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With the exponential growth of time-stamped data from social media, e-commerce and sensor systems, time series data analysis is of growing interests for extracting useful insights. In many real-world applications, there is usually a large amount of unlabeled data but limited labeled data, which can be difficult to obtain. In this paper, we present a graph-based semi-supervised learning framework which...
In this paper, an online predictive maintenance approach is proposed for monitoring health of semiconductor equipment. It includes two phases, the first is online prediction of the health indicator and the second phase is the classification of the indicator to one of the health states for making maintenance decisions. Kernel recursive least square (KRLS) algorithm is used for online prediction which...
There are few methods to deal with the coal-mine surveillance video, the only few existing methods all make use of the traditional image engineering ideas. Visual attention has the prominent functions that can reduce computation and accelerate the computing speed. This paper firstly analyzes the limitations of existing down-top attention model in the application of coal-mine surveillance video, then...
Triggered by a market relevant application that involves making joint predictions of pedestrian and public transit flows in urban areas, we address the question of how to utilize hidden common cause relations among variables of interest in order to improve performance in the two related regression tasks. Specifically, we propose stacked Gaussian process learning, a meta-learning scheme in which a...
With citypsilas rapid development, environment and resource problem is more and more prominent. For the sake of achieving the basic aim of the city environmental sustainable development, we need establishing an assessment model for predict the environment effectiveness of China city. In this paper, a new environment effect assessment system for China city is presented. The assessment system based...
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