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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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Open AccessArticle Drilling Rig Hoisting Platform Security Monitoring System Design and Application
Machines 2017, 5(3), 19; doi:10.3390/machines5030019
Received: 1 June 2017 / Revised: 2 August 2017 / Accepted: 3 August 2017 / Published: 22 August 2017
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Abstract
Drilling rig hoisting platform security monitoring system has played a very important role in oil exploration. And drilling parameters and working condition of workers are particularly important, because these parameters indicate that whether the drilling work is safe and effective directly. A security
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Drilling rig hoisting platform security monitoring system has played a very important role in oil exploration. And drilling parameters and working condition of workers are particularly important, because these parameters indicate that whether the drilling work is safe and effective directly. A security monitoring system is established to provide the real-time parameters for drilling safety is the purpose of this study. The monitoring system includes a top drive, a traveling block hook, an oil derrick and a driller room, and the controller of the system is programmable logic controller PLC. The procedure of the system is written by the RSLogix5000 software, the PC configuration is used force control monitor configuration software. According to the system, top drive, driller room and environment wind speed parameters in real-time are collected and displayed in the configuration of upper computer, the collected parameters can be used to determine the working conditions of the top drive and to send timely warnings for inspection maintenance to avoid drilling safety accidents. Work fatigue remind of driller and regularly remind of derrick check can be as much as possible to reduce safety accidents. And automatic operation of traveling block hook reached the default point faster and more smoothly than manual operation given the other uncontrollable factors in manual operation. The application of the system is successfully working in the drilling work site. Full article
(This article belongs to the Special Issue Machinery Condition Monitoring and Industrial Analytics)
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Open AccessArticle Root Cause Identification of Machining Error Based on Statistical Process Control and Fault Diagnosis of Machine Tools
Machines 2017, 5(3), 20; doi:10.3390/machines5030020
Received: 3 April 2017 / Revised: 1 September 2017 / Accepted: 4 September 2017 / Published: 6 September 2017
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Abstract
The essence of the machining process is the interaction that occurs between machine tools and a workpiece under certain conditions of cutting parameters. Root cause identification (RCI) is critical to the quality control and productivity improvement of machining processes. The geometric error caused
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The essence of the machining process is the interaction that occurs between machine tools and a workpiece under certain conditions of cutting parameters. Root cause identification (RCI) is critical to the quality control and productivity improvement of machining processes. The geometric error caused by fixture faults can be identified in most RCI methods; however, the influence of machine tool degradation on workpiece quality is usually neglected. In this paper, a novel root cause identification scheme of machining error based on statistical process control and fault diagnosis of machine tools is proposed. With the pattern recognition of control charts, quality fluctuations can be detected in a timely manner. Once the machining error occurs, the fault diagnosis of machine tools are carried out. The relationship between machine tool condition and workpiece quality is established and the root cause identification of the machining error can be achieved. A case study of the machining of a complex welded box-type workpiece is presented to illustrate the feasibility of the proposed scheme. It is found that the coaxiality error of the two holes in two sides of the box’s wall is caused by the wear of the worm gear in the rotary work table of the machine tool. Full article
(This article belongs to the Special Issue Advances in Process Machine Interactions)
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