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Deep Learning-based Modeling of Gasoline controlled Auto-Ignition

IFAC-PapersOnLine 2023年第2期56卷 8248-8253页

作者： Xu Chen Maximilian Basler Maike Stemmler Alexander Winkler Jakob Andert Dirk Abel Institute of Automatic Control Department of Mechanical Engineering RWTH Aachen University 52074 Aachen Germany Teaching and Research Area Mechatronics in Mobile Propulsion RWTH Aachen University 52074 Aachen Germany

Internal combustion engines face increasingly stringent pollution and greenhouse gas emission restrictions. In this regard, the technology of low-temperature combustion is the scope of research, as it can reduce pollutant emissions while increasing engine efficiency. However, the system dynamics are very complicated and have a high sensitivity concerning the thermodynamics in the combustion chamber. Modeling with first principle models would be time-consuming and requires accurate parameter estimations. Therefore, this work presents deep learning-based models to capture the dynamics of the cylinder pressure trace for gasoline controlled auto-ignition. These models trained with experimental data can learn the internal pattern of the combustion cycle dynamics. Based on information from the current cycle, they enable the prediction of the combustion pressure trace for the following. The results show that the models precisely predict the pressure traces and ensure high prediction accuracy of engine performance parameters derived from the predicted traces. R 2 values of 0.95 or more are obtained for global parameters, whereas values of 0.8 or more are obtained for local parameters.

关键词： Neural Network models Internal Combustion Engines Low Temperature Combustion controlled Auto Ignition

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Trends and Challenges of Sustainable Agriculture in the Iraqi Environment

Trends and Challenges of Sustainable Agriculture in the Iraq...

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2022 IEEE International Conference on Blockchain, Smart Healthcare and Emerging Technologies, SmartBlock4Health 2022

作者： Al-Asadi, Hanan Hussein Goga, Maria Ali, Haider Abdullah Popa, Ramona Cristina Oludele, Awodele University POLITEHNICA of Bucharest Faculty of Automatic Control and Computers Bucharest Romania University of Technology-Iraq Baghdad Iraq Technical University of Civil Engineering of Bucharest UTCB Department for Training of Teaching Personnel Bucharest Romania Madenat Alelem University College Baghdad Iraq Babcock University Computer Science Department Ogun State Nigeria

ISBN: (纸本)9781665460149

The use of Agriculture Intelligent Systems (AISs) in Iraq has become popular among farmers. The greenhouse is a solution for plant growth, which uses an effective microclimate to simulate seasons and to use alternative energy sources like solar, thermal, and photovoltaic systems for microclimate control. Greenhouses are considered a successful solution with developments and achievements in the technology of this field. However, problems like high costs, difficulties in installation and maintenance, or the need for some procedures should be considered. Because Iraq is close to the equator (3669 km), it receives sufficient solar power. It also has large areas available for the building of solar greenhouse systems. The farmers need an affordable and functional smart greenhouse to enable and encourage them to use the environment efficiently. We intend to develop low-cost, accurate, and easy-to-use greenhouse systems controlled by artificial intelligence algorithms. Farmers will be able to control the greenhouse by using simple commands. Machine learning algorithms provide real-time and effective analysis that could support farmers in decision-making and provide relevant data for researchers and policymakers. AISs algorithms can lead to a sustainable and productive agriculture system. The system can be used in Iraq with sufficient government support and subsidy. © 2022 IEEE.

关键词： Intelligent systems

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Machine Learning Integrated with Model Predictive control for Imitative Optimal control of Compression Ignition Engines

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IFAC-PapersOnLine 2022年第24期55卷 19-26页

作者： Armin Norouzi Saeid Shahpouri David Gordon Alexander Winkler Eugen Nuss Dirk Abel Jakob Andert Mahdi Shahbakhti Charles Robert Koch Department of Mechanical Engineering University of Alberta Edmonton Canada Teaching and Research Area Mechatronics in Mobile Propulsion RWTH Aachen University Aachen Germany Institute of Automatic Control RWTH Aachen University Germany

The high thermal efciency and reliability of the compression-ignition engine makes it the first choice for many applications. For this to continue, a reduction of the pollutant emissions is needed. One solution is the use of Machine Learning (ML) and Model Predictive control (MPC) to minimize emissions and fuel consumption, without adding substantial computational cost to the engine controller. ML is developed in this paper for both modeling engine performance and emissions and for imitating the behaviour of a Linear Parameter Varying (LPV) MPC. Using a support vector machine-based linear parameter varying model of the engine performance and emissions, a model predictive controller is implemented for a 4.5 L Cummins diesel engine. This online optimized MPC solution offers advantages in minimizing the NO x emissions and fuel consumption compared to the baseline feedforward production controller. To reduce the computational cost of this MPC, a deep learning scheme is designed to mimic the behavior of the developed controller. The performance in reducing NO x emissions at a constant load by the imitative controller is similar to that of the online optimized MPC, however, the imitative controller requires 50 times less computation time when compared to that of the online MPC optimization.

关键词： Diesel engines Linear Parameter Variable Model Machine learning Support Vector Machine Model Predictive control Deep Learning

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Deep Learning based Model Predictive control for Compression Ignition Engines

arXiv

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arXiv 2022年

作者： Norouzi, Armin Shahpouri, Saeid Gordon, David Winkler, Alexander Nuss, Eugen Abel, Dirk Andert, Jakob Shahbakhti, Mahdi Koch, Charles Robert Mechanical Engineering Department University of Alberta Edmonton Canada Teaching and Research Area Mechatronics in Mobile Propulsion RWTH Aachen University Germany Institute of Automatic Control RWTH Aachen University Germany

Machine learning (ML) and a nonlinear model predictive controller (NMPC) are used in this paper to minimize the emissions and fuel consumption of a compression ignition engine. In this work machine learning is applied in two methods. In the first application, ML is used to identify a model for implementation in model predictive control optimization problems. In the second application, ML is used as a replacement of the NMPC where the ML controller learns the optimal control action by imitating or mimicking the behavior of the model predictive controller. In this study, a deep recurrent neural network including long-short term memory (LSTM) layers are used to model the emissions and performance of an industrial 4.5 liter 4-cylinder Cummins diesel engine. This model is then used for model predictive controller implementation. Then, a deep learning scheme is deployed to clone the behavior of the developed controller. In the LSTM integration, a novel scheme is used by augmenting hidden and cell states of the network in an NMPC optimization problem. The developed LSTM-NMPC and the imitative NMPC are compared with the Cummins calibrated Engine control Unit (ECU) model in an experimentally validated engine simulation platform. Results show a significant reduction in Nitrogen Oxides (NOx) emissions and a slight decrease in the injected fuel quantity while maintaining the same load. In addition, the imitative NMPC has a similar performance as the NMPC but with a two orders of magnitude reduction of the computation time. © 2022, CC BY-NC-ND.

关键词： Long short-term memory

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Machine Learning Integrated with Model Predictive control for Imitative Optimal control of Compression Ignition Engines

arXiv

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arXiv 2022年

作者： Norouzi, Armin Shahpouri, Saeid Gordon, David Winkler, Alexander Nuss, Eugen Abel, Dirk Andert, Jakob Shahbakhti, Mahdi Koch, Charles Robert Department of Mechanical Engineering University of Alberta Edmonton Canada Teaching and Research Area Mechatronics in Mobile Propulsion RWTH Aachen University Aachen Germany Institute of Automatic Control RWTH Aachen University Germany

The high thermal efficiency and reliability of the compression-ignition engine makes it the first choice for many applications. For this to continue, a reduction of the pollutant emissions is needed. One solution is the use of Machine Learning (ML) and Model Predictive control (MPC) to minimize emissions and fuel consumption, without adding substantial computational cost to the engine controller. ML is developed in this paper for both modeling engine performance and emissions and for imitating the behaviour of a Linear Parameter Varying (LPV) MPC. Using a support vector machine-based linear parameter varying model of the engine performance and emissions, a model predictive controller is implemented for a 4.5 L Cummins diesel engine. This online optimized MPC solution offers advantages in minimizing the NOx emissions and fuel consumption compared to the baseline feedforward production controller. To reduce the computational cost of this MPC, a deep learning scheme is designed to mimic the behavior of the developed controller. The performance in reducing NOx emissions at a constant load by the imitative controller is similar to that of the online optimized MPC, however, the imitative controller requires 50 times less computation time when compared to that of the online MPC optimization. © 2022, CC BY-NC-ND.

关键词： Model predictive control

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Trends and Challenges of Sustainable Agriculture in the Iraqi Environment

Trends and Challenges of Sustainable Agriculture in the Iraq...

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Blockchain, Smart Healthcare and Emerging Technologies (SmartBlock4Health), IEEE International Conference on

作者： Hanan Hussein Al-asadi Maria Goga Haider Abdullah Ali Ramona Cristina Popa Awodele Oludele Faculty of Automatic Control and Computers University POLITEHNICA of Bucharest Bucharest Romania University of Technology-Iraq Baghdad Iraq UTCB Department for Training of Teaching Personnel Technical University of Civil Engineering of Bucharest Bucharest Romania Madenat Alelem University College Baghdad Iraq Faculty of Engineering in Foreign Languages University POLITEHNICA of Bucharest Bucharest Romania computer science department Babcock University Ogun State Nigeria

ISBN: (纸本)9781665460156

关键词： Smart agriculture Machine learning algorithms Costs Green buildings Soil control systems Agriculture

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Tremor after long term lithium treatment;Is it cortical myoclonus?

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Cerebellum and Ataxias 2019年第1期6卷 5页

作者： Sarrigiannis, Ptolemaios Georgios Zis, Panagiotis Unwin, Zoe Charlotte Blackburn, Daniel J. Hoggard, Nigel Zhao, Yifan Billings, Stephen A. Khan, Aijaz A. Yianni, John Hadjivassiliou, Marios Department of Clinical Neurophysiology Sheffield Teaching Hospitals NHS Foundation Trust Royal Hallamshire Hospital Sheffield United Kingdom Department of Neurology Sheffield Teaching Hospitals NHS Foundation Trust Sheffield United Kingdom Department of Neuroradiology Sheffield Teaching Hospitals NHS Foundation Trust Sheffield United Kingdom Through-life Engineering Services Centre Cranfield University Bedford MK43 0AL United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield Sheffield S1 3JD United Kingdom Department of Neurosurgery Sheffield Teaching Hospitals NHS Foundation Trust Sheffield United Kingdom

Introduction: Tremor is a common side effect of treatment with lithium. Its characteristics can vary and when less rhythmical, distinction from myoclonus can be difficult. Methods: We identified 8 patients on long-term treatment with lithium that developed upper limb tremor. All patients were assessed clinically and electrophysiologically, with jerk-locked averaging (JLA) and cross-correlation (CC) analysis, and five of them underwent brain MRI examination including spectroscopy (MRS) of the cerebellum. Results: Seven patients (6 female) had action and postural myoclonus and one a regular postural and kinetic tremor that persisted at rest. Mean age at presentation was 58 years (range 42-77) after lengthy exposure to lithium (range 7-40 years). During routine monitoring all patients had lithium levels within the recommended therapeutic range (0.4-1 mmol/l). There was clinical and/or radiological evidence (on cerebellar MRS) of cerebellar dysfunction in 6 patients. JLA and/or CC suggested a cortical generator of the myoclonus in seven patients. All seven were on antidepressants and three additionally on neuroleptics, four of them had gluten sensitivity and two reported alcohol abuse. Conclusions: A synergistic effect of different factors appears to be contributing to the development of cortical myoclonus after chronic exposure to lithium. We hypothesise that the cerebellum is involved in the generation of cortical myoclonus in these cases and factors aetiologically linked to cerebellar pathology like gluten sensitivity and alcohol abuse may play a role in the development of myoclonus. Despite the very limited evidence in the literature, lithium induced cortical myoclonus may not be so rare. © 2019 The Author(s).

关键词： Cerebellar ataxia Cortical myoclonus Gluten sensitivity JLA Lithium MRS

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Decryption of communication by using super chaotic system with uncertain functions

Decryption of communication by using super chaotic system wi...

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2012 International Conference on Systems and Informatics, ICSAI 2012

作者： Jin, Yumeng Jin, Yuqiang Lei, Junwei Meng, Wanjing Communication Station of Automatic Department Xingxing Pipes Co.Ltd Chandan China Department of Training Naval Aeronautical and Astronautical University Yantai China Department of Control Engineering Naval Aeronautical and Astronautical University Yantai China College Foreign Languages Teaching Department Ludong University Yantai China

ISBN: (纸本)9781467301992

Synchronization of chaotic system is used in secure communication because of the random type behavior of chaotic systems. But there is no absolute safe communication system. A new kind of decryption method is proposed in this paper by making use of the robustness of synchronization strategy. Also model with sender, receiver and unfriendly receiver is built and a decryption problem is transformed to be a synchronization problem with uncertainties. © 2012 IEEE.

关键词： Synchronization

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Decryption of communication by using super chaotic system with uncertain functions

Decryption of communication by using super chaotic system wi...

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International Conference on Systems and Informatics (ICSAI)

作者： Yumeng Jin Junwei Lei Yuqiang Jin Wanjing Meng Communication Station of Automatic Department Xingxing Pipes Company Limited Chandan China Department of Training Naval Aeronautical and Astronautical University Yantai China Department of Control Engineering Naval Aeronautical and Astronautical University Yantai China College Foreign Languages Teaching Department Ludong University Yantai China

关键词： Synchronization Chaotic communication Receivers Uncertainty Educational institutions Adaptive systems

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