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Frequent and dramatic viewpoint changes make loop closure detection of hybrid ground/aerial vehicles extremely challenging. To address this issue, we present a robust and efficient loop closure detection approach based on the state-of-the-art simultaneous localization and mapping (SLAM) framework and pre-trained deep learning models. First, the outputs of the SuperPoint network are processed to extract both tracking features and additional features used in loop closure. Next, binary-encoded SuperPoint descriptors are applied with a method based on Bag of VisualWords (BoVW) to detect loop candidates efficiently. Finally, the combination of SuperGlue and SuperPointdescriptors provides correspondences of keypoints to verify loop candidates and calculate relative poses. The system is evaluated on the public datasets and a real-world hybrid ground/aerial vehicles dataset. The proposed approach enables reliable loop detection, even when the relative translation between two viewpoints exceeds 7m or one of the Euler angles is above 50°.

Polyacrylonitrile/Soy protein isolate/polyurethane (PAN/SPI/PU) blends were prepared in dimethylsulfoxide (DMSO). The compatibility and interactive properties of PAN/SPI, SPI/PU, and PAN/PU blend systems were studied using dilute solution viscometry and phase contrast microscopy. It was found that PAN/SPI and PAN/PU were immiscible systems, but that there was an attractive interaction between them. However, the SPI/PU was an almost miscible system. To improve compatibility, the main product of graft copolymerization of acrylonitrile and SPI (AN-g-SPI) and the alkaline hydrolysis polyacrylonitrile (HPAN) were used as compatibilizers. The results showed that the mechanical properties of both the PAN/SPI and PAN/SPI/PU systems were significantly improved.

A novel air leak diagnosis and localization method for vessels is proposed. The tempreture field around the leak changes during air inflation and deflation.The changing phenomenon is acquired by thermal camera and the best detecting time is confirmed by temperature curves.Then a local gray-entropy difference algorithm is used to identify the leak area from infrared images captured during inflation and deflation. The gray information of local gray-entropy enhances the difference between leak area and non-leak area largely meanwhile the entropy information of local gray-entropy improves robustness performance.Experiments verify that the leak localization method is effective and sensitive.

In this paper, we discuss the existence of positive radially symmetric entire solutions of the p-k-Hessian equation σk1kλDi|Du|p−2Dju=α1k(|x|)f(u), and the general p-k-Hessian system σk1kλDi|Du|p−2Dju=α1k(|x|)f1(v)f2(u), σk1kλDi|Dv|p−2Djv=β1k(|x|)g1(u)g2(v).

Aspect-oriented Fine-grained Opinion Extraction (AFOE) aims to extract the aspect terms, corresponding opinion terms and sentiment polarity in a target sentence. Most previous methods treat AFOE as word-level or span-level task, which ignore the complementarity of these two tasks. To integrate the merits of word-level and span-level information, we construct an end-to-end Span-based Multi-Table Labeling (SpanMTL) framework. SpanMTL combines word-based and span-based table labeling to tackle AFOE task. Specifically, in the proposed model, we use two separate BiLSTMs to encode the information of aspect and opinion terms into a word-based 2D representation table. Based on the table, we construct span-based table with CNN by associating the word-pair representations. At last, we integrate the table label distributions of word- and span-based table labeling to generate a multi-table labeling. The proposed method improves the performances of OPE and OTE tasks by introducing span information especially on the data with lots of spans. We have conducted various experiments on AFOE datasets to validate our method. The experimental results show that our method outperforms other baselines when the sentences having lots of span information.

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