An artificial neural network driven decision-making system for manufacturing disturbance mitigation in reconfigurable systems

McLean, Roscoe; Walker, Anthony J.; Bright, Glen

doi:10.1109/icca.2017.8003144

Cited by 7 publications

(5 citation statements)

References 8 publications

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“…Table 3 presents the algorithm for the preprocessing. 15. Generalize all the users with respect to their number of educational and noneducational sites.…”

Section: Preprocessingmentioning

confidence: 99%

“…With that, Self-Organizing Map (SOM) was employed in creating the clusters or making of the class labels because it is an unsupervised learning method to determine dataset's patterns and excellently work with high dimensional data [14]. It is fast and robust since it is an example of an artificial neural network architecture [15,16]. In dealing with SSS problem in LDA, there were several solutions proposed in the literature.…”

Section: Introductionmentioning

confidence: 99%

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Bandwidth Allocation Mechanism based on Users' Web Usage Patterns for Campus Networks

Paredes¹,

Sison²,

Medina³

2022

Int. j. commun. netw. inf. secur.

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Managing the bandwidth in campus networks becomes a challenge in recent years. The limited bandwidth resource and continuous growth of users make the IT managers think on the strategies concerning bandwidth allocation. This paper introduces a mechanism for allocating bandwidth based on the users’ web usage patterns. The main purpose is to set a higher bandwidth to the users who are inclined to browsing educational websites compared to those who are not. In attaining this proposed technique, some stages need to be done. These are the preprocessing of the weblogs, class labeling of the dataset, computation of the feature subspaces, training for the development of the ANN for LDA/GSVD algorithm, visualization, and bandwidth allocation. The proposed method was applied to real weblogs from university’s proxy servers. The results indicate that the proposed method is useful in classifying those users who used the internet in an educational way and those who are not. Thus, the developed ANN for LDA/GSVD algorithm outperformed the existing algorithm up to 50% which indicates that this approach is efficient. Further, based on the results, few users browsed educational contents. Through this mechanism, users will be encouraged to use the internet for educational purposes. Moreover, IT managers can make better plans to optimize the distribution of bandwidth.

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“…Table 3 presents the algorithm for the preprocessing. 15. Generalize all the users with respect to their number of educational and noneducational sites.…”

Section: Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bandwidth Allocation Mechanism based on Users' Web Usage Patterns for Campus Networks

Paredes¹,

Sison²,

Medina³

2022

Int. j. commun. netw. inf. secur.

View full text Add to dashboard Cite

show abstract

“…Abid et al 18 proposed a method to model the sources of disturbance and its management in manufacturing systems. McLean et al 19 proposed a decision-making system based on an artificial neural network, which can mitigate the manufacturing disturbance in reconfigurable systems.…”

Section: Related Researchmentioning

confidence: 99%

A real-time identification and mitigation solution generation method of random disturbance in a manufacturing system

Kang

Gao

Wang

2014

Advances in Mechanical Engineering

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In order to timely identify and mitigate the adverse effect of the random disturbance in the manufacturing system, a method of identifying and mitigating the random disturbance is proposed. In the method, the disturbance is structured into several categories, and each is subdivided into several events. The disturbance data detected by the hardware system are normalized to 10 scales for more accurate monitoring. An evaluation model is built and it has a bi-layer criteria system, which can evaluate every category disturbance and the whole system at the same time. The fuzzy analytic hierarchy process is utilized to calculate the criteria weights and evaluate the impacts of the disturbance. Time and cost used as constraints are combined into the adjustment solution. The BP neural network is used to generate adjustment solution for the disturbance, and then the resource and task scheduler are adjusted to mitigate the loss caused by the disturbance. Finally, the proposed method is illustrated by an example, and the validity of the method is verified.

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“…Over the past three decades consumers have increasingly been demanding a wider variety of goods in smaller batches, resulting in rapid changes in production technologies [28]- [30]. This trend reveals the importance for flexible, reconfigurable and automated production systems for future markets [31]- [33]-which is not usually considered by object manipulation literature. Although flexible robotic hardware is progressively becoming a popular topic, such as the dual-arm, scalable concept developed by ABB [34], [35], the adaptability of the related grasping methodology is not usually considered to the same degree.…”

Section: Introductionmentioning

confidence: 99%

A 3-Stage Machine Learning-Based Novel Object Grasping Methodology

et al. 2020

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The automatic grasping of objects previously unseen by a robotic system is a difficult task-of which there is currently no robust solution. The research presented in this article improves upon previous works that employ depth data and learning techniques to generate and select from a pool of hypothesised grasps by focusing on the pruning and selection process. In this work, a vision-based, sampling methodology that generates candidate grasps through a convolutional neural network is proposed. Each candidate grasp is assessed using scores derived from the candidate itself and other related input modalities-such as the centre of gravity of the object. The final selection is determined by a learning algorithm. To overcome human bias, objective measures of grasp performance are established that comprehensively measure the error introduced by the grasp trial itself. The proposed metrics are empirically demonstrated to quantify grasp quality, offer useful criteria for network training and provide better descriptive power than traditional measures of grasp outcome. Experimentation showed that the proposed methodology can generate a meaningful, final grasp within 1.3 seconds. Trials quantitatively demonstrate a small-object-in-isolation performance of 99%. For unknown objects, this equates to a 10% improvement relative to other similar methodologies. Testing also showed that grasp performance was improved by 5% when implementing the proposed metrics-compared to the baseline.

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An artificial neural network driven decision-making system for manufacturing disturbance mitigation in reconfigurable systems

Cited by 7 publications

References 8 publications

Bandwidth Allocation Mechanism based on Users' Web Usage Patterns for Campus Networks

Bandwidth Allocation Mechanism based on Users' Web Usage Patterns for Campus Networks

A real-time identification and mitigation solution generation method of random disturbance in a manufacturing system

A 3-Stage Machine Learning-Based Novel Object Grasping Methodology

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