Pub Date : 2022-06-01DOI: 10.1177/1063293X221082681
Parham Dadash Pour, Mohammad A Nazzal, Basil M Darras
Industry 4.0 aims to revolutionize the manufacturing sector to achieve sustainable and efficient production. The novel coronavirus pandemic has brought many challenges in different industries globally. Shortage in supply of raw material, changes in product demand, and factories closures due to general lockdown are all examples of such challenges. The adaption of Industry 4.0 technologies can address these challenges and prevent their recurrence in case of another pandemic outbreak in future. A prominent advantage of Industry 4.0 technologies is their capability of building resilient and flexible systems that are responsive to exceptional circumstances such as unpredictable market demand, supply chain interruptions, and manpower shortage which can be crucial at times of pandemics. This work focuses on discussing how different Industry 4.0 technologies such as Cyber Physical Systems, Additive Manufacturing, and Internet of Things can help the manufacturing sector overcome pandemics challenges. The role of Industry 4.0 technologies in raw material provenance identification and counterfeit prevention, collaboration and business continuity, agility and decentralization of manufacturing, crisis simulation, elimination of single point of failure risk, and other factors is discussed. Moreover, a self-assessment readiness model has been developed to help manufacturing firms determine their readiness level for implementing different Industry 4.0 technologies.
{"title":"The role of industry 4.0 technologies in overcoming pandemic challenges for the manufacturing sector.","authors":"Parham Dadash Pour, Mohammad A Nazzal, Basil M Darras","doi":"10.1177/1063293X221082681","DOIUrl":"10.1177/1063293X221082681","url":null,"abstract":"<p><p>Industry 4.0 aims to revolutionize the manufacturing sector to achieve sustainable and efficient production. The novel coronavirus pandemic has brought many challenges in different industries globally. Shortage in supply of raw material, changes in product demand, and factories closures due to general lockdown are all examples of such challenges. The adaption of Industry 4.0 technologies can address these challenges and prevent their recurrence in case of another pandemic outbreak in future. A prominent advantage of Industry 4.0 technologies is their capability of building resilient and flexible systems that are responsive to exceptional circumstances such as unpredictable market demand, supply chain interruptions, and manpower shortage which can be crucial at times of pandemics. This work focuses on discussing how different Industry 4.0 technologies such as Cyber Physical Systems, Additive Manufacturing, and Internet of Things can help the manufacturing sector overcome pandemics challenges. The role of Industry 4.0 technologies in raw material provenance identification and counterfeit prevention, collaboration and business continuity, agility and decentralization of manufacturing, crisis simulation, elimination of single point of failure risk, and other factors is discussed. Moreover, a self-assessment readiness model has been developed to help manufacturing firms determine their readiness level for implementing different Industry 4.0 technologies.</p>","PeriodicalId":55213,"journal":{"name":"Concurrent Engineering-Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9096009/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74663504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-09DOI: 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%.
{"title":"Deep learning based fusion model for COVID-19 diagnosis and classification using computed tomography images","authors":"R.T.Subhalakshmi, S. Balamurugan, S. Sasikala","doi":"10.1177/1063293X211021435","DOIUrl":"https://doi.org/10.1177/1063293X211021435","url":null,"abstract":"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%.","PeriodicalId":55213,"journal":{"name":"Concurrent Engineering-Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79475373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-12-01DOI: 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.
{"title":"Using formal methods to scope performance challenges for Smart Manufacturing Systems: focus on agility.","authors":"Kiwook Jung, K C Morris, Kevin W Lyons, Swee Leong, Hyunbo Cho","doi":"10.1177/1063293X15603217","DOIUrl":"10.1177/1063293X15603217","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":55213,"journal":{"name":"Concurrent Engineering-Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4850556/pdf/nihms763031.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34451933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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