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Bayesian optimization has been demonstrated as an effective methodology for the global optimization. However, it suffers from a computational bottleneck that the inference time grows cubically with the number of observations. In this paper, a Bayesian optimization based on the data-parallel approach is proposed to alleviate this problem. Firstly, an improved geometry motivated clustering algorithm...
Reservoir computing has been widely applied in dynamical system modeling and solving time-dependent problems at low computational expense. However, when confronting some complex tasks that exhibit multiple sets of dynamics, the conventional reservoir computing model with a single reservoir may become ineffective and powerless. Inspired by the modality-independent but functionally connected brain regions,...
In this paper, we address adaptive predictor feedback design for a simplified drilling system in the presence of disturbance and time-delay. The main objective is to stabilize the bottomhole pressure at a critical depth at a desired set-point directly. The stabilization of the dynamic system and the asymptotic tracking are demonstrated by the proposed adaptive control, where the adaptation employs...
Composite services are notoriously prone to failure, this is particularly true for long-running, and data-intensive services. Different composition strategies can be employed to make compositions robust. Any service composition strategy does impact performance at the lower network layer and needs to be assessed. Novel approaches are needed to model and evaluate dynamically reconfigurable service composition...
User profiling plays an important role in online news recommendation systems. In this paper, we analyze the relationship between users' clicking behaviors and the category of the news story to model user's interests by mining web log data of an adaptive news system. We train a Memory-based User Profile (MUP), which imitates human being's learning, remembering and forgetting mechanisms, to predict...
The use of semantic Web technologies and service oriented computing paradigm in Internet of Things research has recently received significant attention to create a semantic service layer that supports virtualisation of and interaction among ``Things''. Using service-based solutions will produce a deluge of services that provide access to different data and capabilities exposed by different resources...
Compared with other large-scale warfare simulation systems, wargame, as a traditional type of warfare simulations, has the advantages of low cost, convenience, practicability, etc. In order to realize Human-Computer Confrontation, Computer Generated Forces (CGF) technology has been added to wargame, which would enhance wargame's training and decision support ability. Based on computerization of “Future:...
Multiple-Instance learning (MIL), which relaxes training annotation granularity from instance level to instance collection (bag) level by applying bag concept, obtains increasing attentions from computer vision community. Due to its flexible annotation mechanism, MIL has been naturally utilized on a variety of computer vision problems. And numerous models have been proposed, each of which is ingeniously...
Based on the fact that the flue gas oxygen content in power plant is hard to detect effectively, a soft-sensing model based on kernel fuzzy C-means clustering and local modeling method is proposed from improving the online self-adaptive ability of the soft-sensing model. Firstly, several sub-sample sets are formed by using kernel fuzzy C-means clustering algorithm to cluster analysis of the history...
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