Concurrent Engineering-Research and Applications最新文献

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Deep learning based fusion model for COVID-19 diagnosis and classification using computed tomography images 基于深度学习的计算机断层图像诊断与分类融合模型

4区工程技术 Q2 Engineering

Concurrent Engineering-Research and Applications

Pub Date : 2021-06-09 DOI: 10.1177/1063293X211021435

R.T.Subhalakshmi, S. Balamurugan, S. Sasikala

Recently, the COVID-19 pandemic becomes increased in a drastic way, with the availability of a limited quantity of rapid testing kits. Therefore, automated COVID-19 diagnosis models are essential to identify the existence of disease from radiological images. Earlier studies have focused on the development of Artificial Intelligence (AI) techniques using X-ray images on COVID-19 diagnosis. This paper aims to develop a Deep Learning Based MultiModal Fusion technique called DLMMF for COVID-19 diagnosis and classification from Computed Tomography (CT) images. The proposed DLMMF model operates on three main processes namely Weiner Filtering (WF) based pre-processing, feature extraction and classification. The proposed model incorporates the fusion of deep features using VGG16 and Inception v4 models. Finally, Gaussian Naïve Bayes (GNB) based classifier is applied for identifying and classifying the test CT images into distinct class labels. The experimental validation of the DLMMF model takes place using open-source COVID-CT dataset, which comprises a total of 760 CT images. The experimental outcome defined the superior performance with the maximum sensitivity of 96.53%, specificity of 95.81%, accuracy of 96.81% and F-score of 96.73%.

最近，COVID-19大流行疫情急剧加剧，而快速检测试剂盒的数量有限。因此，自动COVID-19诊断模型对于从放射图像中识别疾病的存在至关重要。此前的研究重点是开发利用x射线图像诊断新冠肺炎的人工智能技术。本文旨在开发一种基于深度学习的多模态融合技术，称为DLMMF，用于从计算机断层扫描(CT)图像中诊断和分类COVID-19。所提出的DLMMF模型主要包括三个过程:基于Weiner滤波(WF)的预处理、特征提取和分类。该模型使用VGG16和Inception v4模型融合了深度特征。最后，采用基于高斯Naïve贝叶斯(GNB)的分类器对测试CT图像进行识别和分类。DLMMF模型使用开源COVID-CT数据集进行实验验证，该数据集共包含760张CT图像。实验结果表明，该方法的最大灵敏度为96.53%，特异性为95.81%，准确度为96.81%，f评分为96.73%。

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引用次数: 8

Using formal methods to scope performance challenges for Smart Manufacturing Systems: focus on agility. 使用形式化方法确定智能制造系统性能挑战的范围：关注敏捷性。

4区工程技术 Q2 Engineering

Concurrent Engineering-Research and Applications

Pub Date : 2015-12-01 DOI: 10.1177/1063293X15603217

Kiwook Jung, K C Morris, Kevin W Lyons, Swee Leong, Hyunbo Cho

Smart Manufacturing Systems (SMS) need to be agile to adapt to new situations by using detailed, precise, and appropriate data for intelligent decision-making. The intricacy of the relationship of strategic goals to operational performance across the many levels of a manufacturing system inhibits the realization of SMS. This paper proposes a method for identifying what aspects of a manufacturing system should be addressed to respond to changing strategic goals. The method uses standard modeling techniques in specifying a manufacturing system and the relationship between strategic goals and operational performance metrics. Two existing reference models related to manufacturing operations are represented formally and harmonized to support the proposed method. The method is illustrated for a single scenario using agility as a strategic goal. By replicating the proposed method for other strategic goals and with multiple scenarios, a comprehensive set of performance challenges can be identified.

智能制造系统（SMS）需要利用详细、精确和适当的数据进行智能决策，从而灵活地适应新情况。在制造系统的多个层面上，战略目标与运营绩效之间错综复杂的关系阻碍了 SMS 的实现。本文提出了一种方法，用于确定制造系统的哪些方面应加以解决，以应对不断变化的战略目标。该方法使用标准建模技术来指定制造系统以及战略目标与运营绩效指标之间的关系。与制造运营相关的两个现有参考模型被正式表示出来，并进行了协调，以支持所建议的方法。该方法针对以敏捷性为战略目标的单一情景进行了说明。通过对其他战略目标和多种情景复制所提出的方法，可以确定一系列全面的绩效挑战。

引用次数: 0

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