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IoT systems deployed in industrial and smart factory settings generate large volumes of data at high velocity. Context awareness is mandatory for knowledge discovery and actionable insights from such high-velocity, high-volume IoT data streams. Changes to the context of a data stream are represented in the underlying data distribution. Research in concept drift aims to detect and adapt to such changes...
The world is witnessing a remarkable increase in the usage of video surveillance systems. Besides fulfilling an imperative security and safety purpose, it also contributes towards operations monitoring, hazard detection and facility management in industry/smart factory settings. Most existing surveillance techniques use hand-crafted features analyzed using standard machine learning pipelines for action...
Smart Grids are electric networks that employ innovative and intelligent monitoring, control, communication, and self-healing technologies to deliver better connections and operations for generators and distributors, flexible choices for consumers, and reliability and security of electricity supply. Smart Grids are complex and dynamical networks in nature that face many new theoretical and practical...
This paper proposes a Multiple-run Interactive Certainty Network (MRICN) that integrates human decision-making heuristics into probabilistic approaches for knowledge-based systems. MRICN is built upon ideas drawn from “Opinion Pooling”, “Probabilistic Network” and “Interaction”, allowing for reflective searching for the optimal sets of knowledge with the maximal certainty gain. It is implemented and...
This paper proposes an Ontology Based Geometric Recognition System (OBGRS) which can provide Automatic Feature Recognition (AFR) for STEP based geometric model. The system represents the geometric domain knowledge and STEP data in ontology as classes, properties, and semantic rules, which improves the explainability of the system. Meanwhile, ontology-based separating of general domain knowledge and...
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