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Loop closure detection can eliminate the accumulated error of visual SLAM, which is of great significance to the SLAM system. Among them, the widely used visual bag-of-word model algorithm has the problem of visual word identity and ambiguity, which affects the loop closure detection effect. To improve these problems and loop back detection effect, this paper proposed a loop closure detection algorithm based on soft-assigned SIFT features. The algorithm allocated the SIFT feature points extracted from the image to several words with the closest Euclidean distance, according to the distance sorting and weighting, and removed the feature points that were far away from the words to generate more discriminating descriptors. In addition, when filtering candidates, the algorithm added the proportion constraint of the same word feature points and the stability constraint of word shift to filter out a few candidates. In the experimental results, the algorithm compared with the traditional visual bag-of-words model and several loop closure detection algorithms in recent years, the recall rate at 100% accuracy rate improved under three data sets, and the average image query time was about 40 ms. The results show that the algorithm can improve the loop detection effect and ensure the real-time performance. [ABSTRACT FROM AUTHOR]

In order to fix the integer ambiguity quickly and accurately, this paper proposed a new ambiguity search algorithm based on measurement accuracy constraints to solve the problems of the LAMBDA algorithm, such as the wide search range of the integer ambiguity and the low search efficiency. When the least squares problem got optimally weighted, the algorithm took the carrier phase measurement accuracy as the constraint condition to test the fixed ambiguity solution on the basis of the stan-dard differential evolution （DE） algorithm. This algorithm solved the influence of different satellite altitude angles, and could achieve 99% success rate when solving 3D integer ambiguity. Compared with the MLAMBDA algorithm, the standard DE algorithm and the adaptive weighted differential evolution （AWDE） algorithm, the proposed algorithm further improves the efficiency and success rate of ambiguity resolution. [ABSTRACT FROM AUTHOR]

Current sentiment analysis models usually use word2vec, Glo Ve and other methods to generate static word embedding, and traditional convolutional or recurrent depth models cannot fully focus on the context, extract insufficiently features, and reduce the accuracy of sentiment judgment. This paper proposed a Chinese text sentiment analysis model based on ELMo and Bi-SAN. Firstly, through ELMo language model training, the model got the word vector that integrated the word itself and context information to solve the problem of ambiguity of a word. Meanwhile, it used pre-trained skip-gram algorithm to replace the embedding layer of the randomly initialized ELMo model and improved the convergence speed of the model. Then the model used Bi-SAN to extract features. Due to the self-attention mechanism, Bi-SAN could fully focus on the context of each word and extract features more comprehensively. Compared with multiple existing sentiment analysis models, the proposed model achieves higher F, in the hotel review dataset and the NLPCC2014 task2 Chinese dataset, which validates the effectiveness of the model. [ABSTRACT FROM AUTHOR]

In view of the problem that the JPDA algorithm can only track the known number of targets and association ambiguity, simultaneously, the number of hypotheses in MHT algorithm increases exponentially over time. This paper proposed a measurement-oriented m-optimal hypothesis N-scan MITT algorithm. Firstly, it used the method of generating m-optimal hypothesis to generate a specified number of optimal and sub-optimal hypothesis in each frame,and used fixed-value N of hypothesis tree depth to generate optimal feasible hypothesis to complete data association. This paper used two-point difference linear method and global least squares estimation to solve the track initiation of single new target and multiple new targets, respectively. The simulation results show that, comparing with MITT-DAM algorithm, the proposed method achieves a balance between tracking performance and operation time. [ABSTRACT FROM AUTHOR]

In order to improve the classification accuracy of the minority samples, this paper proposed a novel resampling algorithm based on the improved density peaks clustering method, named IDP-SMOTE. First, it improved density peaks clustering algorithm by utilizing Box-Cox transformation and σ-rule for finding the clustering centers and outliers automatically. Second, it combined the improved density peaks clustering algorithm with SMOTE method. With removing the noisy data, the synthetic samples could be generated in the sub-class regions on the basis of the values of local density and nearest distance of the minority samples. The presented algorithm avoids the boundary ambiguity caused by over-sampling, improves the imbalance problem with-in class and reduces the learning difficulty of the boundary data. Meanwhile, it realizes automatic clustering and resampling, and avoids the interference of subjective factors. Through the contrast experiment, the proposed algorithm is effective and adaptive. [ABSTRACT FROM AUTHOR]