As technology advances, we are surrounded by more complex products that can be challenging to use and troubleshoot. We often turn to online resources and the help of others to learn how to use a product's features or fix malfunctions. This is a common issue in both everyday life and industry. The key to be able to use a product or fix malfunctions is having access to accurate information and instructions and to gain the necessary skills to perform the tasks correctly. This paper offers an overview of how Artificial Intelligence, Digital Twins, and the Metaverse - currently popular technologies - can enhance the process of acquiring knowledge, know-how, and skills, with a focus on industrial maintenance. However, the concepts discussed may also be applicable to the maintenance of consumer products.
{"title":"Exploring the Intersection of Metaverse, Digital Twins, and Artificial Intelligence in Training and Maintenance","authors":"M. Bordegoni, F. Ferrise","doi":"10.1115/1.4062455","DOIUrl":"https://doi.org/10.1115/1.4062455","url":null,"abstract":"As technology advances, we are surrounded by more complex products that can be challenging to use and troubleshoot. We often turn to online resources and the help of others to learn how to use a product's features or fix malfunctions. This is a common issue in both everyday life and industry. The key to be able to use a product or fix malfunctions is having access to accurate information and instructions and to gain the necessary skills to perform the tasks correctly. This paper offers an overview of how Artificial Intelligence, Digital Twins, and the Metaverse - currently popular technologies - can enhance the process of acquiring knowledge, know-how, and skills, with a focus on industrial maintenance. However, the concepts discussed may also be applicable to the maintenance of consumer products.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":"39 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82780318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abhijeet S. Raina, Ronak R. Mohanty, Abhirath Bhuvanesh, Divya Prabha J, Manohar Swaminathan, Vinayak R. Krishnamurthy
We present an experimental investigation of spatial audio feedback using smartphones to support direction localization in pointing tasks for people with visual impairments (PVIs). We do this using a mobile game based on a bow-and-arrow metaphor. Our game provides a combination of spatial and non-spatial (sound beacon) audio to help the user locate the direction of the target. Our experiments with sighted, sighted-blindfolded, and visually impaired users shows that (a) the efficacy of spatial audio is relatively higher for PVIs than for blindfolded sighted users during the initial reaction time for direction localization, (b) the general behavior between PVIs and blind-folded individuals is statistically similar, and (c) the lack of spatial audio significantly reduces the localization performance even in sighted blind-folded users. Based on our findings, we discuss the system and interaction design implications for making future mobile-based spatial interactions accessible to PVIs.
{"title":"Pointing Tasks Using Spatial Audio on Smartphones for People With Vision Impairments","authors":"Abhijeet S. Raina, Ronak R. Mohanty, Abhirath Bhuvanesh, Divya Prabha J, Manohar Swaminathan, Vinayak R. Krishnamurthy","doi":"10.1115/1.4062426","DOIUrl":"https://doi.org/10.1115/1.4062426","url":null,"abstract":"\u0000 We present an experimental investigation of spatial audio feedback using smartphones to support direction localization in pointing tasks for people with visual impairments (PVIs). We do this using a mobile game based on a bow-and-arrow metaphor. Our game provides a combination of spatial and non-spatial (sound beacon) audio to help the user locate the direction of the target. Our experiments with sighted, sighted-blindfolded, and visually impaired users shows that (a) the efficacy of spatial audio is relatively higher for PVIs than for blindfolded sighted users during the initial reaction time for direction localization, (b) the general behavior between PVIs and blind-folded individuals is statistically similar, and (c) the lack of spatial audio significantly reduces the localization performance even in sighted blind-folded users. Based on our findings, we discuss the system and interaction design implications for making future mobile-based spatial interactions accessible to PVIs.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":"43 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77841950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nagendra Singh Ranawat, Jatin Prakash, Ankur Miglani, P. K. Kankar
Rags, dusts, foreign particles etc. are primary cause of blockage in centrifugal pump and deteriorates the performance. This study elaborates an experimental and data-driven methodology to identify suction, discharge and simultaneous occurrence of both blockages. The discharge pressure signals are acquired and denoised using CEEMD. The fuzzy recurrence plots obtained from denoised signals are attempted to classify using three pre-trained models: Xception, GoogleNet and Inception. None of these models are trained on such images, thus, features are extracted from different pooling layers which include shallow features too. The features extracted from different layers are fed to four shallow learning classifiers: Quadratic SVM, Weighted KNN, Narrow Neural network, and subspace discriminant classifier. The study finds that subspace discriminant achieves highest accuracy of 97.8% when trained using features from second pooling of Xception model. Furthermore, this proposed methodology is implemented at other blockage condition of the pump. The subspace discriminant analysis outperforms the other selected shallow classifier with an accuracy of 93% for the features extracted from the first pooling layer of the Xception model. Therefore, this study demonstrates an efficient method to identify pump blockage using pre-trained and shallow classifiers.
{"title":"Fuzzy Recurrence Plots for Shallow Learning-Based Blockage Detection in a Centrifugal Pump Using Pre-Trained Image Recognition Models","authors":"Nagendra Singh Ranawat, Jatin Prakash, Ankur Miglani, P. K. Kankar","doi":"10.1115/1.4062425","DOIUrl":"https://doi.org/10.1115/1.4062425","url":null,"abstract":"\u0000 Rags, dusts, foreign particles etc. are primary cause of blockage in centrifugal pump and deteriorates the performance. This study elaborates an experimental and data-driven methodology to identify suction, discharge and simultaneous occurrence of both blockages. The discharge pressure signals are acquired and denoised using CEEMD. The fuzzy recurrence plots obtained from denoised signals are attempted to classify using three pre-trained models: Xception, GoogleNet and Inception. None of these models are trained on such images, thus, features are extracted from different pooling layers which include shallow features too. The features extracted from different layers are fed to four shallow learning classifiers: Quadratic SVM, Weighted KNN, Narrow Neural network, and subspace discriminant classifier. The study finds that subspace discriminant achieves highest accuracy of 97.8% when trained using features from second pooling of Xception model. Furthermore, this proposed methodology is implemented at other blockage condition of the pump. The subspace discriminant analysis outperforms the other selected shallow classifier with an accuracy of 93% for the features extracted from the first pooling layer of the Xception model. Therefore, this study demonstrates an efficient method to identify pump blockage using pre-trained and shallow classifiers.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":"149 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85884496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Product sustainability is a pressing global issue that requires urgent improvement, and low-carbon design is a crucial approach towards achieving sustainable product development. Digital twin technology, which connects the physical and virtual worlds, has emerged as an effective tool for supporting product design and development. However, obtaining accurate product parameters remains a challenge, and traditional low-carbon product design primarily focuses on design parameters. To address these issues, this paper proposes a method for data collection throughout the product lifecycle, leveraging the Internet of Things. The paper envisions the automatic collection of product lifecycle data to enhance the accuracy of product design. Moreover, traditional low-carbon design often has a limited scope that primarily considers product structure and lifecycle stage for optimization. In contrast, combining digital twin technology with low-carbon design can effectively improve product sustainability. Therefore, this paper proposes a three-layer architecture model of product sustainability digital twin, comprising data layer, mapping layer, and application layer. This model sets the carbon footprint as the iterative optimization goal and facilitates the closed-loop sustainable design of the product. The paper envisions sustainable product design based on digital twins that can address cascading problems and achieve closed-loop sustainable design.
{"title":"Digital Twin-Driven Product Sustainable Design for Low Carbon Footprint","authors":"Bin He, Hangyu Mao","doi":"10.1115/1.4062427","DOIUrl":"https://doi.org/10.1115/1.4062427","url":null,"abstract":"\u0000 Product sustainability is a pressing global issue that requires urgent improvement, and low-carbon design is a crucial approach towards achieving sustainable product development. Digital twin technology, which connects the physical and virtual worlds, has emerged as an effective tool for supporting product design and development. However, obtaining accurate product parameters remains a challenge, and traditional low-carbon product design primarily focuses on design parameters. To address these issues, this paper proposes a method for data collection throughout the product lifecycle, leveraging the Internet of Things. The paper envisions the automatic collection of product lifecycle data to enhance the accuracy of product design. Moreover, traditional low-carbon design often has a limited scope that primarily considers product structure and lifecycle stage for optimization. In contrast, combining digital twin technology with low-carbon design can effectively improve product sustainability. Therefore, this paper proposes a three-layer architecture model of product sustainability digital twin, comprising data layer, mapping layer, and application layer. This model sets the carbon footprint as the iterative optimization goal and facilitates the closed-loop sustainable design of the product. The paper envisions sustainable product design based on digital twins that can address cascading problems and achieve closed-loop sustainable design.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":"56 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84520600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-04-25DOI: 10.48550/arXiv.2304.14275
Peter Meltzer, J. Lambourne, Daniele Grandi
Semantic knowledge of part-part and part-whole relationships in assemblies is useful for a variety of tasks from searching design repositories to the construction of engineering knowledge bases. In this work we propose that the natural language names designers use in Computer Aided Design (CAD) software are a valuable source of such knowledge, and that Large Language Models (LLMs) contain useful domain-specific information for working with this data as well as other CAD and engineering-related tasks. In particular we extract and clean a large corpus of natural language part, feature and document names and use this to quantitatively demonstrate that a pre-trained language model can outperform numerous benchmarks on three self-supervised tasks, without ever having seen this data before. Moreover, we show that fine-tuning on the text data corpus further boosts the performance on all tasks, thus demonstrating the value of the text data which until now has been largely ignored. We also identify key limitations to using LLMs with text data alone, and our findings provide a strong motivation for further work into multi-modal text-geometry models. To aid and encourage further work in this area we make all our data and code publicly available.
{"title":"What's in a Name? Evaluating Assembly-Part Semantic Knowledge in Language Models through User-Provided Names in CAD Files","authors":"Peter Meltzer, J. Lambourne, Daniele Grandi","doi":"10.48550/arXiv.2304.14275","DOIUrl":"https://doi.org/10.48550/arXiv.2304.14275","url":null,"abstract":"\u0000 Semantic knowledge of part-part and part-whole relationships in assemblies is useful for a variety of tasks from searching design repositories to the construction of engineering knowledge bases. In this work we propose that the natural language names designers use in Computer Aided Design (CAD) software are a valuable source of such knowledge, and that Large Language Models (LLMs) contain useful domain-specific information for working with this data as well as other CAD and engineering-related tasks. In particular we extract and clean a large corpus of natural language part, feature and document names and use this to quantitatively demonstrate that a pre-trained language model can outperform numerous benchmarks on three self-supervised tasks, without ever having seen this data before. Moreover, we show that fine-tuning on the text data corpus further boosts the performance on all tasks, thus demonstrating the value of the text data which until now has been largely ignored. We also identify key limitations to using LLMs with text data alone, and our findings provide a strong motivation for further work into multi-modal text-geometry models. To aid and encourage further work in this area we make all our data and code publicly available.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":"6 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85029106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bingkun Yuan, Junnan Ye, Xinying Wu, Chaoxiang Yang
With the development of social productivity and the improvement in material living standards, emotional value has become the core driver of the enhancement of product market competitiveness. A medical nursing bed, one of the most typical types of medical devices, is designed with little attention to the emotional experience of the users. Therefore, this paper proposes an innovative perceptual design approach under the Kansei engineering (KE) framework for resource-limited and information-poor companies. It guides the aesthetic design of medical nursing beds by constructing a mapping relationship between users' perceptual needs and the design characteristics of medical nursing beds to maximize users' emotions. First, latent Dirichlet allocation (LDA) is used to extract usable Kansei semantics from big data, compensating for the subjectivity of traditional KE data input. Then, the design characteristics obtained after deconstructing a medical nursing bed are simplified with rough set theory (RST). Finally, a mapping model between users' perceptual needs and the core design characteristics of nursing beds is established through support vector regression (SVR), and the optimal design solution is obtained by weighting calculation. The optimal combination of design characteristics for medical nursing beds is finally obtained. The results suggest that the design method proposed in this paper can help designers accurately grasp users' emotional perceptions in terms of aesthetic design and scientifically guide and complete the design of new medical nursing beds, verifying the feasibility and scientificity of the proposed method in terms of aesthetic design.
{"title":"Applying Latent Dirichlet Allocation and Support Vector Regression to the Aesthetic Design of Medical Nursing Beds","authors":"Bingkun Yuan, Junnan Ye, Xinying Wu, Chaoxiang Yang","doi":"10.1115/1.4062350","DOIUrl":"https://doi.org/10.1115/1.4062350","url":null,"abstract":"\u0000 With the development of social productivity and the improvement in material living standards, emotional value has become the core driver of the enhancement of product market competitiveness. A medical nursing bed, one of the most typical types of medical devices, is designed with little attention to the emotional experience of the users. Therefore, this paper proposes an innovative perceptual design approach under the Kansei engineering (KE) framework for resource-limited and information-poor companies. It guides the aesthetic design of medical nursing beds by constructing a mapping relationship between users' perceptual needs and the design characteristics of medical nursing beds to maximize users' emotions. First, latent Dirichlet allocation (LDA) is used to extract usable Kansei semantics from big data, compensating for the subjectivity of traditional KE data input. Then, the design characteristics obtained after deconstructing a medical nursing bed are simplified with rough set theory (RST). Finally, a mapping model between users' perceptual needs and the core design characteristics of nursing beds is established through support vector regression (SVR), and the optimal design solution is obtained by weighting calculation. The optimal combination of design characteristics for medical nursing beds is finally obtained. The results suggest that the design method proposed in this paper can help designers accurately grasp users' emotional perceptions in terms of aesthetic design and scientifically guide and complete the design of new medical nursing beds, verifying the feasibility and scientificity of the proposed method in terms of aesthetic design.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":"42 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89095496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abheek Chatterjee, Cade Helbig, Richard Malak, Astrid Layton
Abstract System of systems (SoS) are networked integration of constituent systems that together achieve new capabilities not possible through the operation of any single system. SoS can be found across all aspects of modern life such as power grids, supply chains, and disaster monitoring and tracking services. Their resilience (being able to withstand and recover from disruptions) is a critical attribute whose evaluation is nontrivial and requires detailed disruption models. Engineers rely on heuristics (such as redundancy and localized capacity) for achieving resilience. However, excessive reliance on these qualitative guidelines can result in unacceptable operation costs, erosion of profits, over-consumption of natural resources, or unacceptable levels of waste or emissions. Graph-theoretic approaches provide a potential solution to this challenge as they can evaluate architectural characteristics without needing detailed performance simulations, supporting their use in early stage SoS architecture selection. However, no consensus exists as to which graph-theoretic metrics are most valuable for SoS design and how they should be included in the design process. In this work, multiple graph-theoretic approaches are analyzed and compared, on a common platform, for their use as design tools for resilient SoS. The metrics central point dominance, modularity, specialized predator ratio, generalization, vulnerability, and degree of system order are found to be viable options for the development of early stage decision-support tools for resilient SoS design.
{"title":"A Comparison of Graph-Theoretic Approaches for Resilient System of Systems Design","authors":"Abheek Chatterjee, Cade Helbig, Richard Malak, Astrid Layton","doi":"10.1115/1.4062231","DOIUrl":"https://doi.org/10.1115/1.4062231","url":null,"abstract":"Abstract System of systems (SoS) are networked integration of constituent systems that together achieve new capabilities not possible through the operation of any single system. SoS can be found across all aspects of modern life such as power grids, supply chains, and disaster monitoring and tracking services. Their resilience (being able to withstand and recover from disruptions) is a critical attribute whose evaluation is nontrivial and requires detailed disruption models. Engineers rely on heuristics (such as redundancy and localized capacity) for achieving resilience. However, excessive reliance on these qualitative guidelines can result in unacceptable operation costs, erosion of profits, over-consumption of natural resources, or unacceptable levels of waste or emissions. Graph-theoretic approaches provide a potential solution to this challenge as they can evaluate architectural characteristics without needing detailed performance simulations, supporting their use in early stage SoS architecture selection. However, no consensus exists as to which graph-theoretic metrics are most valuable for SoS design and how they should be included in the design process. In this work, multiple graph-theoretic approaches are analyzed and compared, on a common platform, for their use as design tools for resilient SoS. The metrics central point dominance, modularity, specialized predator ratio, generalization, vulnerability, and degree of system order are found to be viable options for the development of early stage decision-support tools for resilient SoS design.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135708616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Practical manufacturing system operates in highly dynamic and uncertain environments, where stochastic disturbances disrupt the execution of production schedule as originally developed. Previous dynamic scheduling mainly focuses on the constructing predictive models for machine unavailability, with little studies on the adaptive and self-learning capacities for changing scheduling environments. Therefore, a digital twin (DT) driven scheduling with dynamic feedback mechanism is proposed, in which a reinforcement learning (RL) based adaptive scheduling is developed in DT to make corrective decisions for the disturbances during production runs. In the proposed architecture, the happening disturbance is firstly detected in the virtual layer by the status continuously updating in accordance with the physical workshop. Furthermore, the reschedule triggering condition is determined in real-time through quantization of the progress deviations resulting from disturbances. For the scheduling approach, the multi-agents RL (MARL) based adaptive scheduling method is built to perceive the dynamic production status from virtual environment and implement corrective strategies to hedge against the occurred disturbances. Finally, the proposed method is verified by a practical job shop case and the corresponding DT system is developed to show the effectiveness and advantages after a practical implementation.
{"title":"An Adaptive Job Shop Scheduling Mechanism for Disturbances by Running Reinforcement Learning in Digital Twin Environment","authors":"Weiguang Fang, Hao Zhang, Weiwei Qian, Yuhao Guo, Shaoxun Li, Zeqing Liu, Chenning Liu, Dongpao Hong","doi":"10.1115/1.4062349","DOIUrl":"https://doi.org/10.1115/1.4062349","url":null,"abstract":"\u0000 Practical manufacturing system operates in highly dynamic and uncertain environments, where stochastic disturbances disrupt the execution of production schedule as originally developed. Previous dynamic scheduling mainly focuses on the constructing predictive models for machine unavailability, with little studies on the adaptive and self-learning capacities for changing scheduling environments. Therefore, a digital twin (DT) driven scheduling with dynamic feedback mechanism is proposed, in which a reinforcement learning (RL) based adaptive scheduling is developed in DT to make corrective decisions for the disturbances during production runs. In the proposed architecture, the happening disturbance is firstly detected in the virtual layer by the status continuously updating in accordance with the physical workshop. Furthermore, the reschedule triggering condition is determined in real-time through quantization of the progress deviations resulting from disturbances. For the scheduling approach, the multi-agents RL (MARL) based adaptive scheduling method is built to perceive the dynamic production status from virtual environment and implement corrective strategies to hedge against the occurred disturbances. Finally, the proposed method is verified by a practical job shop case and the corresponding DT system is developed to show the effectiveness and advantages after a practical implementation.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":"1 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83762917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Conceptualization and design of intellectualized, socialized, and personalized cyber-physical systems (CPSs) needs exploration and synthesis of novel knowledge. In turn, it raises the need for a combined use of interdisciplinary, multidisciplinary, and transdisciplinary research. Supradisciplinary research emerged as a new doctrine of combining these research approaches from epistemological, methodological, and procedural perspective. However, no methodology can be found in the literature that could facilitate the practical execution of supradisciplinary research programs and projects. This position paper proposes a conceptual framework that can be used as a blueprint of operationalization of such undertakings. The framework rests on six generic pillars: (i) problematics, (ii) infrastructure, (iii) methodics, (iv) stakeholders, (v) operations, and (vi) knowledge.The framework arranges the concerns in a procedural logic - as they should be considered by the research managers and cyber-physical system developers. In its current form, the framework does not cover the specific societal and personal issues of a successful organization of the inquiry at individual researchers, research teams, and research community levels. Notwithstanding, the framework can facilitate management of research organization tasks, joint formation of shared research infrastructure, setting up concrete research programs, projects, and processes, academic partnering and public stakeholder involvement, process flow management and capacity/competence allocation, and knowledge synthesis, assessment, and consolidation in a holistic manner. Follow up community-based research may focus on the practical application and testing of the framework in concrete cases – a task that an individual researcher cannot address.
{"title":"Framing Supradisciplinary Research for Intellectualized Cyber-Physical Systems: An Unfinished Story","authors":"I. Horváth","doi":"10.1115/1.4062327","DOIUrl":"https://doi.org/10.1115/1.4062327","url":null,"abstract":"\u0000 Conceptualization and design of intellectualized, socialized, and personalized cyber-physical systems (CPSs) needs exploration and synthesis of novel knowledge. In turn, it raises the need for a combined use of interdisciplinary, multidisciplinary, and transdisciplinary research. Supradisciplinary research emerged as a new doctrine of combining these research approaches from epistemological, methodological, and procedural perspective. However, no methodology can be found in the literature that could facilitate the practical execution of supradisciplinary research programs and projects. This position paper proposes a conceptual framework that can be used as a blueprint of operationalization of such undertakings. The framework rests on six generic pillars: (i) problematics, (ii) infrastructure, (iii) methodics, (iv) stakeholders, (v) operations, and (vi) knowledge.The framework arranges the concerns in a procedural logic - as they should be considered by the research managers and cyber-physical system developers. In its current form, the framework does not cover the specific societal and personal issues of a successful organization of the inquiry at individual researchers, research teams, and research community levels. Notwithstanding, the framework can facilitate management of research organization tasks, joint formation of shared research infrastructure, setting up concrete research programs, projects, and processes, academic partnering and public stakeholder involvement, process flow management and capacity/competence allocation, and knowledge synthesis, assessment, and consolidation in a holistic manner. Follow up community-based research may focus on the practical application and testing of the framework in concrete cases – a task that an individual researcher cannot address.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":"191 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79508965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Thakur, Swagatika Sahoo, Arnab Mukherjee, Raju Halder
Lately the importance of swarm robotics has been recognized in a wide range of areas, including logistics, surveillance, disaster management, agriculture, and other industrial applications. The swarm intelligence introduced by the existing paradigm of Artificial Intelligence and Machine Learning often ignores the aspect of providing security and reliability guarantees. Consider a futuristic scenario wherein self-driving cars will transport people, self-driving trucks will carry cargo between warehouses, and a combination of legged robots/drones will ship cargo from warehouses to doorsteps. In the case of such a heterogeneous swarm of robots, it is crucial to ensure a trustful and reliable operating platform for smooth coordination, collaborative decision-making via appropriate consensus, and seamless information sharing while ensuring data security. In this direction, blockchain has been proven to be an effective technology that maintains the transactions (records) in a trustful manner after being validated through consensus. This guarantees accountability, transparency, and trust concerning the storage, safeguarding, and sharing of information among the parties. In this paper, we provide a walkthrough demonstrating the feasibility of using blockchain technology to make the robotic swarm trustful systems in their adoption to critical applications at large-scale. We highlight the pros and cons of the use of cloud vis-a-vis blockchain in swarm robotics. Finally, we present various future research opportunities pertaining to the adoption of blockchain technology in swarm robotics applications.
{"title":"Making Robotic Swarms Trustful: A Blockchain-Based Perspective","authors":"A. Thakur, Swagatika Sahoo, Arnab Mukherjee, Raju Halder","doi":"10.1115/1.4062326","DOIUrl":"https://doi.org/10.1115/1.4062326","url":null,"abstract":"\u0000 Lately the importance of swarm robotics has been recognized in a wide range of areas, including logistics, surveillance, disaster management, agriculture, and other industrial applications. The swarm intelligence introduced by the existing paradigm of Artificial Intelligence and Machine Learning often ignores the aspect of providing security and reliability guarantees. Consider a futuristic scenario wherein self-driving cars will transport people, self-driving trucks will carry cargo between warehouses, and a combination of legged robots/drones will ship cargo from warehouses to doorsteps. In the case of such a heterogeneous swarm of robots, it is crucial to ensure a trustful and reliable operating platform for smooth coordination, collaborative decision-making via appropriate consensus, and seamless information sharing while ensuring data security. In this direction, blockchain has been proven to be an effective technology that maintains the transactions (records) in a trustful manner after being validated through consensus. This guarantees accountability, transparency, and trust concerning the storage, safeguarding, and sharing of information among the parties. In this paper, we provide a walkthrough demonstrating the feasibility of using blockchain technology to make the robotic swarm trustful systems in their adoption to critical applications at large-scale. We highlight the pros and cons of the use of cloud vis-a-vis blockchain in swarm robotics. Finally, we present various future research opportunities pertaining to the adoption of blockchain technology in swarm robotics applications.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":"358 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80189293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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