Perceiving information and extracting insights from data is one of the major challenges in smart manufacturing. Real-time data analytics face several challenges in real-life scenarios, while there is a huge treasure of legacy, enterprise and operational data remaining untouched. The current paper exploits the recent advancements of (deep) machine learning for performing predictive and prescriptive analytics on the basis of enterprise and operational data aiming at supporting the operator on the shopfloor. To do this, it implements algorithms, such as Recurrent Neural Networks for predictive analytics, and Multi-Objective Reinforcement Learning for prescriptive analytics. The proposed approach is demonstrated in a predictive maintenance scenario in steel industry.
Adaptability in non-stationary contexts is a very important property and a constant desire for modern intelligent systems and is usually associated with dynamic system behaviors. In this framework, we present a novel methodology of dynamic resource control and optimization for neurofuzzy inference systems. Our approach involves a neurofuzzy model with structural learning capabilities that adds rule nodes when necessary during the training phase. Sensitivity analysis is then applied to the trained network so as to evaluate the network rules and control their usage in a dynamic manner based on a confidence threshold. Therefore, on one hand, we result in a well-balanced structure with an improved adaptive behavior and, on the other hand, we propose a way to control and restrict the "curse of dimensionality". The experimental results on a number of classification problems prove clearly the strengths and benefits of this approach.
Abstract. The concept of semantic and context aware intelligent systems provides a vision for the Information Society where the emphasis lays on computing applications that can sense context from the people and the environment and wrap that knowledge into adaptable behavior. In this framework the proper and automatic classification of data gathered by sensors is of major importance. Our approach describes a model that operates as a selfevaluating classifier using on-line re-clustering, addressing adequately the basic issues of modern demands. The novelty of the model lies in a flexible and efficient initialization technique that first partitions the data space utilizing Gaussian distributions and then merges clusters so as to produce an effective partitioning.
Recent years have seen a surge of interest in the field of pervasive context-aware computing. In this framework we propose a novel real implementation of an adaptive self-configurable system, applied within the scope of wireless ad-hoc networks. WiDFuNC is an integrated system that consists of an intelligent unit implemented on a real PDA, a number of sensors and a remote server device to form an efficient prototype system that can be applied in various domains. This implementation of WiDFuNC focuses on pure classification problems with satisfactory experimental results, presenting great adaptability and context-awareness.
The general idea of the AMBER/Child Alert System is that by broadcasting and distributing information about a missing child to the community, the public's involvement can trigger critical feedback that would have otherwise been ignored. This feedback, in several cases, can be proved decisive in finding the missing child. Despite the efforts at country and European level to effectively address the issue of missing children, a number of key challenges remain open, including lack of location-focused distribution of alerts, insufficient capture and diffusion of information, and lack of a mechanism that uses and merges all available sources of information. The aim of this paper is to present a novel approach for handling such challenges through a data analytics platform and a mobile application available to all citizens. Using the active research fields of human mobility pattern analysis and machine learning, we show that missing children investigations, as well as search and rescue operations, can be actively supported and enhanced when multiple data sources are combined and analyzed.
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