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Challenges in developing and deploying AI in the engineering, procurement and construction industry

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Challenges in developing and deploying AI in the engineering...

IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Rimma Dzhusupova Jan Bosch Helena Holmströ m Olsson Dept. of Electrical &#x0026 Instrumentation Control &#x0026 Safety Systems McDermott The Hague The Netherlands Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Department of Computer Science and Media Technology Malm&#x00F6 University Malm&#x00F6 Sweden

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

AI in the engineering, Procurement and Construction (EPC) industry has not yet a proven track record in large-scale projects. Since AI solutions for industrial applications became available only recently, deployment experience and lessons learned are still to be built up. Several research papers exist describing the potential of AI, and many surveys and white papers have been published indicating the challenges of AI deployment in the EPC industry. However, there is a recognizable shortage of in-dept. studies of deployment experience in academic literature, particularly those focusing on the experiences of EPC companies involved in large-scale project execution with high safety standards, such as the petrochemical or energy sector. The novelty of this research is that we explore in detail the challenges and obstacles faced in developing and deploying AI in a large-scale project in the EPC industry based on real-life use cases performed in an EPC company. Those identified challenges are not linked to specific technology or a company's know-how and, therefore, are universal. The findings in this paper aim to provide feedback to academia to reduce the gap between research and practice experience. They also help reveal the hidden stones when implementing AI solutions in the industry.

关键词： Industries Procurement Companies Manuals Petrochemicals Software Safety

Automated Optical Inspection for Printed Circuit Board Assembly Manufacturing with Transfer Learning and Synthetic Data Generation

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Automated Optical Inspection for Printed Circuit Board Assem...

Mediterranean Conference on control and Automation (MED)

作者： Syed Saad Saif Kerem Aras Alessandro Giuseppi Dept. of Computer Control and Management Engineering (DIAG) of the University of Rome "La Sapienza" Roma Italy Token Financial Technologies Istanbul Turkey Technical University of Berlin Berlin Germany

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

Automated Optical Inspection (AOI) is among the most common and effective quality checks employed in production lines. This paper details the design of a Deep Learning solution that was developed for addressing a specific quality control in a Printed Circuit Board Assembly (PCBA) manufacturing process. The developed Deep Neural Network exploits transfer learning and a synthetic data generation process to be trained even if the quantity of the data samples available is low. The overall AOI system was designed to be deployed on low-cost hardware with limited computing capabilities to ease its deployment in industrial settings.

关键词： Deep learning Transfer learning Printed circuits Neural networks Optical computing Quality control Production

Evaluation of Students' Learning Based on Cybernetic Models with Differential Equations of Fractional Order

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Evaluation of Students' Learning Based on Cybernetic Models ...

2021 International e-engineering Education Services Conference, e-engineering 2021

作者： Busher, Victor Chornyi, Oleksii Herasymenko, Larisa Tytyuk, Valeriy Khandakji, Kamal Zakis, Janis National University "odessa Maritime Academy" Dept. of Electrical Engineering and Electronics Odessa Ukraine Kremenchuk Mykhailo Ostrohradskyi National University Institute of Electromechanics Energy Saving and Automatic Control Systems Kremenchuk Ukraine Kryvyi Rih Technical University Dept. of Electromechanics Kryvyi Rih Ukraine Tafila Technical University Department of Electrical Power and Mechatronics Tafila Jordan Riga Technical University Institute of Industrial Electronics and Electrical Engineering Riga Latvia

ISBN: (纸本)9781728134703

In this paper, the implementation of a cybernetic learning model based on differential equations of fractional order is justified. Experimental learning results were obtained by testing students during the academic semester. Based on a heterogeneous differential equation with integer order, learning graphs have been compared to a family of models with fractional order equations. Consequently, fractional-order equations are proved to have better approximation properties. Criteria for assessing the learning and differentiation of teaching methods based on the parameters of fractional order equations are proposed. © 2021 IEEE.

关键词： Differential equations

Modeling the Productivity of a Sugar Factory using Machine Learning Methods

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Modeling the Productivity of a Sugar Factory using Machine L...

International computer Sciences and Information Technologies (CSIT)

作者： Nataliia Lutska Lidiia Vlasenko Nataliia Zaiets Vitaliy Lysenko dept. of Automation and Computer Technologies of Control Systems National University of Food Technologies Kyiv Ukraine dept. of Software Engineering and Cyber Security State University of Trade and Economics Kyiv Ukraine dept. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine

ISBN: (纸本)9798350334326

Relevant today is the digitalization of industrial enterprises, which is an evolutionary continuation of the introduction of MES/MOM. At the same time, an important role is played by the construction of mathematical models for various purposes, which are the main component of the Digital Twin development. Digital Twin provides a new philosophy for conducting enterprise production processes based on these models, which is aimed at improving the efficiency of both individual sectors and the enterprise as a whole. To increase the profit of the enterprise, it is necessary to ensure the planned productivity of technological processes, the final indicator of which is the yield of sugar. Taking into account that a significant number of material flows operate at the enterprise, only production flows with which the operator-technologist works are singled out in the work. The selected flows, based on expert assessments, were assigned weighted values of the impact on the overall performance of the sugar production. This made it possible to build a mathematical model that predicts the yield of sugar from input flows. The structure and parameters of this model are determined by machine learning methods. This is a feed-forward neural network MLP 7-23-23-1, which provides forecast accuracy with an error of less than 1%. The model can be used to model, correct, and predict the process conditions of an enterprise, which will increase the overall productivity of the sugar factory.

关键词： Productivity Biological system modeling Machine learning Predictive models Mathematical models Sugar industry Production facilities

Direct data-driven LPV control of nonlinear systems: An experimental result

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arXiv 2022年

作者： Verhoek, Chris Abbas, Hossam S. Tóth, Roland Control Systems Group Dept. of Electrical Engineering Eindhoven University of Technology Eindhoven5600MB Netherlands Institute for Electrical Engineering in Medicine Universität zu Lübeck Lübeck23558 Germany Systems and Control Lab Institute for Computer Science and Control Budapest1111 Hungary

We demonstrate that direct data-driven control of nonlinear systems can be successfully accomplished via a behavioral approach that builds on a Linear Parameter-Varying (LPV) system concept. An LPV data-driven representation is used as a surrogate LPV form of the data-driven representation of the original nonlinear system. The LPV data-driven control design that builds on this representation form uses only measurement data from the nonlinear system and a priori information on a scheduling map that can lead to an LPV embedding of the nonlinear system behavior. Efficiency of the proposed approach is demonstrated experimentally on a nonlinear unbalanced disc system showing for the first time in the literature that behavioral data-driven methods are capable to stabilize arbitrary forced equilibria of a real-world nonlinear system by the use of only 7 data points. © 2022, CC BY-NC-SA.

关键词： Nonlinear systems

Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder

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arXiv 2022年

作者： van Haren, Max Poot, Maurice Kostić, Dragan van Es, Robin Portegies, Jim Oomen, Tom Control Systems Technology Dept. of Mechanical Engineering Eindhoven University of Technology Eindhoven Netherlands ASM Pacific Technology Centre of Competency Beuningen Netherlands CASA Dept. of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands Delft Center for Systems and Control Delft University of Technology Delft Netherlands

Mechatronic systems have increasingly stringent performance requirements for motion control, leading to a situation where many factors, such as position-dependency, cannot be neglected in feedforward control. The aim of this paper is to compensate for position-dependent effects by modeling feedforward parameters as a function of position. A framework to model and identify feedforward parameters as a continuous function of position is developed by combining Gaussian processes and feedforward parameter learning techniques. The framework results in a fully data-driven approach, which can be readily implemented for industrial control applications. The framework is experimentally validated and shows a significant performance increase on a commercial wire bonder. © 2022, CC BY.

关键词： Gaussian distribution

Analysis of Heart Rate Variability during the Performance of the Wim Hof Method in Healthy Subjects

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Analysis of Heart Rate Variability during the Performance of...

Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Beatriz F. Giraldo Giraldo David Ferré López Jordi Solà-Soler CIBER de Bioingeniería Biomateriales y Nanomedicina (CIBER-BBN) Automatic Control Dept. (ESAII) the Barcelona East School of Engineering (EEBE) Universitat Politècnica de Catalunya (UPC) Institute for Bioengineering of Catalonia (IBEC) – The Barcelona Institute of Science and Technology Barcelona Spain Barcelona East School of Engineering (EEBE) Universitat Politècnica de Catalunya (UPC) Spain

Cardiorespiratory interaction is related to the heart rate variability (HRV) synchronized with respiration. These metrics help to comprehend the autonomic nervous system (ANS) functionality in cardiovascular mechanisms. In this work, we aim to study the HRV in healthy subjects aged 18-24 years during the breathing techniques based on deep breaths followed by apnoeas, developed by Wim Hof (WHM). The attributes of all participates have been treated as a group and therefore, separated by gender. A total of 11 intervals have been distinguished: starting of basal respiration (SRI = 1), controlled deep breaths (CDB = 3), long expiratory apnoea (LEA = 3), short inspiratory apnoea (SIA = 3) and ending with basal respiration again (FRI = 1). To strengthen the HRV knowledge extraction from these scenarios, time and frequency analysis is conducted. In general, breathing and apnoea intervals presented significant statistically differences (p < 0.05), heart rate (HR) mean between SRI and FRI (p < 0.001), RR variability of LEA intervals (p < 0.01), root mean square of RR intervals during CDB (p < 0.05), maximum high frequency (HF) peak amplitude between SRI and FRI (p = 0.016), and low frequency (LF) area for LEA intervals (p < 0.001). When performing the frequency analysis, it has been observed that the sympathetic nervous system (SNS) has a higher contribution in the apnoea intervals. In conclusion, the WHM method implementation seems to involve a decrease in the HR. Specific breathing techniques could help to control the body in different *** Relevance— The WHM seems to imply a decrease on HR. Furthermore, after the implementation of the WHM, women presented higher HRV.

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Uncertain AoI in Stochastic Optimal control of Networked and Constrained LTI Systems

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arXiv 2021年

作者： Hahn, Jannik Stursberg, Olaf Control and System Theory Dept. of Electrical Engineering and Computer Science University of Kassel Kassel34121 Germany

— This paper addresses finite-time horizon optimal control of single-loop networked control systems with stochastically modeled communication channel and disturbances. To cope with the uncertainties, an optimization-based control scheme is proposed which uses a disturbance feedback and the age of information as central aspects. The disturbance feedback is an extension of the control law used for balanced stochastic optimal control previously proposed for control systems without network. Balanced optimality is understood as a compromise between minimizing of expected deviations from the reference and minimization of the uncertainty of future states. Time-varying state constraints as well as time-invariant input constraints are considered, and the controllers are synthesized by semi-definite programs. © 2021, CC BY.

关键词： Networked control systems

LPV Modeling of the Atmospheric Flight Dynamics of a Generic Parafoil Return Vehicle

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arXiv 2022年

作者： de Lange, Matthis H. Verhoek, Chris Preda, Valentin Tóth, Roland Control Systems Group Dept. of Electrical Engineering Eindhoven University of Technology Eindhoven5600MB Netherlands ESTEC European Space Agency Noordwijk2200AG Netherlands Systems and Control Lab Institute for Computer Science and Control Budapest1111 Hungary

Obtaining models that can be used for control is of utmost importance to ensure the guidance and navigation of spacecraft, like a Generic Parafoil Return Vehicle (GPRV). In this paper, we convert a nonlinear model of the atmospheric flight dynamics of an GPRV to a Linear Parameter-Varying (LPV) description, such that the LPV model is suitable for navigation control design. Automated conversion methods for nonlinear models can result in complex LPV representation, which are not suitable for controller synthesis. We apply several state-of-the-art techniques, including learning based approaches, to optimize the complexity and conservatism of the LPV embedding for an GPRV. The results show that we can obtain an LPV embedding that approximates the complex nonlinear dynamics sufficiently well, where the balance between complexity, conservatism and model performance is optimal. © 2022, CC BY-NC-SA.

关键词： Principal component analysis