Sustainable industrial systems within kernel density analysis of artificial intelligence and industry 4.0

Abstract

This paper conveys the theoretical perspectives of sustainable industrial systems of On board Unit (OBU) in Electronic Toll and its transportation industry. It proposes kernel density analysis of artificial intelligence approach to industry 4.0. Regulation for technology developments in artificial intelligence and robotics are deemed as one of beneficial yet structurally neglected domain. This domain refers to human perspective on augmenting automation. This regulation was emphasized in 2017 by the European Parliament report level. The mentioned regulation comprises attention in Indonesia transportation industry indicating positive innovation domains in term of safeguards and regulations are needed. Prior, current and posterior trends of the Internet of Things, Industry 4.0, and Physical Internet constitutes the results of the data development and understanding. Therefore, the topical framework of automation and robotics are triggered by these developments. The mention triggers has impacted the most important wide range implementation of industries in the future. Electronic Toll Collection (ETC) system is 40% higher in cost efficiency than Manual Toll Collection (MTC) system. In this situation, Indonesian government has already issued full swing policy implementation on non-contact freeway toll collection system by the end of 2018. Structural Equation Modelling (SEM) is capitalized to proceed to data processing. Result of this research shown the driver's characteristics that significantly affect willingness to pay an on-board unit are education expenses, distance and frequency. The average value of driver willing to pay an on-board unit was 225.781 IDR and factors that affecting values of the willingness to pay an on-board unit are expenses and distance.

Policy interactions with research trajectories: The case of cyber-physical convergence in manufacturing and industrials

“…Cyber-based design for additive manufacturing using artificial neural networks for Industry 4.0 (Elhoone et al, 2020) Intelligence Sustainable industrial systems within kernel density analysis of artificial intelligence and industry 4.0 (Soebandrija et al, 2018) Modelling: Modelling includes operational research techniques that have long been used in manufacturing (Greasley, 2005, Smith et al, 1994 to "understand the states, conditions and behaviors of the systems under control and those of peer systems by interpreting and correlating data gathered from sensors and peer systems" (Lin et al, 2015, p. 29). Virtual models built from sensor data provide valuable insights into existing or planned systems and thus enable better decision-making during activities such as shop floor layout optimisation (Eklin et al, 2009), production process control (Iassinovski et al, 2008) and inventory control (Rezg et al, 2005).…”