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IEEE Transactions on Radar Systems 2024年 2卷 484-497页

作者： Biswas, Sabyasachi Manavi Alam, Ahmed Gurbuz, Ali C. Mississippi State University Department of Electrical and Computer Engineering Information Processing and Sensing Laboratory StarkvilleMS39672 United States

Micro-Doppler signatures (mu -DSs) are widely used for human activity recognition (HAR) using radar. However, traditional methods for generating mu -DS, such as the short-time Fourier transform (STFT), suffer from limitations, such as the tradeoff between time and frequency resolution, noise sensitivity, and parameter calibration. To address these limitations, we propose a novel deep learning (DL)-based approach to reconstruct high-resolution mu -DS directly from a 1-D complex time-domain signal. Our DL architecture consists of an autoencoder (AE) block to improve signal-to-noise ratio (SNR), an STFT block to learn frequency transformations to generate pseudo spectrograms, and, finally, a U-Net block to reconstruct high-resolution spectrogram images. We evaluated our proposed architecture on both synthetic and real-world data. For synthetic data, we generated 1-D complex time-domain signals with multiple time-varying frequencies and evaluated and compared the ability of our network to generate high-resolution mu -DS and perform in different SNR levels. For real-world data, a challenging radar-based American Sign Language (ASL) dataset consisting of 100 words was used to evaluate the classification performance achieved using the mu -DS generated by the proposed approach. The results showed that the proposed approach outperforms the classification accuracy of traditional STFT-based mu -DS by 3.48%. Both synthetic and experimental mu -DSs show that the proposed approach learns to reconstruct higher resolution and sparser spectrograms. © 2023 IEEE.

关键词： Signal to noise ratio

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Towards Green AI by Reducing Training Effort of Recurrent Neural Networks Using Hyper-Parameter Optimization with Dynamic Stopping Criteria 22

Towards Green AI by Reducing Training Effort of Recurrent Ne...

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22nd IEEE International Symposium on Intelligent Systems and Informatics, SISY 2024

作者： Podgorelec, Vili Fister, Iztok Vrbancic, Grega University of Maribor Intelligent Systems Laboratory Faculty of Electrical Engineering and Computer Science Slovenia

ISBN: (纸本)9798350385601

Neural networks have become a leading model in modern machine learning, able to model even the most complex data. For them to be properly trained, however, a lot of computational resources are required. With the carbon footprint of ever-growing adoption of neural networks in mind, an approach to reduce the required training resources would be very welcome. We designed a new training effort reduction method based on the calculation of area under the normalized loss curve and assessed it on the electricity consumption forecasting problem with the recurrent neural networks. The results show that the proposed method was able to considerably reduce the amount of computational resources, while maintaining the predictive performance, and thus contributing towards the Green AI. © 2024 IEEE.

关键词： Green AI hyper-parameter optimization machine learning neural networks resource optimization

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Net Load Prediction Using Machine Learning Methods: A Case Study of Residential Households 3

Net Load Prediction Using Machine Learning Methods: A Case S...

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3rd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Herodotou, Panagiotis Tziolis, Georgios Makrides, George Georghiou, George E. PV Technology Laboratory Department of Electrical and Computer Engineering Cyprus for Intelligent Nicosia Cyprus

ISBN: (纸本)9798350375923

As residential solar photovoltaic (PV) systems are increasingly integrated into distribution networks globally, there is a growing necessity to shift from traditional load forecasting to net load forecasting (i.e., predicting the combination of PV and load collectively). Understanding and predicting the daily net energy of a typical household is crucial for designing and sizing appropriate renewable energy systems and storage solutions. This paper compares the performance of different machine learning methods in predicting the net load patterns of a typical household. The database contained PV production and energy load records from 68 actual households in Cyprus, over a period of one year. The results showed that the eXtreme Gradient Boosting (XGBoost) model outperformed all other models yielding a normalized root mean square error of 5.52%. Despite the inherent unpredictability of net load patterns, the findings indicate that the proposed XGBoost model can achieve high prediction accuracies for household electricity net load profiles. © 2024 IEEE.

关键词： Prediction models

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Rotor Angle Stability Prediction Using Temporal and Topological Embedding Deep Neural Network Based on Grid-informed Adjacency Matrix

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Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 695-706页

作者： Peiyuan Sun Long Huo Xin Chen Siyuan Liang the School of Electrical Engineering Xi’an Jiaotong UniversityXi’an 710049China the School of Electrical Engineering and the Center of Nanomaterials for Renewable EnergyState Key Laboratory of Electrical Insulation and Power EquipmentXi’an Jiaotong UniversityXi’an 710049China the Department of Computer Science and Engineering The Chinese University of Hong KongShatin999077Hong KongChina

Rotor angle stability(RAS)prediction is critically essential for maintaining normal operation of the interconnected synchronous machines in power *** wide deployment of phasor measurement units(PMUs)promotes the development of data-driven methods for RAS *** paper proposes a temporal and topological embedding deep neural network(TTEDNN)model to accurately and efficiently predict RAS by extracting the temporal and topological features from the PMU *** grid-informed adjacency matrix incorporates the structural and electrical parameter information of the power *** the small-signal RAS with disturbance under initial operating conditions and the transient RAS with short circuits on transmission lines are *** studies of the IEEE 39-bus and IEEE 300-bus power systems are used to test the performance,scalability,and robustness against measurement uncertainties of the TTEDNN *** show that the TTEDNN model performs best among existing deep learning ***,the superior transfer learning ability from small-signal RAS conditions to transient RAS conditions has been proved.

关键词： Rotor angle stability prediction topological embedding deep learning graph convolution network

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A high speed dynamic comparator with automatic offset compensation

A high speed dynamic comparator with automatic offset compen...

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2024 Panhellenic Conference on Electronics and Telecommunications, PACET 2024

作者： Tsirmpas, George Kontelis, Spyros Souliotis, George Plessas, Fotis University of Patras Electronics Laboratory Physics Department Patras Greece University of Peloponnese Department of Electrical and Computer Engineering Greece University of Thessaly Department of Electrical and Computer Engineering Volos Greece

ISBN: (纸本)9798350318845

A high-speed dynamic comparator with automatic dc offset compensation is proposed in this paper. The comparator is based on a double-tail topology and reduces the dc offset by a calibration circuit. Avoiding using the commonly used capacitive calibration, it can operate at a higher speed compared to other topologies. Designed in a CMOS 65nm technology node, it operates with 1 V supply voltage and clock speed at 10 GHz. © 2024 IEEE.

关键词： Comparators (optical)

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Mamba State Space Model (SSM) Method for the Automatic Segmentation of Organs at Risk from Head and Neck CT Images 4

Mamba State Space Model (SSM) Method for the Automatic Segme...

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4th International Conference on electrical, computer, Communications and Mechatronics engineering, ICECCME 2024

作者： Vagenas, Theodoros P. Georgas, Konstantinos Matsopoulos, George K. School of Electrical and Computer Engineering National Technical University of Athens Biomedical Engineering Laboratory Athens Greece

ISBN: (纸本)9798350391183

Radiotherapy (RT) is one of the main treatment modalities using high-energy radiation to eliminate cancer cells in Head and Neck cancer (HNC) for patients worldwide. Advances such as intensity-modulated radiotherapy (IMRT) improve the precision of targeting tumors and prevent damaging healthy tissues. Segmentation of Organs At Risk (OARs) on Computed Tomography (CT) images is crucial for RT treatment planning in order to avoid damaging healthy structures during the sessions. However, manual delineation of OARs is challenging as their size varies and both small and large organs are present in images with low contrast against the background. In this regard, manual delineation cannot be performed in a timely manner and suffers from potential interobserver variability among clinicians, leading to delays in clinical workflow. In this study, we propose an automatic Deep Learning segmentation framework utilizing effectively the recently introduced Mamba State Space Models (SSM) to localize and delineate multiple OARs with high accuracy from the 3D HNC CT images. Due to the Mamba-based encoder, the model can learn the long-range dependencies of the largest organs by efficiently handling large sequences while also segmenting the smaller ones. The proposed model was evaluated on two public datasets, demonstrating superior performance with an average Dice Similarity Coefficient (DSC) of 75.45% and a Hausdorff distance of 8.22 mm against established deep learning models, such as UNet-based and Transformer-based, in segmenting 9 OARs, indicating promising results to support the clinical workflow. © 2024 IEEE.

关键词： computerized tomography

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Programmable Photonics Using Dynamically Modulated Coupled Resonances 16

Programmable Photonics Using Dynamically Modulated Coupled R...

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Piao, Xianji School of Electrical and Computer Engineering University of Seoul Wave Engineering Laboratory Seoul02504 Korea Republic of

ISBN: (纸本)9798350372076

We introduce the platform for photonic computation using dynamically modulated coupled resonances. By realizing the interference between the couplings with tunable gauge fields, we implement reconfigurable and universal SU(2) time gates, which are the unit elements for achieving universal U(N) operations in an adaptable manner. In the talk, the application of the proposed platform to quantum gates and quantum-optical analogy as examples. © 2024 IEEE.

关键词： Photonics

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Data-driven modeling of power system dynamics:Challenges,state of the art,and future work

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iEnergy 2023年第3期2卷 200-221页

作者： Heqing Huang Yuzhang Lin Yifan Zhou Yue Zhao Peng Zhang Lingling Fan Department of Electrical and Computer Engineering University of Massachusetts LowellLowellMA 01852USA Department of Electrical and Computer Engineering Stony Brook UniversityStony BrookNY 11794USA Interdisciplinary Science Department Brookhaven National LaboratoryUptonNY 11973USA Department of Electrical and Computer Engineering University of South FloridaTampaFL 33620USA

With the continual deployment of power-electronics-interfaced renewable energy resources,increasing privacy concerns due to deregulation of electricity markets,and the diversification of demand-side activities,traditional knowledge-based power system dynamic modeling methods are faced with unprecedented ***-driven modeling has been increasingly studied in recent years because of its lesser need for prior knowledge,higher capability of handling large-scale systems,and better adaptability to variations of system operating *** paper discusses about the motivations and the generalized process of datadriven modeling,and provides a comprehensive overview of various state-of-the-art techniques and *** also comparatively presents the advantages and disadvantages of these methods and provides insight into outstanding challenges and possible research directions for the future.

关键词： Data-driven modeling machine learning model construction parameter identification power system dynamics system identification

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Analysis of Channel Uncertainty in Trusted Wireless Services via Repeated Interactions

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2248-2265页

作者： Chen, Bingwen Ling, Xintong Cao, Weihang Wang, Jiaheng Ding, Zhi Southeast University National Mobile Communications Research Laboratory Nanjing211189 China Purple Mountain Laboratories Nanjing210023 China University of Maryland Department of Electrical and Computer Engineering College ParkMD20742 United States University of California at Davis Department of Electrical and Computer Engineering DavisCA95616 United States

The coexistence of heterogeneous sub-networks in 6G poses new security and trust concerns and thus calls for a perimeterless-security model. Blockchain radio access network (B-RAN) provides a trust-building approach via repeated interactions rather than relying on pre-established trust or central authentication. Such a trust-building process naturally supports dynamic trusted services across various service providers (SP) without the need for perimeter-based authentications;however, it remains vulnerable to environmental and system unreliability such as wireless channel uncertainty. In this study, we investigate channel unreliability in the trust-building framework based on repeated interactions for secure wireless services. We derive specific requirements for achieving cooperation between SPs and clients via a repeated game model and illustrate the implications of channel unreliability on sustaining trusted wireless services. We consider the framework design and optimization to guarantee SP-client cooperation, given the worst channel condition and/or the least cooperation willingness. Furthermore, we explore the maximum cooperation area to enhance service resilience and reveal the trade-off relationship between transmission efficiency, security integrity, and cooperative margin. Finally, we present simulations to demonstrate the system performance over fading channels and verify our results. © 1983-2012 IEEE.

关键词： Fading channels

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Dynamic Services Migration Based on Dueling Deep Q-Network in MEC 15

Dynamic Services Migration Based on Dueling Deep Q-Network i...

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15th International Conference on Communication Software and Networks, ICCSN 2023

作者： Zhang, Xinyang Bu, Chao School of Computer Science and Engineering Tianjin University of Technology Key Laboratory of Computer Vision and System of Ministry of Education Tianjin China

ISBN: (纸本)9798350329360

As an extended computing paradigm of cloud computing, Mobile Edge Computing (MEC) facilitates real-time service responses by deploying resources near network edges. However, services should frequently move among multiple edge computing servers because of the mobility of most users, which accordingly leads to increased network operation costs and influences service quality. In this paper, we formulate the service migration problem as a Markov Decision Process (MDP) and introduce the dueling Deep Q-Network (DQN) to solve the problem, so as to reduce the network operating cost without lowering the service quality. We also propose a trajectory prediction approach to further optimize the service migration. Simulation experimental results demonstrate that the proposed mechanism can achieve a lower network operation cost without reducing the service quality. © 2023 IEEE.

关键词： Markov processes

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