Abstract: A method according to certain embodiments generally involves operating a system including an unmanned aerial vehicle (UAV) and a base station. The base station includes a nest including an upper opening having an upper opening diameter and a lower opening having a lower opening diameter less than the upper opening diameter. The lower opening is accessible from within the base station. The method generally includes landing the UAV within the nest such that a portion of the UAV is accessible via the lower opening, releasably attaching a load to the UAV, and operating the UAV to deliver the load to a destination.

Abstract: The present invention provides a method for separating out a defect image from a thermogram sequence based on weighted naive Bayesian classifier and dynamic multi-objective optimization, we find that different kinds of TTRs have big differences in some physical quantities. The present invention extracts these features (physical quantities) and classifies the selected TTRs into K categories based on their feature vectors through a weighted naive Bayesian classifier, which deeply digs the physical meanings contained in each TTR, makes the classification of TTRs more rational, and improves the accuracy of defect image's separation. Meanwhile, the multi-objective function does not only fully consider the similarities between the RTTR and other TTRs in the same category, but also considers the dissimilarities between the RTTR and the TTRs in other categories, thus the RTTR selected is more representative, which guarantees the accuracy of describing the defect outline.

Abstract: The present invention provides a method for separating out a defect image from a thermogram sequence based on feature extraction and multi-objective optimization, we find that different kinds of TTRs have big differences in some physical quantities, such as the energy, temperature change rate during endothermic process, temperature change rate during endothermic process, average temperature, maximum temperature. The present invention extract these features (physical quantities) and cluster the selected TTRs into L clusters based on their feature vectors, which deeply digs the physical meanings contained in each TTR, makes the clustering more rational, and improves the accuracy of defect separation. Meanwhile, the present invention creates a multi-objective function to select a RTTR for each cluster based on multi-objective optimization.

Abstract: The present invention provides a method for separating out a defect image from a thermogram sequence based on feature extraction and multi-objective optimization, we find that different kinds of TTRs have big differences in some physical quantities, such as the energy, temperature change rate during endothermic process, temperature change rate during endothermic process, average temperature, maximum temperature. The present invention extract these features (physical quantities) and cluster the selected TTRs into L clusters based on their feature vectors, which deeply digs the physical meanings contained in each TTR, makes the clustering more rational, and improves the accuracy of defect separation. Meanwhile, the present invention creates a multi-objective function to select a RTTR for each cluster based on multi-objective optimization.

Abstract: A method for separating out a defect image from a thermogram sequence based on weighted naive Bayesian classifier and dynamic multi-objective optimization. A method extracts these features and classifies the selected TTRs into K categories based on their feature vectors through a weighted naive Bayesian classifier, which deeply digs the physical meanings contained in each TTR, makes the classification of TTRs more rational, and improves the accuracy of defect image's separation. Meanwhile, the multi-objective function does not only fully consider the similarities between the RTTR and other TTRs in the same category, but also considers the dissimilarities between the RTTR and the TTRs in other categories, thus the RTTR selected is more representative, which guarantees the accuracy of describing the defect outline.

Abstract: Provided are 1-(3-aminopropyl) substituted cyclic amine compounds as represented by frmula (I), pharmaceutically acceptable salts, enantiomers, diastereoisomers, racemates and mixtures thereof, and a method of synthesizing said 1-(3-aminopropyl) substituted cyclic amine compounds by using aromatic heterocyclic formaldehyde as raw material. Said compounds can be used as CCR 5 antagonist for the treatment of HIV infection.

Abstract: Provided are 1-(3-aminopropyl) substituted cyclic amine compounds as represented by formula (I), pharmaceutically acceptable salts, enantiomers, diastereoisomers, racemates and mixtures thereof, and a method of synthesizing said 1-(3-aminopropyl) substituted cyclic amine compounds by using aromatic heterocyclic formaldehyde as raw material. Said compounds can be used as CCR 5 antagonist for the treatment of HIV infection.