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8th International Conference on Power and Renewable Energy, ICPRE 2023

作者： Tan, Di Fang, Xiaohan Fan, Yuan Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Hefei230601 China

ISBN: (纸本)9798350328813

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. © 2023 IEEE.

关键词： Community microgrid Demand response mechanism Genetic algorithm Internal prices Stackelberg game model

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Optimization Strategies for Hydrogen Mixing Scheduling in Natural Gas Networks in Integrated Energy Systems 8

Optimization Strategies for Hydrogen Mixing Scheduling in Na...

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8th International Conference on Power and Renewable Energy, ICPRE 2023

作者： Zhang, Zixun Fang, Xiaohan Fan, Yuan Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Hefei230601 China

ISBN: (纸本)9798350328813

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. © 2023 IEEE.

关键词： hybrid hydrogen transportation hydrogen energy Integrated energy systems natural gas pipeline network

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An Empirical Study on Neural Networks Pruning: Trimming for Reducing Memory or Workload 5

An Empirical Study on Neural Networks Pruning: Trimming for ...

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5th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2023

作者： Xiao, Kaiwen Cai, Xiaodong Xu, Ke School of Artificial Intelligence Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei China

ISBN: (纸本)9798350393521

Most of existing studies on neural network pruning only consider memory-based pruning strategies. However pruning for computational workload is often more important in hardware deployments due to a greater focus on model computation reductions. In addition, most pruning schemes restore model accuracy during pruning at the expense of adding hyperparameters, extending training time and training complexity. This work proposes a statistical-based globally soft iterative pruning scheme. With little extra calculation, an extremely sparse model can be obtained without additional hyperparameters and extended training time. Moreover, this work proposes the concept of computational intensity to balance model memory and computational workload during pruning. Focusing on memory orientated pruning, we can achieve 303×, 100× and 25× parameter compression on LeNet-5 (MNIST), VGG (CIFAR-10) and AlexNet (ImageNet) models, respectively. In particular, combined with cluster quantization, the LeNet-5 model parameters can be compressed to 3232×. Focusing on workload orientated pruning, we can reduce the computation by 7}.6× on the AlexNet model, without accuracy loss, significantly higher than prior work. In addition, in order to verify the versatility of the pruning method, we also migrate the pruning task to the object detection and implement 10× parameter compression and 2.8× computation compression for YOLOv2 with reduced mAP within 1%. © 2023 IEEE.

关键词： Iterative methods

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Real-time pricing method through market auction based on multi-agent reinforcement learning 8

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

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8th International Conference on Power and Renewable Energy, ICPRE 2023

作者： Hong, Peng He, Shuping Fang, Xiaohan Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Hefei230601 China

ISBN: (纸本)9798350328813

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. © 2023 IEEE.

关键词： Market auction Multi-agent reinforcement learning Real-time pricing

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A small target detection algorithm for aerial image with improved YOLOv5 3

A small target detection algorithm for aerial image with imp...

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3rd International Conference on Electronic Information Engineering, Big Data, and Computer Technology, EIBDCT 2024

作者： Xue, Xianli Jiao, Weidong Zhang, Siyuan Qu, Wenkang Tianjin Key Laboratory of Intelligent Signal and Lmage Processing Civil Aviation University of China TIANJIN300300 China

ISBN: (纸本)9781510680449

The problem of complex backgrounds, dramatic changes in the scale of objects and the presence of a large number of dense small targets in the UAV aerial images. In this study, a detection algorithm for small targets is proposed based on the YOLOv5s algorithm. Firstly, a new BottleNeck: C3LK module is designed for the C3 module using depthwise separable convolution to construct a large convolutional kernel on the YOLOv5 backbone network, which improves the ability to capture global information and compensates for the lack of local information for detecting small targets. Secondly, a small target detection layer and two feature fusion layers are added. At the same time, In order to better detect objects of different scales in complex backgrounds, a module for multi-scale feature extraction is designed by combining dilated convolution and attention mechanism. The experimental results show that the proposed algorithm improved 5.4% over YOLOv5s in terms of mAP@0.5 and over YOLOv7-tiny. The method meet the requirement of real-time detection in aerial image small target detection, and improve the detection performance in small target scenes. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Feature extraction

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A Portrait Segmentation Algorithm Combining MobileNeXt and Attention Mechanism 24

A Portrait Segmentation Algorithm Combining MobileNeXt and A...

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2024 International Conference on computing, Machine Learning and Data Science, CMLDS 2024

作者： Cheng, Yan Huang, Zou Wang, Guixi Lai, Jianming Xiong, Yuqi School of Software Jiangxi Normal University Jiangxi Provincial Key Laboratory of Intelligent Information Processing and Affective Computing China School of Digital Industry Jiangxi Normal University Jiangxi Provincial Key Laboratory of Intelligent Information Processing and Affective Computing China

ISBN: (纸本)9798400716393

Existing portrait segmentation methods are easily affected by the background. To address this challenge, we propose a simple convolution-based portrait segmentation algorithm to solve the problems of complex background, low accuracy and poor efficiency in real environments. Combined with multi-scale convolution, we design a multi-scale sandglass module structure (MultiScale-Sandglass-CA, MSC), which expands the depth and width of the network without increasing the number of parameters, and enhances the feature learning ability of the model for complex backgrounds;and the MSC module is used for feature extraction, and the introduction of coordinate attention mechanism not only improves the learning ability of the portrait features, but also enhances the network's ability to learn the key information. The introduction of the coordinate attention mechanism not only improves the learning ability of the portrait features, but also enhances the focusing of the network on the key information;at the same time, the introduction of the hybrid loss function, which helps to obtain clear portrait edges. Experiments are conducted on Matting and EG1800 public datasets, and the results show that the algorithm can significantly improve the mean intersection and merger ratio (mIoU) and pixel accuracy (Acc), etc. The mIoU can reach up to 97%, and the pixel accuracy can reach up to 10%. Among them, the mIoU index can reach 97.91% (Matting) and 94.72% (EG1800). Compared with the mIoU predicted by PortraitNet, it is 1.29% and 1.19% higher, respectively. In addition, experiments are conducted for the MSC structure proposed in this paper, which proves the superiority of the model in this paper. © 2024 ACM.

关键词： Convolution

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A Wideband GCPW-SIW Fed Slots Antenna for Millimeter-Wave Applications

A Wideband GCPW-SIW Fed Slots Antenna for Millimeter-Wave Ap...

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

作者： Run, Penghao Sun, Yufa Anhui University Key Lab of Intelligent Computing and Signal Processing Ministry of Education Hefei230601 China

ISBN: (纸本)9781733509657

A wideband GCPW-SIW fed slots antenna in millimeter wave band is designed in this paper. The structure is composed of four inclined slots which are placed on the one side of the SIW with three shorting vias and fed by GCPW transmission line. The simulated results show that the operating frequency band of the proposed antenna is 24.15-27.59GHz (13.23%), the antenna has a stable gain with a maximum value of 9.67dBi and high radiation efficiency. © 2023 Applied Computational Electromagnetics Society (ACES).

关键词： Slot antennas

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Broadband Circularly Polarized Hybrid Metasurface Antenna for Wearable Devices

Broadband Circularly Polarized Hybrid Metasurface Antenna fo...

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

作者： Liu, Zhiyi Sun, Yufa Anhui University Key Lab of Intelligent Computing & Signal Processing Ministry of Education HeFei230601 China

ISBN: (纸本)9781733509657

A 38×38×4mm3 wideband circularly polarized (CP) antenna with Hybrid Metasurface (MTS) for wearable devices is proposed. The proposed antenna achieves CP radiation by cutting corners on a square microstrip patch. A hybrid MTS is placed above a square patch with cut corners, achieving broadband radiation performance. The working mechanism of hybrid MTS is analyzed by characteristic mode analysis (CMA). The simulated -10dB bandwidth is 40.1% (4.61-6.82 GHz) and the 3dB axial ratio bandwidth is 25.2% (5.16-6.55 GHz) with a peak gain of 8.1 dBi. In addition, the antenna has a front-to-back ratio of approximately 20 dB, which makes it suitable for wearable applications. © 2023 Applied Computational Electromagnetics Society (ACES).

关键词： Bandwidth

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PRPFD: Polynomial Resampling Particle Filter Based Deconvolution Approach for Resting-State BOLD-fMRI Data Analysis 24

PRPFD: Polynomial Resampling Particle Filter Based Deconvolu...

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5th International Symposium on Artificial Intelligence for Medicine Science, ISAIMS 2024

作者： Luo, Yunying Zeng, Weiming Key Laboratory for Digital Imaging Processing and Intelligent Computing School of Information Engineering Shanghai Maritime University Shanghai China

ISBN: (纸本)9798400717826

Functional Magnetic Resonance Imaging (fMRI) stands out in brain science research due to its non-invasive, non-intrusive, radiation-free, high spatial resolution, and precise localization advantages. However, the BOLD-fMRI signals it generates exhibit a several-second delay compared to neural activity, making it unable to reflect brain neural activity directly. This discrepancy may lead to differences from the actual situation. Therefore, this study aims to enhance the deconvolution of fMRI by refining existing methods to reconstruct the brain’s neural signals more faithfully. Building upon the foundation of particle filtering deconvolution, this paper introduced a novel approach to deconvolving resting-state fMRI data: the polynomial resampling particle filtering deconvolution (PRPFD). They adapted the particle distribution better to match the actual state through weight computation and resampling. We performed a simulation-based experiment to validate the feasibility of our approach. Besides, we validated the performance of our method using group data, focusing on both effective connectivity and functional connectivity, providing strong evidence for the usability and stability of our approach, and compared our method with other deconvolution techniques, demonstrating improvements in accuracy and computational efficiency. © 2024 Copyright held by the owner/author(s).

关键词： Polynomials

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APFA:Ameliorated Pathfinder Algorithm for Engineering Applications

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Journal of Bionic Engineering 2024年第3期21卷 1592-1616页

作者： keyu Zhong Fen Xiao Xieping Gao Key Laboratory of Computing and Stochastic Mathematics of Ministry of Education Hunan Normal UniversityChangsha410081China School of Mathematics and Statistics Hunan Normal UniversityChangsha410081China School of Computer Science Xiangtan UniversityXiangtan411105China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal UniversityChangsha410081China

Pathfinder algorithm(PFA)is a swarm intelligent optimization algorithm inspired by the collective activity behavior of swarm animals,imitating the leader in the population to guide followers in finding the best food *** algorithm has the characteristics of a simple structure and high ***,PFA faces challenges such as insufficient population diversity and susceptibility to local optima due to its inability to effectively balance the exploration and exploitation *** paper proposes an Ameliorated Pathfinder Algorithm called APFA to solve complex engineering optimization ***,a guidance mechanism based on multiple elite individuals is presented to enhance the global search capability of the ***,to improve the exploration efficiency of the algorithm,the Logistic chaos mapping is introduced to help the algorithm find more high-quality potential solutions while avoiding the worst ***,a comprehensive following strategy is designed to avoid the algorithm falling into local optima and further improve the convergence *** three strategies achieve an effective balance between exploration and exploitation overall,thus improving the optimization performance of the *** performance evaluation,APFA is validated by the CEC2022 benchmark test set and five engineering optimization problems,and compared with the state-of-the-art metaheuristic *** numerical experimental results demonstrated the superiority of APFA.

关键词： Pathfinder algorithm Swarm intelligent Metaheuristic Engineering problems

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