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Machines, Volume 5, Issue 3 (September 2017)

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Research

Open AccessArticle Do We Understand the Relationship between Affective Computing, Emotion and Context-Awareness?
Machines 2017, 5(3), 16; doi:10.3390/machines5030016
Received: 28 June 2017 / Revised: 18 July 2017 / Accepted: 19 July 2017 / Published: 27 July 2017
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Abstract
Historically, the utilization of context, the range and scope of context-aware systems, and the levels of computational intelligence in such systems have been very limited. While the inherent complexity is a significant factor, a principal reason for these limitations lies in the failure
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Historically, the utilization of context, the range and scope of context-aware systems, and the levels of computational intelligence in such systems have been very limited. While the inherent complexity is a significant factor, a principal reason for these limitations lies in the failure to incorporate the emotional component. Affective computing technologies are designed to implement innate emotional capabilities and the capability to simulate emotions and empathy; thus, intelligent context-aware systems with affective computing provide a basis upon which we may effectively enable the emotional component. Moreover, machine cognition relies upon affective computing technologies to provide a basis upon which the emotional component may be incorporated. This paper poses the question: do we understand the relationship between affective computing, emotion and context-awareness? The conclusion drawn is that while affective computing and the need for the incorporation of the emotional component is generally understood and domain-specific strategies to enable implementation have been proposed, there remain important challenges and open research questions in relation to the cognitive modelling and the effective incorporation of affective computing and the emotional component in intelligent context-aware systems. Full article
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Open AccessArticle Survivability Analysis on a Cyber-Physical System
Machines 2017, 5(3), 17; doi:10.3390/machines5030017
Received: 27 May 2017 / Revised: 14 July 2017 / Accepted: 28 July 2017 / Published: 1 August 2017
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Abstract
A cyber-physical system (CPS) is composed of interdependent physical-resource and cyber-resource networks that are tightly coupled. The malfunction of nodes in a network may trigger failures to the other network and further cause cascading failures, which would potentially lead to the complete collapse
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A cyber-physical system (CPS) is composed of interdependent physical-resource and cyber-resource networks that are tightly coupled. The malfunction of nodes in a network may trigger failures to the other network and further cause cascading failures, which would potentially lead to the complete collapse of the entire system. The number and communication of operating nodes at stable state are closely related to the initial failure nodes and the topology of the network system. To address this issue, this paper studies the survivability of CPS in the presence of initial failure nodes, proposes (m, k)—survivability, which is defined as the probability that at least k nodes are still working in CPS after m nodes are attacked, and discusses the problem of cascading failure based on reliability (CFR). Further, we propose an algorithm to calculate (m, k)—survivability and find that the minimum survivability of system with regular allocation strategy decreases with k for a fixed m, and the proportion of initial failure node groups that cause the system to completely fragment increases with m. The simulation shows the properties and the result of CFR of the system with 12 nodes. Full article
(This article belongs to the Special Issue Cyber-Physical System Cybersecurity)
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Open AccessArticle An Ensemble-Boosting Algorithm for Classifying Partial Discharge Defects in Electrical Assets
Machines 2017, 5(3), 18; doi:10.3390/machines5030018
Received: 18 July 2017 / Revised: 3 August 2017 / Accepted: 4 August 2017 / Published: 8 August 2017
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Abstract
This paper presents an ensemble-boosting algorithm (EBA) for classifying partial discharge (PD) patterns in the condition monitoring of insulation diagnosis applied for electrical assets. This approach presents an optimization technique for creating a sequence of artificial neural network (ANNs), where the training data
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This paper presents an ensemble-boosting algorithm (EBA) for classifying partial discharge (PD) patterns in the condition monitoring of insulation diagnosis applied for electrical assets. This approach presents an optimization technique for creating a sequence of artificial neural network (ANNs), where the training data for each constituent of the sequence is selected based on the performance of previous ANNs. Four different PD faults scenarios were manufactured in the high-voltage (HV) laboratory to simulate the PD faults of cylindrical voids in methacrylate, point-air-plane configuration, ceramic bushing with contaminated surface and a transformer affected by the internal PD. A PD dataset was collected, pre-processed and prepared for its use in the improved boosting algorithm using statistical techniques. In this paper, the EBA is extensively compared with the widely used single artificial neural network (SNN). Results show that the proposed approach can effectively improve the generalization capability of the PD patterns. The application of the proposed technique for both online and offline practical PD recognition is examined. Full article
(This article belongs to the Special Issue Machinery Condition Monitoring and Industrial Analytics)
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