Chinese entity relationship is usually stored in the form of a triple, usually based on dependent on its syntactic and semantic role labeling way of information extraction, the method to extract the entities may be greatly influenced by noise, this paper USES neural network is optimized by the recognition of Chinese entities triples, abstracted from the first extracts the initial triples, and then use neural network to the initial triples, which can identify the entity through our experiment, found that this method not only can well remove the noise of the entity, and can be controlled by neural networks,Allows the result of a triple that can only be parsed as expected.
Aiming at the problems of low positioning accuracy and high data dimension of traditional WIFI fingerprint locating method, propose the WIFI fingerprint indoor locating method based on TSNE-KNN method to solution the problem. In the offline stage, the WIFI fingerprint database is dimensionalized by using the TSNE (t-distributed embedding), and the TSNE parameters are adjusted to obtain the 2d(two-dimensional) WIFI fingerprint database with high differentiation. In the online phase: firstly, the real-time WIFI signal strength collected together with the original WIFI fingerprint database is used as the input of TSNE. The 2d WIFI fingerprint database obtained in the offline phase is used as the initial solution, and a set of arbitrary data is added as the initial solution. The TSNE parameters obtained in the offline phase are used to calculate the dimensionality reduction data. Then use KNN (k-nearestneighbor) algorithm to achieve WIFI location; Finally, the fingerprint database on the fourth floor of EE building of XJTLU north campus is used as input in the experiment. Experiments show that the TSNE-KNN can effectively display the characteristics of high-dimensional datas with low-dimensional datas, and improve the location accuracy also.
The Pythagorean fuzzy set is characterized by five parameters, namely membership degree, non-membership degree, indeterminacy degree, strength of commitment about membership, and direction of commitment. And distance measure is an important index in Pythagorean Hesitant fuzzy (PHF) environment when solving the multicriteria decision-making problem. However, the existing distance measure considers the difference between the member ship degrees, the non-membership degrees, and the degrees of indeterminacy, but ignores the influence of the difference between the directions of PHF sets (PHFSs). The existing distance measure method may lead to unreasonable results sometimes. Inspired by above, the five parameters of PFS are extended to Pythagorean Hesitant Fuzzy set (PHFS) fully in this paper, generating new distance measures of PHFS and introducing some properties and theorems firstly. Then, the proposed method is applied in MCDM with PHF information by considering the distance between the positive ideal solution and each alternative. Finally, to validate the effectiveness of the proposed method, a pragmatic experiment is introduced for comparisons with existing methods.