Kalman Filter

Welch, George R.

doi:10.1007/978-0-387-31439-6_716

Cited by 29 publications

(21 citation statements)

References 6 publications

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“…Therefore, as shown in Figure 6, the preprocessing step consists of four steps to translate continuous percentage utilization into interval probability values and generate monitoring vectors of events ( -events) by using the method proposed in [19]. We adopted this method with a filtering dataset to remove and process the outlier data and noise by using Extended Kalman Filter (EKF) [40,41] and we generated a new method algorithm for transforming a numerical data to binning data, as shown in Algorithm 1.…”

Section: Determination Of Network Parametersmentioning

confidence: 99%

A New Theoretical Approach: A Model Construct for Fault Troubleshooting in Cloud Computing

Alkasem

Liu

Shafiq

et al. 2017

Mobile Information Systems

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In cloud computing, there are four effective measurement criteria: (I) priority, (II) fault probability, (III) risk, and (IV) the duration of the repair action determining the efficacy of troubleshooting. In this paper, we propose a new theoretical algorithm to construct a model for fault troubleshooting; we do this by combining a Naïve-Bayes classifier (NBC) with a multivalued decision diagram (MDD) and influence diagram (ID), which structure and manage problems related to unambiguous modeling for any connection between significant entities. First, the NBC establish the fault probability based on a Naïve-Bayes probabilistic model for fault diagnosis. This approach consists of three steps: (I) identifying the network parameters to also show the reliance for probability relationship among the entire set of nodes; (II) determining the structure of the network topology; (III) assessing the probability of the fault being propagated. This calculates the probability of each node being faulty given the evidence. Second, the MDD combines the influential factors of four measurements and determines the utility value of prioritizing their actions during each step of the fault troubleshooting which in turn assesses which fault is selected for repair. We demonstrate how the procedure is adapted by our method, with the host server's failure to initiate a case-study. This approach is highly efficient and enables low-risk fault troubleshooting in the field of cloud computing.

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Section: Determination Of Network Parametersmentioning

confidence: 99%

A New Theoretical Approach: A Model Construct for Fault Troubleshooting in Cloud Computing

Alkasem

Liu

Shafiq

et al. 2017

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…B k is the control input model which is applied to the control vector and U k , w k is the process noise which is assumed to be drawn from a zero mean multivariate normal distribution with covariance Q k ;w k ∼N(0,Q k ) [15]. At time k an observation (or measurement) Z k of the true state X k is made according to,…”

Section: Kalman Filtermentioning

confidence: 99%

“…where,H k is the observation model which maps the true state space into the observed space and V k is the observation noise which is assumed to be zero mean Gaussian white noise with covariance R k ;V k ∼N(0,R k ) [15]. The initial state, and the noise vectors at each step {x 0 , w 1 , ..., w k , v 1 , ..., v k } are all assumed to be mutually independent [15].…”

Section: Kalman Filtermentioning

confidence: 99%

See 2 more Smart Citations

Motion tracking by sensors for real-time human skeleton animation

Arachchi

Xiang

Kumara

et al. 2016

Int J on Adv. in ICT for Emerging Countries

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Abstract-Human Computer Interaction based Research has emerged in the early 1980s with the advent of computer technology. Human Motion Capture is the process of recording the movement of people. Among many kinds of human motion capture devises, Microsoft Kinect sensor and inertial sensors are most popular nowadays. In this paper we propose an efficient motion tracking mechanism to construct real time human skeleton animation using inertial sensors. We compare the results of our proposed method with the Microsoft Kinect sensor over the complicated motion tracking and joint position. During the experiment we observed that our results are much steady than Microsoft Kinect results. Some motions like hand cross over or leg cross over, our method showed better results than Kinect because the Kinect may lose skeleton of the blocked parts. On the other hand, since we use radio frequency inertial sensors, our method has a larger working area than Kinect.

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Improving the estimation of Lake Kinneret's heat balance and surface fluxes using the Kalman Filter algorithm

Nussboim

Rimmer

Lechinsky

et al. 2017

Limnology & Ocean Methods

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High frequency (10 min) meteorological measurements are usually the basis for surface fluxes calculations (net radiation, sensible and latent heat) over a lake surface. Data from simultaneous high frequency monitoring of the lake‐water temperature profile can be used as additional information for calculating these fluxes more accurately, if the large random fluctuations of such data could be overcome. This challenge can be achieved using an algorithm that filters out the natural noise of both the surface fluxes calculation (“model”) and the monitoring data (“measurement”), such as the Kalman Filter (KF). The KF uses statistics of the uncertainty in the dynamics of the heat balance model and the measurements, and improves the calculated heat storage estimate. The KF algorithm was applied for studying the energy balance at the surface of Lake Kinneret, Israel. We tested its operation using different algorithms, in light of seasonal variations associated with meteorological and lake temperature conditions. Typically, during the spring and summer the uncertainty of the heat storage data result in low Kalman Gain, K, whereas during calm lake conditions, in the autumn, the gain was high. Using only data already measured in “the past” and the current measurement, the KF is more suitable for cases where information regarding surface fluxes is required online than other filters (such as a moving average), which need data from “the future.”

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Kalman Filter

Cited by 29 publications

References 6 publications

A New Theoretical Approach: A Model Construct for Fault Troubleshooting in Cloud Computing

A New Theoretical Approach: A Model Construct for Fault Troubleshooting in Cloud Computing

Motion tracking by sensors for real-time human skeleton animation

Improving the estimation of Lake Kinneret's heat balance and surface fluxes using the Kalman Filter algorithm

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