Modelling and reasoning of large scale fuzzy petri net using inference path and bidirectional methods

The state explosion problem has limited further research of Fuzzy Petri Net (FPN). With the rising scale of FPN, the algorithm complexity for related applications using FPN has also rapidly increased. To overcome this challenge, this research proposed three algorithms, which are transformation algorithm, decomposition algorithm and bidirectional reasoning algorithm to solve the state explosion problems of knowledge-based system (KBS) modelling and reasoning using FPN. Based on the goal of this research, the entire research is separated into two tasks, which are KBS modelling and reasoning using FPN. In modelling, a transformation algorithm has been proposed while in reasoning, decomposition and bidirectional reasoning algorithms have been proposed. In transformation, the algorithm is proposed to generate an equivalent large-scale FPN for the corresponding large-size KBS using a novel representation method of Fuzzy Production Rule (FPR). In decomposition, the algorithm is proposed to separate a large-scale FPN into a group of sub-FPNs by using a presented index function and incidence matrix. In bidirectional reasoning, the algorithm for optimal path is proposed to implement inference operations. Experimental results show that all proposed algorithms have successfully accomplished the requirements of each link of KBS modelling and reasoning using large-scale FPN. First, the proposed transformation algorithm owns ability to generate the corresponding FPN for the large-size KBS automatically. Second, the proposed decomposition owns ability to divide a large-scale FPN into a group of sub-FPNs based on the inner-reasoning-path. Lastly, the proposed bidirectional reasoning algorithm owns ability to implement inference for the goal output place in an optimal reasoning path by removal of irrelevant places and transitions. These results indicate that all proposed algorithms have ability to overcome the state explosion problem of FPN

Chinese sentence similarity calculation based on modifiers

To compute the similarity of Chinese sentences accurately, a revised Chinese sentence similarity approach is proposed though enhancing the importance of the modifiers of stem of sentence. After extracting the modified part of the sentence by Language Technology Platform (LTP), this part of each structure could be removed the longest common substring, to better capture the similarities of modified parts. The entire method includes three phases, which are to split the sentences into principal and predicate object structures using the syntactic analysis tool, to generate modifiers and sentence stem vectors and calculate the similarity between the vectors using the Word2Vec, and to obtain the similarity between two sentences by weighting each part. Experimental results on 200 sentences of the LCQMC dataset and corresponding analysis reveal that the proposed method can obtain more accurate similarity calculation results by effectively gaining the modified part - which affects the whole sentence meaning effectively-of the sentence structure

Improved ORB-SLAM2 Algorithm Based on Information Entropy and Image Sharpening Adjustment

Simultaneous Localization and Mapping (SLAM) has become a research hotspot in the field of robots in recent years. However, most visual SLAM systems are based on static assumptions which ignored motion effects. If image sequences are not rich in texture information or the camera rotates at a large angle, SLAM system will fail to locate and map. To solve these problems, this paper proposes an improved ORB-SLAM2 algorithm based on information entropy and sharpening processing. The information entropy corresponding to the segmented image block is calculated, and the entropy threshold is determined by the adaptive algorithm of image entropy threshold, and then the image block which is smaller than the information entropy threshold is sharpened. The experimental results show that compared with the ORB-SLAM2 system, the relative trajectory error decreases by 36.1% and the absolute trajectory error decreases by 45.1% compared with ORB-SLAM2. Although these indicators are greatly improved, the processing time is not greatly increased. To some extent, the algorithm solves the problem of system localization and mapping failure caused by camera large angle rotation and insufficient image texture information

Additional file 1 of Whole-genome resequencing provides insights into the diversity and adaptation to desert environment in Xinjiang Mongolian cattle

Additional file 1. Supplementary Figure 1. The output produced by OptM. A total of 5 iterations were run for each possible number of migration edges, m = 1–10. (A) The mean and standard deviation (SD) for the composite likelihood L(m) (left axis, black circles) and proportion of variance explained (right axis, red “x”s). (B) The second-order rate of change (Δm) across values of m. The arrow indicates the peak in Δm at m = 2 edges. Supplementary Figure 2. Cross-validation plot for the 161 genomes. Supplementary Table 1. Summary of sequencing data. Supplementary Table 2. Functional classification of the detected SNPs. Supplementary Table 3. Functional classification of the exonic SNPs. Supplementary Table 4. list of selected regions in Xinjiang Mongolian cattle. Supplementary Table 5. The top ten significant GO terms from the enrichment analysis of selected candidate genes Supplementary Table 6. The top ten significant KEGG pathways from the enrichment analysis of selected candidate genes