Automatic threshold tracking of sensor data using Expectation Maximization algorithm

Arnaout, Arghad; Esmael, Bilal; Frühwirth, R.; Thonhauser, Gerhard

doi:10.1109/his.2011.6122164

Cited by 5 publications

(5 citation statements)

References 4 publications

(1 reference statement)

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“…According to [34], the statistic quantity τ F of the Friedman test needs to satisfy the F-distribution with degrees of freedom (K − 1) and (K − 1)(N − 1) (i.e., τ F > F(K − 1, (K − 1)(N − 1))), indicating that the generalization ability of the adopted methods on the benchmark datasets has a diference. We calculate its statistic quantity τ F as follows:…”

Section: Evaluation Criterionmentioning

confidence: 99%

“…We obtain c * by taking α * into equation (33), and take α * and c * into equation (34) to obtain the optimal solution for ω as follows:…”

mentioning

confidence: 99%

“…(20). It is obvious that the optimization for w is a convex function, but we cannot obtain an analytical solution for w. We use the Expectation-Maximization (EM) [34,35] strategy to optimize the w. Te idea of the EM strategy is to optimize the i-th element value in the vector w, fxing the n − 1 element values in the vector w other than the i-th element value. Te optimal solution of w i is obtained using this strategy in the order of element positions.…”

mentioning

confidence: 99%

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Bayes-Decisive Linear KNN with Adaptive Nearest Neighbors

Zhang,

Bian,

Wang

2024

International Journal of Intelligent Systems

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While the classical KNN (k nearest neighbor) shares its avoidance of the consistent distribution assumption between training and testing samples to achieve fast prediction, it still faces two challenges: (a) its generalization ability heavily depends on an appropriate number k of nearest neighbors; (b) its prediction behavior lacks interpretability. In order to address the two challenges, a novel Bayes-decisive linear KNN with adaptive nearest neighbors (i.e., BLA-KNN) is proposed to obtain the following three merits: (a) a diagonal matrix is introduced to adaptively select the nearest neighbors and simultaneously improve the generalization capability of the proposed BLA-KNN method; (b) the proposed BLA-KNN method owns the group effect, which inherits and extends the group property of the sum of squares for total deviations by reflecting the training sample class-aware information in the group effect regularization term; (c) the prediction behavior of the proposed BLA-KNN method can be interpreted from the Bayes-decision-rule perspective. In order to do so, we first use a diagonal matrix to weigh each training sample so as to obtain the importance of the sample, while constraining the importance weights to ensure that the adaptive k value is carried out efficiently. Second, we introduce a class-aware information regularization term in the objective function to obtain the nearest neighbor group effect of the samples. Finally, we introduce linear expression weights related to the distance measure between the testing and training samples in the regularization term to ensure that the interpretation of Bayes-decision-rule can be performed smoothly. We also optimize the proposed objective function using an alternating optimization strategy. We experimentally demonstrate the effectiveness of the proposed BLA-KNN method by comparing it with 7 comparative methods on 15 benchmark datasets.

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Section: Evaluation Criterionmentioning

confidence: 99%

“…We obtain c * by taking α * into equation (33), and take α * and c * into equation (34) to obtain the optimal solution for ω as follows:…”

mentioning

confidence: 99%

mentioning

confidence: 99%

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Bayes-Decisive Linear KNN with Adaptive Nearest Neighbors

Zhang,

Bian,

Wang

2024

International Journal of Intelligent Systems

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“…EM provides the possibility to find and describe main clusters in the data by estimating description parameters of each cluster. Segmenting of data based on a cluster will be a minor task if the parameters are estimated [3]. The Expectation Maximization algorithm considered with stable performance in data with less amount of noise [4].…”

Section: A U T H O R C O P Ymentioning

confidence: 99%

“…Arnaout et al [3] discussed in detail the use of hookload data to determine automatically the threshold value for separation of InSlips and OutOfSlips states.…”

Section: High Level Drilling Time Series Segmentationmentioning

confidence: 99%

Distributed recognition system for drilling events detection and classification

Arnaout

O’Leary

Esmael

et al. 2013

HIS

Self Cite

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Several sensor measurements collected from drilling rig during oil well drilling process. These measurements carry information not only about operational states of drilling rig but also about all high-level operations and activities performed by drilling crew. The work presented in this paper shed the light on analysis of hidden lost time in drilling process through automatic detection and classification of drilling operations. This paper develops a novel algorithm for detecting drilling events and operations in sensor data of drilling rig. Expectation Maximization EM and Piecewise Linear Approximation PLA algorithms applied for detecting drilling events. The Expectation Maximization algorithm performs high-level segmentation on hook-load sensor data. In addition, Piecewise Linear Approximation algorithm slices standpipe pressure; pump flow rate; rotational speed and torque of top drive motor into labeled segments (low-level segmentation). Merging results from both Expectation Maximization and Piecewise Linear Approximation gives the suggested algorithm ability to detect all drilling events and activities performed by drilling rig and crew. Moreover, this paper shows the usage of discrete orthonormal basis functions (Gram basis) as a tool to classify drilling operations from detected segments in drilling time series. The classification process performed in cooperation with the concept of Patterns Templates Base. The optimal polynomial degree to represent drilling operations has been concluded through analysis of polynomial spectrum of each drilling operation.

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Detection of Liver Cirrhosis in Ultrasonic Images from GLCM Features and Classifiers

Karthikamani¹,

Rajaguru²

2022

IFMBE Proceedings

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Automatic threshold tracking of sensor data using Expectation Maximization algorithm

Cited by 5 publications

References 4 publications

Bayes-Decisive Linear KNN with Adaptive Nearest Neighbors

Bayes-Decisive Linear KNN with Adaptive Nearest Neighbors

Distributed recognition system for drilling events detection and classification

Detection of Liver Cirrhosis in Ultrasonic Images from GLCM Features and Classifiers

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