Pub Date : 2024-12-15DOI: 10.1016/j.jii.2024.100749
Biswajit Sarkar , Andreas Se Ho Kugele , Mitali Sarkar
Multi-stage production systems produce a severe amount of defective items as a result of an irregular quota of defectiveness. It is very important to remanufacture those imperfect pieces that are wasted and try to keep the model as reality close as possible. Two different models are developed in this study: a smart production system with numerous stages and with only one cycle and a smart production system with numerous stages and numerous cycles. The essential objective of this study is to scale down the overall waste and lower the final total cost in both models at the same time by optimizing the planned batch size, investments in each stage, and the production rate based on the demand. The remanufacturing of the defective products occurs in two ways. While the remanufacturing process in the smart multi-stage production system with a single cycle occurs within the cycle, the reworking in the case of a smart multi-stage production system with numerous cycles occurs in different cycles. Numerical examples are conducted and compared to illustrate the model quantitatively. It is found that in both scenarios of both models, the total cost is minimized.
{"title":"Two non-linear programming models for the multi-stage multi-cycle smart production system with autonomation and remanufacturing in same and different cycles to reduce wastes","authors":"Biswajit Sarkar , Andreas Se Ho Kugele , Mitali Sarkar","doi":"10.1016/j.jii.2024.100749","DOIUrl":"10.1016/j.jii.2024.100749","url":null,"abstract":"<div><div>Multi-stage production systems produce a severe amount of defective items as a result of an irregular quota of defectiveness. It is very important to remanufacture those imperfect pieces that are wasted and try to keep the model as reality close as possible. Two different models are developed in this study: a smart production system with numerous stages and with only one cycle and a smart production system with numerous stages and numerous cycles. The essential objective of this study is to scale down the overall waste and lower the final total cost in both models at the same time by optimizing the planned batch size, investments in each stage, and the production rate based on the demand. The remanufacturing of the defective products occurs in two ways. While the remanufacturing process in the smart multi-stage production system with a single cycle occurs within the cycle, the reworking in the case of a smart multi-stage production system with numerous cycles occurs in different cycles. Numerical examples are conducted and compared to illustrate the model quantitatively. It is found that in both scenarios of both models, the total cost is minimized.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"44 ","pages":"Article 100749"},"PeriodicalIF":10.4,"publicationDate":"2024-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143159068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-12DOI: 10.1016/j.jii.2024.100735
Madhuri Chouhan , R Rajesh , Rajendra Sahu
This study investigates the impact of Industry 4.0 (I4.0) enabling technologies in enhancing the resilience of supply chain systems and the barriers to adopting Industry 4.0 in the supply chains. We use the novel grey influence analysis (GINA) to examine the influence relations among supply chain resilience enhancers and barriers. The study has identified seven enhancers and nine barriers to adopting I4.0 that can improve supply chain resilience. While analyzing the enhancers, the research findings indicate that visibility and coordination in the supply chain are the major enhancers. For barriers, based on the overall influence score, financial constraints, lack of skills and expertise, and inaccessibility of new technology have ranked first, second, and third, respectively. The identified barriers to adopting I4.0 indicate that reducing financial constraints could facilitate the implementation of Industry 4.0 technologies. This study is among the initial investigations to analyze the supply chain resilience enhancers and barriers together for adoption of Industry 4.0. The findings of this study can assist decision-makers and practitioners in overcoming the identified barriers, thereby focusing on the important enhancers of resilience for facilitating the effective adoption of Industry 4.0 in supply chains.
{"title":"Resilience enhancers and barriers analysis for Industry 4.0 in supply chains using grey influence analysis (GINA)","authors":"Madhuri Chouhan , R Rajesh , Rajendra Sahu","doi":"10.1016/j.jii.2024.100735","DOIUrl":"10.1016/j.jii.2024.100735","url":null,"abstract":"<div><div>This study investigates the impact of Industry 4.0 (I4.0) enabling technologies in enhancing the resilience of supply chain systems and the barriers to adopting Industry 4.0 in the supply chains. We use the novel grey influence analysis (GINA) to examine the influence relations among supply chain resilience enhancers and barriers. The study has identified seven enhancers and nine barriers to adopting I4.0 that can improve supply chain resilience. While analyzing the enhancers, the research findings indicate that <em>visibility</em> and <em>coordination</em> in the supply chain are the major enhancers. For barriers, based on the overall influence score, <em>financial constraints, lack of skills and expertise,</em> and <em>inaccessibility of new technology</em> have ranked <em>first, second</em>, and <em>third</em>, respectively. The identified barriers to adopting I4.0 indicate that reducing financial constraints could facilitate the implementation of Industry 4.0 technologies. This study is among the initial investigations to analyze the supply chain resilience enhancers and barriers together for adoption of Industry 4.0. The findings of this study can assist decision-makers and practitioners in overcoming the identified barriers, thereby focusing on the important enhancers of resilience for facilitating the effective adoption of Industry 4.0 in supply chains.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"43 ","pages":"Article 100735"},"PeriodicalIF":10.4,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142757767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-01DOI: 10.1016/j.jii.2024.100714
Sarthak Acharya, Arif Ali Khan, Tero Päivärinta
Context:
Industry 4.0/5.0 has brought together technologies like Internet of Things (IoTs), Industrial IoT (IIoT), Cyber–Physical Systems (CPS), Edge Computing, big data analytics, communication technologies (4G/5G/6G) and Digital Twins (DTs), aiming for more intelligent, interconnected systems. However, their real-time efficiency hinges on how well these components integrate and interact. This paper examines the interoperability levels and challenges of integrating Digital Twins within edge-enabled Cyber–Physical Systems.
Objective:
Our research explores the existing methods and frameworks for integrating multiple digital twins into a CPS setup. This study focuses on two key research objectives. The first is delineating interoperability levels within DT deployments in CPS setups. The second examines various interoperability challenges in integrating DTs in the context of CPS.
Method:
A literature survey is conducted on the existing scholarly literature, industrial use cases, and reports.
Results:
We identified 77 interoperability challenges and proposed an interoperability framework for DTs, involving six levels i.e. technical, syntactic, semantic, pragmatic, dynamic and organizational. We further categorized all the 77 challenges into 33 subthemes and mapped them across the 6 levels.
Conclusions:
The findings synthesize these challenges into a framework, offering a structured lens through which practitioners can view the adoption and effective use of interconnected DTs in CPS. This contribution is paving the way for future research and development endeavors, aiming to explore fully integrated, efficient, and intelligent CPS within the realm of Industry 4.0/5.0.
{"title":"Interoperability levels and challenges of digital twins in cyber–physical systems","authors":"Sarthak Acharya, Arif Ali Khan, Tero Päivärinta","doi":"10.1016/j.jii.2024.100714","DOIUrl":"10.1016/j.jii.2024.100714","url":null,"abstract":"<div><h3>Context:</h3><div>Industry 4.0/5.0 has brought together technologies like Internet of Things (IoTs), Industrial IoT (IIoT), Cyber–Physical Systems (CPS), Edge Computing, big data analytics, communication technologies (4G/5G/6G) and Digital Twins (DTs), aiming for more intelligent, interconnected systems. However, their real-time efficiency hinges on how well these components integrate and interact. This paper examines the interoperability levels and challenges of integrating Digital Twins within edge-enabled Cyber–Physical Systems.</div></div><div><h3>Objective:</h3><div>Our research explores the existing methods and frameworks for integrating multiple digital twins into a CPS setup. This study focuses on two key research objectives. The first is delineating interoperability levels within DT deployments in CPS setups. The second examines various interoperability challenges in integrating DTs in the context of CPS.</div></div><div><h3>Method:</h3><div>A literature survey is conducted on the existing scholarly literature, industrial use cases, and reports.</div></div><div><h3>Results:</h3><div>We identified 77 interoperability challenges and proposed an interoperability framework for DTs, involving six levels i.e. <em>technical, syntactic, semantic, pragmatic, dynamic</em> and <em>organizational</em>. We further categorized all the 77 challenges into 33 subthemes and mapped them across the 6 levels.</div></div><div><h3>Conclusions:</h3><div>The findings synthesize these challenges into a framework, offering a structured lens through which practitioners can view the adoption and effective use of interconnected DTs in CPS. This contribution is paving the way for future research and development endeavors, aiming to explore fully integrated, efficient, and intelligent CPS within the realm of Industry 4.0/5.0.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"42 ","pages":"Article 100714"},"PeriodicalIF":10.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate soil type categorization is very important for resource management in space exploration. Using a complete system including a space station, rovers, and a deep learning framework, this study proposes an advanced deep learning model for soil type categorization in space informatics. Gathering and preprocessing multispectral and hyperspectral soil data, the rovers send it to the space station for in-depth study. Our model had a test accuracy of about 80%. For space informatics, the suggested method guarantees strong and accurate soil categorization, therefore enabling efficient decision-making.
{"title":"Advance deep learning for soil type classification in space informatics","authors":"Brij B. Gupta , Akshat Gaurav , Varsha Arya , Razaz Waheeb Attar","doi":"10.1016/j.jii.2024.100712","DOIUrl":"10.1016/j.jii.2024.100712","url":null,"abstract":"<div><div>Accurate soil type categorization is very important for resource management in space exploration. Using a complete system including a space station, rovers, and a deep learning framework, this study proposes an advanced deep learning model for soil type categorization in space informatics. Gathering and preprocessing multispectral and hyperspectral soil data, the rovers send it to the space station for in-depth study. Our model had a test accuracy of about 80%. For space informatics, the suggested method guarantees strong and accurate soil categorization, therefore enabling efficient decision-making.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"42 ","pages":"Article 100712"},"PeriodicalIF":10.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-01DOI: 10.1016/j.jii.2024.100724
Kelvin K.L. Wong, Kavimbi Chipusu, Muhammad Awais Ashraf, Andrew W.H. Ip, Chris W.J. Zhang
The integration of cybernetic principles into space technology has led to a significant shift in spacecraft guidance systems and space station operations. This scholarly work provides a comprehensive analysis of the profound impact that cybernetics has had on space technology, particularly focusing on the development and implementation of closed-loop control systems. Building on foundational contributions to cybernetics, this paper offers a detailed analysis of the complex interplay between control mechanisms, behavioral dynamics, and information management in space informatics. The essential role of cybernetic control systems is highlighted through their critical function in enabling precise spacecraft maneuvers, as demonstrated by the guidance systems used in Apollo lunar missions during the crucial descent and landing phases. In modern space station programs, cybernetic intelligence operates much like a neural network, processing data in real-time and coordinating the various elements of the station's infrastructure. This cybernetic system excels in a wide range of tasks, from space docking maneuvers to waste management, thereby safeguarding astronaut welfare and ensuring mission success. Information serves as the cornerstone of problem analysis and processing, providing a holistic understanding of information flow within complex systems. The study concludes by highlighting the application of a cybernetically intelligent neural network in developing adaptive learning systems for nonlinear and intricate industrial processes. Ultimately, this paper underscores the transformative role of cybernetics in shaping the future of space exploration, emphasizing the seamless integration of control, information, and behavior in space technology to advance our understanding of celestial systems while enhancing the efficiency and safety of space missions.
{"title":"In-space cybernetical intelligence perspective on informatics, manufacturing and integrated control for the space exploration industry","authors":"Kelvin K.L. Wong, Kavimbi Chipusu, Muhammad Awais Ashraf, Andrew W.H. Ip, Chris W.J. Zhang","doi":"10.1016/j.jii.2024.100724","DOIUrl":"10.1016/j.jii.2024.100724","url":null,"abstract":"<div><div>The integration of cybernetic principles into space technology has led to a significant shift in spacecraft guidance systems and space station operations. This scholarly work provides a comprehensive analysis of the profound impact that cybernetics has had on space technology, particularly focusing on the development and implementation of closed-loop control systems. Building on foundational contributions to cybernetics, this paper offers a detailed analysis of the complex interplay between control mechanisms, behavioral dynamics, and information management in space informatics. The essential role of cybernetic control systems is highlighted through their critical function in enabling precise spacecraft maneuvers, as demonstrated by the guidance systems used in Apollo lunar missions during the crucial descent and landing phases. In modern space station programs, cybernetic intelligence operates much like a neural network, processing data in real-time and coordinating the various elements of the station's infrastructure. This cybernetic system excels in a wide range of tasks, from space docking maneuvers to waste management, thereby safeguarding astronaut welfare and ensuring mission success. Information serves as the cornerstone of problem analysis and processing, providing a holistic understanding of information flow within complex systems. The study concludes by highlighting the application of a cybernetically intelligent neural network in developing adaptive learning systems for nonlinear and intricate industrial processes. Ultimately, this paper underscores the transformative role of cybernetics in shaping the future of space exploration, emphasizing the seamless integration of control, information, and behavior in space technology to advance our understanding of celestial systems while enhancing the efficiency and safety of space missions.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"42 ","pages":"Article 100724"},"PeriodicalIF":10.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-01DOI: 10.1016/j.jii.2024.100718
Rashmi Pathak , Badal Soni , Naresh Babu Muppalaneni , Muhammet Deveci
Blockchain technology (BT) is a digitally decentralized, distributed and public ledger, which considers a secure and viable solution for storing and accessing the record transactions in a public or private peer-to-peer network and assures a smart world of automation of complex services. This paper aims to evaluate the factors persuading the BT adoption and also to assess the possible application areas for the adoption of BT. Implementation of the BT process considers multiple criteria and alternatives with uncertain information; therefore, it can be considered as an uncertain multi-criteria decision-making problem. In this context, a hybrid approach is proposed based on the criteria interaction through inter-criteria correlation (CRITIC) and the complex proportional assessment (COPRAS) methods with interval-valued q-rung orthopair fuzzy set (IVq-ROFS) named as the “IVq-ROF-CRITICCOPRAS” framework. In this approach, the significance values of the factors are computed through novel score function-based CRITIC model, whereas the CRITIC-based COPRAS model is employed to rank different domains/application areas for the adoption of BT under IVq-ROFSs environment. In this regard, a novel score function is introduced with its desirable characteristics. Further, the developed model is executed on a case study of BT adoption considering 14 factors, which confirms the efficiency and applicability of introduced approach. Based on the results, the healthcare BT alternative is most appropriate among the others. To evaluate its permanence, sensitivity analysis with regard to various ratings of decision strategic coefficient is presented under IVq-ROFS context. The strength of the developed approach is emphasized by comparing it with some of the former models under the context of IVq-ROFSs. The proposed extension of the COPRAS approach can be utilized to solve other complex group decision-making problems with uncertainties.
{"title":"Interval-valued q-rung orthopair fuzzy complex proportional assessment-based approach and its application for evaluating the factors of blockchain technology in various domains","authors":"Rashmi Pathak , Badal Soni , Naresh Babu Muppalaneni , Muhammet Deveci","doi":"10.1016/j.jii.2024.100718","DOIUrl":"10.1016/j.jii.2024.100718","url":null,"abstract":"<div><div>Blockchain technology (BT) is a digitally decentralized, distributed and public ledger, which considers a secure and viable solution for storing and accessing the record transactions in a public or private peer-to-peer network and assures a smart world of automation of complex services. This paper aims to evaluate the factors persuading the BT adoption and also to assess the possible application areas for the adoption of BT. Implementation of the BT process considers multiple criteria and alternatives with uncertain information; therefore, it can be considered as an uncertain multi-criteria decision-making problem. In this context, a hybrid approach is proposed based on the criteria interaction through inter-criteria correlation (CRITIC) and the complex proportional assessment (COPRAS) methods with interval-valued q-rung orthopair fuzzy set (IVq-ROFS) named as the “IVq-ROF-CRITIC<img>COPRAS” framework. In this approach, the significance values of the factors are computed through novel score function-based CRITIC model, whereas the CRITIC-based COPRAS model is employed to rank different domains/application areas for the adoption of BT under IVq-ROFSs environment. In this regard, a novel score function is introduced with its desirable characteristics. Further, the developed model is executed on a case study of BT adoption considering 14 factors, which confirms the efficiency and applicability of introduced approach. Based on the results, the healthcare BT alternative is most appropriate among the others. To evaluate its permanence, sensitivity analysis with regard to various ratings of decision strategic coefficient is presented under IVq-ROFS context. The strength of the developed approach is emphasized by comparing it with some of the former models under the context of IVq-ROFSs. The proposed extension of the COPRAS approach can be utilized to solve other complex group decision-making problems with uncertainties.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"42 ","pages":"Article 100718"},"PeriodicalIF":10.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142653178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-01DOI: 10.1016/j.jii.2024.100737
Mohammad A. Edalatpour , Amir M. Fathollahi-Fard , Seyed Mohammad Javad Mirzapour Al-e-Hashem , Kuan Yew Wong
Industrial information integration plays a crucial role in modern supply chains by ensuring the smooth flow of data across all stages, including recovery, recycling, and disposal, which is essential for the successful implementation of a closed-loop supply chain (CLSC) model. Building on this, our paper addresses a global CLSC problem by incorporating International Commercial Terms (Incoterms) and international transportation modes, bridging global supply chain operations with sustainability criteria. This innovative approach advances the development of a globally sustainable CLSC by focusing on the integration of economic, environmental, and social factors, i.e., the triple bottom line of sustainability. Specifically, we address environmental concerns through the introduction of carbon taxation and enhance social sustainability by exploring the impact of advertising on customer satisfaction. To further refine this model, we classify customers based on their sustainability engagement and apply a fuzzy programming approach to account for uncertainty in customer demand influenced by advertising. To solve this complex global CLSC model, we conduct a thorough analysis of constraints and develop a robust Lagrangian relaxation reformulation. While the initial solution may result in infeasibility, we propose a heuristic algorithm that ensures feasible solutions. Our efficient Lagrangian-based heuristic, incorporating an adaptive strategy, is capable of solving large-scale networks with an approximate 10 % optimality gap. Ultimately, this research provides both a comprehensive framework for practitioners to improve the environmental performance and global operations of their supply chains, as well as significant theoretical contributions to the field of industrial information systems.
{"title":"Global sustainable closed-loop supply chain network considering Incoterms rules and advertisement impacts","authors":"Mohammad A. Edalatpour , Amir M. Fathollahi-Fard , Seyed Mohammad Javad Mirzapour Al-e-Hashem , Kuan Yew Wong","doi":"10.1016/j.jii.2024.100737","DOIUrl":"10.1016/j.jii.2024.100737","url":null,"abstract":"<div><div>Industrial information integration plays a crucial role in modern supply chains by ensuring the smooth flow of data across all stages, including recovery, recycling, and disposal, which is essential for the successful implementation of a closed-loop supply chain (CLSC) model. Building on this, our paper addresses a global CLSC problem by incorporating International Commercial Terms (Incoterms) and international transportation modes, bridging global supply chain operations with sustainability criteria. This innovative approach advances the development of a globally sustainable CLSC by focusing on the integration of economic, environmental, and social factors, i.e., the triple bottom line of sustainability. Specifically, we address environmental concerns through the introduction of carbon taxation and enhance social sustainability by exploring the impact of advertising on customer satisfaction. To further refine this model, we classify customers based on their sustainability engagement and apply a fuzzy programming approach to account for uncertainty in customer demand influenced by advertising. To solve this complex global CLSC model, we conduct a thorough analysis of constraints and develop a robust Lagrangian relaxation reformulation. While the initial solution may result in infeasibility, we propose a heuristic algorithm that ensures feasible solutions. Our efficient Lagrangian-based heuristic, incorporating an adaptive strategy, is capable of solving large-scale networks with an approximate 10 % optimality gap. Ultimately, this research provides both a comprehensive framework for practitioners to improve the environmental performance and global operations of their supply chains, as well as significant theoretical contributions to the field of industrial information systems.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"42 ","pages":"Article 100737"},"PeriodicalIF":10.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142653228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-01DOI: 10.1016/j.jii.2024.100731
Ignacio Huitzil , Miguel Molina-Solana , Juan Gómez-Romero , Marco Schorlemmer , Pere Garcia-Calvés , Nardine Osman , Josep Coll , Fernando Bobillo
Semantic Building Information Modeling (BIM) consists in translating data expressed using BIM formats (namely IFC) into Semantic Web files using RDF serializations (e.g., Turtle). This enables the inference of new knowledge and constraint checking, among other advantages. While several software tools for translating BIM models into Semantic Web languages have been proposed in the literature, they differ in the features exposed.
This paper analyzes and empirically compares some of these tools (namely, IFC converters translating an input IFC model into an RDF graph), identifying their strengths and main limitations. Our methodology includes measuring computation times of common tasks (file conversion, query and inference over output files), assessing the retention of knowledge (particularly, geometric information) and examining reasoning capabilities (complexity and completeness of the resulting models). Our results show that IFCtoLBD is the best option in many cases. IFCtoRDF and IFC2LD are slower but better preserve geometric information, while KGG is faster at the expense of losing information in the translation.
{"title":"Semantic Building Information Modeling: An empirical evaluation of existing tools","authors":"Ignacio Huitzil , Miguel Molina-Solana , Juan Gómez-Romero , Marco Schorlemmer , Pere Garcia-Calvés , Nardine Osman , Josep Coll , Fernando Bobillo","doi":"10.1016/j.jii.2024.100731","DOIUrl":"10.1016/j.jii.2024.100731","url":null,"abstract":"<div><div>Semantic Building Information Modeling (BIM) consists in translating data expressed using BIM formats (namely IFC) into Semantic Web files using RDF serializations (e.g., Turtle). This enables the inference of new knowledge and constraint checking, among other advantages. While several software tools for translating BIM models into Semantic Web languages have been proposed in the literature, they differ in the features exposed.</div><div>This paper analyzes and empirically compares some of these tools (namely, IFC converters translating an input IFC model into an RDF graph), identifying their strengths and main limitations. Our methodology includes measuring computation times of common tasks (file conversion, query and inference over output files), assessing the retention of knowledge (particularly, geometric information) and examining reasoning capabilities (complexity and completeness of the resulting models). Our results show that IFCtoLBD is the best option in many cases. IFCtoRDF and IFC2LD are slower but better preserve geometric information, while KGG is faster at the expense of losing information in the translation.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"42 ","pages":"Article 100731"},"PeriodicalIF":10.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-01DOI: 10.1016/j.jii.2024.100739
Hemant Kumar Apat, Bibhudatta Sahoo
With the rapid development of Internet of Things (IoT) devices, the volume of data generate across various fields, such as smart healthcare, smart home, smart transportation has significantly increased. This surge raises serious concerns about the secure storage of sensitive data for e.g., biometric information (e.g., fingerprints and facial recognition) and medical records etc. The centralized cloud computing paradigm provides various cost-effective services to IoT applications users. Despite of various benefits of centralized cloud, it fails to adequately meet the strict latency and security requirement of various IoT applications. Fog computing is proposed to enhance the real-time data processing for various latency sensitive IoT applications by extending the cloud computing services closer to the data sources. In this paper we proposed a novel blockchain based distributed fog computing model that ensures secure distributed storage for various IoT data. The blockchain network acts a trusted third party aimed at establishing secure communication among IoT devices and fog node within the fog layer. It details a distinctive Elliptic Curve Diffie–Hellman (ECDH) protocol for reliable and secure data storage and retrieval based on requests and responses from heterogeneous IoT devices. Additionally, a Merkle tree-based data structure is used to verify data integrity, ensuring secure and tamper-proof data management within the blockchain-enabled fog computing framework. It provides a formal security proof using AVISPA tools for the proposed scheme, ensuring that it meets the necessary security standards and can be trusted for protecting sensitive IoT data. Finally, the proposed scheme is compared with existing security schemes, such as AES, ABE, RSA, and Hybrid RSA in terms of resource utilization, computational cost, communication cost and execution cost. The experimental results exemplify that the proposed scheme outperform other state of the art schemes.
{"title":"A Blockchain assisted fog computing for secure distributed storage system for IoT Applications","authors":"Hemant Kumar Apat, Bibhudatta Sahoo","doi":"10.1016/j.jii.2024.100739","DOIUrl":"10.1016/j.jii.2024.100739","url":null,"abstract":"<div><div>With the rapid development of Internet of Things (IoT) devices, the volume of data generate across various fields, such as smart healthcare, smart home, smart transportation has significantly increased. This surge raises serious concerns about the secure storage of sensitive data for e.g., biometric information (e.g., fingerprints and facial recognition) and medical records etc. The centralized cloud computing paradigm provides various cost-effective services to IoT applications users. Despite of various benefits of centralized cloud, it fails to adequately meet the strict latency and security requirement of various IoT applications. Fog computing is proposed to enhance the real-time data processing for various latency sensitive IoT applications by extending the cloud computing services closer to the data sources. In this paper we proposed a novel blockchain based distributed fog computing model that ensures secure distributed storage for various IoT data. The blockchain network acts a trusted third party aimed at establishing secure communication among IoT devices and fog node within the fog layer. It details a distinctive Elliptic Curve Diffie–Hellman (ECDH) protocol for reliable and secure data storage and retrieval based on requests and responses from heterogeneous IoT devices. Additionally, a Merkle tree-based data structure is used to verify data integrity, ensuring secure and tamper-proof data management within the blockchain-enabled fog computing framework. It provides a formal security proof using AVISPA tools for the proposed scheme, ensuring that it meets the necessary security standards and can be trusted for protecting sensitive IoT data. Finally, the proposed scheme is compared with existing security schemes, such as AES, ABE, RSA, and Hybrid RSA in terms of resource utilization, computational cost, communication cost and execution cost. The experimental results exemplify that the proposed scheme outperform other state of the art schemes.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"42 ","pages":"Article 100739"},"PeriodicalIF":10.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-01DOI: 10.1016/j.jii.2024.100725
Jun Yang , Baoping Cai , Xiangdi Kong , Xiaoyan Shao , Bo Wang , Yulong Yu , Lei Gao , Chao yang , Yonghong Liu
As the complexity of modern engineering systems increases, traditional fault detection models face growing challenges in achieving accuracy and reliability. This paper presents a novel Digital Twin-assisted fault diagnosis framework specifically designed for hydraulic systems. The framework utilizes a virtual model, constructed using Modelica, which is integrated with real-time system data through a first-of-its-kind bidirectional data consistency evaluation mechanism. The integrated data is further refined using a two-dimensional signal warping algorithm to enhance its reliability. This optimized twin data is then employed to train a multi-channel one-dimensional convolutional neural network-gated recurrent unit model, effectively capturing both spatial and temporal features to improve fault detection. The subsea blowout preventer in lab is used to study the performance of the method. The results show that the accuracy is 95.62 %. Compared to current methods, this is a significant improvement. By integrating DT technology, data consistency optimization, and advanced deep learning techniques, this framework provides a scalable and reliable solution for predictive maintenance in complex engineering systems.
{"title":"A digital twin-assisted intelligent fault diagnosis method for hydraulic systems","authors":"Jun Yang , Baoping Cai , Xiangdi Kong , Xiaoyan Shao , Bo Wang , Yulong Yu , Lei Gao , Chao yang , Yonghong Liu","doi":"10.1016/j.jii.2024.100725","DOIUrl":"10.1016/j.jii.2024.100725","url":null,"abstract":"<div><div>As the complexity of modern engineering systems increases, traditional fault detection models face growing challenges in achieving accuracy and reliability. This paper presents a novel Digital Twin-assisted fault diagnosis framework specifically designed for hydraulic systems. The framework utilizes a virtual model, constructed using Modelica, which is integrated with real-time system data through a first-of-its-kind bidirectional data consistency evaluation mechanism. The integrated data is further refined using a two-dimensional signal warping algorithm to enhance its reliability. This optimized twin data is then employed to train a multi-channel one-dimensional convolutional neural network-gated recurrent unit model, effectively capturing both spatial and temporal features to improve fault detection. The subsea blowout preventer in lab is used to study the performance of the method. The results show that the accuracy is 95.62 %. Compared to current methods, this is a significant improvement. By integrating DT technology, data consistency optimization, and advanced deep learning techniques, this framework provides a scalable and reliable solution for predictive maintenance in complex engineering systems.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"42 ","pages":"Article 100725"},"PeriodicalIF":10.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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