Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Mining Deviants in a Time Series Database
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Automatic generation of fuzzy rule-based models from data by genetic algorithms
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on recent advances in soft computing
Finding surprising patterns in a time series database in linear time and space
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
SSDBM '00 Proceedings of the 12th International Conference on Scientific and Statistical Database Management
Online novelty detection on temporal sequences
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
An information granulation based data mining approach for classifying imbalanced data
Information Sciences: an International Journal
ACM Computing Surveys (CSUR)
Passive learning and input-to-state stability of switched Hopfield neural networks with time-delay
Information Sciences: an International Journal
An optimal method for prediction and adjustment on byproduct gas holder in steel industry
Expert Systems with Applications: An International Journal
Boundary-based lower-bound functions for dynamic time warping and their indexing
Information Sciences: an International Journal
Data-driven based model for flow prediction of steam system in steel industry
Information Sciences: an International Journal
From model-based control to data-driven control: Survey, classification and perspective
Information Sciences: an International Journal
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The accuracy of the acquired data is very significant for the decision-making process for the purpose of the safety and reliability of the energy system in steel industry. However, owing to the instability and vulnerability of industrial system of supervisory control and data acquisition (SCADA), the anomaly data usually exist in practice. In this study, considering the data feature of the energy system, we classify the anomalies as the trend anomaly for the pseudo-periodic data and the deviants for the generic data. As for the trend anomaly, a dynamic time warping (DTW) based method combining with adaptive fuzzy C means (AFCM) is proposed by referencing the similar industrial processes; while, as for the deviants detection, a k-nearest neighbor AFCM algorithm (KNN-AFCM) is designed here for the local anomaly detection for the generic data. To verify the effectiveness of the proposed method, the real-world energy data coming from a steel plant are employed to perform the experiments, and the results indicate that the proposed method exhibits a higher precision compared to the other methods for the anomaly detection.