66results about How to "Overcoming the problem of low precision" patented technology

The invention provides a text similarity measuring system based on multi-feature fusion and relates to the field of intelligent information processing. According to the system, the text similarity is measured by fusing multiple features based on word frequencies, word vectors and Wikipedia labels. The invention aims to solve the problem of semantic loss caused by non-considering of contexts in a conventional text similarity measuring system and the problem of low similarity result accuracy caused by larger text length difference. The text similarity measuring system is implemented by the following steps: carrying out preprocessing such as word segmentation and stop word removal on a training text; training corpora of the processed training text as a word vector model; measuring the similarity based on the word frequencies, the similarity based on the word vectors and the similarity based on the Wikipedia labels between input text pairs to be computed, and carrying out weighted summation to obtain a final text semantic similarity measuring result. According to the system, the measurement accuracy of the text similarities can be improved, so that the requirement on intelligent information processing is met.

The invention discloses a high-order multi-scale condition random field CRF unsupervised synthetic aperture radar SAR image segmentation method. The method comprises the following steps: performing wavelet transform on the input SAR image; computing histogram features; computing semivariance feature; forming eigenvector; calculating the conditional probability of the local class; performing initial segmenting of synthetic aperture radar sar image; calculating an edge probability of each pixel point; calculating a joint posterior edge probability of each pixel point; calculating a posterior edge probability of each pixel point; estimating parameters; segmenting SAR image segmentation. The invention solves the model parameters through the iteration of the relevant parameters and the iteration of the characteristic parameters, fully utilizes the characteristics of the image itself, and greatly reduces the demand for the quantity of the training data.

The invention discloses a wifi and geomagnetism fused indoor positioning system and method based on an HMM (Hidden Markov Model). The method comprises the following steps: matching a wifi signal acquired by a mobile terminal in real time with a pre-established wifi fingerprint library to obtain a local position area; initializing the position in the area to obtain an initial probability distribution matrix; obtaining a state transition probability matrix by using a position relationship of label points in the local position area; and fusing a geomagnetic signal with a pre-established geomagnetic fingerprint library to obtain an observation probability matrix, and estimating the position of a user according to a forward algorithm. When the user is positioned in the local position area determined by the wifi signal, the current real-time geomagnetic signal is used to update the observation probability matrix, and the position of the user is estimated by the observation probability matrixand the state transition matrix; otherwise, the wifi signal is introduced to re-establish the local position area, and the initial probability distribution matrix, the state transition probability matrix and the observation probability matrix are updated according to the preceding method. According to the wifi and geomagnetism fused indoor positioning method based on the provided by the invention, various information is effectively fused, and the positioning accuracy of the system is improved.