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IEEE Green Technologies Conference (IEEE-Green)

作者： Ola Ali Osama A. Mohammed Department of Electrical and Computer Engineering Energy Systems Research Laboratory Florida International University Miami FL USA

ISBN: (数字)9798331520182

ISBN: (纸本)9798331520199

This paper presents a communication emulation model for enhancing the efficiency of multi-microgrid (MMG) networks, employing Network Simulator 3 (ns3). MMG systems require robust communication frameworks for coordination, particularly under varying traffic conditions. The introduced model accommodates multiple local area networks (LANs) that support efficient communication across multiple autonomous AC microgrids. The developed model facilitates seamless bidirectional information exchange between the microgrids' local controllers and the MMG control center (MMGCC), employing TCP/IP protocol to ensure high data transmission integrity and low latency. A detailed design in C++ is demonstrated, including the configuration of the communication nodes, topology, communication links, and quality of service (QoS) analysis. The network performance is rigorously assessed across low and high traffic conditions, with performance metrics such as throughput, delay, and percentage packet loss at each node. Results confirm the effectiveness of the introduced model in sustaining minimal packet loss, high throughput, and low delays under dynamic operating scenarios. These findings establish the developed model as a viable solution for meeting the real-time communication requirements of MMG energy management systems.

关键词： Emulation Packet loss TCPIP Quality of service Microgrids C++ languages Throughput Real-time systems Delays Load modeling

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Resilient Operation of DC Standalone Microgrids: Experimental Validation of Hybrid RNN-PI Control for Pulse Load Management

Resilient Operation of DC Standalone Microgrids: Experimenta...

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IEEE Green Technologies Conference (IEEE-Green)

作者： Ahmed Aghmadi Osama A. Mohammed Department of Electrical and Computer Engineering Energy Systems Research Laboratory Florida International University Miami FL USA

ISBN: (数字)9798331520182

ISBN: (纸本)9798331520199

Effective energy storage system (ESS) management is critical for enhancing the performance of standalone DC microgrids, particularly when integrating renewable energy sources such as photovoltaic (PV) or wind systems. This research describes a new hybrid control technique to improve DC microgrid operations' efficiency and resiliency, especially when handling critical loads. This research presents a hybrid closed-loop control technique that uses Recurrent Neural Networks (RNNs) and Proportional-Integral (PI) controllers to address the complex issues of dynamic load situations and transient disturbances. The RNN evaluates voltage errors to generate accurate reference currents, which the PI controllers then adjust to improve the performance of the bidirectional converter. Unlike standard PI-PI controls, our hybrid approach is particularly effective at regulating pulsed power loads (PPLs) and enhancing system response to transitory situations. The experimental results show that the RNN-PI control scheme significantly improves voltage regulation and transient responsiveness, exceeding conventional performance and resilience techniques. This approach provides a convincing solution for enhancing microgrid systems' stability and efficiency in various real-world applications.

关键词： Renewable energy sources Recurrent neural networks PI control Voltage fluctuations Microgrids Robustness Voltage control Transient analysis Standards Resilience

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On the Performance of Joint Resource Allocation and Control in Industrial IoT

On the Performance of Joint Resource Allocation and Control ...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Wang, Ying Wu, Shaohua Jiao, Jian Zhang, Ning Zhang, Qinyu The Department of Electronics and Information Engineering Shenzhen518055 China The Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Shenzhen518055 China Peng Cheng Laboratory Shenzhen518055 China University of Windsor Department of Electrical and Computer Engineering WindsorONN9B 3P4 Canada

ISBN: (纸本)9798350345384

In this paper, a wireless networked control system with a local controller and remote controller is investigated. The observed states can be controlled directly by the local controller or controlled by the remote controller via an unreliable channel. In this scenario, we focus on a more realistic problem that the network might be faced with, that is, the lifespan of the network. However, this issue has received little attention. The lifespan of the network is considered in two-folds: 1) a stochastic sample and transmission scheme is adopted to reduce the power consumption and 2) the transmit power is optimized to guarantee the total transmission power limitation. In order to balance the control performance and the network lifespan, an optimization problem on communication and control co-design is formulated. In particular, we aim to jointly optimize the resource allocation and local/remote controls by minimizing the linear quadratic regulation (LQR) control cost under the constraints of transmit power and transmission probability. The results of the considered optimization problem show that there exists an optimal transmit power and the corresponding transmission probability that can minimize the control cost function. Numerical results are further provided to verify the theoretical analysis. © 2023 IEEE.

关键词： Resource allocation

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FAD-SAR: A Novel Fishing Activity Detection System via Synthetic Aperture Radar Images Based on Deep Learning Method

FAD-SAR: A Novel Fishing Activity Detection System via Synth...

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2024 International Symposium on Intelligent Signal Processing and Communication systems, ISPACS 2024

作者： Bai, Yanbing Li, Siao Ju, Rui-Yang Yang, Zihao Yu, Jinze Chiang, Jen-Shiun Center for Applied Statistics School of Statistics Renmin University of China Beijing100872 China Graduate Institute of Networking and Multimedia National Taiwan University Taipei City106335 Taiwan Department of Mechanical Systems Engineering Tokyo University of Agriculture and Technology Tokyo183-8538 Japan Department of Electrical and Computer Engineering Tamkang University New Taipei City251301 Taiwan

ISBN: (纸本)9798350389210

Illegal, unreported, and unregulated (IUU) fishing activities seriously affect various aspects of human life. However, traditional methods for detecting and monitoring IUU fishing activities at sea have limitations. Although synthetic aperture radar (SAR) can complement existing vessel detection systems, extracting useful information from SAR images using traditional methods remains a challenge, especially in IUU fishing. This paper proposes a deep learning based fishing activity detection system, which is implemented on the xView3 dataset using six classical object detection models: SSD, RetinaNet, FSAF, FCOS, Faster R-CNN, and Cascade R-CNN. In addition, this work employs different enhancement techniques to improve the performance of the Faster R-CNN model. The experimental results demonstrate that training the Faster R-CNN model using the Online Hard Example Mining (OHEM) strategy increases the Avg-F1 value from 0.212 to 0.216. © 2024 IEEE.

关键词： Deep learning

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Quantitative comparison between reflection and transmission modes for the inspection of composite materials using pulsed thermography 45

Quantitative comparison between reflection and transmission ...

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Thermosense: Thermal Infrared Applications XLV 2023

作者： Alhammad, M. Avdelidis, N.P. Ibarra-Castanedo, C. Torbali, M.E. Zolotas, A. Maldague, X.P.V. School of Aerospace Transport and Manufacturing Cranfield University United Kingdom Universite Laval Computer Vision and Systems Laboratory Department of Electrical and Computer Engineering Quebec CityG1V 0A6 Canada

ISBN: (纸本)9781510661868

Composite materials have extensively been used in the aerospace industry. There are several inspection methods to ensure the safety of these composites;pulse thermography (PT) is one of the most promising ones. Both reflection and transmission modes of PT could be applied. However, few studies reveal the advantages and disadvantages of these two modes. This paper presents a quantitative comparison of pulsed thermal imaging in two modes of reflection and gradual heat transfer in carbon fibres. Experimental work was conducted on carbon fibre-reinforced plastic (CFRP) samples with different thicknesses and thermal images were recorded in both modes. Thermal images were further processed using statistical analysis and machine learning algorithms. Comparing the results from both modes, there is a marked improvement in the accuracy when the reflection mode is employed. © 2023 SPIE.

关键词： Nondestructive examination

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LSTM-Based Digital Twin Modeling for LVDC Microgrid Operation with Pulsed Load

LSTM-Based Digital Twin Modeling for LVDC Microgrid Operatio...

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IEEE Industry Applications Society Annual Meeting (IAS)

作者： Mahmoud S. Abdelrahman Mustafa Esoofally Hossam Hussein Ibtissam Kharchouf Osama A. Mohammed Department of Electrical and Computer Engineering Energy Systems Research Laboratory Florida International University Miami USA

ISBN: (数字)9798350372717

ISBN: (纸本)9798350372724

Proper control and energy management is crucial for the effective, practical, and reliable operation of DC microgrids. In DC microgrids, the high certainties of renewables and the dynamic behavior of particular loads propose different modeling, operation, and control challenges. With recent advancements in information and communication technologies offering energy solution services relying on data analytics, machine learning (ML), and IoT technologies, the concept of digital twins (DT) has emerged as a feasible and active model to enhance the operation and resilience of microgrids. This paper proposes a long short-term memory (LSTM) neural networkbased strategy to construct a data-driven digital twin (DT) model of DC microgrids. The model is tested and validated with a standalone DC MG containing Renewable Energy Sources (RES), Energy Storage Sources (ESSs), and different load types. The accuracy of the created twin model is tested and evaluated under different scenarios of the microgrid operation, such as a sudden decrease in renewable generation and energizing pulsed load to the MG.

关键词： Renewable energy sources Accuracy Simulation Microgrids Real-time systems Digital twins Reliability Long short term memory Load modeling Resilience

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Design of Wave Scattering From Gain and Loss Disordered Materials

Design of Wave Scattering From Gain and Loss Disordered Mate...

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Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Ikbeom Lee Kunwoo Park Sunkyu Yu Department of Electrical and Computer Engineering Intelligent Wave Systems Laboratory Seoul National University Seoul Korea

We propose the design of scattering from gain and loss materials using the structure factor. Increased degrees of freedom in the structure factor allow the suppression across the target momentum space.

ISBN: (数字)9798350372076

ISBN: (纸本)9798350372083

关键词： Scattering Lasers and electrooptics Electro-optical waveguides

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Multi-Level Deep Generative Adversarial Networks for Brain Tumor Classification on Magnetic Resonance Images

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Intelligent Automation & Soft Computing 2023年第4期36卷 127-143页

作者： Abdullah A.Asiri Ahmad Shaf Tariq Ali Muhammad Aamir Ali Usman Muhammad Irfan Hassan A.Alshamrani Khlood M.Mehdar Osama M.Alshehri Samar M.Alqhtani Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajranSaudi Arabia Department of Clinical Laboratory Sciences Collage of Applied Medical ScienceNajran UniversityNajranSaudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

The brain tumor is an abnormal and hysterical growth of brain tissues,and the leading cause of death affected patients *** in this technol-ogy-based arena,brain tumor images with proper labeling and acquisition still have a problem with the accurate and reliable generation of realistic images of brain tumors that are completely different from the original *** artiﬁcially created medical image data would help improve the learning ability of physicians and other computer-aided systems for the generation of augmented *** over-come the highlighted issue,a Generative Adversarial Network(GAN)deep learn-ing technique in which two neural networks compete to become more accurate in creating artiﬁcially realistic data for MRI *** GAN network contains mainly two parts known as generator and ***,a generator is the convolutional neural network,and a discriminator is the deconvolutional neural *** this research,the publicly accessible Contrast-Enhanced Mag-netic Resonance Imaging(CE-MRI)dataset collected from 2005-to 2020 from different hospitals in China consists of four classes has been *** proposed method is simple and achieved an accuracy of 96%.We compare our technique results with the existing results,indicating that our proposed technique outper-forms the best results associated with the existing methods.

关键词： GAN network CE-MRI images convolutional neural network brain tumor classiﬁcation

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On a novel tracking differentiator design based on iterative learning in a moving window

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Control Theory and Technology 2023年第1期21卷 46-55页

作者： Xiangyang Li Rafal Madonski Zhiqiang Gao Senping Tian Key Laboratory of Autonomous Systems and Network Control Ministry of EducationSchool of Automation Science and EngineeringSouth China University of TechnologyGuangzhou 510641China Energy and Electricity Research Center Jinan UniversityZhuhai 519070China Department of Electrical and Computer Science Cleveland State UniversityClevelandOH 44115USA

Differential signals are key in control engineering as they anticipate future behavior of process variables and therefore are critical in formulating control laws such as proportional-integral-derivative(PID).The practical challenge,however,is to extract such signals from noisy measurements and this difficulty is addressed first by *** in the form of linear and nonlinear tracking differentiator(TD).While improvements were made,TD did not completely resolve the conflict between the noise sensitivity and the accuracy and timeliness of the *** two approaches proposed in this paper start with the basic linear TD,but apply iterative learning mechanism to the historical data in a moving window(MW),to form two new iterative learning tracking differentiators(IL-TD):one is a parallel IL-TD using an iterative ladder network structure which is implementable in analog circuits;the other a serial IL-TD which is implementable digitally on any computer *** algorithms are validated in simulations which show that the proposed two IL-TDs have better tracking differentiation and de-noise performance compared to the existing linear TD.

关键词： Tracking differentiator(TD) Iterative learning Iterative learning tracking differentiator(IL-TD) Active disturbance rejection control(ADRC)-Two-dimensional system(2-D system)

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Inverse Design Method of Hyperuniform Materials Using System Factorization

Inverse Design Method of Hyperuniform Materials Using System...

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Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Kunwoo Park Ikbeom Lee Sunk Yu Department of Electrical and Computer Engineering Intelligent Wave Systems Laboratory Seoul National University Seoul Korea

ISBN: (数字)9798350372076

ISBN: (纸本)9798350372083

关键词： Design methodology Lasers and electrooptics Electro-optical waveguides

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