A multi-dimensional fusion strategy similarity measure method for patent application technology disclosure document.

Patent application technology disclosure document is one of the important bases for judging patent novelty and uniqueness. Automated evaluation can effectively solve the problems of long time and strong subjectivity of human evaluation. The text similarity evaluation algorithm based on corpus and deep learning technology has problems such as insufficient amount of cross-library learning data and insufficient core content tendency in the similarity judgment of patent application technology disclosure document, which limits their performance and practical application. In this paper, we propose a similarity evaluation method of patent application technology disclosure document based on multi-dimensional fusion strategy to realize the similarity measurement of patents. Firstly, in the text preprocessing section, word segmentation reconstruction and similarity evaluation optimization strategies based on word frequency and part-of-speech score weighted fusion are proposed. Then, a similarity calculation method of patent application technology disclosure document based on two new mapping spaces of dot matrix and image is proposed to achieve a more diversified comprehensive evaluation. The algorithm was evaluated by using four published text similarity matching datasets (containing 0-5 or 0/1 labels) and a set of patent application technology disclosure documents. Experimental results show that on the published text similarity matching datasets, the similarity evaluation method under the multi-dimensional fusion strategy proposed in this paper has a discrimination accuracy improvement of about 10% compared to traditional vector semantic model, and can match the discriminative ability of lightweight deep learning models without the need for training. At the same time, the discrimination accuracy of the proposed method on the sample dataset of patent application technology disclosure document is superior to traditional vector semantic model (20%) and various deep learning models (1%-8%), and the precision and recall rate are relatively balanced. The visual analysis results on the dataset of the patent application technology disclosure documents also prove the effectiveness and reliability of the similarity calculation method proposed in the dot matrix and image space, which provide a new idea and method for the similarity evaluation between patent application technology disclosure document.

[FTIR-based vehicles paint comparison].

In order to determine a range on vehicle types by the vehicle paints left on the accident site, 940 infrared spectra of vehicle paint from 287 vehicle samples were collected, and then the infrared spectrum database on vehicle body paint was established. The vehicle paints comparison was implemented by characteristic peaks method and correlation coefficient method, and the comparison tests on different vehicle scrap paints were carried out. The test results show that the key of vehicle paint comparison is the spectrum of topcoat layer and the coating layer for the integrated scrap paint, and spectrum should be searched after layer separating for partial scrap paint. For aging paint, topcoat layer spectrum should be main emphasis and the range of suspect vehicle should be extended.

[Infrared spectrum characteristics of vehicle body paint].

Two hundred eighty seven samples of vehicle paint were collected, and 940 spectra were obtained by Fourier transform infrared micro spectrometer. The spectral features of varnish, finish layer, and coated layers of different models and different color were analyzed, and the spectra similarities were compared. The results show that the varnish similarity on the same models with different color is 99.5%, and some similar model with the same manufacturer had high similarity. The finish spectra have remarkable differences with different model and different color, and the similarity degree is under 70%. The coated layer similarity varies between 83.33% and 96.91% among the common lacquer putty, and it ranges between 70.12% and 96.44% among the water-based lacquer putty. The metal components of paint will influence the spectrum characteristics. The spectra of the vehicle paint will change with the usage time.