19 results about "Discrimination function" patented technology

The present application relates to the technical field of safety monitoring and industrial automation, and discloses a silo safety monitoring method and system, which synchronously acquires coal pile surface point cloud, lateral pressure, vertical load and environmental data through the arrangement of a laser scanning unit, an annular pressure sensing belt, a weighing sensor and a temperature and humidity node; a high-precision three-dimensional morphology is reconstructed based on an adaptive grid refinement algorithm, and a joint representation model is constructed by fusing mechanical data; a stability discrimination function is established in combination with coal body physical parameters to identify the instability critical region; and through pattern matching with a historical stability morphology library, hierarchical early warning of abnormal working conditions such as suspended arch, bonding and blockage and generation of blockage removal strategies are realized. The present application constructs a joint representation model of coal pile three-dimensional morphology and mechanical state by fusing laser point cloud, pressure distribution and load sensing data, breaks through the limitation of traditional single-point material level measurement which cannot reflect the overall coal pile structure, and realizes high-precision measurement with a storage calculation error of less than 2%.

The invention discloses a radar track target classification method based on statistical knowledge and a neural network, and the method comprises the steps: collecting original track data, and carrying out the marking and cleaning processing, and obtaining a standard track data set; extracting kinematics, bistatic radar cross section and track morphological features based on the standard track data set, and constructing an original feature set; performing feature weight learning through a neighborhood component analysis method, and screening according to a preset weight threshold to obtain an effective feature subset; performing statistical distribution analysis on the effective feature subsets according to target categories, determining significant features of each target category, and setting a threshold interval; performing preliminary classification based on a statistical knowledge classifier, and determining whether the to-be-classified track is an easily-classified sample or a difficultly-classified sample through a category discrimination function; and classifying the samples difficult to classify based on a back propagation neural network classifier, outputting the probability value of each target category through a neural network model, and taking the target category with the maximum probability value as a final classification result. According to the invention, the target classification accuracy and real-time performance can be improved.

The invention provides a test paper automatic detection method, electronic equipment and a storage medium, and belongs to the technical field of test paper instant detection. The detection method comprises the following steps: acquiring a plurality of detection points and color characteristics of the detection points in a test paper reaction region; and calculating the distance between the detection point and the center of the reaction area so as to endow the detection point with a decision weight. And combining the color features of the plurality of detection points into an observation matrix, and combining the basis matrixes of the plurality of detection points based on the decision weight into a block diagonal matrix. And based on the observation matrix and the block diagonal matrix, obtaining an optimal coefficient matrix of the color features of the detection points so as to reconstruct the color features of the detection points. And respectively establishing a background model of each detection point, and verifying the consistency of each background model so as to filter the color of the test paper. And estimating the concentration of the detection point according to the color feature of the detection point, and carrying out substance identification through a discrimination function and a decision weight. And outputting the concentration and name of the substance in the reaction area. According to the method, the accuracy of a detection result can be improved.

The invention relates to a polyhedral structure sound wave orientation sensor module, an array and a sound wave orientation method in the technical field of sound wave detection. The sound wave directional sensor module comprises a sensor and a calculation unit. The sensor comprises a rigid backing and n piezoelectric sensing units. And the calculation unit is used for determining a primary direction surface and a secondary direction surface according to the signal amplitude of the sensor, calculating the amplitude ratio P of the primary direction surface and the secondary direction surface, inquiring the amplitude ratio-incident angle mapping database based on the P to obtain the azimuth angle theta of the sound wave on the primary direction surface, and calculating to obtain the sound wave direction based on the theta and the primary direction surface. According to the invention, through the innovative polyhedral integrated design, the sound wave direction discrimination function which can be completed by a sensor array originally is realized in a single compact device, the problem of direction fuzziness of a traditional single-unit sensor is solved from the physical principle, and in addition, through a two-step decoding algorithm which is tightly combined with the sensor structure, the sound wave direction discrimination function is realized. And an optimal closed loop from physical perception to information output is formed.

The invention relates to a rainfall data analysis method based on multi-modal data fusion and space-time correlation perception, and the method comprises the steps: collecting historical non-blocking operation and maintenance data of a rain gauge in a target region in a set time period through a system, and storing the historical non-blocking operation and maintenance data in a computer system in a partitioned manner to construct an equipment historical baseline library; the system receives real-time multi-mode monitoring data of a preset number of rain gauges in a target area to construct a real-time monitoring data set, converts historical non-blocking operation and maintenance data and time sequence data of the real-time monitoring data set into scores capable of being quantized in a unified mode according to time step column values, and determines analysis points and peripheral points in the target area. And calculating a score residual error between each time step column value and a baseline midpoint value in a historical baseline library, generating a two-dimensional baseline residual error matrix, calculating a dynamic deviation index and a state continuity parameter, constructing a blockage judgment function, and combining an operation result with a preset threshold value to complete blockage judgment. According to the method, full-process automation can be realized, and efficient analysis is realized.

The invention provides a blast furnace condition trend early warning system based on PCA (Principal Component Analysis) and LDA (Latent Dirichlet Analysis), which comprises a data preprocessing module used for cleaning and standardizing multi-source heterogeneous data of a blast furnace; the feature extraction module ensures feature quality; the working condition identification module is connected with the feature extraction module to realize intelligent identification and classification of the blast furnace operation working condition; and the early warning decision optimization module forms an optimal early warning decision. According to the method, a PCA technology is used for carrying out dimension reduction on operation parameters, then through analysis of load vectors, principal components are clearly mapped into characteristic indexes with direct process significance, the characteristic indexes are input into an LDA model, through maximization of the ratio of inter-class divergence to intra-class divergence, the optimal projection direction is found, the characteristics are projected to a low-dimension discrimination space, and finally, the optimal projection direction is obtained. And a classification rule based on a discrimination function is constructed, normal, early warning and serious abnormal working conditions are identified, and the classification accuracy of the system on complex operation modes is improved.

The invention relates to the technical field of safety monitoring and industrial automation, and discloses a silo safety monitoring method and system.The method comprises the steps that a laser scanning unit, an annular pressure sensing belt, a weighing sensor and a temperature and humidity node are arranged, and coal pile surface point cloud, lateral pressure, vertical load and environment data are synchronously obtained; a high-precision three-dimensional form is reconstructed based on an adaptive mesh refinement algorithm, and mechanical data are fused to construct a joint representation model; establishing a stability discrimination function in combination with coal physical parameters, and identifying an instability critical region; through mode matching with a historical stable form library, graded early warning and blockage clearing strategy generation of abnormal working conditions such as suspension arching, bonding and blockage are realized. According to the method, the laser point cloud, the pressure distribution and the load sensing data are fused, a combined characterization model of the three-dimensional form and the mechanical state of the coal pile is constructed, the limitation that traditional single-point material level measurement cannot reflect the whole coal pile structure is broken through, and high-precision measurement with the reserve calculation error smaller than 2% is achieved.

The invention discloses a method for predicting the service life of a relay for an operating mechanism based on a grey theoretical model, and relates to the technical field of a relay for a high-voltage circuit breaker operating mechanism of a power system, and the method comprises the following steps: simulating a salt mist aging test parameter test system through an electromagnetic relay, and simulating the operation environment of the relay for the operating mechanism; an alternating salt spray test is carried out, and characteristic parameters of the relay are extracted; performing dimension reduction processing on the characteristic parameters of the relay by using a principal component analysis method, and extracting digital characteristics of the characteristic parameters; on the basis of the digital characteristics of the characteristic parameters, the characteristic parameter with the maximum discriminant function value is selected by utilizing Fisher discriminant criteria; and taking the characteristic parameter with the maximum discrimination function value as a prediction variable, constructing a gray theoretical model, predicting the service life of the relay, and completing the service life prediction of the relay for the operating mechanism based on the gray theoretical model. The problem that the existing relay life prediction precision is low is solved.

The application relates to the technical field of computers and discloses a transportation track generation method and device, equipment and a storage medium, wherein GPS track data is obtained by acquiring road GPS point data and carrying out noise filtering processing; a state-action value function, a strategy function and a discrimination function are established based on the GPS track data and five elements of a Markov decision process; parameters of the state-action value function, the strategy function and the discrimination function are updated to construct a generated imitation reinforcement learning model; a meta-learning algorithm is used to train the generated imitation reinforcement learning model, and a transportation track is generated according to task-specific parameters obtained by training the meta parameters; the transportation track generation method and device, the terminal and the storage medium provided by the application can construct the generated imitation reinforcement learning model, improve the accuracy of the model, generate a city transportation track, and thus adapt to a complex travel environment.

The invention discloses a deep neural network black box test method based on key region analysis, comprising the following steps: firstly, proposing a partition strategy to guide initialization of an image region and dynamic adjustment in an iteration process for an original input image; secondly, performing regional importance analysis on an input image by designing a discrimination function, and automatically positioning a key region on which a model decision depends; then, dynamically adjusting the initialized region, carrying out fine-grained analysis by utilizing a discriminant function, and carrying out iteration until the region is subdivided into a predefined size; and finally, based on the regional importance score, preferentially applying semantic preserving disturbance to the key region, and generating a targeted test sample through iterative query and feedback. According to the method, the decision dependency of the deep neural network is utilized, key region positioning can be achieved through model input and output, and compared with a traditional black box test method, the query frequency is effectively reduced; and a self-adaptive disturbance strategy is adopted, so that the sample semantic rationality is maintained while the test effectiveness is ensured. Meanwhile, the generated input image can be used for finely adjusting a target model, and the robustness of the model is improved.

The present application relates to a binary function similarity detection method using hypergraph twin neural network, belonging to the field of computer and information science technology. The present application firstly constructs a directed heterogeneous hypergraph containing data flow and control flow hyperedges based on the abstract syntax tree of binary functions; then uses the improved hypergraph twin network to obtain hypergraph embedding vectors, and calculates the cosine distance as the similarity measure of function pairs, effectively learns the structure and semantic features of long-distance statements through multi-layer convolution, uses the initial residual connection and identity mapping to retain the initial features of the statements and avoid the performance degradation caused by the stacking of network layers; finally, the similarity measure is compared with the threshold to determine whether the function pairs are similar. The present application aims at the problem that the existing methods do not generate sufficient information representation of function semantics and structure, and it is difficult to accurately model the mutual influence between long-distance statements in the function, and proposes a binary function similarity detection method using hypergraph twin neural network, which improves the detection accuracy.

This invention proposes a method for quality evaluation and multi-class discrimination of yam based on PCA and LDA, belonging to the technical field of agricultural product quality evaluation. The method includes the following steps: A) Constructing a discrimination model, specifically including A1) Data acquisition; A2) Data preprocessing and feature selection; A3) Constructing a linear discriminant model; B) Distinguishing yam categories, B1) Measuring 12 original indicator data of the sample to be tested according to the method described in A1); B2) Calculating the 12 original indicator data x... j Standardized to Z j B3) Z j Substitute the values ​​into the LDA discriminant function to calculate the values ​​for each type of yam; compare them, and the category corresponding to the maximum value is the discrimination result. When users apply the discrimination method of this invention, they do not need to understand the principle of PCA; they only need to perform simple standardization and a single linear combination calculation to complete the classification, which greatly reduces the threshold for using the model and the computational cost.

The application discloses a radar single-pulse angle discrimination curve fitting method based on cost sensitivity weighting, which comprises the following steps: 1, measuring sum-difference beam patterns of multiple wave positions in a microwave darkroom or an ideal field environment, and extracting original measurement values of angle discrimination curves in an angle range of interest; 2, constructing a measurement value combination for joint fitting of the angle discrimination function of multiple wave positions; 3, constructing a polynomial fitting optimization problem based on a least square criterion; 4, designing a cost sensitivity function and related parameters according to actual application requirements, and further correcting the cost function; 5, calculating intermediate variables and solving fitting coefficients; and 6, in a radar actual working project, on-line single-pulse angle measurement is completed based on the angle discrimination function obtained through fitting. Through simple parameter setting, the fitting accuracy of targets near the beam pointing direction and targets far away from the beam pointing direction can be flexibly adjusted according to actual application requirements.

The application discloses a method for quickly evaluating a safe area on the surface of an extraterrestrial object by using a single image feature, and belongs to the field of deep space exploration. The method comprises the following steps: collecting a single image of the surface of the extraterrestrial object, removing image noise and rocks smaller than a given size in the image by using a side window filtering method, retaining other rocks, and using a gradient sharpening method to strengthen edge information to obtain a preprocessed image; constructing a ruggedness discrimination operator based on local pixel statistical characteristics, performing image segmentation on the preprocessed image by using the ruggedness discrimination operator, evaluating a safe area, and obtaining a screened binary image of the safe area on the surface of the extraterrestrial object by combining a morphological filtering method; calculating the size of a landing area by using a safety radius spiral search algorithm, combining a calculated value of a ruggedness discrimination function of the safe area, constructing a landing point weighted evaluation method which comprehensively considers the flatness of a landing point and the size of the landing area, and realizing optimization of the landing point. The application can improve the accuracy and efficiency of the safe area and the optimized landing point.

Systems and methods are described for converting input content. A first model may convert input content to an output content that exhibits one or more desired properties. A second model may determine if the conversion meets a desired quality of conversion using a discriminating function. The discriminating function may determine a difference between properties of the output content and properties of desired content, where the difference corresponds to the success of the conversion applying the desired properties. Updated control data may be generated by a third model using information from the second model, where the updated control data may be used by the first model to reduce the determined difference. After updated control data has been generated, the foregoing steps may be repeated based upon the updated control data. One of a plurality of different actions may be determined in response to the difference.

An print medium specification method includes (a) step for acquiring first physical property information related to the print medium; (b) step for acquiring second physical property information different from the first physical property information related to the print medium; (c) step for acquiring a discrimination information for discriminating the type of the print medium by inputting the first physical property information to a discrimination function configured as a learned machine learning model; and (d) step for specify a type of the print medium using the discrimination information and the second physical property information not used for machine learning.

The application discloses a UAV flight program control system with trajectory detection and discrimination functions and belongs to the technical field of unmanned aerial vehicles (UAVs). In order to solve the problem that positioning is easily affected by signals and the accuracy of trajectory discrimination is affected, a trajectory integration module integrates the flight map positioning and satellite map positioning of a UAV to generate a UAV flight trajectory; a detection and discrimination module detects and discriminates the UAV flight trajectory, detects and matches the UAV flight trajectory and a predetermined flight route, so that the UAV flight trajectory can be matched with satellite positioning when the UAV flight trajectory is generated, the satellite map positioning can be calibrated according to the flight map positioning, the problem of inaccurate satellite positioning caused by signals is avoided, the accuracy of the UAV flight trajectory when the UAV flight trajectory is generated is ensured, the UAV flight trajectory is convenient for subsequent detection and discrimination, and the accuracy of UAV trajectory discrimination is improved.

This invention discloses a traveling wave protection method, device, system, and medium based on heterogeneous computing, belonging to the field of power system relay protection. The method is based on a heterogeneous computing architecture that coordinates CPU and FPGA, executing high-speed data sampling, phase-mode transformation, start-up discrimination, and integral calculation in parallel via the FPGA; the CPU is responsible for parameter configuration, receiving preprocessing results from the FPGA, and executing the final protection logic judgment. Specifically, the start-up discrimination uses an integer arithmetic formula optimized for FPGA to quickly capture faults; the integral calculation obtains the integral value of the first-mode current within a specific time window; the CPU calculates its correlation with the reference waveform using a discrimination function to distinguish between faults inside and outside the fault zone. This invention significantly improves the computational efficiency and operating speed of traveling wave protection, exhibiting high reliability and strong tolerance to transition resistance.

The application relates to a glass classification method and device based on a classification model, equipment and a medium. The method comprises the following steps: automatically extracting chemical composition discrimination features of glass sample data by constructing a machine learning-based classification model, constructing a linear discrimination function according to the discrimination features and the discrimination units in the classification model, preliminarily classifying the glass sample data based on the linear discrimination function, further using a sub-classification unit in the classification model to perform detailed sub-classification on the preliminary classification result by using a K-means clustering algorithm, and obtaining a sub-classification result. By using the method, the classification model can be learned and trained through large-scale data, different chemical composition features of the glass sample data can be extracted based on the classification model, the glass sample data can be preliminarily classified and sub-classified, and the accuracy and reliability of glass classification are improved.