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Decentralized, Distributed, and Hybrid ICT Architectures: Hierarchical Multitier Big data driven Management for Smart, Sustainable, Scalable and Reliable Cities 11

Decentralized, Distributed, and Hybrid ICT Architectures: Hi...

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conference on Technologies for Sustainability (SusTech)

作者： Sinaeepourfard, Amir Shaik, Shehenaz Mesgaribarzi, Niusha IEEE Piscataway NJ 08854 USA E Tennessee State Univ Johnson City TN USA Univ Sorost Norge USN Notodden Norway

ISBN: (纸本)9798350394351;9798350394344

A smart city hopes to intelligently manage a city's resources to address urban challenges and complexities through urban development and digital transformation strategies. This involves the participation of all stakeholders, including private and public sectors and city residents, in addition to the municipality itself. data is an essential resource in smart cities, often called green oil. To effectively organize data in smart cities, managing all data lifecycle stages, from creation to consumption, is essential, emphasizing the critical role of information and communications technology (ICT) and information technology (IT) in resource and data management. Sustainable technology focuses on designing, organizing, and managing Internet of Things (IoT) technologies within ICT and IT architectural solutions in smart cities, addressing management of data/databases, resources, network communication, cybersecurity issues, and software services development. This paper reviews various smart city ICT architectures, such as centralized, decentralized-to-centralized (DC2C-ICT), distributed-to-centralized (D2C-ICT), and hybrid-ICT architectures. This discussion comprehensively covers key concepts of big data-driven architecture, including various design perspectives, management, control, and monitoring systems, as well as a range of ICT/IoT technologies and techniques. It involves organizing city data within IoT networks, utilizing multiple computing nodes and platforms in edge-to-cloud orchestration. Emphasis is on the convergence and integration of ICT, IoT, and Big data with artificial intelligence (AI) and machine learning for efficient organization and management. The paper introduces an innovative big data-driven architecture for hierarchical multitier ICT management in smart city IoT networks, rooted in edge-to-cloud orchestration. This architecture can incorporate diverse ICT and IoT technologies across different multilayer ICT architectures over time, making it adaptabl

关键词： Smart City IoT Sustainable Technology data Life Cycle model ICT Architecture Big data driven Architecture Big data driven control and Monitoring System

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Transformer-driven Resource Allocation for Enhanced Multi-Carrier NOMA Downlink 25

Transformer-Driven Resource Allocation for Enhanced Multi-Ca...

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ieee Wireless Communications and Networking conference (ieee WCNC)

作者： Dong, Liang (Leon) Baylor Univ Dept Elect & Comp Engn Waco TX 76798 USA

ISBN: (纸本)9798350303582;9798350303599

This paper presents a transformer-driven resource allocation strategy to optimize channel assignment and power allocation in multi-carrier non-orthogonal multiple access (NOMA) downlink systems, aiming to maximize the sum data rate. Our approach configures the system to support two users per channel. We employ an attention-based deep learning model, specifically an encoder-only transformer, to process the channel-gain-to-noise ratio matrix and derive the optimal channel-assignment matrix. The transformer model includes a tailored loss function that addresses the requirements of the output channel-assignment matrix, precisely steering the optimization to meet the system demands. Simulation results demonstrate the superior performance of our method, confirming its effectiveness in enhancing wireless communication optimization through the integration of deep learning techniques.

关键词： Multi-carrier NOMA resource allocation channel assignment power allocation transformer deep learning

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data-driven Modeling of Comprehensive Academic Level and Physical/Moral Education for Students in control Discipline 36

Data-driven Modeling of Comprehensive Academic Level and Phy...

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36th Chinese control and Decision conference (CCDC)

作者： Zhao, Ziyan Li, Shuangru Li, Shipeng Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China

ISBN: (纸本)9798350387780;9798350387797

Physical education and moral education, as two important components, have a profound impact on the overall development of students. This work deeply studies the relationship between physical/moral education and students' comprehensive academic level through data-driven modeling. Based on students' grades in physical/moral-related courses, three machine learning models, logistic regression, support vector machine and random forest, are constructed to realize the high-accuracy classification of the students having different comprehensive academic level. Experimental results show that the constructed data-driven models can achieve significant accuracy and stability in predicting students' comprehensive academic level. It proves that physical education and moral education have obvious positive correlation with students' comprehensive academic level, and thus highlights the importance of physical/moral education in students' all-round development.

关键词： five education simultaneously machine learning data-driven modeling academic level prediction

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Image Recognition of Chinese Herbal Medicine Using Adaptive Gamma Correction Based on Convolutional Neural Network 13

Image Recognition of Chinese Herbal Medicine Using Adaptive ...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Ji, Honghai Liu, Xinjing Wang, Li Fan, Lingling Liu, Shida School of Electronics & Control Engineering North China University of Technology Beijing100144 China School of Automation Beijing Information Science and Technology University Beijing100096 China

ISBN: (纸本)9798350361674

Chinese herbal medicine (CHM) has gained significant attention due to its potential in treating various diseases. CHM image classification is crucial in fields such as smart medical care and medical teaching. However, accurately classifying CHM images has been a challenging task due to their fine-grained variability. To achieve automatic identification and classification of Chinese herbal medicines, we designed a method that combines image processing and deep learning. We established a dataset of CHM images, which includes 10 kinds of common Chinese herbal medicines, with 15,719 images in total. The dataset covers medicinal materials in various natural backgrounds and provides ample samples for training deep learning models. Regarding image processing, we proposed a method based on adaptive gamma correction(AGC) and compared it with CLAHE(Contrast Limited Adaptive Histogram Equalization)and traditional gamma correction(TGC) methods. Subsequently, we employed three pre-training models of convolutional neural networks based on transfer learning, namely GoogLeNet, ResNet-18, and MobileNetV2, to identify CHM images. The results of the verification indicate that the AGC-CNN model outperforms the CLAHE-CNN and TGC-CNN models in terms of classification accuracy and image quality improvement. © 2024 ieee.

关键词： Contrastive learning

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A Cyberphysical System for data-driven Real-Time Traffic Prediction on the Las Vegas I-15 Freeway

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ieee INTELLIGENT TRANSPORTATION systems MAGAZINE 2023年第1期15卷 23-35页

作者： Guzman, Jose A. Morris, Brendan T. Nunez, Felipe Pontificia Univ Catolica Chile Santiago 7820436 Chile Univ Nevada Las Vegas NV 89119 USA

Mobility and transportation services in modern large-scale cities face traffic congestion as one of the main sources of discomfort and economic losses. In this context, taking preventive measures based on traffic predictions looks like an appealing alternative to mitigate congestion. The increasing availability of detectors in the transportation infrastructure has allowed tackling the traffic prediction problem by using a purely data-driven approach, where deep learning models have excelled. Unfortunately, the implementation of these techniques in real time is still under development. This work presents the implementation of a real-time traffic prediction application in the Las Vegas, NV, USA, urban area, built as a cyberphysical system with real-time data streaming from field sensors to a cloud-like environment where deep learning-based traffic predictors are hosted. Implementation results show the feasibility of doing traffic prediction in real time with the current technology and the usefulness of periodic retraining to maintain prediction accuracy.

关键词： Detectors Predictive models Traffic control data models Real-time systems Simple object access protocol Urban areas

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Slow Feature Thermography for Non-Destructive Evaluation of Defects in Composite Materials 13

Slow Feature Thermography for Non-Destructive Evaluation of ...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Liu, Kaixin Zhong, Xin Chen, Ping Dai, Yun Liu, Yi North University of China The State Key Lab for Electronic Testing Technology China North University of China Department of Information and Communication Engineering Taiyuan030051 China College of Mechanical Engineering Nantong Vocational University Nantong226007 China Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China

ISBN: (纸本)9798350361674

In recent years, various methods for processing and analyzing thermographic data have been applied in non-destructive testing to improve the visibility of defects. Representatively, traditional linear dimensionality reduction methods (e.g. principal component analysis) have achieved good results in thermographic data analysis. In this work, a slow feature thermography (SFT) method for non-destructive evaluation of defects in composite materials. Thermographic data can be regarded as a continuous time series generated by an infrared imager at different moments. Slow feature analysis algorithm is subsequently used to extract slow features of the thermal image sequence. Slow features refer to features that are unchanged or slowly changing in time-series data, such as the background of a thermal image. By extracting the slow features of data, the inhomogeneous background, noise and defect features contained in thermal images can be effectively distinguished. Test results on carbon fiber reinforced polymer specimens demonstrate the feasibility and effectiveness of the SFT method. © 2024 ieee.

关键词： Carbon fiber reinforced plastics

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Design and Stability Analysis of Vehicle Platooning control in Zero-Trust Environment 13

Design and Stability Analysis of Vehicle Platooning Control ...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Zhu, Ling Huang, Darong Na, Yuhong Li, Xinrong Institute of Information Science and Engineering Chongqing Jiao Tong University Chongqing400074 China School of Artificial Intelligence Anhui University Hefei230001 China

ISBN: (纸本)9798350361674

Vehicle platoon control constitutes a pivotal aspect of intelligent transportation system development, most of the previous research on vehicle platoon control is carried out under mutual default trust, without considering the trust factor between interconnected vehicles, the trust factor between vehicles has a significant influence on the cooperative behavior of the vehicles, the way of information sharing, and the security of the vehicles, etc. In the actual traffic scenario, vehicle cooperative process is often In real transportation scenarios, the process of vehicle collaboration is often subject to various network attacks, so vehicle collaboration is always facing security threats. Zero-trust network is a kind of de-boundary network, which requires individuals in the network to 'never trust and verify in real-time', and vehicle platoon control can be placed in the zero-trust environment to avoid these problems. Firstly, we employ the C-F uncertainty inference method to assess the trust of nodes of interconnected vehicles. Secondly, we constructed a vehicle platoon dynamics model containing node trust, and based on this, we constructed a vehicle platoon dynamics model containing node trust, and designed a distributed controller. Finally, the effectiveness of the method proposed in this paper is verified by numerical simulation experiments. © 2024 ieee.

关键词： Maneuverability

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Formal Synthesis of Safety controllers for Unknown systems Using Gaussian Process Transfer learning

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ieee control systems LETTERS 2023年 7卷 3741-3746页

作者： Awan, Asad Ullah Zamani, Majid Tech Univ Munich Dept Elect & Comp Engn D-80333 Munich Germany Ludwig Maximilian Univ Munich Dept Comp Sci D-80539 Munich Germany Univ Colorado Boulder Dept Comp Sci Boulder CO 80309 USA

In this letter, we propose a data-driven approach for synthesizing safety controllers for unknown nonlinear control systems using Gaussian Process (GP) transfer learning. Our approach involves two steps. The first step involves learning a GP model using data sampled from the system. Our method allows for leveraging a previously learned GP model of a related system, known as the source system (e.g., robot deployed in slightly different environmental conditions), to learn a GP model for the system at hand, known as the target system. This is required in situations where data collection for the target system is expensive or time consuming. In the second step, we compute a control barrier function together with a corresponding controller based on the learned GP model. In addition, we quantify the lower bound on the probability of safety satisfaction for the target system equipped with the synthesized controller. We demonstrate the effectiveness of the proposed approach by applying it to a jet engine case study.

关键词： Formal synthesis Gaussian process safety controllers transfer learning control barrier certificates

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Integrating Multidimensional Teaching-learning Methods: A Trans-disciplinary Approach to Engineering Education driven by Advances in Neurosciences

Integrating Multidimensional Teaching-Learning Methods: A Tr...

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Latin American Electron Devices conference (LAEDC)

作者： Villavicencio, Liliana M. Singh, Pallavi Cruz, Luis Fernando Quevedo, Luis Miguel M. Moreno, Wilfrido Alejandro Univ S Florida Elect Engn Dept Tampa FL 33620 USA Univ Manuela Beltran CCR Res Grp Bogota Colombia ISA Tampa FL USA IEEE Tampa FL USA

ISBN: (纸本)9798350316384;9798350316377

This paper underlines the crucial role of transdisciplinary approaches in overcoming complex educational challenges to accelerate learning and meet semiconductor industry workforce needs by combining neurosciences, psychology, education, and engineering. It examines the brain's learning processes, focusing on sensory and neuroendocrine pathways, and adopts a neuroscience approach from a control systems perspective to enhance personalized learning. Using an unconventional analogy between human learning and industrial processes, the study introduces the Psychophysiological-Based Hypermedia Adaptive Automation System (PHAS) model. Employing Model-Based systems Engineering (MBSE) with systems Modeling Language (SysML), it develops the Engineering learning Analytic System ( ELAS) framework, featuring a rigorous Verification and Validation (VV) process to align stakeholder needs with system capabilities. ELAS simulations offer predictive insights into soft skill development, facilitating targeted educational interventions. The study aims to develop comprehensive engineers with critical thinking, creativity, problem- solving, communication, and teamwork skills, aligning with initiatives such as the Chips for America by integrating Multidimensional Teaching/learning Methods to create more effective, inclusive, and holistic educational systems.

关键词： Adaptive control systems Integral Engineer Brain Computer Interface (BCI) Model-Based systems Engineering systems Modeling Language (SysML) Neurosciences Educational Management Engineering learning Analytic System (ELAS) Soft Skill Development Personalized learning Automation Pyramid

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Steady-State Error Compensation for Reinforcement learning-Based control of Power Electronic systems

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ieee ACCESS 2023年 11卷 76524-76536页

作者： Weber, Daniel Schenke, Maximilian Wallscheid, Oliver Paderborn Univ Dept Power Elect & Elect Drives D-33098 Paderborn Germany

data-driven approaches like reinforcement learning (RL) allow a model-free, self-adaptive controller design that enables a fast and largely automatic controller development process with minimum human effort. While it was already shown in various power electronic applications that the transient control behavior for complex systems can be sufficiently handled by RL, the challenge of non-vanishing steady-state control errors remains, which arises from the usage of control policy approximations and finite training times. This is a crucial problem in power electronic applications which require steady-state control accuracy, e.g., voltage control of grid-forming inverters or accurate current control in motor drives. To overcome this issue, an integral action state augmentation for RL controllers is introduced that mimics an integrating feedback and does not require any expert knowledge, leaving the approach model free. Therefore, the RL controller learns how to suppress steady-state control deviations more effectively. The benefit of the developed method both for reference tracking and disturbance rejection is validated for two voltage source inverter control tasks targeting islanded microgrid as well as traction drive applications. In comparison to a standard RL setup, the suggested extension allows to reduce the steady-state error by up to 52% within the considered validation scenarios.

关键词： ~control disturbance rejection power electronic systems reference tracking reinforcement learning steady-state error

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