Database construction and data retrieval for optimal casting design

Abstract

The existing optimal casting design data retrieval system enables a user to easily retrieve only the needed text data from a huge text database. While the necessary data can be found quickly through search terms, however, it is difficult to access know how gained in the field and the shape data on casting design models. To solve this problem, an optimal system has been developed to retrieve all the shape data generated in the field. The features and keywords of comparative optimal casting designs are extracted and stored in the shape feature keyword database. Through the user interface, the two-dimensional and three-dimensional shape data and shape keywords of a product model are compared with those of casting designs stored in the database. In this way, the optimal casting design most similar to a good model is chosen.     

The Influence of Heat on the Sensitivity of a Photographic Film to Low Intensity Light

A fast radiation monitoring film, the lIford PM I, was exposed at 0 per cent relative humidity to low intensity u-v light from a plastic scintillator, the exposure temperoture being varied between +10 and +75°C. Films were also heated at 60°C. and 0 per cent RH and subsequently exposed to the scintillator light at 30O°C, the results indicating the presence of latent sub-image in the heated film at the end of the heating. From the temperature dependence of the net film density obtained with constant heating time the activation energy of 126 eV was derived, this corresponding to the formation energy of a Frenkel defect in silver bromide. The results suggest that the effect of increased temperature in increasing light absorption is a minor one in this instance.     

Image Recoloring with Valence‐Arousal Emotion Model

We introduce an affective image recoloring method for changing the overall mood in the image in a numerically measurable way. Given a semantically segmented source image and a target emotion, our system finds reference image segments from the collection of images that have been tagged via crowdsourcing with numerically measured emotion labels. We then recolorize the source segments using colors from the selected target segments while preserving the gradient of the source image to generate a seamless and natural result. User study confirms the effectiveness of our method in accomplishing the stated goal of altering the mood of the image to match the target emotion level.