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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Dou, Jiangling Zhao, Chenyang Song, Jian Wang, Qingwang Shen, Tao Kunming University of Science and Technology School of Information Engineering and Automation Kunming650500 China Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China Kunming University of Science and Technology Graduate School Kunming650500 China

ISBN: (纸本)9798350375909

As antenna structure grow in complexity, the problem of time-consuming in electromagnetic (EM) simulation have become more common. This problem undoubtedly brings a series of the challenges in raising the effectiveness of antenna optimization. In order to overcome the existing difficulties, an antenna optimization method based on RF-GWO method is proposed. This approach consists of three major components. Firstly, an artificial neural network (ANN) is used to build the dataset. Compared with the traditional sampling method, the data filtered by the artificial neural network (ANN) are closer to the optimization objective. Consequently, it can better responsive to the relationship between the antenna parameters and the optimization objective. Then, the Random Forest method (RF) is utilized to analyze the impact of the ten selected parameters based on the electromagnetic (EM) simulation results on the antenna performance. Random Forest Algorithm (RF) has strong interpretability, allowing for the assessment of the contribution of different parameters to the optimization objective, which can effectively reduce the number of design variables. Finally, the antenna optimization is performed utilizing the Grey Wolf Optimization (GWO) algorithm. Combined with the sensitivity analysis results, the Gray Wolf Algorithm (GWO) can effectively search for improved results in a faster manner. In order to evaluate the algorithm's performance, the proposed method is employed to optimize a multiband miniaturized microstrip antenna. The result demonstrates that substantial improvement in impedance bandwidth is achieved by the proposed method, with an approximate 29% increase compared to conventional manual optimization methods. This outcome has proved potential and effectiveness of the proposed method in practical implications of antenna optimization. © 2024 IEEE.

关键词： Sensitivity analysis

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A Novel Two-Stream Algorithm for Spatio-Temporal Fusion in Remote Sensing

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IEEE Geoscience and Remote Sensing Letters 2025年

作者： Wang, Shiying Zou, Xuechao Wang, Wentao Liu, Yao Zhang, Shun Cao, Tengfei Tao, Pin Qinghai University School of Computer Technology and Application Xining China Qinghai University Intelligent Computing and Application Laboratory of Qinghai Province China School of Computer Science & Technology Beijing China Tsinghua University Department of Computer Science and Technology Beijing China Ministry of Education Key Laboratory of Pervasive Computing China

The spatio-temporal fusion technology can effectively solve the problem of missing time series data. However, existing algorithms often struggle to accurately capture surface feature changes and fine details. To overcome these limitations, this paper introduces FemSTF, a flexible two-stream algorithm for remote sensing spatio-temporal fusion, which consists of three main stages: image sharpening, image fusion, and edge optimization. In the first stage, techniques such as interpolation, classification, and blending are employed to address missing pixel values caused by minimal input data. In the second stage, a two-stream strategy is introduced to enhance fusion accuracy, enabling the algorithm to capture regional detail changes with high sensitivity to surface variations. The third stage focuses on edge optimization, generating high-resolution predictive images under minimal input conditions (three images) while retaining rich texture and edge details. Notably, FemSTF achieves a performance comparable to that of methods requiring five input images. Experimental results demonstrate that FemSTF outperforms mainstream algorithms across multiple datasets. Ablation experiments confirm the effectiveness of the two-stream strategy, highlighting its role in enhancing remote sensing data processing accuracy. This study offers an efficient solution for spatio-temporal fusion, demonstrating great potential in the field. © 2004-2012 IEEE.

关键词： Spatio-temporal data

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A Single-Loop Narrow-Band Force Rebalance Control Method with Temperature Self-Compensation for a MEMS Gyroscope 38

A Single-Loop Narrow-Band Force Rebalance Control Method wit...

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38th IEEE International Conference on Micro Electro Mechanical Systems, MEMS 2025

作者： He, Chunhua Xu, Yingyu Wu, Heng Huang, Qinwen Zhao, Qiancheng Yan, Guizhen Guangdong University of Technology School of Computer China Ceprei Science and Technology on Reliability Physics and Application Technology of Electronic Component Laboratory China Guangdong University of Technology School of Automation China Peking University School of Integrated Circuits Beijing China National Key Lab of Micro/Nano Fabrication Technology Beijing China

ISBN: (纸本)9798331508890

This paper presents a novel single-loop narrow-band force rebalance control method with temperature self-compensation for a MEMS gyroscope. The narrow-bandwidth force rebalance control for the sense mode is achieved using a simple bandpass filter (BPF) and without modulation or demodulation. The center frequency of the BPF is adjusted automatically to match the resonant frequency of the sense mode. Meanwhile, its gain is tuned based on the real-time quality factor of the sense mode. Experimental results demonstrate that the bias drift of the gyroscope with self-compensation is reduced from 1320 deg/h to 16 deg/h, while the scale factor drift is reduced from 0.616 mV/deg/s to 0.075 mV/deg/s. © 2025 IEEE.

关键词： Gyroscopes

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Mobile edge computing-enabled blockchain: contract-guided computation offloading

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Journal of Supercomputing 2023年第7期79卷 7970-7996页

作者： Li, Yijun Lin, Ziqiong Zhang, Wenjie Zheng, Yifeng Yang, Jingmin School of Computer Sciences Minnan Normal University Fujian Zhangzhou363000 China Key Laboratory of Data Science and Intelligence Application Fujian Province University Fujian Zhangzhou363000 China Department of Electronic Engineering National Taipei University of Technology Taipei106335 Taiwan

The core problem of blockchain is to solve the proof-of-work puzzle, which is a high energy-consumption task. To address the resource limit problem, the combination of blockchain and mobile edge computing (MEC) has attracted widespread concern. In this paper, we consider a resource trading problem for a MEC-enabled blockchain system with multiple miners and one edge computing service provider (ESP), in which ESP has no previous knowledge of miners’ private information. We focus on how to perform resource trading between miners and ESP to maximize ESP’s profit under incomplete information? To tackle this problem, the resource trading process is modeled as a monopoly market using contract theory. The ESP acts as a monopolist and needs to set a contract containing service time and price for the miners. The miners can be classified into multiple types based on the private information and select one contract item designed for its type. We first analyze the necessary and sufficient conditions for feasible contracts and derive the optimal contract that maximizes the ESP’s profit for complete information scenario. For incomplete information, the service demand and the number of transactions contained in the block are the private information of miners and not known by ESP, we explore the properties of a feasible contract to reduce the complexity and propose a Lagrange multiplier algorithm to obtain the optimal contract in this scenario. Finally, the feasibility and optimality of the contract have been shown through simulation results. In addition, our proposed method can achieve results close to that of complete information scenario and has a maximum performance improvement of 17.19% comparing with linear pricing strategy in incomplete information scenario. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Blockchain

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Enhanced Fine-Tuning of Lightweight Domain-Specific Q&A Model Based on Large Language Models 35

Enhanced Fine-Tuning of Lightweight Domain-Specific Q&A Mode...

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35th IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2024

作者： Zhang, Shenglin Zhu, Pengtian Ma, Minghua Wang, Jiagang Sun, Yongqian Li, Dongwen Wang, Jingyu Guo, Qianying Hua, Xiaolei Zhu, Lin Pei, Dan Nankai University China Haihe Laboratory of Information Technology Application Innovation China Microsoft Tsinghua University China Tianjin Key Laboratory of Software Experience and Human Computer Interaction China China Mobile Research Institute China Beijing National Research Center for Information Science and Technology China

ISBN: (纸本)9798350367041

Large language models (LLMs) excel at general question-answering (Q&A) but often fall short in specialized domains due to a lack of domain-specific knowledge. Commercial companies face the dual challenges of privacy protection and resource constraints when involving LLMs for fine-tuning. This paper propose a novel framework, Self-Evolution, designed to address these issues by leveraging lightweight open-source LLMs through multiple iterative fine-tuning rounds. To enhance the efficiency of iterative fine-tuning, Self-Evolution employ a strategy that filters and reinforces the knowledge with higher value during the iterative process. We employed Self-Evolution on Qwen1.5-7B-Chat using 4,000 documents containing rich domain knowledge from China Mobile, achieving a performance score 174% higher on domain-specific question-answering evaluations than Qwen1.5-7B-Chat and even 22% higher than Qwen1.5-72B-Chat. Self-Evolution has been deployed in China Mobile's daily operation and maintenance for 117 days, and it improves the efficiency of locating alarms, fixing problems, and finding related reports, with an average efficiency improvement of over 18.6%. In addition, we release Self-Evolution framework code in https://***/Zero-Pointer/Self-Evolution. © 2024 IEEE.

关键词： Data mining

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Specific Emitter Identification based on CNN and Transformer 5

Specific Emitter Identification based on CNN and Transformer

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5th International Academic Exchange Conference on Science and technology Innovation, IAECST 2023

作者： Wang, Bin Gao, Ning Wang, Feng The 38th Research Institute of China Electronics Technology Group Corporation Key Laboratory of Aperture Array and Space Application Key Laboratory of Intelligent Information Processing Anhui Hefei230088 China Hefei University of Technology School of Computer and Information Hefei230601 China

ISBN: (纸本)9798350357738

Specific emitter identification(SEI) plays an increasingly crucial and potential role in both military and civilian scenarios. It refers to a process to discriminate individual emitters from each other by analyzing extracted characteristics from given radio signals. In this paper, we present a novel radiation source identification method named CNNFormer based on a hybrid architecture of Convolutional Neural Network and Transformer, harnessing the strengths of both architectures to enhance feature extraction and sequence modeling. We demonstrate the effectiveness of our approach on simulation datasets, achieving state-of-the-art accuracy in emitter identification. We showcase outstanding performance of our method on simulation datasets, achieving 98.29% three-class identification accuracy the emitter dataset with three devices and 97.60% ten-class identification accuracy on an RF fingerprint dataset with ten XSPR transmitters. Furthermore, our model allows for insight into the key features contributing to identification. This work contributes to enhancing the security and reliability of wireless communication systems. © 2023 IEEE.

关键词： Convolutional neural networks

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Multi-Scale Automated Self-Supervised Learning for Recommendation

Multi-Scale Automated Self-Supervised Learning for Recommend...

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2023 International Conference on Intelligent Management and Software Engineering, IMSE 2023

作者： Yan, Yuhang Gao, Yujie Tian, Liqin School of Computer Science Qinghai Normal University Xining China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining China School of Computer North China Institute of Science and Technology Yanjiao China

ISBN: (纸本)9798350394146

Automated self-supervised learning frameworks have shown significant results in the recommendation domain. However, existing methods such as AutoCF use graph convolutional networks and graph attention mechanisms that are limited to a single expression granularity and cannot fully capture the multi-granularity of user interests and product attributes. To address this problem, we propose MS-AutoCF, a multi-scale automated self-supervised learning recommender system that obtains user and product representations at different granularities through multi-layer graph convolution to match the global and local details of their interest preferences. Meanwhile, we design a query key value-based multi-head graph attention module that can adaptively aggregate and enhance the representations with different granularities. Experimental results show that the MS-AutoCF system achieves significant improvements over existing single-scale methods on several public datasets. ©2023 IEEE.

关键词： Supervised learning

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TMAS:A transaction misbehavior analysis scheme for blockchain

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Blockchain(Research and applications) 2024年第3期5卷 42-52页

作者： Shiyong Huang Xiaohan Hao Yani Sun Chenhuang Wu Huimin Li Wei Ren Kim-Kwang Raymond Choo School of Computer Science China University of GeosciencesWuhan430074China Artificial Intelligence Thrust Information HubHong Kong University of Science and TechnologyGuangzhou511400China Yunnan Key Laboratory of Blockchain Application Technology Yunnan Innovation Institute of Beihang UniversityKunming650233China Fujian Key Laboratory of Financial Information Processing Putian UniversityPutian351100China Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX 78249-0631USA

Blockchain-based cryptocurrencies,such as Bitcoins,are increasingly ***,the decentralized and anonymous nature of these currencies can also be(ab)used for nefarious activities such as money laundering,thus reinforcing the importance of designing tools to effectively detect malicious transaction *** this paper,we propose TMAS,a transaction misbehavior analysis scheme for blockchain-based ***,the proposed system includes ten features in the transaction graph,two heuristic money laundering models,and an analysis method for account linkage,which identifies accounts that are distinct but controlled by an identical *** evaluate the effectiveness of our proposed indicators and models,we analyze 100 million transactions and compute transaction features,and are able to identify a number of suspicious ***,the proposed methods can be applied to other cryptocurrencies,such as token-based cryptocurrencies(e.g.,Bitcoins)and account-based cryptocurrencies(e.g.,Ethereum).

关键词： Blockchain Anti-money laundering Bitcoin Cyptocurrency

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Research on expressway traffic flow prediction model based on MSTA-GCN

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 9317-9328页

作者： Ning, Tao Wang, Jiayu Duan, Xiaodong Institute of Computer Science and Engineering Dalian Minzu University Dalian116000 China Big Data Application Technology Key Laboratory of State Ethnic Affairs Commission Dalian Minzu University Dalian116000 China Institute of Information Dalian Maritime University Dalian116021 China

The traffic flow prediction of expressway is highly nonlinear and complex, so it is technically and operationally difficult to predict it accurately. In order to predict the traffic flow of expressway and alleviate its traffic congestion, a multi-component spatial–temporal graph convolution network model based on attention mechanism (MSTA-GCN) is proposed, which combines GCN, standard CNN and attention mechanism. Firstly, near period, daily period and weekly period are modeled to capture the nonlinear correlation characteristics in multivariable urban spatial–temporal series. Then input the sample data into the integrated model for training and extract the characteristics of traffic flow data. Finally, the model is tested on California Expressway PeMS D7 dataset. The experimental results show that MSTA-GCN is superior to classical shallow learning model, benchmark deep learning model and other traffic flow deep learning models, and has lower prediction error. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Convolution

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Cooperative Carrier Aircraft Support Operation Scheduling via Multi-Agent Reinforcement Learning 24

Cooperative Carrier Aircraft Support Operation Scheduling vi...

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24th IEEE International Conference on Mobile Data Management, MDM 2023

作者： Hao, Hongjie Zhang, Xueqin Chi, Yuan Gao, Rongxin Xie, Anke Xu, Mingliang Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou China Beihang University Yunnan Innovation Institute Kunming China Yunnan Key Laboratory of Blockchain Application Technology Kunming China

ISBN: (纸本)9798350341010

Carrier Aircraft support operation is an important component of the carrier aviation support system, and its efficiency is closely related to the aircraft sortie rate. However, the carrier deck faces significant challenges such as limited space, scarce resources and constantly changing environmental conditions. These factors have a profound impact on the aircraft support operations, particularly in complex scenarios, requiring careful and rational scheduling. To solve above problems, we propose a collaborative fast scheduling framework for Multi-Agent support operations, which is based on a distributed partially observable Markov decision process (Dec-POMDP) model. Within this framework, we develop a dynamic scheduling algorithm based on centralized training with decentralized execution framework (CTDE). To evaluate the performance of our approach, we establish a simulation environment for aircraft scheduling and compare our algorithm's performance with VDN and QMIX in various carrier aircraft scenarios. Experimental results demonstrate that our algorithm outperforms others in terms of both training efficiency and scheduling quality. © 2023 IEEE.

关键词： Reinforcement learning

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