Hong Kong Applied Science and Technology Research Institute Company, Limited

Laifa Fang

Kar-Wing Edward Lor

Zheng Long Li

King Wai Chow

The framework predicts water-quality data from observation data associated with raw features, e.g., rainfall. In the framework, an artificial neural network (ANN) computes the predicted water-quality data from feature data associated with impact features and derived from the observation data. The impact features are learnable, and are selected from impact-feature candidates comprising directional change- (DC-based) features each indicating occurrence of DC events in a corresponding raw feature. Including the DC-based features in the candidates enhances the ANN's ability of capturing significant change patterns of water quality due to extreme/unexpected events. The ANN architecture is also configurable according to model hyperparameters, which are learnable. The impact features and model hyperparameters are learnt by differential evolution for maximizing a prediction performance achieved by the ANN, thereby enabling the ANN architecture and impact features to be automatically optimized without requiring manual adjustment by domain experts in applying the ANN to different situations.

Self-adaptive optimization framework for water quality prediction

For an augmented reality (AR) content creation system having a marker database, when a user requests this system to use a first sub-image of an image to update the marker database, this system computes a suitability score of the first sub-image for rating feature richness of the first sub-image and uniqueness thereof against existing markers in the marker database. When the suitability score is less than a threshold value, a second sub-image of the image having a suitability score not less than the threshold value and completely containing the first sub-image is searched. Then the second sub-image, the suitability score thereof and the suitability score of the first sub-image are substantially-immediately presented to the user for real-time suggesting the user to use the second sub-image instead of the first sub-image as a new marker in updating the marker database to increase feature richness or uniqueness of the new marker.

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Hong Kong, China

Laifa Fang