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Estimation of Non-integer Order Models to Represent the Pressure Dynamics in Common-rail Natural Gas Engines

IFAC-PapersOnLine 2017年第1期50卷 14551-14556页

作者： Kapetina, Mirna Lino, Paolo Maione, Guido Rapai, Milan University of Novi Sad Faculty of Technical Sciences Computing and Control Department Trg D. Obradovica 6 Novi Sad Serbia Politecnico di Bari Department of Electrical and Information Engineering Via E. Orabona 4 Bari Italy

Automotive natural gas engines are designed to dramatically reduce the emission of polluting gases, particulate matter, and harmful substances associated to combustion of Diesel or gasoline fuels. Their performance depends on the accurate metering of the air/fuel ratio, which is pursued by controlling the gas injection timing and the pressure in the common rail volume. To this aim, an accurate control-oriented model is necessary to represent gas pressure dynamics in the main parts of the injection system. This work focuses on the gas transition from inner parts of the system to the common rail. The aim is to identify a linear non-integer-order model that describes the process in a more effective and compact way than ARX integer-order models of high order. Identification is performed in a non-parametric setting, trying to identify the optimal smooth fit in the frequency domain by minimization of an appropriate discrepancy criterion, between the actual measurements and the model outputs. In this way, the model structure will be obtained as part of the identification process and will not be fixed a priori. © 2017

关键词： Compressed natural gas Diesel engines Frequency response Gas engines Gases Particle swarm optimization (PSO) Actual measurements control oriented models Fractional order systems Harmful substances Identification process Injection systems Model identification Particulate Matter

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Iterative ECG signal filtering for better malfunction recognition and diagnosis 5

Iterative ECG signal filtering for better malfunction recogn...

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5th IFAC Symposium on Modelling and control in Biomedical Systems

作者： Szilágyi, SM Benyó, Z Dávid, L Faculty of Technical and Human Science Sapientia Hungarian Science University of Transylvania Hungary Department of Control Engineering and Information Technology Budapest University of Technology and Economics Hungary

ISBN: (纸本)0080441599

This paper presents an adaptive, iteratively functioning ECG signal filtering method. After a conventional pre-filtering, the waves from the signal are localized and the model's parameters are determined. The gained information allows an iterative-type filtering in permanent concordance with the aimed processing manner. The structure of the algorithm allows real-time adaptation to the heart's state. Using these methods for one channel of the MIT-BIH database, the detection rate of QRS complexes is above 99.8%. The negative influence of various noise types, like 50/60 Hz power line, abrupt baseline shift or drift, and low sampling rate was almost completely eliminated. IFAC.

关键词： medical applications real-time systems neural network signal processing spectrum analysis iterative methods regression algorithm systems

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Applicability of RESTful web services in control center software integrations

Applicability of RESTful web services in control center soft...

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International Conference on Innovations in Information Technology

作者： Varga, Ervin Lendak, Imre Gavrić, Milan Erdeljan, Aleksandar Department of Power Electronics and Communication Engineering Faculty of Technical Sciences Novi Sad Serbia Computing and Control Department Faculty of Technical Sciences Novi Sad Serbia CIM and GIS Group Telvent DMS Llc Novi Sad Serbia

ISBN: (纸本)9781457703140

This paper shows the result of an analysis pertaining to the applicability of RESTful web services (RESTful WS) in solving various software and data integration scenarios in control centers for the electric power system domain. There are many possible ways to enhance power system software/data integration by combining RESTful WS and the IEC 61970 set of standards. The goal of this paper is to highlight those lines of integration in control centers, where the benefits of leveraging RESTful WS are most noticeable. The importance of selecting the most prominent integration scenarios from the viewpoint of RESTful WS stems from the fact, that not all integration problems are naturally fitted for RESTful WS. Assuming a type of RESTful WS, which wraps the resource oriented power system data model defined in the IEC 61970-301 Common Information Model (CIM) and the generic query interface defined in IEC 61970-403 Generic Data Access (GDA), the paper demonstrates that such a RESTful WS may be successfully utilized in many concrete integration situations. As an added advantage, a RESTful WS atop of existing IEC 61970 standards can significantly loose the couplings between different software packages used in power system control centers. We have realized several pilot projects using real life power system data models (some used in CIM interoperability tests) to assert the outcome of our feasibility study. This study clearly shows that RESTful web services are a powerful tool for solving numerous power system software and data integration problems in control centers. © 2011 IEEE.

关键词： Data integration

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Unified resilience model using deep learning for assessing power system performance

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Heliyon 2025年第4期11卷 e42802页

作者： Artemchuk, Volodymyr Garbuz, Iurii Jamil Alsayaydeh, Jamil Abedalrahim Shkarupylo, Vadym Oliinyk, Andrii Bin Yusof, Mohd Faizal Herawan, Safarudin Gazali Department of Mathematical and Econometric Modelling G.E. Pukhov Institute for Modelling in Energy Engineering of the NAS of Ukraine Kyiv Ukraine Department of Environmental Protection Technologies and Radiation Safety Center for Information-analytical and Technical Support of Nuclear Power Facilities Monitoring of the NAS of Ukraine Kyiv Ukraine Department of Іnformation Systems in Economics Kyiv National Economic University Named After Vadym Hetman Kyiv Ukraine Department of Computerized Control System National Aviation University Kyiv Ukraine Department of Engineering Technology Fakulti Teknologi Dan Kejuruteraan Elektronik Dan Komputer (FTKEK) Universiti Teknikal Malaysia Melaka (UTeM) Melaka 76100 Malaysia Department of Computer Systems Networks and Cybersecurity of the National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Department of Software Tools Faculty of Computer Science and Technology Zaporizhzhia Polytechnic National University Zaporizhzhia Ukraine Research Section Faculty of Resilience Rabdan Academy Abu Dhabi United Arab Emirates Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta 11480 Indonesia

Energy resilience in renewable energy sources dissemination components such as batteries and inverters is crucial for achieving high operational fidelity. Resilience factors play a vital role in determining the performance of power systems, regardless of their operating environment and interruptions. This article introduces a Unified Resilience Model (URM) using Deep Learning (DL) to enhance power system performance. The proposed model analyzes environmental factors impacting the resilience of batteries and energy storage devices. This deep learning approach trains performance-impacting factors using previously known low resilience drain data. The learning output is utilized to augment various strengthening factors, thereby improving resilience. Drain mitigation and performance improvements are combined for direct impact verification. This process validates the model's fidelity in enhancing power system performance, with a specific focus on the impact of weather factors. © 2025 The Authors

关键词： Deep learning Energy resilience Fidelity Weather impact

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Defeasible AceRules: A prototype 12

Defeasible AceRules: A prototype

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12th International Conference on Computational Semantics, IWCS 2017

作者： Diller, Martin Wyner, Adam Strass, Hannes Faculty of Informatics Technical University of Vienna Austria Department of Computing Science University of Aberdeen United Kingdom Computer Science Institute Leipzig University Germany

The paper adapts the syntax of the AceRules system, an existing controlled natural language (CNL), and pipes the resulting output rules to a defeasible inference engine. With this, we can represent and reason with knowledge bases (KB) with strict and defeasible rules. For strict rules, the consequent of a strict rule must hold if the premises hold;for a defeasible rule, the consequent of a defeasible rule ought to hold if the premises hold. Strict rules are always applied whenever applicable, while defeasible rules are maximally applied (in terms of rule subsets), subject to consistency. In the CNL, defeasible rules are expressed in terms of what is "usual". The CNL expressions of rules are parsed and semantically represented so as to be used by an inference engine to accurately generate possible states of affairs. A verbaliser expresses the results in natural language, which facilitates understanding. The approach to defeasible rules is contrasted with available approaches, e.g. negation-as-failure, exceptions, and the use of abnormality predicates. The work is broadly set in the context of argumentation. The paper overviews the underlying formalism and the CNL;it provides an extended example. © 12th International Conference on Computational Semantics, IWCS 2017 - Long Papers. All rights reserved.

关键词： Inference engines

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Performance Analysis on Denial of Service attack using UNSW-NB15 Dataset 7

Performance Analysis on Denial of Service attack using UNSW-...

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7th International Conference on Software Engineering and Computer Systems and 4th International Conference on Computational science and Information Management, ICSECS-ICOCSIM 2021

作者： Edzereiq Kamarudin, Imran Faizal Ab Razak, Mohd Firdaus, Ahmad Izham Jaya, M. Ti Dun, Yau University Malaysia Pahang Faculty of Computing College of Computing and Applied Science Pahang Pekan26600 Malaysia SysArmy Sdn Bhd Technical Department Kuala Lumpur Malaysia

ISBN: (纸本)9781665414074

With the advancement of network technology, users can now easily gain access to and benefit from networks. However, the number of network violations is increasing. The main issue with this violation is that irresponsible individuals are infiltrating the network. Network intrusion can be interpreted in a variety of ways, including cyber criminals forcibly attempting to disrupt network connections, gaining unauthorized access to valuable data, and then stealing, corrupting, or destroying the data. There are already numerous systems in place to detect network intrusion. However, the systems continue to fall short in detecting and counter-attacking network intrusion attacks. This research aims to enhance the detection of Denial of service (DoS) by identifying significant features and identifying abnormal network activities more accurately. To accomplish this goal, the study proposes an Intrusion Analysis System for detecting Denial of service (DoS) network attacks using machine learning. The accuracy rate of the proposed method using random forest was demonstrated in our experimental results. It was discovered that the accuracy rate with each dataset is greater than 98.8 percent when compared to traditional approaches. Furthermore, when features are selected, the detection time is significantly reduced. © 2021 IEEE.

关键词： Malware

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Enhancing Collaborative and Geometric Multi-Kernel Learning Using Deep Neural Network

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Computers, Materials & Continua 2022年第9期72卷 5099-5116页

作者： Bareera Zafar Syed Abbas Zilqurnain Naqvi Muhammad Ahsan Allah Ditta Ummul Baneen Muhammad Adnan Khan Department of Mechatronics and Control Engineering University of Engineering and TechnologyLahore54890Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department Software Gachon UniversitySeongnam13120Korea

This research proposes a method called enhanced collaborative andgeometric multi-kernel learning (E-CGMKL) that can enhance the CGMKLalgorithm which deals with multi-class classification problems with non-lineardata distributions. CGMKL combines multiple kernel learning with softmaxfunction using the framework of multi empirical kernel learning (MEKL) inwhich empirical kernel mapping (EKM) provides explicit feature constructionin the high dimensional kernel space. CGMKL ensures the consistent outputof samples across kernel spaces and minimizes the within-class distance tohighlight geometric features of multiple classes. However, the kernels constructed by CGMKL do not have any explicit relationship among them andtry to construct high dimensional feature representations independently fromeach other. This could be disadvantageous for learning on datasets with complex hidden structures. To overcome this limitation, E-CGMKL constructskernel spaces from hidden layers of trained deep neural networks (DNN).Due to the nature of the DNN architecture, these kernel spaces not onlyprovide multiple feature representations but also inherit the compositionalhierarchy of the hidden layers, which might be beneficial for enhancing thepredictive performance of the CGMKL algorithm on complex data withnatural hierarchical structures, for example, image data. Furthermore, ourproposed scheme handles image data by constructing kernel spaces from aconvolutional neural network (CNN). Considering the effectiveness of CNNarchitecture on image data, these kernel spaces provide a major advantageover the CGMKL algorithm which does not exploit the CNN architecture forconstructing kernel spaces from image data. Additionally, outputs of hiddenlayers directly provide features for kernel spaces and unlike CGMKL, do notrequire an approximate MEKL framework. E-CGMKL combines the consistency and geometry preserving aspects of CGMKL with the compositionalhierarchy of kernel spaces extracted from DNN hidde

关键词： CGMKL multi-class classification deep neural network multiplekernel learning hierarchical kernel spaces

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Retraction notice to "The investigation of energy management and atomic interaction between coronavirus structure in the vicinity of aqueous environment of H2O molecules via molecular dynamics approach" [J. Mol. Liq. 341 (2021) 117430] (Journal of Molecular Liquids (2021) 341, (S0167732221021541), (10.1016/***.2021.117430))

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Journal of Molecular Liquids 2025年 428卷

作者： Guo, Hui-Hui Bajuri, Mohd Yazid Alrabaiah, Hussam Muhammad, Taseer Mohammad Sajadi, S. Ghaemi, Ferial Baleanu, Dumitru Karimipour, Arash Zhejiang Provincial Key Laboratory of Media Biology and Pathogenic Control Central Laboratory The First Affiliated Hospital of Huzhou University Zhejiang Huzhou313000 China Kuala Lumpur Malaysia College of Engineering Al Ain University Al Ain United Arab Emirates Department of Mathematics College of Science Tafila Technical University Tafila Jordan Department of Mathematics College of Sciences King Khalid University Abha61413 Saudi Arabia Department of Nutrition Cihan University-Erbil Kurdistan Region Iraq Department of Phytochemistry SRC Soran University KRG Iraq Selangor Bangi43600 Malaysia Department of Mathematics Faculty of Arts and Sciences Cankaya University Ankara Turkey Institute of Space Sciences Magurele Romania Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan Department of Mechanical Engineering Sapienza University of Rome Rome Italy

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://***/about/policies/article-withdrawal). This article has been retracted at the request of the Editor-in-Chief. Post-publication, the editor discovered suspicious changes in authorship between the original submission and the revised version of this paper. During revision one author was removed. The author Hussam Rabai'ah was added to the revised paper without explanation and without the exceptional approval by the journal editor, which is contrary to the journal policy on changes to authorship. In addition, there are serious concerns about images, specifically: Figures 2,3, 5 and 6 and the relevancy of some of the citations. The editor reached out to the authors for an explanation, but they failed to provide a satisfactory explanation to these issues. Post-publication, an investigation conducted on behalf of the journal by Elsevier's Research Integrity & Publishing Ethics team also discovered that acceptance of this article was solely based upon the positive advice of a reviewer who was closely linked to one of the authors, Arash Karimipour. This compromised the editorial process and breached the journal's policies. The Ethics team has also determined that the authors were requested by one of the reviewers to insert redundant references to their papers during the peer-review process. The investigation also discovered suspicious email addresses used by the authors during submission that were not associated with legitimate researcher accounts. Overall, the editor has determined that the authorship and the findings of the article cannot be relied upon, and has decided to retract the article. The authors disagree with the retraction of the article and dispute the grounds for it. © 2025 Elsevier B.V.

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Graphical framework-supported digital design 6

Graphical framework-supported digital design

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6th IFAC Workshop on Programmable Devices and Systems, PDS 2003

作者： Kasik, Vladimir Department of Measurement and Control Faculty of Electrical Engineering and Computer Science VSB Technical University Ostrava Ostrava Czech Republic

The architecture features of today's FPGA devices together with a capability for implementing highly parallel arithmetic architectures makes the them ideally suited for creating high-performance custom data path processors for DSP applications and many others. In this project an open program system for digital design applications, suited to Programmable logic devices, especially Field Programmable Gate Arrays (FPGA) is presented. Fundamental implementation idea is based on usage of Matlab and its integrated part - Simulink. Copyright © 2003 IFAC.

关键词： Computer circuits

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Development of embedded control system for mobile objects using UML

Development of embedded control system for mobile objects us...

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2004 IFAC Workshop on Programmable Devices and Systems, PDS 2004

作者： Kotzian, Jiří Srovnal, Vilém VŠB - Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Department of Measurement and Control Czech Republic

This paper present methodology of developing a microprocessor based embedded system for the distributed control system. The control system is realized for controlling of small electro mobile. The system controls speed, direction, breaking, etc. There is used wireless communication. Methodology of developing is based on Unified Modelling Language UML. Whole system is modelled on PC and then implemented to given target. We are using 68HC12 Motorola microprocessors and RiMC UML modelling environment from I-logix. Distributed control system is based on CAN bus, which is also modelled. © 2004 IFAC.

关键词： Embedded systems

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