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Proceedings of the Winter Simulation Conference

作者： David Tian Hazel Y. Squires Charlotte Buckley Duncan Gillespie Harry Tattan-Birch Lion Shahab Robert West Alan Brennan Jamie Brown Robin C. Purshouse Dept. of Automatic Control and Systems Engineering University of Sheffield UK Sheffield Centre for Health and Related Research School of Medicine and Population Health University of Sheffield UK Institute of Epidemiology and Health University College London UK

ISBN: (纸本)9798331534202

Modeling trajectories in cigarette smoking prevalence, initiation and quitting for populations and subgroups of populations is important for policy planning and evaluation. This paper proposes an agent-based model (ABM) design for simulating the smoking behaviors of a population using the Capability, Opportunity, Motivation - Behavior (COM-B) model. Capability, Opportunity and Motivation are modeled as latent composite attributes which are composed of observable factors associated with smoking behaviors. Three forms of the COM-B model are proposed to explain the transitions between smoking behaviors: initiating regular smoking uptake, making a quit attempt and quitting successfully. The ABM design follows object-oriented principles and extends an existing generic software architecture for mechanism-based modeling. The potential of the model to assess the impact of smoking policies is illustrated and discussed.

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One-step Local Feature Extraction using CNN

One-step Local Feature Extraction using CNN

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2020 IEEE International Conference on Networking, Sensing and control, ICNSC 2020

作者： Zhou, Yunpeng Zhu, Zhangqing Xin, Bo Nanjing University Dept. Control and Systems Engineering Nanjing China

ISBN: (纸本)9781728168531

We propose a one-step Local Feature Extraction Network framework to solve the sparse feature matching problem. In our network, we use raw camera data and the Structure from Motion (SfM) algorithm to restore the corresponding relationships of the different feature map. Our network combines the detector and descriptor as one step to build an end-to-end Local Feature Extraction network. At the same time, the whole process is differentiable and we train our network by the loss of feature map. Finally, we train our network on indoor datasets and prove its accuracy and rapidity advantage over other methods. © 2020 IEEE.

关键词： Convolutional neural networks

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Plant model frequency scale decomposition for identification and control design 3

Plant model frequency scale decomposition for identification...

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3rd International Conference on Industrial Artificial Intelligence, IAI 2021

作者： Albertos, Pedro Esparza, Alicia Instituto de Automática e Informática Industrial Universitat Politècnica de València Dept. of Systems Engineering and Control Spain

ISBN: (纸本)9781665435178

There are processes whose dynamic behavior is defined at different frequencies, their models being difficult to deal with as a whole. The modeling and the control design procedures can be simplified if the process is split in different components characterizing its behavior in a given range of frequencies. This idea was successfully applied in the iterative identification and control design strategy and it was reported in some previous papers. In this paper, assuming the full model of the plant, the control is designed dealing with partial process models being appropriate to represent the plant behavior in the frequency range where this control is intended to act. This brings some advantages: first, the control design is simplified as it only takes care of a range of frequencies. Moreover, in order to save resources, the control can be implemented in a multirate scheme. Several examples of processes with flexible components are considered to design their control. © 2021 IEEE.

关键词： Frequency domain analysis

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USDOT Tier-1 University Transportation Center for Advancing Cybersecurity Research and Education

USDOT Tier-1 University Transportation Center for Advancing ...

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Mobility, Operations, Services and Technologies (MOST), IEEE International Conference on

作者： Yunpeng Jack Zhang Kailai Wang Lingguang Song Amaury Lendasse Houbing Herbert Song Zhu Han Rakesh Verma Arlei Silva Carlos Rubio-Medrano Zhixia Richard Li Guohui Zhang Dept. of Information Science Technology University of Houston Houston TX USA Dept. of Construction Management University of Houston Houston TX USA Dept. of Information Systems University of Maryland Baltimore County Baltimore MD USA Dept. of Electrical & Computer Engineering University of Houston Houston TX USA Dept. of Computer Science University of Houston Houston TX USA Dept. of Computer Science Rice University Houston TX USA Dept. of Computer Science Texas A&M University-Corpus Christi Corpus Christi TX USA Dept. of Civil and Architectural Engineering and Construction Management University of Cincinnati Cincinnati OH USA Dept. Civil and Environmental Engineering University of Hawai’i at Mānoa Honolulu HI USA

The Transportation Cybersecurity Center for Advanced Research and Education (CYBER-CARE) is a US Department of Transportation (USDOT) Tier-1 University Transportation Center (UTC) funded in 2023. CYBER-CARE primarily focuses on the USDOT statutory research priority area of “Reducing Transportation Cybersecurity Risks.” CYBER-CARE aims to establish a fundamental knowledge basis and explore advanced theory to mitigate the impacts of large-scale cyberattacks on transportation infrastructure and connected and automated vehicle (CAV) systems. The research projects at CYBER-CARE will develop conceptual frameworks, construct comprehensive datasets, explore novel analytical approaches, support the implementation of public policies and infrastructure investments, and build a high-quality industry workforce through education. All CYBER-CARE research projects can be organized into four thrusts: CAV cybersecurity, transportation data security, advanced traffic management system (ATMS) cybersecurity, and next-generation transportation cybersecurity systems. In addition, CYBER-CARE will accelerate industry collaborations, foster new technologies, and provide professionals with the skills and opportunities needed to become successful leaders in their fields. Notably, as CYBER-CARE will prioritize engagement with underrepresented minorities, these communities stand to benefit from professional development training in transportation cybersecurity.

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Cross-Coupled Iterative Learning control for Complex systems: A Monotonically Convergent and Computationally Efficient Approach *

Cross-Coupled Iterative Learning Control for Complex Systems...

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IEEE Conference on Decision and control

作者： Leontine Aarnoudse Johan Kon Koen Classens Max van Meer Maurice Poot Paul Tacx Nard Strijbosch Tom Oomen Dept. of Mechanical Engineering Control Systems Technology Eindhoven University of Technology Eindhoven The Netherlands IBS Precision Engineering Eindhoven The Netherlands Delft Center for Systems and Control Delft University of Technology Delft The Netherlands

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Cross-coupled iterative learning control (ILC) can achieve high performance for manufacturing applications in which tracking a contour is essential for the quality of a product. The aim of this paper is to develop a framework for norm-optimal cross-coupled ILC that enables the use of exact contour errors that are calculated offline, and iteration-and time-varying weights. Conditions for the monotonic convergence of this iteration-varying ILC algorithm are developed. In addition, a resource-efficient implementation is proposed in which the ILC update law is reframed as a linear quadratic tracking problem, reducing the computational load significantly. The approach is illustrated on a simulation example.

关键词： Simulation Computational modeling Aerospace electronics Manufacturing Computational efficiency Printers Complex systems

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

arXiv

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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

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Impact of Dataset Balancing on Machine Learning-Based Intrusion Detection systems

Impact of Dataset Balancing on Machine Learning-Based Intrus...

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IEEE International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA)

作者： Muhammad Iqbal Yusri Mohamed Hadi Habaebi Teddy Surya Gunawan Hasmah Mansor Mira Kartiwi Levy Olivia Nur Electrical and Computer Engineering Dept. International Islamic University Malaysia Kuala Lumpur Malaysia Information Systems Department International Islamic University Malaysia Kuala Lumpur Malaysia School of Electrical Engineering Telkom University Bandung Indonesia

ISBN: (数字)9798350365283

ISBN: (纸本)9798350365290

Intrusion Detection systems (IDS) are indispensable for cybersecurity, as they safeguard networks from increasingly sophisticated and sophisticated cyberattacks. This paper assesses the influence of dataset balancing on the performance of machine learning-based IDS, thereby addressing the challenge of imbalanced data in detecting network intrusions. We concentrate on three IDS implementations: Tree-based Intelligent IDS, Multi-Tiered Hybrid IDS (MTH-IDS), and Leader Class and Confidence Decision Ensemble (LCCDE). We utilized the Synthetic Minority Over-Sampling Technique (SMOTE) to balance data and implemented feature selection and hyperparameter optimization to improve the model's performance using the CICIDS 2017 dataset. The LCCDE model exhibits the highest performance, as our comparative analysis demonstrates that the combination of SMOTE and feature selection enhances the F1 scores. The results underscore the significance of advanced ensemble techniques and data preprocessing in developing resilient IDS. This research emphasizes the necessity of ongoing optimization and evaluation of IDS models to guarantee effective protection against the development of cyber threats.

关键词： Heuristic algorithms System performance Roads Intrusion detection Feature extraction Real-time systems Delays

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Extended Adaptive Data-Rate (X-ADR) Technique for Optimal Resource Allocation in Smart City Applications

Extended Adaptive Data-Rate (X-ADR) Technique for Optimal Re...

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IEEE International Symposium on World of Wireless Mobile and Multimedia Networks (WoWMoM)

作者： Nikumani Choudhury Manik Gupta Moustafa M. Nasralla Satoshi Fujita Dept. of Computer Science and Information Systems Birla Institute of Technology & Science Pilani Hyderabad India Dept. of Communications & Networks Engineering Prince Sultan University Riyadh Saudi Arabia Graduate School of Advanced Science and Engineering Hiroshima University Japan

With the rapid growth of urbanization, the need for sustainable, energy-efficient, and smart solutions for home, industry, governance, traffic, and in general, the need for improved quality of life and health has risen. As an enabling technology, the Internet of Things (IoT) must facilitate several advanced applications with varied QoS requirements for smart cities. In this regard, Long Range (LoRa) technologies can be an ideal communication protocol for resource-constrained IoT devices. The LoRa Wide Area Network (LoRaWAN) defines physical layer options and the medium access control (MAC) sub-layer protocols for facilitating low-power, low-rate communications among battery-operated wireless IoT devices. With the enormous number of IoT devices and growing QoS requirements, it is imperative to optimally allocate resources (bandwidth, spreading factor, and transmit power) to these constrained devices so that network lifetime can be improved. The Adaptive Data-Rate (ADR) technique is used by the Network Servers (NS) to adapt the transmission parameters of the devices optimally. This is based on several received parameters from the end devices as well as the network settings. In this work, we extend the ADR technique to the LoRa gateways to consider the class of LoRa devices and the frequency of transmissions further to extend the energy efficiency and lifespan of IoT devices. Additionally, there is a considerable delay in adapting to optimal settings for the end-devices resulting in excess power dissipation. Further, a novel mechanism is proposed to address the congestion issue in the network using the Spreading Factor parameter (SF).

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Cross-Coupled Iterative Learning control for Complex systems: A Monotonically Convergent and Computationally Efficient Approach

arXiv

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arXiv 2022年

作者： Aarnoudse, Leontine Kon, Johan Classens, Koen van Meer, Max Poot, Maurice Tacx, Paul Strijbosch, Nard Oomen, Tom The Dept. of Mechanical Engineering Control Systems Technology Eindhoven University of Technology Eindhoven Netherlands IBS Precision Engineering Eindhoven Netherlands The Delft Center for Systems and Control Delft University of Technology Delft Netherlands

Cross-coupled iterative learning control (ILC) can achieve high performance for manufacturing applications in which tracking a contour is essential for the quality of a product. The aim of this paper is to develop a framework for norm-optimal cross-coupled ILC that enables the use of exact contour errors that are calculated offline, and iteration- and time-varying weights. Conditions for the monotonic convergence of this iteration-varying ILC algorithm are developed. In addition, a resource-efficient implementation is proposed in which the ILC update law is reframed as a linear quadratic tracking problem, reducing the computational load significantly. The approach is illustrated on a simulation example. Copyright © 2022, The Authors. All rights reserved.

关键词： Iterative methods

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Feedback Optimizing MPC for Load Frequency control and Economic Dispatch

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IFAC-PapersOnLine 2023年第2期56卷 10935-10940页

作者： Amba Asuk Paul Trodden National Grid ESO Faraday House Warwick CV34 6DA UK Dept. of Automatic Control & Systems Engineering University of Sheffield S1 3JD UK

With increasing renewable generation, demand response, and deregulation, power networks are becoming more uncertain, time-varying, and strongly coupled. As a result, the conventional approach of performing separate economic dispatch (ED) and load-frequency control (LFC) operations may no longer guarantee smooth and cost-efficient regulation of frequency across interconnected power networks. To address this, we present a tracking model predictive control (MPC) algorithm which simultaneously achieves economic dispatch and secondary frequency control in a multi-area power network. A unique feature of the proposed algorithm is that it exploits the implicit feedback in MPC to regulate the interconnected power system towards steady-state equilibria that solve a multi-area economic dispatch problem, without explicitly computing the latter as a reference to be followed or estimating the unknown disturbances. This feedback-based optimization approach endows the algorithm with inherent robustness to uncertainty (such as unknown step changes in the demand). Simulation results for a two-area power network show improved steady-state economic performance compared to standard MPC-based frequency control schemes, and better dynamic performance compared to other feedback-based optimization schemes.

关键词： control and optimization of power systems Renewable energy sources Smart grids control of energy storage systems Power system planning and operation

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