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In this paper, a recursive filtering problem is analyzed for nonlinear systems with sensor saturation under duty cycle scheduling (DCS). The sensor saturation is taken into account to describe practical engineering better. The DCS is introduced to conserve energy by alternating sensor nodes between active and dormant states. The considered problem aims to design a collaboration-prediction-based recursive filtering algorithm for nonlinear systems with sensor saturation such that, under the sparse measurements due to DCS, satisfactory filtering performance is guaranteed. By solving a set of matrix difference equations, the upper bound on the filtering error covariance is first obtained, and then the gain matrix of the filter that minimizes the upper limit is calculated. In addition, the boundedness of the upper bound of the filtering error covariance is analyzed. Finally, the effectiveness of the proposed collaboration-prediction-based recursive filtering algorithm is verified by the simulation example. [ABSTRACT FROM AUTHOR]

In view of problems that existing coal and gas outburst prediction technology is difficult to achieve omnidirectional continuous prediction in the field of time and space,has low utilization rate of anti-outburst prediction results and lag in the release of prediction information, a set of multi-factor, omnidirectional, time and space continue coal and gas outburst collaborative prediction technology system for modern high-yield and efficient mines was proposed. According to space-time dimension of coal and gas outburst, coal and gas outburst risk prediction is divided into regional outburst risk prediction based on space dimension and local outburst risk prediction based on time dimension. Through in-depth mining and analysis of regional and local outburst prediction data such as geological structure, coal seam depth, coal quality, soft coal distribution, drill cuttings index, initial gas velocity, etc., regional and local outburst risk prediction results are obtained and effectively integrated in accordance with certain rules to achieve continuous monitoring and early warning in time and space dimension. Supplementary anti-outburst information integrated management and control platform and WTC-1 gas outburst data acquisition device are developed to provide software and hardware support for the realization of the coal and gas outburst collaborative prediction technology. Field application results show that prediction accuracy of the technology exceeds 90%, single approval time of anti-outburst prediction result is shortened to 22.5% of the original time, and the anti-outburst information comprehensive management and control platform has captured the danger of coal and gas outburst many times in advance, which effectively improves the mine anti-outburst information utilization efficiency.

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Many patients suffer from comorbidity conditions; for example, obese patients often develop type-2 diabetes and hypertension. In the U.S., 80% of Medicare spending is for managing patients with these multiple coexisting conditions. Predicting potential comorbidity conditions for an individual patient can promote preventive care and reduce costs. Predicting possible comorbidity progression paths can provide important insights into population heath and aid with decisions in public health policies. Discovering the comorbidity relationships is complex and difficult, due to limited access to electronic health records by privacy laws. In this paper, we present a collaborative comorbidity prediction method to predict likely comorbid conditions for individual patients, and a trajectory prediction graph model to reveal progression paths of comorbid conditions. Our prediction approaches utilize patient generated health reports on online social media, called social health records (SHR). The experimental results based on one SHR source show that our method is able to predict future comorbid conditions for a patient with coverage values of 48% and 75% for a top-20 and a top-100 ranked list, respectively. For risk trajectory prediction, our approach is able to reveal each potential progression trajectory between any two conditions and infer the confidence of the future trajectory, given any observed condition. The predicted trajectories are validated with existing comorbidity relations from the medical literature. [ABSTRACT FROM PUBLISHER]

To improve the accuracy of real-time public transport information release system, a collaborative prediction model was proposed based on cyber-physical systems architecture. In the model, the total bus travel time was divided into three parts: running time, dwell time and intersection delay time, and the data were divided into three categories of historical data, static data and real-time data. The bus arrival time was obtained by fusion computing the real-time data in perception layer together with historical data and static data in collaborative layer. The validity of the collaborative model was verified by the data of a typical urban bus line in Shanghai, and 1538 sets of data were collected and analyzed from three different perspectives. By comparing the experimental results with the actual results, it is shown that the experimental results are with higher prediction accuracy, and the collaborative prediction model adopted is able to meet the demand for bus arrival prediction. [ABSTRACT FROM AUTHOR]

Bayesian networks (BNs) have become increasingly popular tools for learning and prediction in the context of user modeling. However, due to the lacks of individual training data especially for inactive or new users, separate treatments of individual users often become quite problematic. In this article, we propose a novel scheme of 'collaborative' prediction, in which multiple BNs (each for a single user) are cooperatively employed for prediction of a single target user's actions. The core idea is to simply use the Bayesian principle, but in a rather intricate way. Our method is simple, and thus readily applicable to rather difficult cases with heterogeneous and preliminary unknown structures of individual BNs. We demonstrate the usefulness of our method with a real dataset related to our motivating user modeling task, i.e., printer usage prediction. The results show that our Bayesian collaboration method effectively improves the prediction accuracy from both non-collaborative and other naive collaborative approaches, especially in cases with small sample sizes. It also appears that our method can improve such a low prediction accuracy that is possibly caused by poor optimization in the structure learning. [ABSTRACT FROM AUTHOR]