Flooding of alarms is a very crucial problem in process industries. An alarm flood makes an operator ineffective of taking necessary actions, and often risking an emergency shutdown or a major upset. In this work, the flooding of alarms is discussed based on the standards presented in ISA 18.2. A new analysis method is proposed to investigate similar alarm floods from the historic alarm data and group them on the basis of the patterns of alarm occurrences. A case study on real industrial alarm data is also presented to demonstrate the utility of the proposed analysis.Note to Practitioners-In an industrial plant, alarms are designed to inform the operator about any fault or abnormality in the operation. However, alarm systems are not always designed, implemented, and maintained properly. This usually results in an excessive number of enunciated alarms, most of which are false or nuisance. Specifically, during a plant upset, operators receive far more alarms that they can handle (hundreds or even thousands of alarms in a short period of time). This is known as an alarm flood or alarm shower. Our study shows that in some cases, alarm floods follow similar patterns. As a result by studying and classifying flood patterns, it is possible to find the root cause of an abnormality based on the history of the plant and previous similar floods. This allows the operator to react to a plant upset at the early stages of or even before an alarm flood.

The departments of transportation worldwide are facing various challenges despite introducing and incorporating various vehicular features. One of such challenges is to make vehicles autonomous, intelligent, and capable of self-learning to evolve their knowledge repository. In this paper, human cognition is proposed to be implemented in vehicles so that they can perform human-like decisions. Therefore, the process of vehicular route decision is debated cognitively in order to provide route information intelligently. The in-vehicle routes provided by the GPS are not optimal and lack on-demand user requirements. GPS connectivity issues, in certain conditions, make it difficult for vehicles to take real-time decisions. This leads to the idea of self-decision by the vehicle controller. We propose a cognitive framework for vehicles to make self-decisions that use cognitive memory for storing route experiences. The framework strengthens the existing in-vehicle route finding capability and its provision in a more realistic manner. The user is provided with all available route-related information that is required for the journey. In addition, the route episodes are learned, stored, and accessed inside the cognitive memory for an optimal route provision. The vehicle learns about the routes and matures with route-experience by itself with the passage of time. In simulations, fuzzy modeling is used to validate the impact of cognitive parameters over static/conventional parameters. Moreover, artificial neural networks are used to minimize the error rate in learning to achieve cognitive route decisions. The proposed in-vehicle cognitive framework outperforms the existing route provision system that is inadequate and provokes the user's anxieties during driving. Besides, the proposed scheme gradually gets mature in delivering optimal as well as latest route-related information. INDEX TERMS VANET, cognition, fuzzy model, artificial neural network, vehicle route.

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Clustering is an important descriptive model in data mining. It groups the data objects into meaningful classes or clusters such that the objects are similar to one another within the same cluster and are dissimilar to other clusters. Spatial clustering is one of the significant techniques in spatial data mining, to discover patterns from large spatial databases. In recent years, several basic and advanced algorithms have been developed for clustering spatial datasets. Clustering technique can be categorized into six types namely partitioning, hierarchical, density, grid, model, and constraint based models. Among these, the density based technique is best suitable for spatial clustering. It characteristically consider clusters as dense regions of objects in the data space that are separated by regions of low density (indicating noise).The clusters which are formed based on the density are easy to understand, filter out noise and discover clusters of arbitrary shape. This paper presents a comparative study of different density based spatial clustering algorithms, and the merits and limitations of the algorithms are also evaluated.

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