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CVNN-based Channel Estimation and Equalization in OFDM Systems Without Cyclic Prefix

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arXiv 2023年

作者： dos Santos Sousa, Heitor Soares, Jonathan Aguiar Mayer, Kayol Soares Arantes, Dalton Soares The Digital Communications Laboratory - Com-Lab Department of Communications School of Electrical and Computer Engineering University of Campinas - Unicamp SP Campinas13083-852 Brazil

In modern communication systems operating with Orthogonal Frequency-Division Multiplexing (OFDM), channel estimation requires minimal complexity with one-tap equalizers. However, this depends on cyclic prefixes, which must be sufficiently large to cover the channel impulse response. Conversely, the use of cyclic prefix (CP) decreases the useful information that can be conveyed in an OFDM frame, thereby degrading the spectral efficiency of the system. In this context, we study the impact of CPs on channel estimation with complex-valued neural networks (CVNNs). We show that the phase-transmittance radial basis function neural network offers superior results, in terms of required energy per bit, compared to classical minimum mean-squared error and least squares algorithms in scenarios without CP. © 2023, CC BY.

关键词： Orthogonal frequency division multiplexing

Operating-Envelopes-Aware Decentralized Welfare Maximization for Energy Communities

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arXiv 2023年

作者： Alahmed, Ahmed S. Cavraro, Guido Bernstein, Andrey Tong, Lang School of Electrical and Computer Engineering Cornell University IthacaNY United States Power System Engineering Center National Renewable Energy Laboratory GoldenCO United States

We propose an operating-envelope-aware, prosumer-centric, and efficient energy community that aggregates individual and shared community distributed energy resources and transacts with a regulated distribution system operator (DSO) under a generalized net energy metering tariff design. To ensure safe network operation, the DSO imposes dynamic export and import limits, known as dynamic operating envelopes, on end-users’ revenue meters. Given the operating envelopes, we propose an incentive-aligned community pricing mechanism under which the decentralized optimization of community members’ benefit implies the optimization of overall community welfare. The proposed pricing mechanism satisfies the cost-causation principle and ensures the stability of the energy community in a coalition game setting. Numerical examples provide insights into the characteristics of the proposed pricing mechanism and quantitative measures of its performance. © 2023, CC BY.

关键词： Dynamics

Subject-Independent Ankle Joint Power Estimation with Two IMUs During Flat and Inclined Walking

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Subject-Independent Ankle Joint Power Estimation with Two IM...

International Workshop on Wearable and Implantable Body Sensor Networks (BSN)

作者： Hong Wang Dongxuan Li Kairan Liang Peter Shull State Key Laboratory of Mechanical System and Vibration Shanghai Jiaotong University Shanghai China Department of Electrical and Computer Engineering Northeastern University Boston America

Assessing ankle joint power during real-life scenarios is crucial for analyzing human push-off and detecting abnormal gait patterns. However, traditional joint power monitoring methods require expensive and professional equipment, limiting their use to gait laboratories. To address this limitation, we propose a portable and robust two-stage approach that estimates ankle joint power using two inertial measurement units (IMU) sensors placed on the shank and foot, respectively. Our subject-independent CNN model accurately assessed ankle joint power during flat and inclined walking across 28 walking speeds and 6 ramp inclines. This solution facilitates ankle joint power assessment outside of gait laboratories and could serve as a foundation to enable gait abnormality evaluation in patients in hospitals, clinics, and home-based settings.

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Improving Quantum Circuit Synthesis with Machine Learning

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arXiv 2023年

作者： Weiden, Mathias Younis, Ed Kalloor, Justin Kubiatowicz, John Iancu, Costin Department of Electrical Engineering and Computer Science University of California Berkeley United States Computational Research Division Lawrence Berkeley National Laboratory United States

In the Noisy Intermediate Scale Quantum (NISQ) era, finding implementations of quantum algorithms that minimize the number of expensive and error prone multi-qubit gates is vital to ensure computations produce meaningful outputs. Unitary synthesis, the process of finding a quantum circuit that implements some target unitary matrix, is able to solve this problem optimally in many cases. However, current bottom-up unitary synthesis algorithms are limited by their exponentially growing run times. We show how applying machine learning to unitary datasets permits drastic speedups for synthesis algorithms. This paper presents QSeed, a seeded synthesis algorithm that employs a learned model to quickly propose resource efficient circuit implementations of unitaries. QSeed maintains low gate counts and offers a speedup of 3.7× in synthesis time over the state of the art for a 64 qubit modular exponentiation circuit, a core component in Shor's factoring algorithm. QSeed's performance improvements also generalize to families of circuits not seen during the training process. © 2023, CC BY.

关键词： Machine learning

Adversarial Purification for Data-Driven Power System Event Classifiers with Diffusion Models

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arXiv 2023年

作者： Cheng, Yuanbin Yamashita, Koji Follum, Jim Yu, Nanpeng The Department of Electrical and Computer Engineering University of California RiversideCA92521 United States The Pacific Northwest National Laboratory RichlandWA United States

The global deployment of the phasor measurement units (PMUs) enables real-time monitoring of the power system, which has stimulated considerable research into machine learning-based models for event detection and classification. However, recent studies reveal that machine learning-based methods are vulnerable to adversarial attacks, which can fool the event classifiers by adding small perturbations to the raw PMU data. To mitigate the threats posed by adversarial attacks, research on defense strategies is urgently needed. This paper proposes an effective adversarial purification method based on the diffusion model to counter adversarial attacks on the machine learning-based power system event classifier. The proposed method includes two steps: injecting noise into the PMU data;and utilizing a pre-trained neural network to eliminate the added noise while simultaneously removing perturbations introduced by the adversarial attacks. The proposed adversarial purification method significantly increases the accuracy of the event classifier under adversarial attacks while satisfying the requirements of real-time operations. In addition, the theoretical analysis reveals that the proposed diffusion model-based adversarial purification method decreases the distance between the original and compromised PMU data, which reduces the impacts of adversarial attacks. The empirical results on a large-scale real-world PMU dataset validate the effectiveness and computational efficiency of the proposed adversarial purification method. © 2023, CC BY-NC-ND.

关键词： Phasor measurement units

TARGETED DEMAND RESPONSE FOR FLEXIBLE ENERGY COMMUNITIES USING CLUSTERING TECHNIQUES

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arXiv 2023年

作者： Pelekis, Sotiris Pipergias, Angelos Karakolis, Evangelos Mouzakitis, Spiros Santori, Francesca Ghoreishi, Mohammad Askounis, Dimitris Decision Support Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Greece ASM Terni S.p.A. Italy

The present study proposes clustering techniques for designing demand response (DR) programs for commercial and residential prosumers. The goal is to alter the consumption behavior of the prosumers within a distributed energy community in Italy. This aggregation aims to: a) minimize the reverse power flow at the primary substation, occuring when generation from solar panels in the local grid exceeds consumption, and b) shift the system wide peak demand, that typically occurs during late afternoon. Regarding the clustering stage, we consider daily prosumer load profiles and divide them across the extracted clusters. Three popular machine learning algorithms are employed, namely k-means, k-medoids and agglomerative clustering. We evaluate the methods using multiple metrics including a novel metric proposed within this study, namely peak performance score (PPS). The k-means algorithm with dynamic time warping distance considering 14 clusters exhibits the highest performance with a PPS of 0.689. Subsequently, we analyze each extracted cluster with respect to load shape, entropy, and load types. These characteristics are used to distinguish the clusters that have the potential to serve the optimization objectives by matching them to proper DR schemes including time of use, critical peak pricing, and real-time pricing. Our results confirm the effectiveness of the proposed clustering algorithm in generating meaningful flexibility clusters, while the derived DR pricing policy encourages consumption during off-peak hours. The developed methodology is robust to the low availability and quality of training datasets and can be used by aggregator companies for segmenting energy communities and developing personalized DR policies. © 2023, CC BY.

关键词： Electric load flow

Short-term household load forecasting model based on variational mode decomposition and gated recurrent unit with attention mechanism

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Journal of Network Intelligence

Journal of Network Intelligence 2021年第1期6卷 143-153页

作者： Lin, Fu Liu, Shi-Jian Chao, Han-Chieh Pan, Jeng-Shyang College of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Department of Electrical Engineering National Dong Hwa University Hualien Taiwan Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

The household load is an important part of the load. Accurate short-term household load forecasting is of great help to issues such as power price formulation, demand response, or power transmission. This paper proposed a forecasting model based on Variational Mode Decomposition (VMD) and Gated Recurrent Unit (GRU) combined with self-attention mechanism (SAM) and attention mechanism (AM) for short-term household load forecasting. First, we use VMD to decompose household electricity load data, extract sub-sequences with strong periodic information, and reorganize the original data sets with sub-sequences and data pre-processed. The new data sets contain more abundant information. Secondly, GRU combined with SAM and AM forecasting model for learning and training to obtain results. Finally, RMSE and MAPE evaluation functions are used for method evaluation to verify the superiority of the proposed scheme. The RMSE and MAPE of the proposed method are 0.193 kW and 15.567%, respectively, which are more favorable than traditional GRU. © 2021, Taiwan Ubiquitous Information. All rights reserved.

关键词： Variational mode decomposition

High-repetition-rate strong-field terahertz source by optical rectification in DSTMS crystals

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High Power Laser Science and engineering 2024年第5期12卷 70-75页

作者： Zhuorui Zheng Kang Wang Hongyang Li Xianze Meng Ye Tian Liwei Song State Key Laboratory of High Field Laser Physics and CAS Center for Excellence in Ultra-intense Laser Science Shanghai Institute of Optics and Fine MechanicsChinese Academy of SciencesShanghaiChina Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijingChina School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghaiChina School of Physics Science and Engineering Tongji UniversityShanghaiChina

We present the generation of high-repetition-rate strong-field terahertz(THz)pulses from a thin 4-N,N-dimethylamino-4’-N’-methyl-stilbazolium 2,4,6-trimethylbenzenesulfonate(DSTMS)organic crystal pumped by an ytterbium-doped yttrium aluminum garnet *** generated THz pulse energy reaches 932.8 nJ at 1 kHz repetition rate,with a conversion efficiency of 0.19%and a peak electric field of 819 kV/*** a repetition rate of 10 kHz,it is able to maintain a peak electric field of 236 kV/cm and an average THz power of 0.77 *** high-repetition-rate,strong-field THz source provides a convenient tool for the study of THz matter manipulation and THz spectroscopy.

关键词： high repetition rate optical rectification strong-field terahertz source

Differentially Private Reward Functions for Markov Decision Processes

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Differentially Private Reward Functions for Markov Decision ...

Control Technology and Applications (CCTA),

作者： Alexander Benvenuti Calvin Hawkins Brandon Fallin Bo Chen Brendan Bialy Miriam Dennis Matthew Hale School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Department of Mechanical and Aerospace Engineering University of Florida Gainesville FL USA Munitions Directorate Air Force Research Laboratory FL USA

ISBN: (数字)9798350370942

ISBN: (纸本)9798350370959

Markov decision processes often seek to maximize a reward function, but onlookers may infer reward functions by observing agents, which can reveal sensitive information. Therefore, in this paper we introduce and compare two methods for privatizing reward functions in policy synthesis for multi-agent Markov decision processes, which generalize Markov decision processes. Reward functions are privatized using differential privacy, a statistical framework for protecting sensitive data. The methods we develop perturb (i) each agent’s individual reward function or (ii) the joint reward function shared by all agents. We prove that both of these methods are differentially private and show approach (i) provides better performance. We then develop an algorithm for the numerical computation of the performance loss due to privacy on a case-by-case basis. We also exactly compute the computational complexity of this algorithm in terms of system parameters and show that it is inherently tractable. Numerical simulations are performed on waypoint guidance of an autonomous vehicle which shows that privacy induces only negligible performance losses in practice.

关键词： Privacy Differential privacy Accuracy Markov decision processes Perturbation methods Observers Numerical simulation