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16th International Conference on Wireless Communications and signal processing, WCSP 2024

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

ISBN: (纸本)9798350390643

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. © 2024 IEEE.

关键词： Bit error rate

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Optimizing Antenna Design through AI Integration: A Concise Review 7

Optimizing Antenna Design through AI Integration: A Concise ...

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7th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2024

作者： Liu, Xiaotian Chung, Kwok L. Li, Wenbiao Li, Ruihong Huang, Shuwei Li, Yingsong Huizhou University School of Computer Science and Engineering Huizhou516007 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China

ISBN: (纸本)9798350384437

The integration of artificial intelligence with antenna design and optimization represents a promising research frontier. This paper provides a concise overview of current research, emphasizing the computational cost challenges associated with implementing machine learning in antenna design and optimization. It explores the advantages and limitations of utilizing the Kriging model and the Surrogate-assisted Differential Evolution Algorithm (SADEA) series to tackle these cost constraints. © 2024 IEEE.

关键词： Smart antennas

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A Microwave Sensor for Detection of Water Adulteration and Spoilage in Milk Based on Split-Ring Resonator 7

A Microwave Sensor for Detection of Water Adulteration and S...

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7th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2024

作者： Ding, Shuying Li, Yingsong Huang, Zhixiang Sinkevich, Eugene Mordachev, Vladimir Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China Belarusian State University of Informatics and Radioelectronics EMC R and D Laboratory Minsk Belarus

ISBN: (纸本)9798350384437

The paper introduces a microwave sensor based on split-ring resonators (SRR) for detecting the moisture content and spoilage of milk. This sensor is characterized by its simple structure and high sensitivity, utilizing SRR to enhance the local electric field intensity of the electromagnetic field. By measuring the transmission characteristics of microwave signals, it can accurately detect the moisture content and spoilage of milk samples. The sensor operates at a frequency of 6 GHz and has dimensions of 30×25×1.6 mm. The study establishes the relationship between resonance frequency and water content in milk through simulated data, which provides an effective method for monitoring milk quality and holds potential applications in the food industry, contributing significantly to ensuring food safety. © 2024 IEEE.

关键词： Microwave sensors

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An Evolutionary Multitasking Algorithm for Efficient Multiobjective Recommendations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 518-532页

作者： Tian, Ye Ji, Luke Hu, Yiwei Ma, Haiping Wu, Le Zhang, Xingyi Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230601 China Anhui University Institutes of Physical Science and Information Technology Hefei230601 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Hefei230029 China

Represented by evolutionary algorithms and swarm intelligence algorithms, nature-inspired metaheuristics have been successfully applied to recommender systems and amply demonstrated effectiveness, in particular, for multiobjective recommendation. Owing to the population-based search paradigm, these algorithms can produce a number of recommendation lists, making diverse tradeoffs between multiple metrics and meeting the requirements of accuracy, novelty, diversity, and other user preferences. However, these algorithms are criticized for the low efficiency of the optimization process, especially when the number of users is large. To address this issue, this article proposes an evolutionary multitasking-based recommendation method, where each task corresponds to a user and all the tasks are optimized simultaneously, thus highly improving the efficiency of recommendation. To enhance the convergence speed, all the users are divided into multiple populations according to the similarity between their preferences, where each population evolves with internal knowledge transfer between users, and all the populations evolve with external knowledge transfer between populations. Experimental results on various datasets verify that the proposed method can better balance between multiple metrics than classical and deep neural network-based recommendation methods and exhibits significantly higher efficiency than evolutionary multiobjective optimization-based recommendation methods. © 2024 IEEE. All rights reserved.

关键词： Multiobjective optimization

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Accuracy verification of sulfur dioxide data based on ultraviolet hyperspectral solar radiometer observations 3

Accuracy verification of sulfur dioxide data based on ultrav...

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3rd International Conference on Environmental Remote Sensing and Geographic Information Technology, ERSGIT 2024

作者： Zeng, Hao Wang, Shun Xun, Lina The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Anhui University Hefei China

ISBN: (纸本)9781510690219

One of the primary air pollutants that has a detrimental effect on both the environment and human health is sulfur dioxide. The ecosystem and climate greatly depend on the high-precision monitoring of sulfur dioxide. In this study, SO2 ground-based data in Xi'an and Hangzhou were calculated using the UV continuous spectral inversion method, and the accuracy was confirmed using OMI satellite data that had undergone bilinear spatial interpolation. Although there are still some discrepancies in the comparative analysis results, the results indicate a good correlation between the ground-based measurement results of the UV hyperspectral solar radiometer and OMI data products;the correlation numbers are 0.938 and 0.941, respectively, and the relative errors are roughly 10%. The ultraviolet hyperspectral solar radiometer can complement this study's findings with accuracy and is a valuable tool for environmental monitoring. © 2025 SPIE.

关键词： Air quality

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Evolutionary Multi-Objective Neural Architecture Search for Generalized Cognitive Diagnosis Models 5

Evolutionary Multi-Objective Neural Architecture Search for ...

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

作者： Yang, Shangshang Zhen, Cheng Tian, Ye Ma, Haiping Liu, Yuanchao Zhang, Panpan Zhang, Xingyi School of Artificial Intelligence Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei China Institutes of Physical Science and Information Technology Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei China Automation for Process Industries Northeastern University State Key Laboratory of Synthetical Shenyang China

ISBN: (纸本)9798350393521

Cognitive diagnosis models (CDMs) with high generalization are essential for intelligent education systems to reveal students' knowledge states in multiple datasets. However, existing CDMs' architectures are designed dependent on researcher expertise and experience from observing and summarizing partial students' learning behaviors, which makes handcrafted models not cover all learning behaviors well and thus limits their generalization. To develop generalized CDMs, this paper proposes an evolutionary neural architecture search to design CDMs' architectures effective on multiple datasets automatically. Specifically, we first formulate the search task as a multi-objective optimization problem (MOP), which maximizes model performance on multiple datasets containing learning behaviors as many as possible to ensure model generalization. Then, an expressive search space is devised to contain as many potential architectures as possible, where each architecture is denoted by a unified form, taking three given inputs and integrating them in a linear or no-linear manner for prediction. Finally, an evolutionary algorithm with a tailored deduplication strategy is employed to solve the MOP, where each architecture is further represented by a single-root computation tree for easy optimization. Experiments on multiple datasets demonstrate the generalization and effectiveness of the best architecture searched by the proposed approach, and the searched architecture also holds as good interpretability as handcrafted architectures. © 2023 IEEE.

关键词： Multiobjective optimization

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Stochastic SEIR epidemic models with virus mutation and logistic growth of susceptible populations

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Mathematics and Computers in Simulation 2023年第1期212卷 289-309页

作者： Wang, Qi Xiang, Kainan Zhu, Chunhui Zou, Lang Hunan Key Laboratory for Computation and Simulation in Science and Engineering & Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education & School of Mathematics and Computational Science Xiangtan University Hunan Province Xiangtan City411105 China National Center for Applied Mathematics in Hunan China

This article introduces a kind of stochastic SEIR models containing virus mutation and logistic growth of susceptible populations, whose deterministic versions have a positively invariant set and globally asymptotically stable equilibrium points. For these stochastic SEIR epidemic models, we prove they have a unique global positive solution, and also obtain sufficient conditions respectively for survival and extinction of the infectious disease. Eventually, we validate our theoretical findings using numerical simulations. © 2023 International Association for Mathematics and Computers in Simulation (IMACS)

关键词： Viruses

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Optimal Dispatch Strategy of Electric Vehicles under Coupled Transportation Network and Power Grid 8

Optimal Dispatch Strategy of Electric Vehicles under Coupled...

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

作者： Luo, Keming 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

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. © 2023 IEEE.

关键词： Charging Dispatch Coupled Transportation Network and Power Grid Dynamic Pricing Mechanism Electric Vehicles (EVs)

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An Optimization Method Based on Deep Learning for Electromagnetic Inverse Scattering Problems

An Optimization Method Based on Deep Learning for Electromag...

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2024 International Applied computational Electromagnetics Society Symposium, ACES-China 2024

作者： Hu, Wangchao Xie, Guoda Dai, Jinpeng Hou, Guilin Li, Xiang Huang, Zhixiang Anhui University The Key Laboratory of Intelligent Computing and Signal Processing Hefei China Anhui Province Key Laboratory of Target Recognition and Feature Extraction Education of Anhui Province Luan237000 China Ministry of Education The Key Laboratory of Intelligent Computing and Signal Processing Hefei China Anhui Province Anhui University Key Laboratory of Electromagnetic Environmental Sensing Hefei230601 China Anhui University Hefei China

ISBN: (纸本)9798350355581

In this paper, a deep learning optimization method combining U-net model and cycle generative adversarial network (Cycle-GAN) is proposed to efficiently solve the electromagnetic inverse scattering (EMIS) problems. Firstly, a U-net model is used to obtain a rough preliminary reconstruction. Through down-sampling and up-sampling processes, the basic features of the object are extracted and reconstructed from the measured scattered field, providing a coarse dielectric parameter distribution. Then, a Cycle-GAN model further refines the output of the U-net. The Cycle-GAN model, through its adversarial training mechanism involving a generator and a discriminator, enhances the details of the preliminary reconstruction, resulting in a finely detailed dielectric parameter distribution his method improves the problem of rough results obtained by using U-net method alone, and can effectively solve and optimize EMIS problems. © 2024 IEEE.

关键词： Generative adversarial networks

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Stackelberg Game Model for Optimal Dispatching of Electricity Consumption for Community Microgrid Considering Demand Response Mechanism 8

Stackelberg Game Model for Optimal Dispatching of Electricit...

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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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