Predicting Safety Solutions via an Artificial Neural Network

Štohl, Radek; Stibor, Karel

doi:10.1016/j.ifacol.2019.12.711

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…Implementing the principles of I4.0 into industrial applications is a slow process, mainly due to the generally nonsystematic approach. At present, relevant technologies involve and rely on digitization, robotics, non-optimal data acquisition, virtual reality, IoT, and advanced data processing [ 21 , 22 , 23 , 24 , 25 , 26 , 27 ]; simultaneously, however, application standards remain undeveloped or are lacking completely, and a similar deficiency also affects corporate economy and common initiative in any given field [ 28 , 29 , 30 , 31 , 32 ]. Conversely, these separate technologies help to accelerate the implementation of I4.0 principles and open new opportunities and challenges for technical development; in the given context, such benefits were considered unfeasible 5–7 years ago.…”

Section: Introductionmentioning

confidence: 99%

Automated Design and Integration of Asset Administration Shells in Components of Industry 4.0

Arm

Benešl

Marcoň

et al. 2021

Sensors

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One of the central concepts in the principles of Industry 4.0 relates to the methodology for designing and implementing the digital shell of the manufacturing process components. This concept, the Asset Administration Shell (AAS), embodies a systematically formed, standardized data envelope of a concrete component within Industry 4.0. The paper discusses the AAS in terms of its structure, its components, the sub-models that form a substantial part of the shell’s content, and its communication protocols (Open Platform Communication—Unified Architecture (OPC UA) and MQTT) or SW interfaces enabling vertical and horizontal communication to involve other components and levels of management systems. Using a case study of a virtual assembly line that integrates AASs into the technological process, the authors present a comprehensive analysis centered on forming AASs for individual components. In the given context, the manual AAS creation mode exploiting framework-based automated generation, which forms the AAS via a configuration wizard, is assessed. Another outcome consists of the activation of a virtual assembly line connected to real AASs, a step that allows us verify the properties of the distributed manufacturing management. Moreover, a discrete event system was modeled for the case study, enabling the effective application of the Industry 4.0 solution.

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Section: Introductionmentioning

confidence: 99%

Automated Design and Integration of Asset Administration Shells in Components of Industry 4.0

Arm

Benešl

Marcoň

et al. 2021

Sensors

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show abstract

“…Dolayısıyla, yapay sinir ağlarının regresyon analizine alternatif bir yöntem olabileceği sonucuna ulaşılmıştır. [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. YSA ile ilgili literatürde birçok çalışma olmasına rağmen konum bilgilerine dayalı YSA kullanımıyla ilgili çalışmalar oldukça kısıtlıdır.…”

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Height Modeling with Artificial Neural Networks (Samsun-Mert River Basin)

Demir¹,

KESKİN²

2020

GMBD

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In many engineering applications, it is needed topographical and surface topography is required to determine the appropriate accuracy. As applications based on digital elevation model case study of height values using horizontal and vertical position information may be determined by appropriate methods. In this study, Samsun Mert river basin and at the intersection of the city limits, the present progress has been providing horizontal and vertical elevation values of the point where the coordinates of the map information have been studied to predict. Multi-Layer Artificial Neural Networks (MLANN), and regression analysis were used. The study was tested in three different combinations. These include: (i) X coordinate of the estimated height information; (ii) the estimated height of the Y coordinate information; (iii) the estimated height information in the form of X and Y coordinates. As a comparison criterion Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and the determination coefficients (R 2 ) were used. When the models were compared, Artificial Neural Networks (ANN) model gave a better fit (fit-line) than Multiple Linear Regression (MLR) and linear regression (LR) models. The highest R² value (0.948) was found from the ANN model where the combination (iii) was used as input data. The lowest R² value (0.132) was obtained from the regression model where the combination (ii) was used as input data. As a result of modeling, heights have been estimated more successfully with the (iii) combination of ANN. Therefore, ANN was determined as an alternative method to regression analysis.

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A review on supervised machine learning for accident risk analysis: Challenges in Malaysia

Chong

Razak

Biak

et al. 2022

Process Safety Progress

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The new Fourth Industrial Revolution (IR 4.0) trend is driven by the concept of automation and artificial intelligence (AI). However, Malaysia is slightly behind Singapore in terms of adopting AI innovation among ASEAN countries. This paper aims to conduct a literature review of machine learning to overcome subjectivity and bias in risk ranking decision‐making. An introduction to machine learning concerning accident risk analysis is presented, and the challenges of its application in Malaysia are discussed. Existing machine learning features were evaluated to identify the feasible application in industrial accident analysis and ensure safety decision‐making consistency. This review observed how the IR 4.0 approaches were used in the risk analysis, especially on supervised machine learning. This study also highlights the finding from the previous works on challenges in utilizing supervised machine learning, which is the need to have publicly accessible large database from industries and agencies such as the Department of Occupational Safety and Health (DOSH) Malaysia for the development of algorithms, which can potentially improve accident risk analysis and safety, especially for Malaysian industries.

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Predicting Safety Solutions via an Artificial Neural Network

Cited by 4 publications

References 5 publications

Automated Design and Integration of Asset Administration Shells in Components of Industry 4.0

Automated Design and Integration of Asset Administration Shells in Components of Industry 4.0

Height Modeling with Artificial Neural Networks (Samsun-Mert River Basin)

A review on supervised machine learning for accident risk analysis: Challenges in Malaysia

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