33 results about "Similitude" patented technology

A method of calculating and encoding a score of semantic and spatial similarity between concepts of an ontology stored in hierarchically numbered trellis form, in which the score (NSSij) is calculated and encoded (1) for each concept (Ci) with respect to a central concept (Cj), taken two-by-two, by convergence relative to their common semantic characteristics respectively by separation according to their distance. The method is applicable to the consultation of ontologies representative of knowledge in the technical or non-technical domain.

The present disclosure proposes a library function identification and detection method and system based on a convolutional autoencoder, including: extracting the instruction sequence corresponding to the binary function from each software to be tested, and then extracting the operation code in the instruction sequence; The continuous sequence of codes is counted to construct a co-occurrence matrix for each function, which is used as input to train the convolutional autoencoder model, and the convolutional autoencoder model is used to extract bottleneck features and complete model training; use training The final model encodes the library functions, and then performs similarity analysis on the library functions, and identifies the library function with the highest similarity coefficient as the final match. It has good versatility, and can directly mark various newly introduced matching objects, and obtain good results.

The invention discloses a 3D model nesting and placing method based on model similarity. The method comprises the following steps that 1, carrying out voxelization on the surface of a 3D model; 2, combining and classifying the voxelized 3D model according to the similarity, and dividing the 3D model into similar model group and non-similar model; 3, nesting and placing the similar model group and the non-similar model group according to a yzx axis sequence to obtain a placing area; and 4, in the placing area, carrying out iterative nested placement on the similar model group and the non-similar model according to a yxz axis sequence, and updating the area after each iteration till that the iteration is completed. The method comprises the following steps: firstly, according to a voxelized 3D model, performing similarity classification combination on the 3D model, then performing nested placement in the yzx direction to obtain a placing area, finally performing nested placement in the yxz direction, and iteratively adjusting the size in the xy direction to minimize the placing area each time. And through similarity combination, supporting materials between the models are reduced, and the models are easy to take down after printing is completed.

The invention discloses a tool feature recognition method based on deep learning, which is used to solve the technical problem of poor practicability of the existing non-standard special tool design method. The technical solution is to start with image vision, dig out the relationship between the machining features of the workpiece and the two-dimensional engineering drawing of the tool, and realize the reuse of the tool design scheme. First, based on the convolutional deep belief network generation model, the structure information and size information of the two-dimensional engineering drawing are fused, and the feature extraction of the high-dimensional tool image is performed to obtain the low-dimensional tool feature vector; then combined with the CDBN discriminant model and transfer learning, in the reuse On the basis of tool feature extraction network parameters, the relationship between processing features and tool feature vectors is mined; finally, through the two steps of tool classification based on Sofmax and similarity matching based on the cosine method of included angle, the tool feature vector to historical tool design scheme is realized. The mapping is good in practicability.

The invention discloses a similarity-based multi-example dictionary learning and classification method, comprising: obtaining a training set; for each positive candidate example selected from a positive bag set, repeatedly performing the following steps, cyclic iteration, training a classifier, Until the preset iteration stop condition is met: calculate the similarity weights of each example in the training set for the positive class and the negative class respectively; learn each example in the training set through dictionary learning to obtain the sparse coding of each example; learn a projection dictionary, The sparse coding of each example is re-represented; based on each example re-represented by projection and the similarity weight of each example, a classifier is trained; based on the trained classifier, the category of the target package is obtained. By applying the technical solution provided by the embodiment of the present invention, the category of the target package can be accurately determined, and the classification effect of the classifier is improved. The invention also discloses a multi-instance dictionary learning and classification device based on similarity, which has corresponding technical effects.

The invention relates to semantic analysis, and provides an artificial intelligence-based method, device, equipment and medium for determining test scores. The present invention can accurately determine whether the user's answer is plagiarized through the Hamming distance of the standard vector and the user vector, and respectively analyzes the standard vector, the user vector and the test question vector through the preset encoder, and can make the test question feature vector, standard feature vector and Each element in the user feature vector integrates the contextual semantic information of the corresponding vocabulary. Through the similarity weighted operation of the standard feature vector and the user feature vector, the words with greater similarity between the user answer and the standard answer can be used in subsequent tasks. The semi-open-ended question can be accurately determined by analyzing the semantic similarity between the user's answer and the standard answer, and the matching degree between the user's answer and the test questions to be scored. In addition, the present invention also relates to blockchain technology, and the target score can be stored in the blockchain.

The present invention proposes a software partial plagiarism detection method based on dynamic instruction dependency graph birthmark, including: 1) using dynamic instrumentation to monitor the program at the instruction level to capture the instruction track of each function; 2) recording each function’s dynamic instruction trajectory, carry out data dependency and control dependency analysis, and construct a dynamic instruction dependency graph birthmark; 3) calculate the similarity between instruction dependency graph birthmarks, and realize the measurement of similarity between functions; 4) based on a given threshold, for the plaintiff Each function in the program builds a suspicious function table; 5) Extract the static function call graph of the program, and under the guidance of the call dependency, perform accurate one-to-one pairing of suspicious functions; 6) Based on the call dependency, assemble the matching function pair to generate Plagiarism evidence map, measuring the proportion of suspected plagiarism. The invention realizes the detection of partial plagiarism by constructing function-level birthmarks; the invention proposes the concept of plagiarism evidence map for the first time, which can greatly enhance the effectiveness of evidence.

The invention discloses a method for nesting and arranging 3D models based on model similarity, which comprises the following steps: step 1: voxelize the surface of the 3D model; step 2: voxelize the 3D model according to the similarity Combination classification, divided into similar model group and non-similar model; Step 3: Nest and arrange similar model group and non-similar model according to the order of yzx axis to get the placement area; Step 4: In the placement area, similar The model group and non-similar models are placed iteratively and nested in the order of the yxz axis, and the area is updated after each iteration until the iteration is completed. Firstly, according to the voxelized 3D model, make similar classification and combination of 3D models, then nest and place them in the yzx direction to get a placement area, and finally nest and place them in the yxz direction, iteratively adjust the xy direction size so that each The area of ​​the second placement is the smallest. Through the combination of similarities, the support material between the models is reduced, and the models are easy to remove after printing.