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Adoption of Industry 4.0 in Lean Manufacturing
Published in Sudan Jha, Usman Tariq, Gyanendra Prasad Joshi, Vijender Kumar Solanki, Industrial Internet of Things, 2022
The cyber-physical device design is based on the architecture of 5C. These levels are structured to translate the big data obtained at the first level to the tiny yet precious data at the fifth level. The “communication level” devices are designed in the first stage to have the ability to self-connect and self-sense to collect data from three sources: sensors, controllers, and cloud data. Data from the connected devices are tracked and translated to the information in the “conversion stage” to identify the critical problems and monitor the health of the computer. This can be known as the system’s self-awareness capacity; these data can be used in the next stage for future prediction of possible problems [27]. A cyber twin is created for each machine in the system in the “cyber level” and each machine can further examine its health by comparing its functionality with its cyber-twin, complex deep learning, and machine learning algorithms are used in this level to examine the health of the computer and its quality of operation. The outcomes of the previous levels will be provided to the user or decision-making software at the “cognition level” to decide on further actions according to the system situation, and finally at the “configuration level,” the system can be reconfigured according to risk criteria [31]. The general overview of CPS can be divided into four main stages according to [32] as Figure 6.1.
Epilogue
Published in David R. Green, Billy J. Gregory, Alex R. Karachok, Unmanned Aerial Remote Sensing, 2020
David R. Green, Billy J. Gregory, Alex R. Karachok
More recent developments include the growing use of AI to extract information from the aerial photography for input to decision-making software. Already a number of apps have been developed to facilitate this. One example is PA applications. A perfect example of this sort of development is an app called Skippy Scout from DroneAg Limited in the UK. This application uses low-altitude aerial imagery from RTF off-the-shelf drones to generate information on crop growth stage, green area index, weeds, and disease enabling the farmer to fly, capture, process, and utilise crop information for decision-making. Other examples, such as those provided by Parrot, make use of similar technology for their fixed-wing and multi-rotor platforms and multispectral sensors to provide information on agricultural and horticultural crops processed in the cloud.
Thermal Processing of Canned Foods
Published in Dennis R. Heldman, Daryl B. Lund, Cristina M. Sabliov, Handbook of Food Engineering, 2018
Computer-based intelligent on-line control systems make use of these models as part of the decision-making software in a computer-based on-line control system. Instead of specifying the retort temperature as a constant boundary condition, the actual retort temperature is read directly from sensors located in the retort and is continually updated with each iteration of the numerical solution. Using only the measured retort temperature as input to the control system, the model operates as a subroutine calculating the internal product cold spot temperature at small time intervals for computer iteration in carrying out the numerical solution to the heat conduction equation by finite differences. At the same time, the model also calculates the accomplishing process lethality from the cold spot temperature in real time as the process is underway. At each time step, the subroutine simulates the additional lethality that will be contributed by the cooling phase if cooling were to begin at that time. In this way, the control system decision of when to end heating and begin cooling is withheld until the model has determined that final target process lethality will be reached at the end of cooling.
What factors may influence decision-making in the operation of Maritime autonomous surface ships? A systematic review
Published in Theoretical Issues in Ergonomics Science, 2022
Kirsty M. Lynch, Victoria A. Banks, Aaron P. J. Roberts, Stewart Radcliffe, Katherine L. Plant
The articles were selected using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) selection process (Moher et al. 2009), shown in Figure 1 and was used to reduce the initial total of articles from 932 articles to 47 articles. Articles relating to machine learning and decision algorithms were excluded, as they related to the technical development of decision-making software and were not the focus of this research. Articles which solely discussed the design of interfaces were also excluded as they did not specifically focus on the human-machine team decision-making process. Due to the specific nature of the search terms used, literature on the effects of automation on the human decision-making process has also been included in the review (18 articles).
Prioritizing the indicators influencing permit to work system efficiency based on an analytic network process
Published in International Journal of Occupational Safety and Ergonomics, 2022
Soheil Abbasi, Neda Gilani, Mostafa Javanmardi, Seyed Shamseddin Alizadeh, Saeid Jalilpour, Milad Safari
With the data collected from the questionnaire, to get a consensus opinion, the simple geometric mean was used. Due to the complex principles and processes of the ANP, the decision model was built with the help of Super Decisions version windows 2.10 and a list of the pairwise comparisons needed to run the evaluation was automatically created. This decision-making software works based on two MCDMs and is a coupling of two parts. The first involves a control hierarchy or network of indicators and sub-indicators that control the communications in the system under study. The second part is a network of influences among the elements and clusters. Applications may be simple, containing a single network, or complex, consisting of the main network and two or more layers of sub-networks. Each network and sub-network is made in its own window [33].
Patient-specific fluid–structure interaction model of bile flow: comparison between 1-way and 2-way algorithms
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2021
Alex G. Kuchumov, Vasily Vedeneev, Vladimir Samartsev, Aleksandr Khairulin, Oleg Ivanov
To predict and prevent postoperative complications, it is necessary to formulate and introduce new technological approaches, which, in particular, may consist in creating a software product (decision-making system in surgical interventions for gallstone disease and its complications). A proposed model of the biliary system makes it possible to assess choledynamics in normal and pathological conditions, as well as to carry out a numerical assessment of bile flow after surgery (removal of the gallbladder) to predict and prevent complications. The results of this study are realized in the decision-making software developed in Perm National Research Polytechnic University. The software enables to evaluate the mean flow rate after cholecystectomy and common bile duct dilatation. These parameters can tell about efficiency of the operation. So, using the results of this paper, the surgeon can evaluate the circumstances of the cholecystectomy for the each patient before operation and evaluate the results of post-operative bile flow features. The details of the developed software and its application in comparison with clinical cases can be found in Appendix 3.