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International Conference on Data Engineering

作者： Zhenhua Huang Kunhao Li Shaojie Wang Zhaohong Jia Wentao Zhu Sharad Mehrotra Anhui University Hefei China Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Hefei China Amazon Research Seattle USA University of California Irvine Irvine USA

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Despite the Graph Neural Networks' (GNNs) pro-ficiency in analyzing graph data, achieving high-accuracy and interpretable predictions remains challenging. Existing GNN interpreters typically provide post-hoc explanations disjointed from GNNs' predictions, resulting in misrepresentations. Self-explainable GNNs offer built-in explanations during the training process. However, they cannot exploit the explanatory outcomes to augment prediction performance, and they fail to provide high-quality explanations of node features and require additional processes to generate explainable subgraphs, which is costly. To address the aforementioned limitations, we propose a self-explained and self-supervised graph neural network (SES) to bridge the gap between explainability and prediction. SES comprises two processes: explainable training and enhanced predictive learning. During explainable training, SES employs a global mask generator co-trained with a graph encoder and directly produces crucial structure and feature masks, reducing time consumption and providing node feature and subgraph explanations. In the enhanced predictive learning phase, mask-based positive-negative pairs are constructed utilizing the ex-planations to compute a triplet loss and enhance the node representations by contrastive learning. Extensive experiments demonstrate the superiority of SES on multiple datasets and tasks. SES outperforms baselines on real-world node classification datasets by notable margins of up to 2.59% and achieves state-of-the-art (SOTA) performance in explanation tasks on synthetic datasets with improvements of up to 3.0%. Moreover, SES delivers more coherent explanations on real-world datasets, has a fourfold increase in Fidelity+ score for explanation quality, and demonstrates faster training and expla-nation generating times. To our knowledge, SES is a pioneering GNN to achieve SOTA performance on both explanation and prediction tasks.

关键词： Training Bridges Accuracy Reliability engineering Data engineering Graph neural networks Generators

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Optimization Strategies for Hydrogen Mixing Scheduling in Natural Gas Networks in Integrated Energy Systems

Optimization Strategies for Hydrogen Mixing Scheduling in Na...

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

作者： Zixun Zhang 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

Developing a clean and efficient Integrated Energy System (IES) is critical to optimizing the use of renewable energy sources and harnessing new forms of energy to achieve the country’s 2030 carbon peak and 2060 carbon neutrality targets. Existing natural gas pipelines in China facilitate the transport of hydrogen blended into the natural gas network and offer the possibility of storing hydrogen blended into natural gas pipelines. This paper proposes an optimal integrated energy dispatch strategy based on electricity-gas-hydrogen-heat-cooling demand, taking into account carbon emissions and economic efficiency, where renewable energy is no longer connected to the grid or stored in batteries, but converted to hydrogen mixed into the natural gas grid for transport and storage, with the economic objective of minimizing the total IES operating cost, the wind and light abandonment cost and the carbon emission cost. The CPLEX solver is used to solve the problem, and the feasibility of the proposed model is verified by simulation results, which promotes the consumption of renewable and clean energy.

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Enhanced Target Localization based on Double RIS

Enhanced Target Localization based on Double RIS

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International Symposium on Wireless Communication Systems (ISWCS)

作者： Lei He Liping Li Yingsong Li Paulo S. R. Diniz Run Ying The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei The Universidade Federal do Rio de Janeiro Rio de Janeiro

ISBN: (数字)9798350362510

ISBN: (纸本)9798350362527

Reconfigurable intelligent Surface (RIS) has become an enabling technology for economically and efficiently realizing intelligent and reconfigurable wireless communication environments. Previous positioning research based on RIS has mainly focused on single RIS without considering the improvement in positioning accuracy brought by double RIS, thus failing to reveal the full potential of a double RIS-assisted wireless communication system. This paper investigates a target positioning system assisted by double RIS, where double RIS is used to enhance communication between users and the receiver. Firstly, the reception signal model based on the collab.rative effect of double RIS is constructed, and then we formulate the target positioning problem based on this model, aiming to obtain the optimal target signal while minimizing system noise. Moreover, to verify the effectiveness of the localization system with double RIS assistance, we also configured a scenario of a single RIS localization system for comparative analysis. Simulation results indicate that the proposed target positioning method based on double RIS exhibits certain superiority in improving system positioning performance compared to single RIS.

关键词： Location awareness Wireless communication Accuracy Simulation Noise Estimation Collab.ration

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Linear Precoding for Faster-Than-Nyquist signaling with Imperfect Channel State Information

Linear Precoding for Faster-Than-Nyquist Signaling with Impe...

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International Conference on Wireless Communications and signal processing (WCSP)

作者： Yan Wang Qiang Li Yingsong Li Liping Li Xingwang Li Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China School of Physics and Electronic Information Engineering Henan Polytechnic University China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

In faster-than-Nyquist (FTN) transmission over frequency-selective fading channels, it is essential to eliminate both FTN-induced intersymbol-interference (ISI) (FTN-ISI) and channel-induced ISI (channel-ISI). Although frequency domain equalization (FDE) techniques can mitigate channel-ISI at the receiver, their performance is often inadequate. In contrast, cyclic prefix/suffix (CPS)-G-to-minus-half (GTMH) precoding shows superior effectiveness in mitigating channel-ISI. However, existing CPS-GTMH precoding techniques assume the transmitter obtains perfect channel state information (CSI) estimated at the receiver. This is impractical due to the high latency in satellite communication links. Therefore, this paper explores precoding for FTN signaling under imperfect CSI (ICSI) and proposes CPS-minimum mean squared error (MMSE) and linear pre-equalization (LPE)-MMSE precoding schemes based on the MMSE criterion. Both schemes employ a cyclic prefix to transform the channel-ISI matrix into the circular matrix, thereby avoiding inter-block interference. The primary distinction is that CPS-MMSE precoding handles FTN-ISI and channel-ISI together using MMSE precoding. In contrast, LPE-MMSE precoding addresses FTN-ISI with LPE and channel-ISI with MMSE precoding separately. Simulation results indicate that CPS-MMSE and LPE-MMSE precodings outperform CPS-FDE and LPE-FDE in mitigating channel-ISI when the estimation error is 0.005 and 0.01. For instance, with a packing ratio of 0.9, a roll-off factor of 0.25, and an estimation error of 0.01, CPS-MMSE and LPE-MMSE precodings achieve bit error rate (BER) gains of approximately 0.8 dB and 1 dB at a BER of 1e4, respectively. However, when the estimation error increases to 0.1, the BER performance of CPS-FDE and LPE-FDE surpasses that of the CPS-MMSE and LPE-MMSE precodings, indicating the proposed precoding schemes' unsuitability for larger channel estimation errors.

关键词： Estimation error Precoding Simulation Bit error rate Channel estimation Interference Receivers Matrix converters Satellite communications Channel state information

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Stackelberg Game Model for Optimal Dispatching of Electricity Consumption for Community Microgrid Considering Demand Response Mechanism

Stackelberg Game Model for Optimal Dispatching of Electricit...

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

作者： Di Tan 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

Community microgrid is a kind of new power system that acts as an intermediary between community consumers and the grid, playing an important role in realising distributed autonomy and improving the power supply level. To achieve optimal power dispatch in community microgrid, this paper establishes a Stackelberg game model with the community microgrid operator (CMO) as the leader and the consumers aggregator as the follower, and combines the internal pricing method with the demand response mechanism. The paper uses a combination of genetic algorithm and solver to solve the Stackelberg game model to maximise the interests of all parties. Simulation results show that the model can secure the interests of each game subject, balance power supply and demand, and promote peak shaving and valley filling of electrical loads.

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Economic Scheduling of Multi-microgrid Systems Based on Distributed Event-triggered Consensus Strategy

Economic Scheduling of Multi-microgrid Systems Based on Dist...

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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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A High Isolation Design Used Between Broadband Dual Polarization Transceiver Antennas

A High Isolation Design Used Between Broadband Dual Polariza...

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International Symposium on Antennas, Propagation and EM Theory (ISAPE)

作者： Yuke Guo Xiaosheng Zhang Luyu Zhao National Key Laboratory of Radar Detection and Sensing Xidian University Xi'an China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China

ISBN: (数字)9798350353129

ISBN: (纸本)9798350353136

We proposed a high isolation design between two broadband dual polarization transceiver antennas in this paper. It employs periodic metal isolation walls with equal spacing and height to strongly suppress the low-frequency band (LB) coupling. Covering the vertical and horizontal surfaces of the metal with flexible graphene high resistive metasurfaces that easily conform to the structure improves 40 dB in isolation between different polarizations at 6–18 GHz. This device has less impact on the radiation pattern of the antenna. This design not only enhances the isolation between different polarizations but also optimizes equipment heat dissipation and improves device performance.

关键词： Performance evaluation Heating systems Couplings Graphene Metals Metasurfaces Transceivers Broadband communication Broadband antennas Antenna radiation patterns

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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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SGCN: A Scalab.e Graph Convolutional Network with Graph-Shaped Kernels and Multi-Channels

SSRN

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SSRN 2023年

作者： Huang, Zhenhua Zhou, Wenhao Li, Kunhao Jia, Zhaohong School of Internet Anhui University Feixi Road Hefei230039 China Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Hefei China

Graph neural networks (GNNs) have shown great success in graph processing. However, current message-passing-based GNNs have limitations in feature aggregations and update mechanisms that rely on a fixed mode, resulting in inadequate representations of the neighborhood structure's richness. Furthermore, the convolution layer in most GNNs lacks flexibility and multi-channels compared to convolutional neural networks (CNNs). CNNs employ multiple channels, where different kernels capture shared patterns of images. To address the limitations, we propose a novel scalab.e graph convolution network (SGCN). SGCN enhances structural information learning by introducing an expressive feature aggregation mechanism. Drawing inspiration from the design of CNNs, SGCN designs graph-shaped kernels to perform multi-channel convolutions on the subgraph structure. A sampler based on degree centrality is employed to simplify the computation costs. Extensive experiments demonstrate that SGCN achieved state-of-the-art performances on various scales of graph datasets (accuracy improved up to 5.38%). © 2023, The Authors. All rights reserved.

关键词： Convolution

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