can be accurately obtained without addition of hardware on the spaceborne computer.

The invention discloses a bridge crane error tracker with an initial load swing angle and trolley displacement and a method. According to the method, an underactuation bridge crane system initial dynamical model is established; an expected trolley error trace and an expected swing angle error trace are given; error tracking signals for trolley positioning and load swinging are determined; an underactuation bridge crane system error tracking dynamical model is obtained; a target system model enabling a Lyapunov function to be stable is established, and the bridge crane error tracker with the initial load swing angle and trolley displacement is obtained according to the target system model and the underactuation bridge crane system error tracking dynamical model. The bridge crane error tracker has the advantages that under the condition that the initial load swing angle and initial trolley displacement which are zero in a conventional control method are expanded, the initial swing angle of a load and the initial displacement of a trolley are allowed to be of any values.

The invention discloses a Chinese counterfeit domain name detection method and a Chinese counterfeit domain name detection system, which are suitable for detecting Chinese counterfeit domain names formed by using similar Chinese characters. The Chinese counterfeit domain name detection system comprises a domain name preprocessing module, which is used for counting length and a total stroke number of every input domain name; a target domain name filtering module, which is used to compare the lengths and the total stroke numbers of the to-be-detected domain names and that of the target domain names, and is used to filter target domain names of a possibly-imitated target domain name set; a domain name disassembling module, which is used to disassemble the to-be-detected domain names and the filtered target domain names correspondingly into single Chinese characters; a single character similarity calculation module, which is used to convert the Chinese characters into character strings of stroke orders, and is used to calculate single character similarity based on character string editing distance; a domain name similarity calculation module, which is used to calculate the similarity of the integral domain name based on the single character similarity; and a counterfeit domain name decision module, which is used to determine and output the most possibly-imitated target domain name based on the domain name similarity. By adopting the above mentioned method and the above mentioned system, the Chinese counterfeit domain names formed by using the similar Chinese characters are effectively identified, and a wide application prospect is provided in a network safety field.

The invention discloses a verifiable fully homomorphic encryption method based on matrix operation. The method comprises the following steps: preprocessing and converting plaintext data into vectors to obtain a vector set; Then selecting a non-zero random real number and each vector to construct a triangular matrix, and encrypting each triangular matrix to obtain an encrypted matrix set; Generating verification evidences by utilizing the triangular matrix set according to the calculation function type; Performing matrix operation on each encryption matrix according to a certain rule to obtainan encrypted operation result; And finally, decrypting the encrypted operation result to obtain a plaintext of the result, and verifying the correctness of the result by comparing whether the verification evidence is equal to the value of the calculation result. The method has privacy security and result verifiability; Simulation experiment results show that the method has high efficiency in the stages of secret key generation, decryption and verifiability, and the effectiveness and feasibility of the scheme are shown.

The invention discloses a method for diagnosing a fault of an oil-immersed transformer on the basis of a rough set and a bayesian network. The method comprises the following steps that (a) the type of the fault is determined, as much as possible input fault characteristic vectors are selected in an original sample set, and an input attribute set is determined; (b) discretization processing is carried out on a fault data set through a data discretization method in the rough set theory, and a discretization decision table is established; (c) establishment of the bayesian network is carried out through Matlab; (d) a conditional probability table is initialized, wherein all the possible conditional probabilities of each node relative to the father node of the node and the quantitative description of the corresponding problem domain are listed in the conditional probability table; (e) parameter learning is carried out, and a deduction engine is established to carry out deduction after the bayesian network is established; (f) a test sample set is input, the posterior probability is solved, and the type of the fault is judged. The method for the oil-immersed transformer on the basis of the rough set and the bayesian network can simplify the scale of a diagnosis network, enhance the anti-interference performance of the network, diagnose various faults of the transformer rapidly, and reduce the outage rate of the transformer greatly.

The invention provides a self-adaptive robust output feedback control method for a motor position servo system. The method is implemented by the following steps: 1, establishing a motor position servo system model; 2, designing a state-estimation-based motor self-adaptive robust output feedback controller; 3, regulating parameters in the robust controller based on limited time interference estimation to enable the system to meet requirements on control performance indexes. According to the self-adaptive robust output feedback control method for the motor position servo system, the motor position servo system model is established according to the characteristics of the motor position servo system, and the system state is estimated for controller design based on the motor indirect self-adaptive robust output feedback controller of a high-order sliding mode differentiator, so that the influence of measurement noise on the controller is avoided, the problems of parameter uncertainty and uncertain nonlinearity of the motor position servo system can be effectively solved, and the control accuracy of the system meets the requirements on the performance indexes under an interference condition.

The invention discloses a generative conference abstracting method based on a graph convolutional neural network, and relates to the generative conference abstracting method based on the graph convolutional neural network. The method aims to solve the problem that according to an existing method, only sequence structures of sentences and words are used for modeling conference texts, and rich dialogue chapter structure information of a conference is ignored. The method comprises the steps of 1, obtaining a dialogue chapter structure of a conference; 2, constructing a conference chapter structure chart and a dialogue chapter structure between sentences in a conference; 3, constructing a conference chapter structure chart of the pseudo data and the corresponding pseudo data; 4, obtaining a pre-trained generative conference abstract model and initialization parameters of the graph neural network; obtaining a trained generative conference abstract model and model parameters of the graph neural network; and testing the conference to be tested by using the trained generative conference abstract model of the graph neural network to generate an abstract. The method is used for a generativeconference abstracting method in the field of natural language processing.

The invention discloses an error constraint control method for an unmanned surface vehicle considering input saturation, and relates to an error constraint control method for unmanned surface vehicles. The object of the invention is to solve the problem of low accuracy of navigation control for surface unmanned boats. The method comprises the following processes of 1, establishing a closed-loop system for the unmanned surface vehicle; 2, obtaining the closed-loop system for the unmanned surface vehicle considering saturation characteristics; 3, performing error constraint processing on the closed-loop system of the unmanned surface vehicle considering the saturation characteristics obtained in the step 2, and constraining the error variable within the specified range; 4, performing uncertainty processing on the closed-loop system of the unmanned surface vehicle considering the saturation characteristics obtained in the step 2, and estimating the unknown parameters; and 5, determining acontrol law and an adaptive law of the closed-loop system of the unmanned surface vehicle based on the error constraint processing in the step 3 and the uncertainty processing in the step 4. The error constraint control method is used in the field of error control of unmanned surface vehicles.

The invention discloses a topic-oriented multi-microblog time sequence abstracting method. The method comprises the following steps of 1) by taking a time point as a horizontal axis and a microblog updating speed corresponding to a corresponding time point as a longitudinal axis, performing topic-oriented microblog text stream popularity signal modeling; 2) denoising an initial signal in the step 1) by adopting wavelet denoising, selecting a signal maximum point in the signal according to a certain time granularity, and performing sorting according to the corresponding updating speed to detect an important time point; 3) establishing a text sorting model T2ST which reflects the importance of a microblog by fusing an instantaneous time sequence characteristic of a microblog stream popularity signal and the user social contact authority of a social network; and 4) selecting an abstract sentence by adopting a maximum edge related technology and establishing an MMR microblog abstract sentence selection model. According to the method, the important time point in a microblog sequence under a specific topic is detected through a wavelet denoising method, and based on this, multiple microblogs are abstracted by utilizing an improved graph-based random walk algorithm, so that the accuracy of an output result is high.

The invention provides a multiple star sensor timing sequence synchronization processing method based on time division multiplexing. The multiple star sensor timing sequence synchronization processing method includes the steps of controlling relationship between attitude and orbit control system control cycle and star sensor image processing cycle, on one hand, finishing read and write synchronization operations between a satellite attitude and orbit control system and a plurality of star sensors through designing of a time operation sequence based on the control cycle, and on the other hand, switching over data updating rate of the star sensors used in the attitude and orbit control system according to needs through the time sequence arrangement. Therefore, the multiple star sensor timing sequence synchronization processing method can periodically finish data acquisition and sending of instructions for all star sensors on the premise of ensuring normal mode control links, and can ensure that the star sensor data can be updated in every circle and can be used in attitude determining calculation according to a mode with attitude high precision requirements (generally is a station keeping mode), meets the requirements for star sensor data update rate in different modes of the attitude and orbit control system, and ensures attitude determining accuracy of the satellite attitude and orbit control system to a certain extent.

The invention discloses a back electromotive force harmonic compensation control method of a permanent magnetic synchronous motor, wherein the method belongs to the motor control technology field. According to the compensation control method, on the basis of a stator voltage condition required for generating a perfect sine-wave current and a stable torque by a permanent magnetic synchronous motor, comparison with an actual terminal voltage of the motor is carried out by utilizing a stator voltage value obtained by an ideal motor model as a reference so as to obtain voltage distortion and the size of harmonic components, wherein the voltage distortion and harmonic components are caused by unideal characteristics of the motor; and compensation is carried out in a control loop to restrain the effects on the stator winding current and the torque by the back electromotive force harmonic component, thereby reducing operating losses of the motor and improving the stability of the motor torque control.

The invention discloses a wood star detection leveraging flight orbit optimization design method, which comprises the following steps of setting a flight sequence as earth-earth-wood star, and establishing an in-plane elliptical earth and wood star analysis ephemeris model; designing a detector and earth resonance emission algorithm, ensuring that the detector is emitted to leave the earth according to a given resonance ratio, establishing a transfer orbit model from deep space maneuver to earth leveraging by utilizing a Lanbert algorithm, setting an earth leveraging flying height, and establishing a transfer orbit model after earth leveraging; defining the earth borrowing moment and the emission moment as phase angle increments, and aiming at an emission window of a certain year, taking the emission moment, the resonance ratio and the phase angle increments as optimization variables to construct a multi-layer nested optimization strategy. Accurate guessing of the initial value of theoptimization variable can be realized, the robustness and rapidity of wood star detection transfer orbit optimization are effectively improved, and a rapid and effective analysis method is provided for rapid analysis and orbit optimization design of a launch window.

Antibody compositions and methods for treatment of neoplastic disease in a mammalian subject are provided. Methods of diagnosing cancer in a mammalian subject are also provided.

The invention discloses a road detection method based on an SIFT-COF feature optical flow. Firstly, frame image information on a road is collected, and a road area and a non-road area are distinguished; the non-road area is determined to be ROI, then features in the ROI are extracted, and feature extraction is a hierarchical structure in which the SIFT feature and the Harris angular point feature are combined; moreover, the feature optical flow is formed through feature matching among frames, and a feature position is judged according to the calculation of the feature optical flow; finally a passable area and a non-passable area are determined. The hierarchical structure with the SIFT feature and the Harris angular point feature is built, complementary advantages of the SIFT feature and the Harris angular point feature are achieved through the method, and through the scale invariance character of the SIFT feature and the uniform distribution character of the Harris angular point feature, optical flow detection contrast tests of different types of unstructured roads show effectiveness of the method.

The invention provides a hydraulic system adaptive robust control method based on a consistent-robustness accurate differentiator. The method is characterized by comprising the following steps of 1, establishing a position servo system model for a hydraulic cylinder with two outlet rods; 2, designing a hydraulic system adaptive robust controller based on a consistent-robustness accurate differentiator; 3, adjusting the parameters of the controller to meet the control performance indexes.

The invention provides an academic team dynamic community discovery algorithm based on a temporal collaboration network. A temporal collaboration network model is established. On the basis of real-time detection of the importance of author nodes and the strength change of relation edges in a collaboration network, the evolution of an academic team in the academic collaboration network is analyzed and tracked, and a community is created, expanded, contracted, divided and disappeared in order to achieve the purpose of dynamic community discovery. The algorithm is of high accuracy. In addition, the invention provides an academic team community division quality evaluation method based on word features, which is used to evaluate the quality of community division. The algorithm is experimentally verified using a public document information record data set. The experimental results show that the algorithm is effective.

The invention discloses a microgrid group optimization scheduling strategy based on a niche chaos particle swarm algorithm, and the strategy comprises the steps: 1, building a dynamic electricity price model and a load model under the dynamic electricity price, building a cost model and an economic benefit model based on the dynamic electricity price model, and building a target function model based on the cost model and the economic benefit model; 2, respectively setting constraint conditions of microgrid power balance, output, controllable unit output, climbing, energy storage battery operation, power interaction between microgrids and power interaction between the microgrids and a power distribution network; 3, taking the target function model under the constraint condition as a scheduling model; and 4, optimizing the scheduling model by adopting a niche chaos particle swarm algorithm, and solving an optimal cost solution of the scheduling model. According to the invention, space complementation among different power supplies of the micro-grid group, bidirectional regulation of electric energy storage and release of the energy storage device and interactive regulation and control of supply and demand balance of the power supplies and loads are realized, which indicates the effectiveness of an optimized scheduling strategy.

The invention discloses a feature selection method based on a Laplacian operator. The feature selection method not only takes the correlation between a sample and a class tag into account, but also keeps the interdependence relation among samples. Specifically, a proposed Lap-Lasso method comprises two regularization items, and the first regularization item is a sparse regularization item which ensures that only a small quantity of features can be selected. In addition, a new regularization item based on Laplacian is introduced and is used for keeping local adjacent structure information among samples of the same types. Furthermore, an APG namely an Accelerated Proximal Gradient algorithm is adopted to optimize a proposed model. An experimental result in a UCI data set verifies the validity of the Lap-lasso method.