| 1. |
- Menkveld, Albert J., et al.
(författare)
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Nonstandard Errors
- 2024
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Ingår i: JOURNAL OF FINANCE. - : Wiley-Blackwell. - 0022-1082 .- 1540-6261. ; 79:3, s. 2339-2390
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Tidskriftsartikel (refereegranskat)abstract
- In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty-nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.
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| 2. |
- Leng, Jiewu, et al.
(författare)
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Unlocking the power of industrial artificial intelligence towards Industry 5.0: Insights, pathways, and challenges
- 2024
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Ingår i: Journal of manufacturing systems. - : Elsevier BV. - 0278-6125 .- 1878-6642. ; 73, s. 349-363
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Forskningsöversikt (refereegranskat)abstract
- With the continuous development of human-centric, resilient, and sustainable manufacturing towards Industry 5.0, Artificial Intelligence (AI) has gradually unveiled new opportunities for additional functionalities, new features, and tendencies in the industrial landscape. On the other hand, the technology-driven Industry 4.0 paradigm is still in full swing. However, there exist many unreasonable designs, configurations, and implementations of Industrial Artificial Intelligence (IndAI) in practice before achieving either Industry 4.0 or Industry 5.0 vision, and a significant gap between the individualized requirement and actual implementation result still exists. To provide insights for designing appropriate models and algorithms in the upgrading process of the industry, this perspective article classifies IndAI by rating the intelligence levels and presents four principles of implementing IndAI. Three significant opportunities of IndAI, namely, collaborative intelligence, self-learning intelligence, and crowd intelligence, towards Industry 5.0 vision are identified to promote the transition from a technology-driven initiative in Industry 4.0 to the coexistence and interplay of Industry 4.0 and a value-oriented proposition in Industry 5.0. Then, pathways for implementing IndAI towards Industry 5.0 together with key empowering techniques are discussed. Social barriers, technology challenges, and future research directions of IndAI are concluded, respectively. We believe that our effort can lay a foundation for unlocking the power of IndAI in futuristic Industry 5.0 research and engineering practice.
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| 3. |
- Li, Xingyu, et al.
(författare)
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Smart Reconfigurable Manufacturing: Literature Analysis
- 2024
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Ingår i: 11th CIRP Global Web Conference, CIRPe 2023. - : Elsevier B.V.. ; , s. 43-48
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Konferensbidrag (refereegranskat)abstract
- Smart manufacturing (SM) enhances the competitiveness of manufacturing companies by promoting automation and overall equipment effectiveness (OEE), targeting to produce 100% qualified products fully automatically. One of the key challenges to the SM initiatives is the continuous demand fluctuations in the specification and quantity, especially when a new product variant comes to the production line. Reconfigurable manufacturing (RM) system provides cost-effective, rapid response to abrupt market changes. It provides a solution by its flexibility in repurposing tools, adding machines, and modifying software to rapidly respond to changing demands at low unit costs. The ability of SM technologies through self-programming and cloud computation may significantly complements RM initiatives. There is increasing evidence that SM and RM may augment each other through their complementary strengths, leading to the new paradigm of smart reconfigurable manufacturing (SRM). To highlight the complementary strengths, this paper investigates the converging trend of RM and SM based on natural language processing, e.g., topic modeling and semantic embedding. Key characteristics and industrial use cases are subsequently summarized to systematically delineate the new SRM paradigm and illustrate its advantages and feasibility in practice.
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| 4. |
- Wang, Baicun, et al.
(författare)
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Human Digital Twin in the context of Industry 5.0
- 2024
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Ingår i: Robotics and Computer-Integrated Manufacturing. - : Elsevier Ltd. - 0736-5845 .- 1879-2537. ; 85
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Forskningsöversikt (refereegranskat)abstract
- Human-centricity, a core value of Industry 5.0, places humans in the center of production. It leads to the prioritization of human needs, spanning from health and safety to self-actualization and personal growth. The concept of the Human Digital Twin (HDT) is proposed as a critical method to realize human-centricity in smart manufacturing systems towards Industry 5.0. HDTs are digital representations of humans, aiming to change the practice of human-system integration by coupling humans’ characteristics directly to the system design and its performance. In-depth analysis, critical insights, and application guidelines of HDT are essential to realize the concept of Industry 5.0 in practice and evolve the smart manufacturing paradigm in modern factories. However, the investigation on the development of HDT to evolve humans’ roles and develop humans to their full potential is limited to date. Recent studies are rarely geared towards designing a standardized framework and architecture of HDT for diverse real-world applications. Thus, this work aims to close this research gap by carrying out a comprehensive survey on HDT in the context of Industry 5.0, summarizing the ongoing evolution, and proposing a proper connotation of HDT, before discussing the conceptual framework and system architecture of HDT and analyzing enabling technologies and industrial applications. This work provides guidance on possible avenues as well as challenges for the further development of HDT and its related concepts, allowing humans to reach their potential and accommodating their diverse needs in the futuristic smart manufacturing systems shaped by Industry 5.0.
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