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IEEE International Conference on Power and Power and Renewable Energy (ICPRE)

作者： Tingting Xu Xiaohan Fang Yuan Fan Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Anhui University Hefei China

Multi-microgrid (MMG) systems offer notable advantages in terms of power sharing and mutual support when compared to individual microgrids (MGs). This paper presents a based on distributed event-triggered consensus strategy for optimizing the economic dispatch of multiple microgrids. The incorporation of the event-triggered mechanism into the multi-microgrid framework significantly reduces unnecessary communication among microgrids in contrast to the conventional time-triggered approach. For the multi-microgrid setup, a two-layer control strategy consisting of inter-microgrid and intra-microgrid layers is employed to achieve coherence by regulating the micro-incremental rates of distributed generation (DG) units. Leaderless event-triggered consistency is utilized for inter-microgrid coordination, while leader event-triggered consistency is implemented for intra-microgrid coordination, ultimately realizing the economic objective of minimizing the operational costs of the multi-microgrid system. Simulation results validate the efficacy of introducing the event-triggered mechanism and its impact on the economic dispatch of the multi-microgrid system.

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DMSS:An Attention-Based Deep Learning Model for High-Quality Mass Spectrometry Prediction

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Big Data Mining and Analytics 2024年第3期7卷 577-589页

作者： Yihui Ren Yu Wang Wenkai Han Yikang Huang Xiaoyang Hou Chunming Zhang Dongbo Bu Xin Gao Shiwei Sun Key Lab of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190Chinaand with University of Chinese Academy of SciencesBeijing 100049China Syneron Technology Guangzhou 510000China College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China Computer Science Program ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia Insitute of Computing Technology Chinese Academy of SciencesBeijing 100190China with Western Institute of Computing Technology Chongqing 400000China

Accurate prediction of peptide spectra is crucial for improving the efficiency and reliability of proteomic analysis,as well as for gaining insight into various biological *** this study,we introduce Deep MS Simulator(DMSS),a novel attention-based model tailored for forecasting theoretical spectra in mass *** has undergone rigorous validation through a series of experiments,consistently demonstrating superior performance compared to current methods in forecasting theoretical *** superior ability of DMSS to distinguish extremely similar peptides highlights the potential application of incorporating our predicted intensity information into mass spectrometry search engines to enhance the accuracy of protein *** findings contribute to the advancement of proteomics analysis and highlight the potential of the DMSS as a valuable tool in the field.

关键词： mass spectrometry proteomics machine learning deep learning

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Real-time pricing method through market auction based on multi-agent reinforcement learning

Real-time pricing method through market auction based on mul...

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IEEE International Conference on Power and Power and Renewable Energy (ICPRE)

作者： Peng Hong Shuping He Xiaohan Fang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Anhui University Hefei China

With the development of distributed energy systems, pricing different energy sources in microgrids has become a significant challenge. To solve this problem, this paper proposes an real-time pricing method through market auction based on multi-agent reinforcement learning. The energy supply side provides different energy production and quotations, while consumers on the energy demand side freely quote and purchase the required energy in the energy market based on their own needs. The final transaction price of different energy sources is determined by the quotes of the buyer and seller who have reached the transaction. The simulation results indicate that the proposed pricing method have a good performance.

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Optimal Dispatch Strategy of Electric Vehicles under Coupled Transportation Network and Power Grid

Optimal Dispatch Strategy of Electric Vehicles under Coupled...

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IEEE International Conference on Power and Power and Renewable Energy (ICPRE)

作者： Keming Luo Xiaohan Fang Yuan Fan Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Anhui University Hefei China

This paper addresses the complex nature of electric vehicles (EVs) as both transportation means and mobile loads, and proposes an optimal dispatching scheduling strategy for EVs in the coupled transportation network and power grid. Firstly, a dynamic traffic network model is established by utilizing a vast amount of real-world traffic data, including Origin-Destination (OD) information, battery states, and departure times. A graph theory-based analysis is then employed to determine the optimal paths for EVs in the traffic network. Secondly, the charging behavior of EVs is integrated with the power grid, and charge-discharge scheduling is conducted for charging stations to optimize the power grid load curve while meeting the charging demands of EVs. Additionally, adaptive electricity prices are formulated based on the power grid’s load generation through a dynamic pricing mechanism, further optimizing the charging strategy for EVs. Lastly, the impact of EV charging on the power grid load is analyzed by contrasting ordered and unordered charging and discharging modes. The research results demonstrate that the proposed optimal charging scheduling strategy for EVs in traffic-aware power grids can significantly reduce peak-valley differences in power grid load, enhance the economic and operational stability of the power grid, and effectively meet the charging demands of EV users. These findings provide valuable insights and promising potential for practical applications and widespread adoption.

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Data-Free Network Debiasing for Long-Tailed Visual Recognition

Data-Free Network Debiasing for Long-Tailed Visual Recogniti...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Cai, Jinmian Wang, Zheng Fu, Huazhu Chen, Jingjing Jiang, Yu-Gang School of Computer Science Fudan University Shanghai Key Lab of Intelligent Information Processing China Shanghai Collaborative Innovation Center on Intelligent Visual Computing China Biren Technology IHPC A-STAR

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Real-world data is often unbalanced and exhibits long-tailed distribution over classes. Vanilla classification models trained on imbalanced datasets inherently exhibit bias towards dominant classes. Existing debiasing methods mostly balance the data or the loss during training. Nevertheless, these data-acquiring methods are not suitable for situations where training data are unavailable. In this paper, we appeal to solutions without access to training data and propose a datafree debiasing (Free-D) method that serves as a plug-and-play module for any standard classification model. Specifically, our method adjusts both the feature representation via feature representation shifting and the classifier weight via class prior compensation in a data-free manner. We evaluate and compare our methods on four long-tailed visual recognition datasets, i.e., long-tailed CIFAR-10/-100, ImageNet-LT, and Places-LT. Extensive experiments demonstrate that the proposed data-free method achieves comparable results of other data-acquired methods. © 2022 IEEE.

关键词： Classification (of information)

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Retrieval Augmented Recipe Generation

Retrieval Augmented Recipe Generation

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Guoshan Liu Hailong Yin Bin Zhu Jingjing Chen Chong-Wah Ngo Yu-Gang Jiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai Collaborative Innovation Center on Intelligent Visual Computing Singapore Management University

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

The growing interest in generating recipes from food images has drawn substantial research attention in recent years. Existing works for recipe generation primarily utilize a two-stage training method—first predicting ingredients from a food image and then generating instructions from both the image and ingredients. Large Multi-modal Models (LMMs), which have achieved notable success across a variety of vision and language tasks, shed light on generating both ingredients and instructions directly from images. Nevertheless, LMMs still face the common issue of hallu-cinations during recipe generation, leading to suboptimal performance. To tackle this issue, we propose a retrieval augmented large multimodal model for recipe generation. We first introduce Stochastic Diversified Retrieval Augmentation (SDRA) to retrieve recipes semantically related to the image from an existing datastore as a supplement, integrating them into the prompt to add diverse and rich context to the input image. Additionally, Self-Consistency Ensemble Voting mechanism is proposed to determine the most confident prediction recipes as the final output. It calculates the consistency among generated recipe candidates, which use different retrieval recipes as context for generation. Extensive experiments validate the effectiveness of our proposed method, which demonstrates state-of-the-art (SOTA) performance in recipe generation on the Recipe1M dataset.

关键词： Training Computer vision Accuracy Computational modeling Stochastic processes Predictive models Reliability Faces

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ETS: An Error Tolerable System for Coreference Resolution 15

ETS: An Error Tolerable System for Coreference Resolution

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15th Conference on Computational Natural Language Learning, CoNLL 2011

作者： Xiong, Hao Song, Linfeng Meng, Fandong Liu, Yang Liu, Qun Lü, Yajuan Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing100190 China

ISBN: (纸本)9781937284084

This paper presents our error tolerable system for coreference resolution in CoNLL-2011(Pradhan et al., 2011) shared task (closed track). Different from most previous reported work, we detect mention candidates based on packed forest instead of single parse tree, and we use beam search algorithm based on the Bell Tree to create entities. Experimental results show that our methods achieve promising results on the development set. © 2011 Association for Computational Linguistics

关键词： Forestry

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An ultra-wideband high-isolation dual-port MIMO antenna design

An ultra-wideband high-isolation dual-port MIMO antenna desi...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Shuangqing Cao Minquan Li Zhenfa Zi Zhonghui Li Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China School of Physics and Materials Engineering Hefei Normal University Hefei China

ISBN: (数字)9798350355581

ISBN: (纸本)9798350355598

In this paper, an antenna for ultra-wideband multiple-input and multiple-output (UWB-MIMO) is designed. The design comprises two hexagonal monopole antennas. In order to achieve ultra-wideband characteristics and mitigate the coupling, a stepped microstrip line structure for impedance matching and T-shaped defective ground structure are employed, which optimizes the broadband characteristics and achieves high isolation characteristics. The antenna achieves a wide impedance bandwidth of 3.5 GHz to 13.9 GHz (relative bandwidth of 119%). Furthermore, the isolation between the two feed ports of the antenna exceeds 18 dB, the envelope correlation coefficient (ECC) is less than 0.012, and it exhibits favorable radiation characteristics.

关键词： Correlation coefficient Time-frequency analysis Impedance matching Bandwidth Microstrip antennas Ultra wideband antennas Envelope detectors Impedance Microstrip Gain

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A Novel Broadband EBG Structure For Simultaneous Switching Noise Suppression

A Novel Broadband EBG Structure For Simultaneous Switching N...

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2023 International Applied Computational Electromagnetics Society Symposium, ACES-Monterey 2023

作者： Sun, Guoxing Yan, Yeqiang Ren, Xingang Cai, Xueyuan Wu, Bo Ju, Huping Tao, Wei Wang, Gang Zou, Bao Huang, Zhixiang Wu, Xianliang Anhui Province Key Laboratory of Target Recognition and Feature Extraction Luan23700 China Engineering Technology Research Center of Specific Power Devices with Novel Semiconductor Materials Anhui Province Chizhou247100 China Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei230601 China

ISBN: (纸本)9781733509633

In this paper, a compact electromagnetic bandgap (EBG) structure for suppressing simultaneous switching noise (SSN) in high-speed circuits is proposed. At the bandwidth level of 0.35GHz to 20GHz, an ultra-wideband SSN suppression feature with a suppression depth of -40dB is achieved. The basic unit structure of the presented EBG is formed by etching an annular groove in the square patch and connecting the separated patches inside and outside the groove with a bridge adjacent to the end. An improved L-type bridge is used between the EBG units to further improve the EBG performance. Compared with the traditional L-type bridge, the suppression depth of the proposed electromagnetic bandgap structure is improved from -30dB to - 40dB. In addition, the time domain eye simulation results show that the structure has good signal integrity. © 2023 ACES.

关键词： Energy gap

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Transient Electro-thermal and Bandgap Narrowing Analysis of the bipolar transistor

Transient Electro-thermal and Bandgap Narrowing Analysis of ...

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2023 International Applied Computational Electromagnetics Society Symposium, ACES-China 2023

作者： Xiao, Hui Ren, Xingang Yan, Yeqiang Huang, Zhixiang Wu, Xianliang Anhui Province Key Laboratory of Target Recognition and Feature Extraction Luan23700 China Engineering Technology Research Center of Specific Power Devices with Novel Semiconductor Materials Anhui Province Chizhou247100 China Anhui University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Hefei230601 China

ISBN: (纸本)9781733509657

Given the bandgap narrowing (BGN) effect caused by heavy doping of the bipolar transistor and the self-heating effect (SHE) in the practical work environment, a transient electro-thermal and bandgap narrowing model is proposed in this paper, which is based on the Slotboom model and the modification of the drift-diffusion equation based on semiconductor equations and the heat conduction equation. The transient characteristics of the bipolar transistor in amplification mode are simulated using the differential method, and the results show that the terminal current of the bipolar transistor decreases after considering the SHE and the BGN effect. The model developed and the results obtained in this work can provide some reference significance for the optimization of the amplification characteristics of the bipolar transistor. © 2023 Applied Computational Electromagnetics Society (ACES).

关键词： Energy gap

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