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American Control Conference (ACC)

作者： Hongli Zhong Zhixiong Zhong Zhijie Huan Hak-Keung Lam School of Computer and Big Data Minjiang University Fuzhou China School of Electrical Engineering and Automation Xiamen University of Technology Xiamen China Department of Engineering King's College London London United Kingdom

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

This paper investigates a novel dual-loop slidingmode control (SMC) scheme to achieve trajectory tracking for a class of nonlinear systems with unknown uncertainties. The inner loop is control loop, which consists of three controllers. An optimal feedback controller based on solving Hamilton-Jacobi-Bellman equation is proposed, which optimizes system performance for the nominal tracking system. A cerebellar model articulation control (CMAC) neural network is introduced for approximating the unknown uncertainties, and is embedded in the so-called CMAC-based sliding-mode controller. An adaptive compensator is used to dispel the negative effect from the approximated error of the CMAC. The outer loop is learning loop, which consists of the CMAC-based approximator and the quasi-sliding-model learning law. We formulate the learning problem of CMAC into a robust control framework of discretetime nonlinear system. A new online learning law based on discrete-time quasi-SMC strategy is developed to ensure the global and fast convergence and eliminates the chattering. It can be shown that the explicit expressions of gain parameters of the dual-loop sliding-mode scheme are derived in terms of the established sufficient conditions. Our proposed techniques are applied to a salient permanent magnet synchronous motor with excellent performance.

关键词： Robust control Sufficient conditions Uncertain systems Uncertainty Trajectory tracking System performance Neural networks

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Exploring the Impact of Real and Synthetic Data in Image Classification: A Comprehensive Investigation Using CIFAKE Dataset

Exploring the Impact of Real and Synthetic Data in Image Cla...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Amila Akagic Emir Buza Medina Kapo Mahdi Bohlouli Department of Computer Science and Informatics Faculty of Electrical Engineering University of Sarajevo Sarajevo Bosnia and Herzegovina Research and Innovation Department Petanux GmbH Bonn Germany

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

This research explores into the utilization of synthetic data within image classification tasks and evaluates its efficiency in comparison to the utilization of real data. To facilitate this investigation, we employ the CIFAKE dataset, comprising the well-established CIFAR10 dataset and an equivalent number of images synthetically generated using the Latent Diffusion Model (LDM). The increasing demand for diverse and abundant labeled datasets has prompted the emergence of synthetic data as a potential solution to address data scarcity. Within this study, we scrutinize the performance of image classification models trained on both real and synthetic datasets. To ensure a comprehensive evaluation, we alternately apply test data across different models. Our analysis encompasses diverse factors, including classification accuracy, generalization capabilities, and robustness in various scenarios. The findings provide valuable insights into the efficacy of synthetic data as a viable alternative or complement to real data in the realm of image classification.

关键词： Training Measurement Neural networks Process control Data models Robustness Information technology Synthetic data Image classification Testing

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Sensitivity Analysis of Weighting Factors for Conditional and Importance Criteria in Gas Insulated Substation Health and Importance Indices

Sensitivity Analysis of Weighting Factors for Conditional an...

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International electrical engineering Congress (iEECON)

作者： Cattareeya Suwanasri Makkrapong Somboon Pongwisit Rattanasri Thanapong Suwanasri Department of Electrical and Computer Engineering Faculty of Ebgineering Bangkok Thailand The Sirindhorn International Thai-German Graduate School of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

The effective maintenance of Gas Insulated Switchgear (GIS) in high-voltage (HV) substations is essential for ensuring system reliability and minimizing unplanned downtime. The percentage Health and Importance Indices (%HI and %ImI) are key metrics used to assess the criticality of various factors influencing GIS performance, such as equipment age, maintenance history, insulation quality, environmental conditions, and operational performance. This research performs a sensitivity analysis on the weighting factors applied to the %HI and %ImI, examining how changes in these weightings affect the indices. Using a weighting and scoring technique, the analysis investigates the impact of varying each parameter’s weight on the overall %HI and %ImI, identifying the most sensitive factors that influence maintenance decisions. The actual data for both the conditional factors and importance criteria are applied in these calculations. Subsequently, the actual scores of six units GIS are computed using ten different weighting sets. The results demonstrate that the weighting values follow a linear approach, where higher weight values indicate a greater influence on the respective criterion. In addition, the sensitivity of the weighting factors significantly impacts the process of adjusting the range for %HI and %ImI, ultimately affecting GIS health and importance identification and informing maintenance planning.

关键词： electrical engineering Substations Sensitivity analysis Switchgear High-voltage techniques Maintenance Planning Gas insulation Indexes History

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A Cross-Domain Few-Shot Learning Method Based on Domain Knowledge Mapping

arXiv

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arXiv 2025年

作者： Chen, Jiajun Yin, Hongpeng Yang, Yifu School of Automation Chongqing University Chongqing400044 China Bradley Department of Electrical & Computer Engineering Virginia Polytechnic Institute and State University BlacksburgVA24061 United States

In task-based few-shot learning paradigms, it is commonly assumed that different tasks are independently and identically distributed (i.i.d.). However, in real-world scenarios, the distribution encountered in few-shot learning can significantly differ from the distribution of existing data. Thus, how to effectively leverage existing data knowledge to enable models to quickly adapt to class variations under non-i.i.d. assumptions has emerged as a key research challenge. To address this challenge, this paper proposes a new cross-domain few-shot learning approach based on domain knowledge mapping, applied consistently throughout the pre-training, training, and testing phases. In the pre-training phase, our method integrates self-supervised and supervised losses by maximizing mutual information, thereby mitigating mode collapse. During the training phase, the domain knowledge mapping layer collaborates with a domain classifier to learn both domain mapping capabilities and the ability to assess domain adaptation difficulty. Finally, this approach is applied during the testing phase, rapidly adapting to domain variations through meta-training tasks on support sets, consequently enhancing the model’s capability to transfer domain knowledge effectively. Experimental validation conducted across six datasets from diverse domains demonstrates the effectiveness of the proposed method. Copyright © 2025, The Authors. All rights reserved.

关键词： Domain Knowledge

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A spatiotemporal multi-scale computational model for FDG PET imaging at different stages of tumor growth and angiogenesis

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SCIENTIFIC REPORTS 2022年第1期12卷 1-16页

作者： Kashkooli, Farshad Moradi Abazari, Mohammad Amin Soltani, M. Ghazani, Mehran Akbarpour Rahmim, Arman Department of Mechanical Engineering K. N. Toosi University of Technology Tehran Iran Department of Electrical and Computer Engineering Faculty of Engineering School of Optometry and Vision Science Faculty of Science University of Waterloo Waterloo Canada Advanced Bioengineering Initiative Center Multidisciplinary International Complex K. N. Toosi University of Technology Tehran Iran Centre for Biotechnology and Bioengineering (CBB) University of Waterloo Waterloo ON Canada Faculty of Mechanical Engineering University of Tabriz Tabriz Iran Department of Integrative Oncology BC Cancer Research Institute Vancouver BC Canada Department of Physics and Astronomy University of British Columbia Vancouver BC Canada Department of Radiology University of British Columbia Vancouver BC Canada

A deeper understanding of the tumor microenvironment (TME) and its role in metabolic activity at different stages of vascularized tumors can provide useful insights into cancer progression and better support clinical assessments. In this study, a robust and comprehensive multi-scale computational model for spatiotemporal transport of F-18 fluorodeoxyglucose (FDG) is developed to incorporate important aspects of the TME, spanning subcellular-, cellular-, and tissue-level scales. Our mathematical model includes biophysiological details, such as radiopharmaceutical transport within interstitial space via convection and diffusion mechanisms, radiopharmaceutical exchange between intracellular and extracellular matrices by glucose transporters, cellular uptake of radiopharmaceutical, as well as its intracellular phosphorylation by the enzyme. Further, to examine the effects of tumor size by varying microvascular densities (MVDs) on FDG dynamics, four different capillary networks are generated by angiogenesis modeling. Results demonstrate that as tumor grows, its MVD increases, and hence, the spatiotemporal distribution of total FDG uptake by tumor tissue changes towards a more homogenous distribution. In addition, spatiotemporal distributions in tumor with lower MVD have relatively smaller magnitudes, due to the lower diffusion rate of FDG as well as lower local intravenous FDG release. Since mean standardized uptake value (SUVmean) differs at various stages of microvascular networks with different tumor sizes, it may be meaningful to normalize the measured values by tumor size and the MVD prior to routine clinical reporting. Overall, the present framework has the potential for more accurate investigation of biological phenomena within TME towards personalized medicine.

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Residual-driven Fuzzy C-Means Clustering for Image Segmentation

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IEEE/CAA Journal of Automatica Sinica 2021年第4期8卷 876-889页

作者： Cong Wang Witold Pedrycz ZhiWu Li MengChu Zhou Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6R 2V4Canada School of Electro-Mechanical Engineering Xidian UniversityXi’an 710071China the Faculty of Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia School of Electro-Mechanical Engineering Xidian UniversityXi’an 710071and also with the Institute of Systems EngineeringMacao University of Science and TechnologyMacaoChina Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China the Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA

In this paper,we elaborate on residual-driven Fuzzy C-Means(FCM)for image segmentation,which is the first approach that realizes accurate residual(noise/outliers)estimation and enables noise-free image to participate in *** propose a residual-driven FCM framework by integrating into FCM a residual-related regularization term derived from the distribution characteristic of different types of *** on this framework,a weighted?2-norm regularization term is presented by weighting mixed noise distribution,thus resulting in a universal residual-driven FCM algorithm in presence of mixed or unknown ***,with the constraint of spatial information,the residual estimation becomes more reliable than that only considering an observed image *** experiments on synthetic,medical,and real-world images are *** results demonstrate the superior effectiveness and efficiency of the proposed algorithm over its peers.

关键词： Fuzzy C-Means image segmentation mixed or unknown noise residual-driven weighted regularization

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A New Hybrid Approach Cluster-Heads Election to reduce the number of clusters in VANETs 5

A New Hybrid Approach Cluster-Heads Election to reduce the n...

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5th International Conference on engineering Technology and its Applications, IICETA 2022

作者： Abbas, Ali Hashim Abdulsattar, Nejood Faisal Mansour, Hassnen Shakir Mutar, Mohammed Hasan Habelalmateen, Mohammed I. Audah, L. Alduais, N.A.M. Mohammed, Admen College of Information Technology Imam ja'Afar Al-Sadiq University Department of Computer Technical Engineering Al-Muthanna 66002 Iraq Engineering College of Technical Engineering Islamic University Department of Computer Technical Najaf54001 Iraq Faculty of Electrical and Electronic Engineering Batu Pahat Malaysia Parit Raja Malaysia Al-Farahidi University Medical Technical College Baghdad Iraq

ISBN: (纸本)9781665472159

Vehicular Ad-hoc Networks (VANETs) is a subpart from mobile Ad-hoc networks (MANETs). The main idea from VANETs is to establish communication between vehicles for the safety of the people. However, mobility of vehicles appears to be the main challenge in VANETs. High speed of vehicles causes topology changed that results in an unstable network. Therefore, clustering is used to reduce the effect of vehicle mobility by combining vehicles in a group called cluster. Network stability is based on clusters stability. Therefore, most of the authors focused on increasing cluster stability to enhance the network. Nevertheless, increasing the number of the cluster in the network affects the network performance negatively. This paper proposes a new method called Hybrid Cluster-Head Election (HCHE) to reduce the number of the cluster in the network to increase network performance. Performance of the proposed method was evaluated through simulation by utilising real-time datasets. Arduino, GPS and external antennas were used to collect real GPS locations from Batu Pahat City - Johor - Malaysia. Overall, the proposed method resulted high performance in the reduction ratio of the number of cluster head compared to other recent methods. The proposed method successfully reduced the number of cluster heads by 30%. © 2022 IEEE.

关键词： Stability

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Microcomputer Prototyping of IEC61850-9-2 with Performance Analysis

Microcomputer Prototyping of IEC61850-9-2 with Performance A...

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IEEE International Conference on Smart Grid Communications (SmartGridComm)

作者： Nikolai Galkin Chen-Wei Yang Nicholas Etherden Valeriy Vyatkin Department of Computer Science Computer and Space Engineering Luleå Tekniska Universitet Sweden Vattenfall R&D Solna Sweden Department of Engineering Sciences and Mathematics Luleå Tekniska Universitet Sweden Department of Electrical Engineering and Automation Aalto University Espoo Finland

The reliability of an electrical system depends on layered Protection, automation, and Control (PAC) functions in which differential relays are considered a fundamental method to isolate the problem. By having a PAC architecture with functionality independent of hardware we hypothesize that the life cycle of PAC devices can be extended by using modern, powerful, and highly reconfigurable IoT devices. This can be achieved by applying modularity to hardware architecture, where an IoT device can be considered a mezzanine module. The Raspberry Pi and BeagleBone manufacturers are keeping the hardware the same size while constantly increasing its power. However, it is not clear how powerful the IoT device should be to meet some of the IEC 61850 requirements (which are usually included in PAC).In this article, we provide a step-by-step process for designing a system that mimics a single-phase transformer differential protection system. First, we provide a requirements analysis (Sampled Values rate, traffic synchronization, reaction time, etc.). Then we analyze the BeagleBone Black device (BBB). We give an example of an electrical system and finally analyze, in terms of IEC 61850 requirements, the performance of two software applications developed for the BBB (based on the operating system and on bare metal).

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IoT-Based Cultivation of Mitragyna Speciosa

IoT-Based Cultivation of Mitragyna Speciosa

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International electrical engineering Congress (iEECON)

作者： S. Meesomklin P. Ruaangchaiwuttikhun J. Duangprateep S. Saleekhao P. Akkaraekthalin Department of Electronics Engineering Technology College of Industrial Technology King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

This paper proposes a smart farm system, applying to cultivate mitragyna speciosa plants in Thailand. The greenhouse system with several sensors, microcontroller, and cultivation devices has been designed. The temperature, light intensity and moisture in the area of cultivation can be measured and the microcontroller has been used to read all data and then they are shown through the website. The cultivation data have been processed and then used for controlling temperature, light intensity and moisture inside the greenhouse. The proposed system is very effective, that the temperature values, light intensity, and humidity can be monthly, daily and hourly viewed and displayed on the computer and smartphone. When the proposed system has been applied for cultivating mitragyna speciose, the better growth results have been found comparing with the traditional cultivation.

关键词： Temperature measurement Smart agriculture Temperature sensors Temperature distribution Microcontrollers Plants (biology) Moisture Process control Sensor systems Temperature control

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Optically Transparent 4-Port UWB Antenna for MIMO Applications

Optically Transparent 4-Port UWB Antenna for MIMO Applicatio...

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Microwave and Antenna Symposium (IMAS), International

作者： Rabia Yahya Haosen Yin David Bertuch Amal El-Ghazaly Department of Physics Faculty of Science and Technology Nouakchott University Nouakchott Mauritania Department of Electrical and Computer Engineering Cornell University Ithaca NY United States

ISBN: (数字)9798350356359

ISBN: (纸本)9798350356366

Herein, we present a novel transparent antenna with ultra-wideband (UWB) performance along with polarization diversity through 4 ports allowing MIMO operation. The antenna design can be of a great interest for a vehicular applications and Internet of Things (IOT) where the dual polarization and multiple ports allows as well as medical glasses and vehicular windows where transparency is needed for visibility. The proposed antenna provides compact size with dimensions of 50 mm×50 mm, through the employment of CPW, dual orthogonal-polarization, isolation level of 20 dB, and envelope correlation coefficient (ECC) of 0.0016 as well as MIMO performance.

关键词： Microwave antennas Correlation coefficient Antenna measurements Wireless communication Glass Ultra wideband antennas Loss measurement Envelope detectors Internet of Things Antenna radiation patterns

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