Cross-modal image-retrieval methods retrieve desired images from a query text by learning relationships between texts and images. Such a retrieval approach is one of the most effective ways of achieving the easiness of query preparation. Recent cross-modal image-retrieval methods are convenient and accurate when users input a query text that can be used to uniquely identify the desired image. However, in reality, users frequently input ambiguous query texts, and these ambiguous queries make it difficult to obtain desired images. To overcome these difficulties, in this study, we propose a novel interactive cross-modal image-retrieval method based on question answering. The proposed method analyzes candidate images and asks users questions to obtain information that can narrow down retrieval candidates. By only answering questions generated by the proposed method, users can reach their desired images, even when using an ambiguous query text. Experimental results show the proposed method’s effectiveness.
Using five-hole pitot tubes, detailed flow measurements were made before, within and after a low-speed three-dimensional turbine stator blade row to obtain quantitative information on the aerodynamic loss mechanism. Qualitative flow visualization tests and endwall static pressure measurements were also made.
An analysis of the tests revealed that many vortical flows promote loss generation. Within a large part of the cascade, a major loss process could be explained simply as the migration of boundary layer low energy fluids from surrounding walls (endwalls and blade surfaces) to the blade suction surface near the trailing edge. On the other hand, complexity exists after the cascade and in the vortical flows near the trailing edge. The strong trailing shedding vortices affect upstream flow fields within the cascade. Detailed flow surveys within the cascade under the effects of blade tip leakage flows are also included.
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