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Time series estimation techniques are usually employed in biomedical research to derive variables less accessible from a set of related and more accessible variables. These techniques are traditionally built from systems modeling approaches including simulation, blind decovolution, and state estimation. In this work, we define target time series (TTS) and its related time series (RTS) as the output...
To estimate intracranial pressure (ICP) noninvasively, a data mining framework was proposed in our previous work. In the procedure, the mapping function plays an important role to estimate ICP based on the feature vector extracted from arterial blood pressure (ABP) and flow velocity (FV), which is translated to the estimated errors by the mapping function for each entry in the database. In this paper,...
A data mining framework has been proposed to estimate intracranial pressure (ICP) non-invasively in our previous work. In the corresponding approach, the feature vector extracted from arterial blood pressure (ABP) and flow velocity (FV) is translated to the estimated errors by the mapping function for each entry in the database. In this paper, three different mapping function solutions, linear least...
This paper studied on time series prediction, and proposes a new prediction algorithm of LS-SVM online learning against the shortcomings in the traditional online learning with least squares support vector machine. This algorithm was researched and used in coal mine gas prediction and had proved effective, compared with the actual data and other relative algorithms.
Change detection is an essential task in equipment monitoring, fault diagnostics and system prognostics. It involves monitoring change to the device state to detect faulty behavior. Early change detection can indicate abnormal conditions that can help in early fault diagnostics. This will allow for timely maintenance actions to be taken before the fault progresses, causes secondary damage to the system,...
Change detection is an important task for remote monitoring, fault diagnostics and system prognostics. When a fault occurs, it will often times cause changes in measurable quantities of the system. Early detection of changes in system measurements that indicate abnormal conditions helps the diagnostics of the fault so that appropriate maintenance action can be taken before the fault progresses, causes...
Prediction of equipment remaining useful life (RUL) is of considerable economic benefit to industry, by permitting the avoidance of unscheduled downtime and costly secondary damage. Detection of change is an important first step in building a prognostic system: when a detectable fault occurs, it will cause changes in one or more sensed parameters of the system. Once a change has been detected, localizing...
There is an urgent need for tools to unravel the complex interactions and functionalities of genes. As such, there has been much interest in reverse-engineering genetic regulatory networks from time series gene expression data. We use an artificial neural network to model the dynamics of complicated gene networks and to learn their parameters. The positive and negative regulations of genes are defined...
Large-scale gene expression data coming from microarray experiments provide us a new means to reveal fundamental cellular processes, investigate functions of genes, and understand relations and interactions among them. To infer genetic regulatory networks from these data with effective computational tools has become increasingly important Several mathematical models, including Boolean networks, Bayesian...
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