Cloud-Based Industrial Cyber–Physical System for Data-Driven Reasoning: A Review and Use Case on an Industry 4.0 Pilot Line

Villalonga, Alberto; Beruvides, Gerardo; Castaño, Fernando; Haber, Rodolfo E.

doi:10.1109/tii.2020.2971057

Cited by 75 publications

(35 citation statements)

References 44 publications

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“…This process requires further research on the stability of the PICCM, which will be a future research direction. In addition, we are developing a method of converting 2D data into renderings through machine learning based on the above discussion, and we will consider building a cloud-based method architecture [ 71 ]. This future study will be beneficial for many industries, such as product design, apparel design, furniture design, and other fields.…”

Section: Discussionmentioning

confidence: 99%

Intelligent Design of Product Forms Based on Design Cognitive Dynamics and a Cobweb Structure

Yang

Zhang

et al. 2021

Computational Intelligence and Neuroscience

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Design is a complex, iterative, and innovative process. By traditional methods, it is difficult for designers to have an integral priori design experience to fully explore a wide range of design solutions. Therefore, refined intelligent design has become an important trend in design research. More powerful design thinking is needed in intelligent design process. Combining cognitive dynamics and a cobweb structure, an intelligent design method is proposed to formalize the innovative design process. The excavation of the dynamic mechanism of the product evolution process during product development is necessary to predict next-generation multi-image product forms from a larger design space. First, different design thinking stimulates the information source and is obtained by analyzing the designers’ thinking process when designing and mining the dynamic mechanism behind it. Based on the nonlinear cognitive cobweb process proposed by Francisco and a natural cobweb structure, the product image cognitive cobweb model (PICCM) is constructed. Then, natural cobweb predation behavior is simulated using a stimulus information source to impact the PICCM. This process uses genetic algorithms to obtain numerous offspring forms, and the PICCM’s mechanical properties are the energy loss parameters in the impact information. Furthermore, feasible solutions are selected from intelligent design sketches by the product artificial form evaluation system based on designers’ cognition, and a new product image cognitive cobweb system is reconstructed. Finally, a case study demonstrates the efficiency and feasibility of the proposed approach.

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Section: Discussionmentioning

confidence: 99%

Intelligent Design of Product Forms Based on Design Cognitive Dynamics and a Cobweb Structure

Yang

Zhang

et al. 2021

Computational Intelligence and Neuroscience

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“…On the other hand, the data-driven decision-making is limited to statistical analysis and data visualization, and the capacities of knowledge acquisition and reasoning are still weak. Further study should focus on developing algorithms and models to discover new knowledge or predict future trends based on the historical monitoring data [97]. Moreover, optimization of construction organization modes based on the application of the CPS should be further explored.…”

Section: Improvement Directionsmentioning

confidence: 99%

Integration of Industry 4.0 Related Technologies in Construction Industry: A Framework of Cyber-Physical System

2020

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The Fourth Industrial Revolution (Industry 4.0) is reshaping the construction industry and bringing it into an intelligent construction era. Emerging technologies, such as the Building Information Modelling, Internet of Things, big data, cloud computing, and artificial intelligence, have penetrated into all stages of the building life cycle and play a significant role. However, the major issue of intelligent construction is integrating multiple technologies to create more potential opportunities rather than their fragmented application. Considering the various special characteristics of the construction industry and the high heterogeneity of these technologies, their integration in the construction industry is challenging and requires in-depth investigations. This paper summarizes the Industry 4.0-related technologies involved in the construction industry based on an analysis of the characteristics of the industry. Further, this study presents a framework of a cyber-physical system to integrate these technologies and improve the overall capabilities of construction organization and management. A case study of the Xiong'an citizen service center is introduced to verify the technological feasibility and preliminary implementation effect of the proposed framework. As forward-looking research, the significance of this paper may also to inspire more efforts in this field.

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“…It contains two modules: the DNN Config Manager handles the configuration of the DNN, while the DNN Training Coordinator (TC) manages the training process. Similar to the work in [ 2 ], DNN models are automatically configured based on the results obtained from pre-processing the training data. By analyzing the structure and type of input data, the DNN Config Manager automatically determines the input and output shapes of the DNN architecture, thus enabling the usage of existing network architectures from the workbench.…”

Section: Eb-ai Toolchainmentioning

confidence: 99%

“…Deep learning (DL) and Deep Neural Networks (DNN) have become leading technologies in many domains, enabling vehicles to perceive their driving environment and take actions accordingly. Although several cloud-based solutions have been proposed for the automatic reconfiguration and adaptation of distributed platforms in Industry 4.0 [ 2 ], automotive grade systems have been scarcely reported.…”

Section: Introductionmentioning

confidence: 99%

Cloud2Edge Elastic AI Framework for Prototyping and Deployment of AI Inference Engines in Autonomous Vehicles

Grigorescu

Cocias

Trasnea

et al. 2020

Sensors

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Self-driving cars and autonomous vehicles are revolutionizing the automotive sector, shaping the future of mobility altogether. Although the integration of novel technologies such as Artificial Intelligence (AI) and Cloud/Edge computing provides golden opportunities to improve autonomous driving applications, there is the need to modernize accordingly the whole prototyping and deployment cycle of AI components. This paper proposes a novel framework for developing so-called AI Inference Engines for autonomous driving applications based on deep learning modules, where training tasks are deployed elastically over both Cloud and Edge resources, with the purpose of reducing the required network bandwidth, as well as mitigating privacy issues. Based on our proposed data driven V-Model, we introduce a simple yet elegant solution for the AI components development cycle, where prototyping takes place in the cloud according to the Software-in-the-Loop (SiL) paradigm, while deployment and evaluation on the target ECUs (Electronic Control Units) is performed as Hardware-in-the-Loop (HiL) testing. The effectiveness of the proposed framework is demonstrated using two real-world use-cases of AI inference engines for autonomous vehicles, that is environment perception and most probable path prediction.

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Cloud-Based Industrial Cyber–Physical System for Data-Driven Reasoning: A Review and Use Case on an Industry 4.0 Pilot Line

Cited by 75 publications

References 44 publications

Intelligent Design of Product Forms Based on Design Cognitive Dynamics and a Cobweb Structure

Intelligent Design of Product Forms Based on Design Cognitive Dynamics and a Cobweb Structure

Integration of Industry 4.0 Related Technologies in Construction Industry: A Framework of Cyber-Physical System

Cloud2Edge Elastic AI Framework for Prototyping and Deployment of AI Inference Engines in Autonomous Vehicles

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