Search Results - (( deviation detection method algorithm ) OR ( variable simulation based algorithm ))
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1
Fault diagnostic algorithm for precut fractionation column
Published 2004“…Hazard and Operability Study (HAZOP) is used to support the diagnosis task. The algorithm has been successful in detecting the deviations of each variable by testing the data set. …”
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2
Development of a rule-based fault diagnostic advisory system for precut fractionation column
Published 2005“…The advisory system algorithm used process history based method and presented by rule-based approach. …”
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3
Fault detection and diagnosis using rule-based support system on fatty acid fractionation column
Published 2003“…Identification of causes, consequences and suggested actions for each deviation assisted by Hazard and Operability Study (HAZOP) analysis are generated into rule-based algorithm. …”
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4
Parameter estimation and outlier detection for some types of circular model / Siti Zanariah binti Satari
Published 2015“…Later, we assess the performance of confidence interval for error concentration parameter for the new functional relationship model via simulation study. Lastly, we consider the problem of detecting multiple outliers in circular regression models based on the clustering algorithm. …”
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5
Deviation detection in text using conceptual graph interchange format and error tolerance dissimilarity function
Published 2012“…We propose a novel error tolerance dissimilarity algorithm to detect deviations in the CGIFs. We evaluate our method in the context of analyzing real world financial statements for identifying deviating performance indicators. …”
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6
Algorithm enhancement for host-based intrusion detection system using discriminant analysis
Published 2004“…Anomaly detection algorithms model normal behavior. Anomaly detection models compare sensor data to normal patterns learned from the training data by using statistical method and try to detect activity that deviates from normal activity. …”
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7
A study on advanced statistical analysis for network anomaly detection
Published 2005“…Anomaly detection algorithms model normal behavior. Anomaly detection models compare sensor data to normal patterns learned from the training data by using statistical method and try to detect activity that deviates from normal activity. …”
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8
A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models
Published 2017“…The results show that the neural network optimized with genetic algorithm and trained with an optimally and intelligently selected input vector containing historical load and meteorological variables produced the best prediction accuracy. © 2017, UK Simulation Society. …”
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9
Parameter estimation and outlier detection in linear functional relationship model / Adilah Abdul Ghapor
Published 2017“…The simulation results indicate that the proposed method is suitable to detect a single outlier. As for the multiple outliers, a clustering algorithm is considered and a dendogram to visualise the clustering algorithm is used. …”
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10
Comparative study of clustering-based outliers detection methods in circular-circular regression model
Published 2021“…This paper is a comparative study of several algorithms for detecting multiple outliers in circular-circular regression model based on the clustering algorithms. …”
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11
Comparative study of clustering-based outliers detection methods in circular-circular regression model
Published 2021“…This paper is a comparative study of several algorithms for detecting multiple outliers in circular-circular regression model based on the clustering algorithms. …”
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12
Comparative study of clustering-based outliers detection methods in circularcircular regression model
Published 2021“…This paper is a comparative study of several algorithms for detecting multiple outliers in circular-circular regression model based on the clustering algorithms. …”
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13
The effect of different distance measures in detecting outliers using clustering-based algorithm for circular regression model
Published 2017“…In this study, we proposed multiple outliers detection in circular regression models based on the clustering algorithm. …”
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14
Experiment and analysis of computer vision-based wrist radial and ulnar deviation exercises
Published 2017“…The researchers have kept on improving the existing methods by creating novel method or developing new algorithms in image processing and artificial intelligence. …”
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15
Autonomous anomaly detection using density-based features in streaming data / Muhammmad Yunus Iqbal Basheer
Published 2023“…Hence, it is critical for an anomaly detection algorithm to detect data anomalies patterns. …”
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16
Feature selection in intrusion detection, state of the art: A review
Published 2016“…These input features give information to the learning algorithms which used in intrusion detection system in the form of the detection method. …”
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17
Computer vision automation system for sorting partially overlapping tiles
Published 2019“…The CbATS consists of three main components which are a color-detection algorithm for distinguishing tiles according to the color, image segmentation that ensures the separation between partially overlapped tiles, and texture features extraction method to determine overturned tiles. …”
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18
Development of a computer vision-based technique for analysis of hand therapy exercise
Published 2018“…For this purpose, the ROM exercise was developed by using the hand detection and tracking, fingertips detection, skeleton joints detection algorithms and joint angle measurement algorithm. …”
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19
Capacitive sensing algorithm for elderly activity detection scheme indoor environment
Published 2018“…The detecting and tracking algorithm uses the sensory data to indicate an associated situation in order to allow elderly people with special requirements. …”
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20
Dynamic Robust Bootstrap Algorithm for Linear Model Selection Using Least Trimmed Squares
Published 2009“…We modified the classical bootstrapping algorithm by developing a mechanism based on the robust LTS method to detect the correct number of outliers in the each bootstrap sample. …”
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