<div><div>Unfortunately, within the framework of blockchain contracting, a significant gap exists in comprehending contractual behavior, and the feasibility of predictive contracts has largely remained unexplored. A principal obstacle stems from the absence of a seamless integration between predictive concepts and blockchain technology. This deficiency is attributed to a failure to consider the inherent characteristics of blockchain when developing solutions aimed at improving predictive capabilities within blockchain-based systems. Many existing predictive approaches function externally to the fundamental blockchain framework, rendering them impractical. This has caused the idea of predictive contracts to be seen as unfeasible due to the character of blockchain smart contracts making it hard to do so. This includes its immutability and the inability for changes to be made once deployed. In this research, we introduce the concept of blockchain-based predictive contracting which stems from the theoretical idea of predictive contracting, and substantiate the feasibility of our approach, enabling blockchain smart contracts to adapt to changes in external environments upon which they depend. We attempt to achieve and prove the first phase of this idea, which we term “recalibration”. Here we provide a means for deployed smart contracts to become structurally changeable while responding to external situations without compromising their security. This we believe is the first phase needed for blockchain smart contracts before they can become predictable. Our approach capitalizes on the key-pair structure scheme utilized in existing blockchain systems to create a data signature, facilitating the identification of new smart contracts. We establish rules encompassing a configuration mechanism, empowering smart contracts to recognize newly-introduced agreements. Additionally, we implement an encoding system to enable the blockchain to respond to dynamic data. This we believe will provide a means for blockchain to be used well in industrial applications such as supply aircraft delivery networks and supply chain networks. To anticipate future scenarios, we devise a multi-versioning system that allows smart contracts to evolve over time. Our innovative concept is also demonstrated within a blockchain-based smart contract prediction scheme, ensuring the adaptability of blockchain-based smart contracts. This scheme comprises a smart contract tracing mechanism, an effective smart contract transitioning procedure, and a protocol for generating new smart contracting terms and conditions while preserving inherent trust within the system. Through extensive experimentation, involving opcode and smart contract ID extraction, Solidity Word2Vec model development, a blockchain-based embedding process, and smart contract versioning detection, we introduce the concept of blockchain-based predictive smart contracts. Notably, we observe a significant enhancement as multiple pa
遗憾的是，在区块链合约框架内，对合约行为的理解还存在很大差距，预测性合约的可行性在很大程度上仍未得到探索。一个主要障碍源于预测概念与区块链技术之间缺乏无缝整合。造成这一缺陷的原因是，在开发旨在提高基于区块链系统的预测能力的解决方案时，未能考虑区块链的固有特性。许多现有的预测方法都是在基本区块链框架之外发挥作用，因此不切实际。由于区块链智能合约的特性使其难以实现，这导致预测性合约的想法被视为不可行。这包括它的不可更改性和一旦部署就无法更改的特性。在这项研究中，我们引入了基于区块链的预测性合约概念，该概念源于预测性合约的理论思想，并证实了我们的方法的可行性，使区块链智能合约能够适应其所依赖的外部环境的变化。我们试图实现并证明这一想法的第一阶段，我们称之为 "重新校准"。在这里，我们为已部署的智能合约提供了一种方法，使其在应对外部环境的同时，在结构上发生变化，而不影响其安全性。我们认为，这是区块链智能合约在变得可预测之前所需的第一阶段。我们的方法利用现有区块链系统中使用的密钥对结构方案来创建数据签名，从而方便识别新的智能合约。我们建立了包含配置机制的规则，使智能合约能够识别新引入的协议。此外，我们还实施了一个编码系统，使区块链能够响应动态数据。我们相信，这将为区块链在供应飞机交付网络和供应链网络等工业应用中的良好应用提供一种手段。为了预测未来的应用场景，我们设计了一个多版本系统，允许智能合约随时间演变。我们的创新理念还在基于区块链的智能合约预测方案中得到了展示，确保了基于区块链的智能合约的适应性。该方案包括一个智能合约追踪机制、一个有效的智能合约过渡程序和一个用于生成新的智能合约条款和条件的协议，同时保持系统内的固有信任。通过大量实验，包括操作码和智能合约 ID 提取、Solidity Word2Vec 模型开发、基于区块链的嵌入过程和智能合约版本检测，我们引入了基于区块链的预测性智能合约的概念。值得注意的是，当多方在区块链上进行复杂操作时，我们观察到了显著的提升，在外生性条件下展示智能合约操作的平均气体成本为 31374215 Wei。这验证了我们的方法比之前的方法更具成本效益。我们的实证结果肯定了我们提出的概念的新颖性和有效性。
{"title":"Bridging the gap: Predictive contracts in blockchain-achieving recalibration for industrial networks","authors":"Bonsu Adjei-Arthur , Sophyani Banaamwini Yussif , Sandra Chukwudumebi Obiora , Daniel Adu Worae , Olusola Bamisile","doi":"10.1016/j.jii.2024.100713","DOIUrl":"10.1016/j.jii.2024.100713","url":null,"abstract":"<div><div>Unfortunately, within the framework of blockchain contracting, a significant gap exists in comprehending contractual behavior, and the feasibility of predictive contracts has largely remained unexplored. A principal obstacle stems from the absence of a seamless integration between predictive concepts and blockchain technology. This deficiency is attributed to a failure to consider the inherent characteristics of blockchain when developing solutions aimed at improving predictive capabilities within blockchain-based systems. Many existing predictive approaches function externally to the fundamental blockchain framework, rendering them impractical. This has caused the idea of predictive contracts to be seen as unfeasible due to the character of blockchain smart contracts making it hard to do so. This includes its immutability and the inability for changes to be made once deployed. In this research, we introduce the concept of blockchain-based predictive contracting which stems from the theoretical idea of predictive contracting, and substantiate the feasibility of our approach, enabling blockchain smart contracts to adapt to changes in external environments upon which they depend. We attempt to achieve and prove the first phase of this idea, which we term “recalibration”. Here we provide a means for deployed smart contracts to become structurally changeable while responding to external situations without compromising their security. This we believe is the first phase needed for blockchain smart contracts before they can become predictable. Our approach capitalizes on the key-pair structure scheme utilized in existing blockchain systems to create a data signature, facilitating the identification of new smart contracts. We establish rules encompassing a configuration mechanism, empowering smart contracts to recognize newly-introduced agreements. Additionally, we implement an encoding system to enable the blockchain to respond to dynamic data. This we believe will provide a means for blockchain to be used well in industrial applications such as supply aircraft delivery networks and supply chain networks. To anticipate future scenarios, we devise a multi-versioning system that allows smart contracts to evolve over time. Our innovative concept is also demonstrated within a blockchain-based smart contract prediction scheme, ensuring the adaptability of blockchain-based smart contracts. This scheme comprises a smart contract tracing mechanism, an effective smart contract transitioning procedure, and a protocol for generating new smart contracting terms and conditions while preserving inherent trust within the system. Through extensive experimentation, involving opcode and smart contract ID extraction, Solidity Word2Vec model development, a blockchain-based embedding process, and smart contract versioning detection, we introduce the concept of blockchain-based predictive smart contracts. Notably, we observe a significant enhancement as multiple pa","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"42 ","pages":"Article 100713"},"PeriodicalIF":10.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701802","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.2024.100711
Qingyang Liu , Yanrong Hu , Hongjiu Liu
The prediction of stock prices is a complex task due to the influence of various factors, high noise, and nonlinearity. This paper focuses on addressing the challenges of low prediction accuracy and poor stability, which have been a key area of interest in academic research. We proposed an optimized ensemble model that combines an LSTM-based attention mechanism and a cyclic multidimensional gray model, utilizing multi-source heterogeneous data. Our results demonstrate that the ensemble model achieves improved prediction accuracy, exhibits a good fitting effect, and outperforms individual models. The ensemble model yields smaller Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) values compared to the LSTM-based attention mechanism model and the multidimensional gray model. Furthermore, the ensemble model shows enhanced coefficient of determination (R2). Comparative analysis with alternative models such as ARIMA, GRU, CNN, and CNN-GRU reveals that the ensemble model achieves significant advancements in prediction accuracy.
{"title":"Enhanced stock price prediction with optimized ensemble modeling using multi-source heterogeneous data: Integrating LSTM attention mechanism and multidimensional gray model","authors":"Qingyang Liu , Yanrong Hu , Hongjiu Liu","doi":"10.1016/j.jii.2024.100711","DOIUrl":"10.1016/j.jii.2024.100711","url":null,"abstract":"<div><div>The prediction of stock prices is a complex task due to the influence of various factors, high noise, and nonlinearity. This paper focuses on addressing the challenges of low prediction accuracy and poor stability, which have been a key area of interest in academic research. We proposed an optimized ensemble model that combines an LSTM-based attention mechanism and a cyclic multidimensional gray model, utilizing multi-source heterogeneous data. Our results demonstrate that the ensemble model achieves improved prediction accuracy, exhibits a good fitting effect, and outperforms individual models. The ensemble model yields smaller Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) values compared to the LSTM-based attention mechanism model and the multidimensional gray model. Furthermore, the ensemble model shows enhanced coefficient of determination (R<sup>2</sup>). Comparative analysis with alternative models such as ARIMA, GRU, CNN, and CNN-GRU reveals that the ensemble model achieves significant advancements in prediction accuracy.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"42 ","pages":"Article 100711"},"PeriodicalIF":10.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696481","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.2024.100730
Simone Agostinelli , Ala Arman , Francesca De Luzi, Flavia Monti , Michele Manglaviti, Massimo Mecella
An important issue in coopetitive supply chains is ensuring business confidentiality when sharing sensitive information among partner actors. This challenge becomes even more complex in blockchain-based supply chains due to inherent transparency, conflicting with businesses’ need to safeguard sensitive information and posing risks to proprietary data. In this paper, we propose an approach based on permissioned blockchains to support transactional business confidentiality in supply chains. The approach is implemented as an open-source platform and evaluated against five non-functional requirements.
{"title":"Supporting business confidentiality in coopetitive scenarios: The B-CONFIDENT approach in blockchain-based supply chains","authors":"Simone Agostinelli , Ala Arman , Francesca De Luzi, Flavia Monti , Michele Manglaviti, Massimo Mecella","doi":"10.1016/j.jii.2024.100730","DOIUrl":"10.1016/j.jii.2024.100730","url":null,"abstract":"<div><div>An important issue in <em>coopetitive</em> supply chains is ensuring business confidentiality when sharing sensitive information among partner actors. This challenge becomes even more complex in blockchain-based supply chains due to inherent transparency, conflicting with businesses’ need to safeguard sensitive information and posing risks to proprietary data. In this paper, we propose an approach based on permissioned blockchains to support transactional business confidentiality in supply chains. The approach is implemented as an open-source platform and evaluated against five non-functional requirements.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"42 ","pages":"Article 100730"},"PeriodicalIF":10.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701803","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.2024.100706
Sabah Suhail , Mubashar Iqbal , Rasheed Hussain , Saif Ur Rehman Malik , Raja Jurdak
Cyber–physical systems (CPSs) are being increasingly adopted for industrial applications, yet they involve a dynamic threat landscape that requires CPSs to adapt to emerging threats during their operation. Recently, digital twin (DT) technology (which refers to a virtual representation of a product, process, or environment) has emerged as a suitable candidate to address the security challenges faced by dynamic CPSs. DT has the capability of strengthening the security of CPSs by continuously mapping the physical to twin counterparts to detect inconsistencies. The existing DT-based security solutions are constrained by untrustworthy data dissemination as well as limited data sharing among the involved stakeholders, which, in turn, limit the ability of DTs to run accurate simulations or make valid decisions. To address these challenges, this paper proposes a modular framework called TRusted and Intelligent cyber-PhysicaL systEm (TRIPLE), that leverages blockchain, DTs, and threat intelligence (TI) to secure CPSs. The blockchain-based DT components in the framework provide data integrity, traceability, and availability for trusted DTs. Furthermore, to accurately and comprehensively model system states, the framework envisions fusing process knowledge for modeling DTs from system specification-based and learning-based information and other sources, including infrastructure-as-code (IaC) and knowledge base (KB). The framework also integrates TI for future-proofing against emerging threats, such that threats can be detected either reactively by mapping the behavior of physical and virtual spaces or proactively by TI and threat hunting. We demonstrate the viability of the framework through a proof of concept. Finally, we formally verify the TRIPLE framework to demonstrate its correctness and effectiveness in enhancing CPS security.
{"title":"TRIPLE: A blockchain-based digital twin framework for cyber–physical systems security","authors":"Sabah Suhail , Mubashar Iqbal , Rasheed Hussain , Saif Ur Rehman Malik , Raja Jurdak","doi":"10.1016/j.jii.2024.100706","DOIUrl":"10.1016/j.jii.2024.100706","url":null,"abstract":"<div><div>Cyber–physical systems (CPSs) are being increasingly adopted for industrial applications, yet they involve a dynamic threat landscape that requires CPSs to adapt to emerging threats during their operation. Recently, digital twin (DT) technology (which refers to a virtual representation of a product, process, or environment) has emerged as a suitable candidate to address the security challenges faced by dynamic CPSs. DT has the capability of strengthening the security of CPSs by continuously mapping the physical to twin counterparts to detect inconsistencies. The existing DT-based security solutions are constrained by untrustworthy data dissemination as well as limited data sharing among the involved stakeholders, which, in turn, limit the ability of DTs to run accurate simulations or make valid decisions. To address these challenges, this paper proposes a modular framework called <strong>TR</strong>usted and <strong>I</strong>ntelligent cyber-<strong>P</strong>hysica<strong>L</strong> syst<strong>E</strong>m (TRIPLE), that leverages blockchain, DTs, and threat intelligence (TI) to secure CPSs. The blockchain-based DT components in the framework provide data integrity, traceability, and availability for trusted DTs. Furthermore, to accurately and comprehensively model system states, the framework envisions fusing process knowledge for modeling DTs from system specification-based and learning-based information and other sources, including infrastructure-as-code (IaC) and knowledge base (KB). The framework also integrates TI for future-proofing against emerging threats, such that threats can be detected either reactively by mapping the behavior of physical and virtual spaces or proactively by TI and threat hunting. We demonstrate the viability of the framework through a proof of concept. Finally, we formally verify the TRIPLE framework to demonstrate its correctness and effectiveness in enhancing CPS security.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"42 ","pages":"Article 100706"},"PeriodicalIF":10.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573306","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.2024.100721
Yuk Ming Tang , Wai Hung Ip , Kai Leung Yung , Zhuming BI
Recently, China and the United States have achieved remarkable success in aerospace science and technology over the years. Space has become another field of competition in the technological advancement of various countries. Through space missions, space tourism, moon and Mars exploration, China and the United States can demonstrate the sophistication of their technologies to the public and audiences around the world. Despite the competitiveness between the big countries, space missions and deep space exploration and exploitation have provided a lot of deep and orbital space information that is beneficial not only for the next space mission but also for enhancing technological development for other domestic uses. Therefore, space industrial information integration (III), or Space III, connecting IoT to form the Internet of Planets, is critically important for deep space explorations. However, few articles have reviewed the existing technologies of space. We are one of the few groups to perform an extensive review, research the space explorations and divide the space information integration systematically based on the information architecture and technologies in the space industries. In this paper, we propose that III can be divided into three different architectures: data, technology, and application, whereas space technology can be divided into six areas. This review is important not only in formulating research in technological integration but also in determining the proposed architecture to facilitate a further extension of applications to large-scale and complex problems in the space industries in the future.
{"title":"Industrial information integration in deep space exploration and exploitation: Architecture and technology","authors":"Yuk Ming Tang , Wai Hung Ip , Kai Leung Yung , Zhuming BI","doi":"10.1016/j.jii.2024.100721","DOIUrl":"10.1016/j.jii.2024.100721","url":null,"abstract":"<div><div>Recently, China and the United States have achieved remarkable success in aerospace science and technology over the years. Space has become another field of competition in the technological advancement of various countries. Through space missions, space tourism, moon and Mars exploration, China and the United States can demonstrate the sophistication of their technologies to the public and audiences around the world. Despite the competitiveness between the big countries, space missions and deep space exploration and exploitation have provided a lot of deep and orbital space information that is beneficial not only for the next space mission but also for enhancing technological development for other domestic uses. Therefore, space industrial information integration (III), or Space III, connecting IoT to form the Internet of Planets, is critically important for deep space explorations. However, few articles have reviewed the existing technologies of space. We are one of the few groups to perform an extensive review, research the space explorations and divide the space information integration systematically based on the information architecture and technologies in the space industries. In this paper, we propose that III can be divided into three different architectures: data, technology, and application, whereas space technology can be divided into six areas. This review is important not only in formulating research in technological integration but also in determining the proposed architecture to facilitate a further extension of applications to large-scale and complex problems in the space industries in the future.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"42 ","pages":"Article 100721"},"PeriodicalIF":10.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573307","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.2024.100720
Arkadiusz Ryś , Lucas Lima , Joeri Exelmans , Dennis Janssens , Hans Vangheluwe
System engineering has been shifting from document-centric to model-based approaches, where assets are becoming more and more digital. Although digitisation conveys several benefits, it also brings several concerns (e.g., storage and access) and opportunities. In the context of Cyber-Physical Systems (CPS), we have experts from various domains executing complex workflows and manipulating models in a plethora of different formalisms, each with their own methods, techniques and tools. Storing knowledge on these workflows can reduce considerable effort during system development not only to allow their repeatability and replicability but also to access and reason on data generated by their execution. In this work, we propose a framework to manage modelling artefacts generated from workflow executions. The basic workflow concepts, related formalisms and artefacts are formally defined in an ontology specified in OML (Ontology Modelling Language). This ontology enables the construction of a knowledge graph that contains system engineering data to which we can apply reasoning. We also developed several tools to support system engineering during the design of workflows, their enactment, and artefact storage, considering versioning, querying and reasoning on the stored data. These tools also hide the complexity of manipulating the knowledge graph directly. Finally, we have applied our proposed framework in a real-world system development scenario of a drivetrain smart sensor system. Results show that our proposal not only helped the system engineer with fundamental difficulties like storage and versioning but also reduced the time needed to access relevant information and new knowledge that can be inferred from the knowledge graph.
{"title":"Model management to support systems engineering workflows using ontology-based knowledge graphs","authors":"Arkadiusz Ryś , Lucas Lima , Joeri Exelmans , Dennis Janssens , Hans Vangheluwe","doi":"10.1016/j.jii.2024.100720","DOIUrl":"10.1016/j.jii.2024.100720","url":null,"abstract":"<div><div>System engineering has been shifting from document-centric to model-based approaches, where assets are becoming more and more digital. Although digitisation conveys several benefits, it also brings several concerns (e.g., storage and access) and opportunities. In the context of Cyber-Physical Systems (CPS), we have experts from various domains executing complex workflows and manipulating models in a plethora of different formalisms, each with their own methods, techniques and tools. Storing knowledge on these workflows can reduce considerable effort during system development not only to allow their repeatability and replicability but also to access and reason on data generated by their execution. In this work, we propose a framework to manage modelling artefacts generated from workflow executions. The basic workflow concepts, related formalisms and artefacts are formally defined in an ontology specified in OML (Ontology Modelling Language). This ontology enables the construction of a knowledge graph that contains system engineering data to which we can apply reasoning. We also developed several tools to support system engineering during the design of workflows, their enactment, and artefact storage, considering versioning, querying and reasoning on the stored data. These tools also hide the complexity of manipulating the knowledge graph directly. Finally, we have applied our proposed framework in a real-world system development scenario of a drivetrain smart sensor system. Results show that our proposal not only helped the system engineer with fundamental difficulties like storage and versioning but also reduced the time needed to access relevant information and new knowledge that can be inferred from the knowledge graph.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"42 ","pages":"Article 100720"},"PeriodicalIF":10.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142653226","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.2024.100717
Rekha Guchhait , Mitali Sarkar , Biswajit Sarkar , Liu Yang , Ali AlArjani , Buddhadev Mandal
A smart production system can be made energy-efficient using renewable energy and is considered to maintain the extended material requirement planning under a logistics system by using radio frequency identification. The tracking technology provides information about products with real-time notification. This study investigates renewable energy usage within a smart production system as renewable energy can contribute to Net Zero Emissions. The logistics framework involves an autonomation technology-based production system, optimum cash flow, logistics, and carbon emissions. Time is an essential influencer for material requirement planning. The model is solved with a Laplace integral transformation, where an associated matrix method is utilized by the input–output analysis. The theoretical concept is elaborated through an illustrative numerical example, where the energy consumption and corresponding net present values are evaluated. Numerical and graphical studies prove the effectiveness of the model for the use of renewable energy within for material planning under a reverse logistics system. The result reveals that efficient renewable energy consumption can save considerable costs and reduce the negative net present value of the system. It is found that skilled workers are worthy of a smart production system, not only in a qualitative aspect but also in an economic aspect.
{"title":"Extended material requirement planning (MRP) within a hybrid energy-enabled smart production system","authors":"Rekha Guchhait , Mitali Sarkar , Biswajit Sarkar , Liu Yang , Ali AlArjani , Buddhadev Mandal","doi":"10.1016/j.jii.2024.100717","DOIUrl":"10.1016/j.jii.2024.100717","url":null,"abstract":"<div><div>A smart production system can be made energy-efficient using renewable energy and is considered to maintain the extended material requirement planning under a logistics system by using radio frequency identification. The tracking technology provides information about products with real-time notification. This study investigates renewable energy usage within a smart production system as renewable energy can contribute to Net Zero Emissions. The logistics framework involves an autonomation technology-based production system, optimum cash flow, logistics, and carbon emissions. Time is an essential influencer for material requirement planning. The model is solved with a Laplace integral transformation, where an associated matrix method is utilized by the input–output analysis. The theoretical concept is elaborated through an illustrative numerical example, where the energy consumption and corresponding net present values are evaluated. Numerical and graphical studies prove the effectiveness of the model for the use of renewable energy within for material planning under a reverse logistics system. The result reveals that efficient renewable energy consumption can save considerable costs and reduce the negative net present value of the system. It is found that skilled workers are worthy of a smart production system, not only in a qualitative aspect but also in an economic aspect.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"42 ","pages":"Article 100717"},"PeriodicalIF":10.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142653231","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}
Despite a small overfitting risk, the deep forest model and its variants cannot automatically match data features; they rely on manual experience and comparative experiments for forest learner selection. This study proposes an automated deep forest model (ATDF) to enhance deep forest automation by automatically determining forest learners’ types and numbers based on training data. The model introduces a forest learner variability measure based on normalized mutual information, serving as a theoretical foundation for the automated process in deep forests. Then, a novel hierarchical clustering algorithm based on normalized mutual information is proposed to group forest learners at different granularities, determining the optimal forest learner type. This advanced technical method enables the determination of the model structure for stacking models, including deep forests. Finally, with the goal of maximizing cross-validation scores, the tree parson estimator-based Bayesian optimization algorithm determines the ideal number of forest learners for each type. Additionally, a standardized method for identifying forest learners is developed to guarantee the consistency of model outcomes. Most importantly, a series of comparative experiments on seven datasets from the UCI Machine Learning Repository confirmed the effectiveness and superiority of the proposed model. The results demonstrate that the proposed model has superior adaptability to new data and tasks, besides having a high level of automation, and performs excellently in the classification task.
{"title":"Making data classification more effective: An automated deep forest model","authors":"Jingwei Guo , Xiang Guo , Yihui Tian , Hao Zhan , Zhen-Song Chen , Muhammet Deveci","doi":"10.1016/j.jii.2024.100738","DOIUrl":"10.1016/j.jii.2024.100738","url":null,"abstract":"<div><div>Despite a small overfitting risk, the deep forest model and its variants cannot automatically match data features; they rely on manual experience and comparative experiments for forest learner selection. This study proposes an automated deep forest model (ATDF) to enhance deep forest automation by automatically determining forest learners’ types and numbers based on training data. The model introduces a forest learner variability measure based on normalized mutual information, serving as a theoretical foundation for the automated process in deep forests. Then, a novel hierarchical clustering algorithm based on normalized mutual information is proposed to group forest learners at different granularities, determining the optimal forest learner type. This advanced technical method enables the determination of the model structure for stacking models, including deep forests. Finally, with the goal of maximizing cross-validation scores, the tree parson estimator-based Bayesian optimization algorithm determines the ideal number of forest learners for each type. Additionally, a standardized method for identifying forest learners is developed to guarantee the consistency of model outcomes. Most importantly, a series of comparative experiments on seven datasets from the UCI Machine Learning Repository confirmed the effectiveness and superiority of the proposed model. The results demonstrate that the proposed model has superior adaptability to new data and tasks, besides having a high level of automation, and performs excellently in the classification task.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"42 ","pages":"Article 100738"},"PeriodicalIF":10.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696482","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.2024.100710
Xin Liu , Gongfa Li , Feng Xiang , Bo Tao , Guozhang Jiang
Human-centered smart manufacturing is an essential direction for the future development of manufacturing. Safe and reliable smart human-robot collaboration is the foundation for realizing human-centered smart manufacturing. Digital twin-based human-robot collaboration has been proposed as a new manufacturing paradigm to devise collaborative strategies, simulate collaborative processes, and ensure worker safety. Establishing a maturity model is essential to accurately assess the capabilities of the constructed human-robot collaboration digital twin. This paper aims to contribute to the formalization and standardization of the human-robot collaboration digital twin. It constructs a novel assessment framework for the overall maturity measurement of existing digital twin-based human-robot collaboration projects. The developed human-robot collaboration digital twin maturity model includes 5 evaluation dimensions and 24 evaluation factors. Additionally, 5 maturity levels and their definitions are defined for each evaluation factor for maturity scoring. The expert opinion aggregation approach is proposed to quantify the evaluation factor metrics and ultimately to obtain a maturity level for the human-robot collaboration digital twin. The effectiveness and feasibility of the proposed method are verified through a collaborative assembly case study. This paper provides a generic method for assessing the competency level of human-robot collaboration digital twins, which can provide insights into the maturity of digital twins for practitioners in the human-robot collaboration field to develop targeted strategies for optimizing and upgrading human-robot collaboration digital twins.
{"title":"Expert opinion aggregation-based decision support for human-robot collaboration digital twin maturity assessment","authors":"Xin Liu , Gongfa Li , Feng Xiang , Bo Tao , Guozhang Jiang","doi":"10.1016/j.jii.2024.100710","DOIUrl":"10.1016/j.jii.2024.100710","url":null,"abstract":"<div><div>Human-centered smart manufacturing is an essential direction for the future development of manufacturing. Safe and reliable smart human-robot collaboration is the foundation for realizing human-centered smart manufacturing. Digital twin-based human-robot collaboration has been proposed as a new manufacturing paradigm to devise collaborative strategies, simulate collaborative processes, and ensure worker safety. Establishing a maturity model is essential to accurately assess the capabilities of the constructed human-robot collaboration digital twin. This paper aims to contribute to the formalization and standardization of the human-robot collaboration digital twin. It constructs a novel assessment framework for the overall maturity measurement of existing digital twin-based human-robot collaboration projects. The developed human-robot collaboration digital twin maturity model includes 5 evaluation dimensions and 24 evaluation factors. Additionally, 5 maturity levels and their definitions are defined for each evaluation factor for maturity scoring. The expert opinion aggregation approach is proposed to quantify the evaluation factor metrics and ultimately to obtain a maturity level for the human-robot collaboration digital twin. The effectiveness and feasibility of the proposed method are verified through a collaborative assembly case study. This paper provides a generic method for assessing the competency level of human-robot collaboration digital twins, which can provide insights into the maturity of digital twins for practitioners in the human-robot collaboration field to develop targeted strategies for optimizing and upgrading human-robot collaboration digital twins.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"42 ","pages":"Article 100710"},"PeriodicalIF":10.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142577924","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.2024.100716
Zelin Wang , Xiangbin Wang , Weizhong Wang , Muhammet Deveci , Zengyuan Wu , Witold Pedrycz
In 2021, United Nations released the "Creating Resilient Cities 2030 Project", which aims to strengthen urban resilience in developing and implementing disaster reduction strategies. Resilient cities are a new type of urban development model that emphasizes the ability of cities to resist natural disasters and social pressures, reduce losses, and allocate resources reasonably to quickly recover from disasters. With the frequent occurrence of public health and safety accidents, the concept of public health safety ecosystem has become increasingly prominent in the field of urban resilience. To effectively manage public health incidents and enhance emergency response capabilities, evaluating the urban public health safety ecosystem is essential. A consensus-based decision-making model that accounts for the social networks among experts to accurately assess urban public health emergency capacity is introduced. To ensure the objectivity of indicator weights, we build up a novel model to calculate the weight of indicators utilizing social network analysis and consensus-reaching process analysis of indicator evaluation value. An illustrative case study on public health emergency capacity in Luoding is presented. This research expands the framework for assessing resilience in urban systems and provides a methodology for improving urban public health and resilience, introducing a novel approach for evaluating the urban public health safety ecosystem through social network analysis.
{"title":"Consensus reaching-based decision model for assessing resilient urban public health safety ecosystem with social network analysis","authors":"Zelin Wang , Xiangbin Wang , Weizhong Wang , Muhammet Deveci , Zengyuan Wu , Witold Pedrycz","doi":"10.1016/j.jii.2024.100716","DOIUrl":"10.1016/j.jii.2024.100716","url":null,"abstract":"<div><div>In 2021, United Nations released the \"Creating Resilient Cities 2030 Project\", which aims to strengthen urban resilience in developing and implementing disaster reduction strategies. Resilient cities are a new type of urban development model that emphasizes the ability of cities to resist natural disasters and social pressures, reduce losses, and allocate resources reasonably to quickly recover from disasters. With the frequent occurrence of public health and safety accidents, the concept of public health safety ecosystem has become increasingly prominent in the field of urban resilience. To effectively manage public health incidents and enhance emergency response capabilities, evaluating the urban public health safety ecosystem is essential. A consensus-based decision-making model that accounts for the social networks among experts to accurately assess urban public health emergency capacity is introduced. To ensure the objectivity of indicator weights, we build up a novel model to calculate the weight of indicators utilizing social network analysis and consensus-reaching process analysis of indicator evaluation value. An illustrative case study on public health emergency capacity in Luoding is presented. This research expands the framework for assessing resilience in urban systems and provides a methodology for improving urban public health and resilience, introducing a novel approach for evaluating the urban public health safety ecosystem through social network analysis.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"42 ","pages":"Article 100716"},"PeriodicalIF":10.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142577925","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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