137results about How to "Increase the number of samples" patented technology

The invention discloses a multi-crack damage identification apparatus and method for a cantilever flexible beam. The apparatus comprises a flexible beam, a mechanical clamping device, a detection device, a vibration exciter, a signal generator, a power amplifier, a fiber Bragg grating demodulator and a computer. One end of the flexible beam with multiple cracks is fixed through the mechanical clamping device; a multi-channel FBG optical fiber sensor group is adhered on the flexible beam and is evenly distributed along the length direction of the flexible beam; a signal generated by the signal generator is amplified by the power amplifier and then enters the vibration exciter; the vibration exciter excites low-order mode vibration of the flexible beam; the vibration results in curvature changes at measurement points of each sensor of the beam; the sensors input the curvature changes into the fiber Bragg grating demodulator in the form of wavelength changes; a demodulated signal enters the computer for modal analysis of the flexible beam, drafting of a modal curvature curve, calculation of damage indexes and establishment of a wavelet neural network identification model; and finally, the purpose of accurate identification of the depth and the location of the cracks of the flexible beam is achieved.

The invention discloses a method and a system for detecting rice panicle blast based on a deep convolutional neural network. The method comprises the following steps: acquiring a hyperspectral image of outdoor rice panicles and performing panicle blast disease calibration; performing data preprocessing and data enhancement on the hyperspectral image of the rice panicles; establishing a deep convolutional neural network model and optimizing model parameters with a stochastic gradient descent algorithm; detecting the hyperspectral image of the rice panicles to be tested with the trained deep convolutional neural network model, and judging whether the rice panicles are infected by the panicle blast disease or not. The system comprises a hyperspectral camera, a computer, a tripod and a reflection board, wherein the rice panicles are hung on the reflection board; the hyperspectral camera is fixed on the tripod, is connected with the computer, and has a lens aiming at the rice panicles on the reflection board. The method and the system provided by the invention can provide technical support for predicting the outdoor rice panicle blast disease and can also play an instructive role in rational and precise application and management of water and fertilizer, pesticide or other agricultural resources in a production process.

The invention relates to the field of big data machine learning, and discloses a method and a system for training a distributed CTR (Click To Rate) prediction model. Logistic regression calculation can be carried out under a distributed parallel framework, parameters and data can be distributed to multiple primary clusters for being processed; the primary clusters can be further subdivided into multiple secondary clusters, a MapReduce framework is adopted by the secondary clusters in each primary cluster, the gradient can be calculated by Map, and the iteration direction and the step length can be calculated by Reduce; and a task backup mechanism can be adopted in the secondary clusters. According to the method and the system, disclosed by the invention, the sample number and the characteristic parameters, which can be processed, in the logistic regression calculation can be increased, the integrated running efficiency of the system can be simultaneously increased, the calculating and processing time can be shortened, and the phenomenon that the whole calculation is interrupted as a node is in massive failure can be prevented from happening.

The invention belongs to the technical field of MIMO radar, and particularly provides an MIMO radar multi-target tracking resource management method. Firstly traversal of controllable parameter combinations is performed, and whether the controllable parameter combinations can meet the detection probability and beam direction constraints is judged so as to form an MIMO radar control vector feasible set; then deviation cost and the energy resource consumption cost between the prediction tracking error covariance and the expected covariance of the targets are calculated under each control vector in the feasible set; and the two cost is integrated, and the optimal radar control vectors are acquired according to the criterion of the minimum integrated cost. The advantages that MIMO radar utilizes single beam to irradiate multiple targets can be fully exerted under the multi-target environment so that the sampling frequency of each target can be increased, the time resource utilization rate of radar can be effectively enhanced and the deviation between the actual tracking error covariance and the expected covariance of the targets can be reduced, and the tracking accuracy of the method can be controlled by the method through adjusting the expected tracking error covariance.

The invention relates to a control method in the information processing technical field, in particular to an asynchronous brain-computer interactive control method for an EEG signal. The method comprises the following steps: acquiring EEG signal data under a continuous motion imagery condition; re-sampling training data; extracting features and establishing a classifier model; carrying out on-line feature projection and pattern classification; calculating duration time in a current status according to dynamic change of the duration time by a post-processing technology; and forming closed brain-computer interactive control by asynchronous brain-computer interactive control and neural feedback. The asynchronous brain-computer interactive control method reduces interactive retardance, improves fault-tolerant capability, breaks through a discrete command control mode, and enhances brain-computer interactive flexibility, and can provide a direct brain-computer interactive communication technology for the handicapped or patients with dyskinesia.

The invention relates to a malicious domain name detection method based on SMOTE and BI-LSTM. The method comprises the following steps of carrying out data preprocessing on domain names in a trainingset in order to obtain a domain name character sequence training set D; carrying out equalized data synthesis on the domain name character sequence training set D by utilizing an improved SMOTE algorithm in order to obtain an enhanced and equalized training set D'; constructing and initializing a neural network model comprising a BI-LSTM layer and a full-connection layer; converting the domain names in the training set D' into representation vectors with the fixed length; inputting the representation vectors of the domain names obtained in the step D into the BI-LSTM layer of the neural network model in order to obtain context vectors of the domain names; inputting he context vectors of the domain names into the full-connection layer of the neural network model in order to obtain a trainedneural network model; and converting the domain names to be judged into a character sequence, inputting the trained neural network model, and outputting a judgment result.

The present invention relates to nucleotide sequences, including expressed sequence tags (ESTs), oligonucleotide probes, polypeptides, vectors and host cells expressing, and immunoadhesions and antibodies to mammalian GFRα3, a novel α-subunit receptor of the GDNF (i.e. GFR) receptor family. It further relates to an assay for measuring activation of an α-subunit receptor by detecting tyrosine kinase receptor activation (i.e., autophosphorylation) or other activities related to ligand-induced α-subunit receptor homo-dimerization or homo-oligomerization.

The invention discloses an age identification method based on an integrated convolution neural network. The method includes following steps: S1, obtaining and expanding training sub-sets in an age identification training database, obtaining the expanded training sub-sets, and selecting M convolution neural network classifiers obtained by training of the expanded training sub-sets as base classifiers; S2, obtaining a to-be-tested face image; and S3, inputting the to-be-tested face image into M base classifiers obtained in step S1 during a test, fusing age categories output by M base classifiers, and obtaining a final age category. According to the method, the accuracy of age identification is high, the dependency on people by age feature extraction of the face image is reduced, ages of a variety of people can be estimated, and the application is wide.

Disclosed herein is an apparatus for sensing characteristics of an object. In a preferred embodiment, the apparatus comprises an array, wherein the array comprises a plurality of nanoscale hybrid semiconductor / metal devices which are in proximity to an object, each hybrid semiconductor / metal device being configured to produce a voltage in response to a perturbation, wherein the produced voltage is indicative of a characteristic of the object. Any of a variety of nanoscale EXX sensors can be selected as the hybrid semiconductor / metal devices in the array. With such an array, ultra high resolution images of nanoscopic resolution can be generated of objects such as living cells, wherein the images are indicative of a variety of cell biologic processes.

The invention discloses a multi-source data fusion-based system and method for predicting pipeline corrosion defect size. The system comprises a sensor group, a lower computer and an upper computer, wherein the sensor group is arranged at the head end or the tail end of a pipeline and is in contact with a transmission medium; the sensor group is connected with the lower computer connected with the upper computer. The method comprises the steps of firstly building a gray GM (1,1) prediction model and a BP neural network model by using historical data; then using the two models to obtain a prediction value; next building an inducted orderly weighted harmonic average (IOWHA) operator combination model according to the historical data and the prediction value of the two models; for new data, using the three models in sequence to predict the corrosion defect size in three directions of axial direction, circumference and depth respectively, so that a corrosion defect size prediction value of the pipeline is finally acquired. By adopting the system and the method, the prediction accuracy is higher, and the input of data is reduced.

The present invention relates to nucleotide sequences, including expressed sequence tags (ESTs), oligonucleotide probes, polypeptides, vectors and host cells expressing, and immunoadhesions and antibodies to mammalian GFRα3, a novel α-subunit receptor of the GDNF (i.e. GFR) receptor family. It further relates to an assay for measuring activation of an α-subunit receptor by detecting tyrosine kinase receptor activation (i.e., autophosphorylation) or other activities related to ligand-induced α-subunit receptor homo-dimerization or homo-oligomerization.

Belonging to the field of metallurgical sample detection, the invention relates to a preparation method of a standard spectrum sample. The method includes the processes of: component selection, preparation of a standard sample through a continuous casting middle ladle top pouring method, stability investigation and conclusion. The method is characterized in that the process of preparing the standard sample by the continuous casting middle ladle top pouring method comprises the steps of: model baking, sample model establishment, sample model pouring by the top pouring method, demolding, segmentation and surface treatment, physical property detection, surface treatment, chemical component uniformity test, and fixed value analysis. The standard sample prepared in the invention not only satisfies the standard sample requirements in terms of physical property, texture, component range and uniformity, but also has reduced preparation cost and increased sampling number. At least 200 pieces can be prepared every time, thus being over 20 times as many as a model casting sampling number. And the preparation number can satisfy the current production requirements. Thus, the standard sample preparation method of the invention boasts very high practical value.

The invention discloses a detecting method for unicellular mass spectrum flow cytometry. A novel encoding method of an isotope metal concentration ratio marking antibody is applied, metals with different ratios is used, thus a new label combination is structured; thus a detecting channel of the mass spectrum flow cytometry is expanded by a few metal label number, a Barcoding technology which moreefficient than the existed technology is developed.

The invention discloses a visible double-row columnar sampler for deep-sea sampling, and relates to the technical field of deep-sea sediment sampling. The sampler comprises a grounding balancer, wherein a visible grounding self-controlled sampling switch assembly is hung at the left end of the grounding balancer; two sets of vertically arranged columnar sampler main bodies are hung at the right end of the grounding balancer; a release connection assembly and a coaxial armored cable are connected with the top of the grounding balancer; the visible grounding self-controlled sampling switch assembly comprises a visible grounding hammer and a grounding hammer connection cable; an underwater camera is mounted in the visible grounding hammer, and connected with the lower end of the cable core of the grounding hammer connection cable; the upper end of the cable core of the grounding hammer connection cable is connected with the cable core of the coaxial armored cable. According to the sampler, visible target sampling can be realized, effective utilization of the sampler in a complex undersea environment, and the sampling success rate is improved; meanwhile, the number of samples obtained in a one-time sampling process is large, the sampling efficiency is high, and the modern oceanographic research demand is met.

A microwave irradiation apparatus proposed for uniformly treating a large number of samples, includes: an irradiation chamber formed in a rectangular resonant cavity of TM (Transverse Magnetic) 110 mode, of which length of X-axis side is “a” (a>0), length of Y-axis side is “b” (b>0) and length of Z-axis side is “c” (c>0); slits respectively formed on Y-Z plane walls of the irradiation chamber; a transfer sheet entering into the irradiation chamber through the slits and moving along a X-Z plane in the irradiation chamber; and a sample holder disposed on the transfer sheet.

A multiphase power supply circuit comprises a PWM (pulse-width modulation) chip and a plurality of sub-circuits. Each sub-circuit comprises an inductor, a first resistor, a first capacitor, a differential line pair, a second resistor and a second capacitor. The first resistor and the first capacitor in each sub-circuit are serially connected, and then are parallelly connected with the inductor in the sub-circuit. The differential line pair in each sub-circuit comprises a first differential line and a second differential line. The first differential line in each sub-circuit is connected between a joint of the corresponding first resistor and the corresponding first capacitor and a terminal of a current sensor of the PWM chip. One end of the second resistor in each sub-circuit is electrically connected to a joint of the corresponding capacitor and the corresponding inductor, one end of the second resistor in each sub-circuit is serially connected with the corresponding second capacitor while the other end of the second capacitor is ground, the second differential line in each sub-circuit is electrically connected between a joint of the corresponding second resistor and the corresponding second capacitor and another terminal of a current sensor of the PWM chip, and the ratio of capacitances of the second capacitors of the sub-circuits is equal to that of lengths of the corresponding differential line pairs. The circuit can prolong sampling time of the long differential line pairs, and increases sampling accuracy.

The present invention provides an oxide-base scintillator single crystal having an extremely large energy of light emission, adoptable to X-ray CT and radioactive ray transmission inspection apparatus, and more specifically to provide a Pr-containing, garnet-type oxide single crystal, a Pr-containing perovskite-type oxide single crystal, and a Pr-containing silicate oxide single crystal allowing detection therefrom light emission supposedly ascribable to 5d-4f transition of Pr.

The invention belongs to the communication field, to be specific, provides an MIMO radar multi-target adaptive tracking method based on a variable analysis moment. The above mentioned method is used to realize MIMO radar adaptive target tracking in multi-target environment. The method comprises steps that a prediction error covariance threshold method is used to determine the next analyzing moments of various targets, and the minimum value is used as the next analyzing moment of the MIMO radar; a controllable parameter combination constituted by working modes, sub-array quantity, and waveform energy is traversed, and whether the controllable parameter combination is capable of satisfying constraints of a detection probability and wave beam directions is determined, and therefore an MIMO radar control vector feasible set is formed; and finally, in every control vector of the feasible set, an optimal radar control vector is acquired according to a lowest resource consumption principle. In the multi-target environment, the advantage of the MIMO radar using the single wave beam to irradiate a plurality of targets is fully used, and the sampling times of the various targets are increased, and the sampling moments of the targets are determined in an adaptive way according to the motion characteristics of the targets, and therefore the time resource utilization rate of the radar is effectively improved.

The invention belongs to the technical field of plant seedling propagation and discloses a micropropagation method of tilia miqueliana. The method comprises steps as follows: A, culture of seedling of the tilia miqueliana; B, adventitious bud induction; C, high-voltage electrostatic field processing; D, subculture and rooting; E, acclimation and seedling hardening. A large quantity of tilia miqueliana explants easy to disinfect can be obtained, the success rate of primary culture is higher than 98%, the proliferation rate reaches 113 times, the survival rate reaches 95%, the problem of shortage of the tilia miqueliana in the market is effectively solved, and the application prospect is wide.

The invention discloses a preparation method of a high-carbon stainless steel standard sample, belonging to the field of detection of metallurgical samples. The preparation method disclosed by the invention comprises the steps of designing a casting mould, baking the casting mould and a steel ladle, selecting components, controlling the process of a casting process, demoulding, detecting physical performances, processing the sample, detecting the homogeneity of chemical components, carrying out deterministic analysis and determining effective height. The physical performances, the structural state, the component range and the homogeneity of the standard sample prepared by the invention can be satisfied; furthermore, the preparation cost is reduced; the sampling number is increased; at least 100 standard samples can be prepared every time; the number is above 20 times of that of die casting sampling; the preparation number can satisfy current production requirements; and thus, the standard sample preparation process has very high practical value and great popularization value in other matter test rooms.

The invention provides a rapid updating mixing assimilation method based on a time lag set. According to a characteristic that a service value forecast system assimilates observation data in a high frequency mode and outputs a forecast field in the high frequency mode, in order to effectively introduce a flow-dependent background error covariance and simultaneously effectively reducing a calculated amount brought by ensemble forecast, the flow-dependent background error covariance obtained and calculated by the time lag set formed by a same moment forecast field obtained by initial fields of different moments based on a history sample is combined with a modeling static background error covariance of a three-dimensional variation so as to expect that assimilation and forecast effects of a current value forecast system based on a variation assimilation method are increased under the condition that calculating cost and storage cost are not increased or only increased a little.

The invention discloses a parallel random forest algorithm for processing unbalanced big data, belongs to the field of unbalanced big data classification, and aims at solving the problem that a parallel random forest algorithm based on a MapReduce mechanism is low in the unbalanced big data classifying efficiency. The parallel random forest algorithm (SBWMR_RF) in which layered self-service sampling and sensitive cost ideas are combined is provided, self-service sampling is carried out on positive and negative classes of each partitioned data set, the information content of the position classis increased while the randomness is guaranteed, a cost sensitive matrix of each partition is calculated dynamically, and the sensitive cost is used for establishment of a base classifier. According to the invention, the unbalanced data is processed in the aspects of data distribution adjustment and algorithm adjustment, influence of data bias is weakened, the classification accuracy of the unbalanced big data is improved, and the algorithm has obvious advantages in the extremely unbalanced environment.

The invention provides a method, a device, a program and a readable storage medium for identifying characters. The method comprises the steps of: identifying the input character samples to generate the identification result; generating degree of confidence of the identification result; in accordance with the degree of confidence, judging whether the input character samples belong to deformed samples, if not, regarding the identification result as the final identification result; if so, identifying the deformed samples and generate the final identification result. The technical proposal of the invention can effectively process the deformed samples by directly regarding the samples which are incorrectly identified at the first level as the basic training samples at the second level, thus improving the identification accuracy of the system.

The invention discloses a smoke and fire identification model establishing method based on deep learning and a smoke and fire identification method. According to the smoke and fire identification model establishing method, a can network is used for synthesizing a smoke and fire picture and a normal picture of a to-be-detected area, the number of samples of a data set in an actual application scenecan be increased, and then the to-be-established smoke and fire identification model is obtained through yolov3 labeling, training and verification. According to the method, the problems of abstractsmoke and fire characteristics, diverse shapes and unobvious and unspecific characteristics in a traditional smoke and fire identification method can be solved, and meanwhile, the situation that smokeand fire image data is small or does not exist in an actual application scene is solved. The smoke and fire identification method is based on the established smoke and fire identification model, andthe misjudgment problem generated in large-scale scene long-distance image identification is solved.

The invention provides an intelligent fire-fighting fire identification method and system based on a neural network algorithm. The method comprises the following steps: reading environment sensing data, and preprocessing and classifying fire conditions; calculating a data change rate and smoothing the data to obtain to-be-classified data; acquiring original fire feature data; preprocessing the original fire feature data; establishing a fire feature database, smoothing fire feature data to construct a perception model, and establishing a feature project; reprocessing the sample input into the combustion stage prediction model; and classifying the environment perception data and the fire feature data after smoothing processing through the model, and judging the type and the combustion stage of the fire. The system comprises an intelligent fire-fighting cloud platform, an administrative management platform, a system access module, a maintenance module and a monitoring center. According to the invention, the fire can be found earlier, the response time is shortened, the fire type is predicted, targeted fire fighting is carried out, data of multiple sensors are cooperatively processed, and the false alarm probability is reduced through comprehensive research and judgment.

The invention discloses a storage device of irradiation supervision test samples of reactor pressure vessel material. The device adopts a well-typed design and a storage well takes underground type, thus occupying no space of a thermal ventricular; a sample storage framework adopts a multi-level structure and makes full use of the space of the storage well, thus the number of the samples for storing is greatly increased and the cost is low. By adopting the storage device of the invention, the storage effect of the irradiation supervision test samples of reactor pressure vessel material can be easy to check and an excellent shielding is conducted on the irradiation of the specimen at the same time.

The invention relates to a UAV (unmanned aerial vehicle) water sampling device. The device comprises a UAV, wherein a control system is arranged in the UAV, and a sampling tank is connected to the lower part of the UAV and provided with a conversion acquisition device, and the conversion acquisition device comprises rotating devices arranged bilaterally symmetrically in the sampling tank, water collecting bottles vertically arranged on the rotating devices in a sleeving manner, hoppers vertically placed on the water collecting bottles, balls placed at the center of the hoppers and electromagnets I tightly attached to the upper right ends of the rotating devices; a reel I is arranged right above the center of each rotating device on the UAV, an electromagnet II is arranged below the reel I,and a connecting wire winds around the reel I and is connected to a sampling bucket; the sampling bucket comprises a lifting ring arranged above the sampling bucket, a sampling bucket cover right below the lifting ring, a water outlet pipe arranged at the bottom of the right end of the sampling bucket cover, a valve arranged on the water outlet pipe, an iron block vertically arranged at the upperend of the valve and a magnet vertically arranged at the lower end of the valve; a reel II is arranged in the middle of the device and wound with a wire of an ultrasonic probe.

The invention discloses a method of analyzing structural reliability of an aeroengine turbine disc; a random variable mathematical model is established according to non-uniformity of turbine disc material, and a stress analysis model is established according to stress condition; PDS (probabilistic design system) in ANSYS environment is used to perform Monte Carlo sampling on random variables of a turbine disc three-dimensional model to obtain maximum stress MAXSTRESS distribution of the turbine disc under different conditions; Monte Carlo simulation tests of different levels are performed on the random variables on MATLAB (matrix laboratory) platform, and reliability of the turbine disc is calculated according to structural functions to obtain the structural reliability of the turbine disc under different conditions; sensitivity of the random variables influencing the structural reliability of the turbine disc is calculated according to sensitivity equation. The method has the advantages of high convergence speed, low cost and high precision, and theoretical basis is provided for the optimal design and reliability management for a turbine disc.

The invention is suitable for the technical field of data processing, and provides a speech recognition method based on artificial intelligence (AI), a device, a terminal and a storage medium. The method comprises the steps of obtaining a to-be-recognized target speech signal; determining a target language type of the target speech signal; and outputting text information of the target speech signal through a real-time speech recognition model corresponding to the target language type, wherein the real-time speech recognition model is obtained by training a training set containing original speech signals and extended speech signals, and the extended speech signal is obtained by converting an existing text based on a basic language type. According to the technical scheme provided by the invention, the number of samples required for training the real-time speech recognition model of the non-basic language can be increased, so that the accuracy and applicability of speech recognition are improved.

A delay line system for high frequency signal transmission comprises a first delay line and a second delay line that are each tapped. The first delay line comprises a first terminal and a second terminal, and the second delay line comprises a first terminal and a second terminal. The first delay line and the second delay line are configured in a manner whereby an analog input signal applied to the first terminal of the first delay line propagates through the first delay line in a first direction, a clock signal applied to the first terminal of the second delay line propagates through the second delay line in a second direction, and the first direction is opposite to the second direction. Further, an oscilloscope for measuring high frequency signals comprises an ADC, which comprises a high frequency sampler, which comprises such a delay line system.