A smart and efficient IoT-AI and ML-based multifunctional system for multilevel power distribution management

Narayanan, Lakshmi; Subbiah, Priyanga; Mary, S.A. Sahaaya Arul; Muralidharan, R. Rengaraj Alias; Gururajan, Iswarya; Sampathkumar, Ranjani; Baskaran, Arun Prasad

doi:10.1016/b978-0-323-91664-6.00008-5

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…As a consequence, there is plenty of room to implement other materials and largesize printing technologies with wide potential use in sectors such as the automotive industry or the construction and engineering fields, where large parts are needed to replace their more factory-like, traditionally-made counterparts at an affordable price while maintaining a well-balanced material performance and consumption [8,9]. Moreover, automation processes are nowadays commoditized, pushed by the power of Supervisory Control and Data Acquisition and Artificial Intelligence (SCADA-AI) integrative applications [10] that allow to obtain large sets of end-effector data and produce behavioral patterns for a variety of practices and routines. For instance, data pattern analysis enables firms to control the overall performance of their production lines and tools in integrated user interfaces, favoring the use of "intelligent devices" able to provide data in real time.…”

Section: Introductionmentioning

confidence: 99%

Advances in Large-Scale Robotic 3D Printing with Plastic Pellets

Serrano¹,

Bares²

2023

Advances in 3D Printing

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3D printing with robotics is reaching an unprecedented level of maturity both in the market and at a technological level. This paper discusses current applications of large-scale robotics applied to 3D printed real-scale final parts for the construction and product design industries, including state-of-the-art methodologies, technologies and applications. Furthermore, an in-depth view of technologies and applications developed by the author will be provided, including robot-end effector integration and the automated generation of machine code through an ad-hoc computer aided design to computer aided manufacturing (CAD-CAM) integration. This integration accounts for parametric capabilities and design-time feedback. Consequently, advances in the seamlessly integrated design-to-manufacturing workflow will be presented: (i) design (by means of employing parametric modeling software), (ii) geometry analysis (by means of ad-hoc machine manufacturing process simulations), (iii) CAD-CAM integration (by means of automated geometry processing and machine code generation), and (iv) manufacturing and testing (by combining 6-axis robots and large-scale plastic extruders).

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Section: Introductionmentioning

confidence: 99%

Advances in Large-Scale Robotic 3D Printing with Plastic Pellets

Serrano¹,

Bares²

2023

Advances in 3D Printing

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“…To the best of our knowledge, no state‐of‐the‐art survey covers all these applications under one umbrella. As shown in Table 1, existing surveys mainly focus on a single application, such as security (Patnaik et al (2021), Al‐Garadi et al (2020), Chaabouni et al (2019), Xiao et al (2018), da Costa et al (2019)), healthcare (Dhillon and Singh (2019), Manogaran and Lopez (2017), Ray (2021), Amulya et al (2021)), smart homes (Ramasamy Ramamurthy and Roy (2018), Ali et al (2019), Bakar et al (2016)), smart cities (Chen et al (2019), Mohapatra and Panda (2019), Ullah et al (2020), Luckey et al (2021)), smart grids (Arif et al (2020), Babar et al (2020), Hasan et al (2020), Narayanan et al (2023)), and smart agriculture (Pathan et al (2020), Sharma et al (2020), Escamilla‐García et al (2020)). In contrast, this study analyzes ML technology, focusing exclusively on its solutions, challenges, and open issues in the mentioned domains.…”

Section: Introductionmentioning

confidence: 99%

Machine learning and internet of things applications in enterprise architectures: Solutions, challenges, and open issues

Rehman,

Tariq,

Moqurrab

et al. 2023

Expert Systems

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SummaryThe rapid growth of the Internet of Things (IoT) has led to its widespread adoption in various industries, enabling enhanced productivity and efficient services. Integrating IoT systems with existing enterprise application systems has become common practice. However, this integration necessitates reevaluating and reworking current Enterprise Architecture (EA) models and Expert Systems (ES) to accommodate IoT and cloud technologies. Enterprises must adopt a multifaceted view and automate various aspects, including operations, data management, and technology infrastructure. Machine Learning (ML) is a powerful IoT and smart automation tool within EA. Despite its potential, a need for dedicated work focuses on ML applications for IoT services and systems. With IoT being a significant field, analyzing IoT‐generated data and IoT‐based networks is crucial. Many studies have explored how ML can solve specific IoT‐related challenges. These mutually reinforcing technologies allow IoT applications to leverage sensor data for ML model improvement, leading to enhanced IoT operations and practices. Furthermore, ML techniques empower IoT systems with knowledge and enable suspicious activity detection in smart systems and objects. This survey paper conducts a comprehensive study on the role of ML in IoT applications, particularly in the domains of automation and security. It provides an in‐depth analysis of the state‐of‐the‐art ML approaches within the context of IoT, highlighting their contributions, challenges, and potential applications.

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