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Meiling Zhu
School of Computer Science and Technology, Tianjin UniversityTianjinPeople's Republic of China

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Research article
Published: 09 October 2018 in IET Intelligent Transport Systems
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A companion of moving objects is an object group that move together in a period of time. Platoon companions are a generalised companion pattern, which describes a group of objects that move together for time segments, each with some minimum consecutive duration of time. This study proposes a method that can instantly discover platoon companions from a special kind of streaming traffic data, called automatic number plate recognition data. Compared to related approaches, the authors transform the companion discovery into a frequent sequence mining problem. The authors propose a data structure, platoon tree (PTree), to record discovered platoon companions. To reduce the cost of tree traversal during mining platoon companions, they utilise the last two together-moving objects of a group to update PTree. Finally, a lot of experiments have been carried out to show the efficiency and effectiveness of the proposed approach.

ACS Style

Meiling Zhu; Chen Liu; Yanbo Han. Approach to discovering companion patterns based on traffic data stream. IET Intelligent Transport Systems 2018, 12, 1351 -1359.

AMA Style

Meiling Zhu, Chen Liu, Yanbo Han. Approach to discovering companion patterns based on traffic data stream. IET Intelligent Transport Systems. 2018; 12 (10):1351-1359.

Chicago/Turabian Style

Meiling Zhu; Chen Liu; Yanbo Han. 2018. "Approach to discovering companion patterns based on traffic data stream." IET Intelligent Transport Systems 12, no. 10: 1351-1359.

Conference paper
Published: 19 July 2018 in Computer Vision
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Predictive maintenance aims at enabling proactive scheduling of maintenance, and thus prevent unexpected equipment failures. Most approaches focus on predicting failures occurring within individual sensors. However, a failure is not always isolated. It probably formed by propagation of trivial anomalies, which are widely regarded as events, among sensors and devices. In this paper, we propose an event correlation discovery algorithm to capture correlations among anomalies/failures. Such correlations can show us lots of clues to the propagation paths. We also extend our previous service hyperlink model to encapsulate such correlations and propose a service-based predictive maintenance approach. Moreover, we have made extensive experiments to verify the effectiveness of our approach.

ACS Style

Meiling Zhu; Chen Liu; Yanbo Han. An Event Correlation Based Approach to Predictive Maintenance. Computer Vision 2018, 232 -247.

AMA Style

Meiling Zhu, Chen Liu, Yanbo Han. An Event Correlation Based Approach to Predictive Maintenance. Computer Vision. 2018; ():232-247.

Chicago/Turabian Style

Meiling Zhu; Chen Liu; Yanbo Han. 2018. "An Event Correlation Based Approach to Predictive Maintenance." Computer Vision , no. : 232-247.

Journal article
Published: 05 June 2018 in Sensors
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Predictive industrial maintenance promotes proactive scheduling of maintenance to minimize unexpected device anomalies/faults. Almost all current predictive industrial maintenance techniques construct a model based on prior knowledge or data at build-time. However, anomalies/faults will propagate among sensors and devices along correlations hidden among sensors. These correlations can facilitate maintenance. This paper makes an attempt on predicting the anomaly/fault propagation to perform predictive industrial maintenance by considering the correlations among faults. The main challenge is that an anomaly/fault may propagate in multiple ways owing to various correlations. This is called as the uncertainty of anomaly/fault propagation. This present paper proposes a correlation-based event routing approach for predictive industrial maintenance by improving our previous works. Our previous works mapped physical sensors into a soft-ware-defined abstraction, called proactive data service. In the service model, anomalies/faults are encapsulated into events. We also proposed a service hyperlink model to encapsulate the correlations among anomalies/faults. This paper maps the anomalies/faults propagation into event routing and proposes a heuristic algorithm based on service hyperlinks to route events among services. The experiment results show that, our approach can reach 100% precision and 88.89% recall at most.

ACS Style

Meiling Zhu; Chen Liu. A Correlation Driven Approach with Edge Services for Predictive Industrial Maintenance. Sensors 2018, 18, 1844 .

AMA Style

Meiling Zhu, Chen Liu. A Correlation Driven Approach with Edge Services for Predictive Industrial Maintenance. Sensors. 2018; 18 (6):1844.

Chicago/Turabian Style

Meiling Zhu; Chen Liu. 2018. "A Correlation Driven Approach with Edge Services for Predictive Industrial Maintenance." Sensors 18, no. 6: 1844.

Conference paper
Published: 18 October 2017 in Computer Vision
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In an IoT (Internet of Things) environment, event correlation becomes more complex as events usually span over many interrelated sensors. This paper refines event correlations in an IoT environment. We extend our previous service hyperlink model to encapsulate such event correlations. To effectively discover service hyperlinks, we transform the event correlation discovery problem into a frequent sequence mining problem and propose CorFinder algorithm. Moreover, we apply our approach to improve anomaly warning in a power plant instead of simulation. Besides the application, we have made extensive experiments to verify the effectiveness of our approach.

ACS Style

Meiling Zhu; Chen Liu; Jianwu Wang; Shen Su; Yanbo Han. An Approach to Modeling and Discovering Event Correlation for Service Collaboration. Computer Vision 2017, 10601, 191 -205.

AMA Style

Meiling Zhu, Chen Liu, Jianwu Wang, Shen Su, Yanbo Han. An Approach to Modeling and Discovering Event Correlation for Service Collaboration. Computer Vision. 2017; 10601 ():191-205.

Chicago/Turabian Style

Meiling Zhu; Chen Liu; Jianwu Wang; Shen Su; Yanbo Han. 2017. "An Approach to Modeling and Discovering Event Correlation for Service Collaboration." Computer Vision 10601, no. : 191-205.