<![CDATA[ Systems, Man, and Cybernetics: Systems, IEEE Transactions on - new TOC ]]> http://ieeexplore.ieee.org TOC Alert for Publication# 6221021 2014 July 07 <![CDATA[Table of contents]]> 44 7 C1 C1 37 <![CDATA[IEEE Transactions on Systems, Man, and Cybernetics publication information]]> 44 7 C2 C2 135 <![CDATA[SMARTSPACE: Multiagent Based Distributed Platform for Semantic Service Discovery]]> 44 7 805 821 18085 <![CDATA[Evolutionary Location and Pricing Strategies in Competitive Hierarchical Distribution Systems: A Spatial Agent-Based Model]]> 44 7 822 833 22433 <![CDATA[Agent-Based Modeling of Competitive and Cooperative Behavior Under Conflict]]> 44 7 834 850 12693 <![CDATA[Intelligent Prognostics for Battery Health Monitoring Using the Mean Entropy and Relevance Vector Machine]]> 44 7 851 862 8220 <![CDATA[A Common Framework for Compilation Techniques Applied to Diagnosis of Linear Dynamic Systems]]> 44 7 863 876 10723 <![CDATA[On the Use of Mean Residual Life as a Condition Index for Condition-Based Maintenance Decision-Making]]> 44 7 877 893 23799 <![CDATA[Decision-Making in Energy Systems With Multiple Technologies and Uncertain Preferences]]> 44 7 894 907 9041 <![CDATA[Determining the Optimal Stopping Time for Automated Negotiations]]> 44 7 908 921 9561 <![CDATA[Uncertainty Management in Differential Evolution Induced Multiobjective Optimization in Presence of Measurement Noise]]> 44 7 922 937 26943 <![CDATA[Optimal Advance-Selling Strategy for Fashionable Products With Opportunistic Consumers Returns]]> 44 7 938 952 2782 <![CDATA[Resource Relocation in Workflow Nets With Time, Resource, and Task Priority Constraints]]> 44 7 953 965 6652 <![CDATA[An Ontology-Based Text Mining Method to Develop D-Matrix From Unstructured Text]]> [7] to capture the hierarchical system-level fault diagnostic information consisting of dependencies between observable symptoms and failure modes associated with a system. Constructing a D-matrix from first principles and updating it using the domain knowledge is a labor intensive and time consuming task. Further, in-time augmentation of D-matrix through the discovery of new symptoms and failure modes observed for the first time is a challenging task. Here, we describe an ontology-based text mining method for automatically constructing and updating a D-matrix by mining hundreds of thousands of repair verbatim (typically written in unstructured text) collected during the diagnosis episodes. In our approach, we first construct the fault diagnosis ontology consisting of concepts and relationships commonly observed in the fault diagnosis domain. Next, we employ the text mining algorithms that make use of this ontology to identify the necessary artifacts, such as parts, symptoms, failure modes, and their dependencies from the unstructured repair verbatim text. The proposed method is implemented as a prototype tool and validated by using real-life data collected from the automobile domain.]]> 44 7 966 977 8741 <![CDATA[IEEE Member digital library]]> 44 7 978 978 1637 <![CDATA[Open Access]]> 44 7 979 979 1157 <![CDATA[Together, we are advancing technology]]> 44 7 980 980 398 <![CDATA[IEEE Systems, Man, and Cybernetics Society Information]]> 44 7 C3 C3 100 <![CDATA[IEEE Transactions on Human-Machine Systems information for authors]]> 44 7 C4 C4 107