Path planning is the focus and difficulty of research in the field of mobile robots, and it is the basis for further research and application of robots. In order to obtain the global optimal path of the mobile robot, an improved Moth-Flame Optimization(IMFO) Algorithm is proposed in this paper. Firstly, referring to the Spotted Hyena Optimization (SHO) Algorithm, the concept of historical best flame average is introduced to improve the Moth-Flame Optimization (MFO) Algorithm update formula to increase the ability of the algorithm to jump out of the local optimum; Secondly, the Quasi-Opposition-based learning(QOBL) is used to perturb the location, increase the population diversity and improve the convergence rate of the algorithm. Combining the above two strategies, this paper proposes an Improved Moth-Flame Optimization (IMFO) Algorithm. In order to evaluate the performance of IMFO algorithm, the IMFO algorithm is compared with other three algorithms on three groups of different types of benchmark functions. The comparative results show that the IMFO algorithm is effective and has good performance in terms of jumping out of local optimum, balancing exploitation ability and exploration ability. Finally, the IMFO algorithm is applied to the path planning of the mobile robot, which provides a new idea for the path planning of the mobile robot.
Background. MicroRNAs (miRNAs) are short noncoding RNAs integral for regulating gene expression at the posttranscriptional level. However, experimental methods often fall short in finding miRNAs expressed at low levels or in specific tissues. While several computational methods have been developed for predicting the localization of mature miRNAs within the precursor transcript, the prediction accuracy requires significant improvement. Methodology/Principal Findings. Here, we present MatPred, which predicts mature miRNA candidates within novel pre-miRNA transcripts. In addition to the relative locus of the mature miRNA within the pre-miRNA hairpin loop and minimum free energy, we innovatively integrated features that describe the nucleotide-specific RNA secondary structure characteristics. In total, 94 features were extracted from the mature miRNA loci and flanking regions. The model was trained based on a radial basis function kernel/support vector machine (RBF/SVM). Our method can predict precise locations of mature miRNAs, as affirmed by experimentally verified human pre-miRNAs or pre-miRNAs candidates, thus achieving a significant advantage over existing methods. Conclusions. MatPred is a highly effective method for identifying mature miRNAs within novel pre-miRNA transcripts. Our model significantly outperformed three other widely used existing methods. Such processing prediction methods may provide important insight into miRNA biogenesis.
Mobile Augmented Reality (MAR) systems are becoming ideal platforms for visualization, permitting users to better comprehend and interact with spatial information. Subsequently, this technological development, in turn, has prompted efforts to enhance mechanisms for registering virtual objects in real world contexts. Most existing AR 3D Registration techniques lack the scene recognition capabilities needed to describe accurately the positioning of virtual objects in scenes representing reality. Moreover, the application of such registration methods in indoor AR-GIS systems is further impeded by the limited capacity of these systems to detect the geometry and semantic information in indoor environments. In this paper, we propose a novel method for fusing virtual objects and indoor scenes, based on indoor scene recognition technology. To accomplish scene fusion in AR-GIS, we first detect key points in reference images. Then, we perform interior layout extraction using a Fully Connected Networks (FCN) algorithm to acquire layout coordinate points for the tracking targets. We detect and recognize the target scene in a video frame image to track targets and estimate the camera pose. In this method, virtual 3D objects are fused precisely to a real scene, according to the camera pose and the previously extracted layout coordinate points. Our results demonstrate that this approach enables accurate fusion of virtual objects with representations of real world indoor environments. Based on this fusion technique, users can better grasp virtual three-dimensional representations on an AR-GIS platform.
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