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IET International Radar Conference 2023, IRC 2023

作者： Wang, Zehua Wang, Zezhong Qiu, Xiaolan Zhang, Zhe Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou China Suzhou Aerospace Information Research Institute Suzhou China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781839539954

Deep neural networks have demonstrated impressive performance in PolSAR image classification. However, the lack of labelled data and the complexity of land boundaries hindering the model's ability to achieve high-quality classification results. In order to address these inherent challenges, this paper introduces an augmentation method designed to maximize the utility of available labelled data. Furthermore, we present an end-to-end PolSAR image classification network, which we refer to as PolSARMixer. Our experimental results indicate that our proposed methodology significantly streamlines the classification process for PolSAR images when compared to traditional methods. Moreover, our proposed network exhibits superior performance in terms of classification accuracy and efficiency. © The Institution of engineering & Technology 2023.

关键词： Image classification

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A Fully 3-D-Printing-Compatible E-Plane Elliptical Waveguide Junction for Power Dividing/Combining Applications

A Fully 3-D-Printing-Compatible E-Plane Elliptical Waveguide...

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2023 IEEE/MTT-S International Microwave Symposium, IMS 2023

作者： Chen, Sicheng Li, Jin Yuan, Tao Guangdong Prov. Mobile Terminal Microwave and Millimeter-Wave Antenna Engineering Research Center College of Electronics and Information Engineering Shenzhen University China Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication China State Key Laboratory of Millimeter Waves China College of Electronics and Information Engineering ATR National Key Laboratory of Defense Technology Shenzhen University China

ISBN: (纸本)9798350347647

In this paper, a new class of 3-D-printing-compatible E-plane elliptical waveguide (EWG) junction is proposed for wideband power dividers/combiners. The junction is based on an EWG architecture with tailored power dividing and impedance-matching structures that are configured in a Y-shape profile. The junction is devised according to a specified orientation for 3-D printing and it features smooth and intrinsically self-supportable waveguide sidewalls. Compared to conventional rectangular waveguide junctions, the proposed EWG junction exhibits dramatically enhanced structural compatibility with additive manufacturing technology. A novel four-way EWG-junction-based power divider is demonstrated at Ka band. The power divider is prototyped monolithically by incorporating Polyjet 3-D printing and electroless copper plating processes. RF performance of the power divider shows a return loss of mostly over 20 dB, transmission coefficients of averagely -6.5 dB, and small amplitude and phase imbalances across the entire Ka band, successfully validating the wideband and low-loss characteristics of the proposed EWG junction. © 2023 IEEE.

关键词： Voltage dividers

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Regulation of morphology and visible light-driven photocatalysis of WO_(3)nanostructures by changing pH

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Rare Metals 2021年第7期40卷 1738-1745页

作者： You-Shu Fan Xiao-Li Xi Yang-Si Liu Zuo-Ren Nie Lin-Yan Zhao Qing-Hua Zhang College of Materials Science and Engineering Key Laboratory of Advanced Functional MaterialsEducation Ministry of ChinaBeijing University of TechnologyBeijing 100124China National Engineering Laboratory for Industrial Big-data Application Technology Beijing University of TechnologyBeijing 100124China

The controlled preparation of hexagonal tungsten trioxide(h-WO_(3))nanostructures was achieved by adjusting the pH of the precursor *** effect of the pH on the morphology,elemental composition,and photocatalytic performance of the samples was characterized via X-ray diffraction(XRD),scanning electron microscopy,energy dispersive X-ray spectroscopy,and Raman ***-visible(UV-Vis)spectra were used to evaluate the absorbance and the photocatalytic performance of methylene ***(PL),electrochemical impedance spectroscopy,photocurrent response and Brunauer-Emmett-Teller(BET)were used to study the optical properties,electrical performance,and specific surface area of the WO_(3)-nanostructures,*** results indicate that the WO_(3) nanorods prepared at pH=1.0 exhibit the highest photocatalytic performance(87.4%in 1 h),whereas the WO_(3) nanoblocks prepared at p H=3.0 show the *** photocatalytic performance of the one dimensional(1 D)-nanorods can be attributed to their high specific surface area and charge transfer *** h-WO_(3) nanostructures were synthesized via a simple method and without a capping *** show an excellent photocatalytic performance,which is promising for their application in environment purification.

关键词： h-WO_(3) Morphology pH Photocatalysis Charge transfer Optoelectrical properties

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Depositional process of hyperpycnal flow deposits:A case study on Lower Cretaceous Sangyuan outcrop in the Luanping Basin,Northeast China

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China Geology 2024年第3期7卷 505-516页

作者： De-zhi Yan Ru-kai Zhu Hao Shou Zhao-hui Xu Wei-hong Liu Si-cheng Zhu Zhi-cheng Lei Jing-ya Zhang Chang Liu Yi Cai Huai-min Xu Research Institute of Petroleum Exploration and Development PetroChinaBeijing 100083China National Energy Tight Oil and Gas R&D Center Beijing 100083China PetroChina Key Laboratory of Oil and Gas Reservoir Beijing 100083China State Key Laboratory of Petroleum Resources and Prospecting China University of Petroleum(Beijing)Beijing 102249China College of Geosciences China University of Petroleum(Beijing)Beijing 102249China Jiangxi Engineering Laboratory on Radioactive Geoscience and Big Data Technology Department of Information and EngineeringEast China University of TechnologyNanchang 330013China

Sedimentary process research is of great significance for understanding the distribution and characteristics of *** the detailed observation and measurement of the Sangyuan outcrop in Luanping Basin,this paper studies the depositional process of the hyperpycnal flow deposits,and divides their depositional process into three phases,namely,acceleration,erosion and *** the acceleration phase,hyperpycnal flow begins to enter the basin nearby,and then speeds up *** developed in the acceleration phase are *** addition,the original deposits become unstable and are taken away by hyperpycnal flows under the eroding *** a result,there are a lot of mixture of red mud pebbles outside the basin and gray mud pebbles within the *** the erosion phase,the reverse deposits are eroded and become thinner or even ***,no reverse grading characteristic is found in the proximal major channel that is closer to the source,but it is still preserved in the middle branch channel that is far from the *** entering the deceleration phase,normally grading deposits appear and cover previous *** final deposits in the basin are *** are reverse,and others are *** are superimposed with each other under the action of hyperpycnal *** analysis of the Sangyuan outcrop demonstrates the sedimentary process and distribution of hyperpycnites,and reasonably explain the sedimentary characteristics of *** is helpful to the prediction of oil and gas exploration targets in gravity flow deposits.

关键词： Hyperpycnal flow Sedimentary characteristics Depositional process Gravity flow deposit Hyperpycnite Red mud pebble Gray mud pebble Oil and gas exploration engineering Luanping Basin

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KAN v.s. MLP for Offline Reinforcement Learning

KAN v.s. MLP for Offline Reinforcement Learning

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Guo, Haihong Li, Fengxin Li, Jiao Liu, Hongyan School of Information Renmin University of China China Institute of Medical Information Medical Library Chinese Academy of Medical Sciences Peking Union Medical College China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China School of Economics and Management Tsinghua University China

ISBN: (纸本)9798350368741

Kolmogorov-Arnold Networks (KAN) is an emerging neural network architecture in machine learning. It has greatly interested the research community about whether KAN can be a promising alternative to the commonly used Multi-Layer Perceptions (MLP). Experiments in various fields demonstrated that KAN-based machine learning can achieve comparable if not better performance than MLP-based methods, but with much smaller parameter scales and are more explainable. In this paper, we explore the incorporation of KAN into the actor and critic networks for offline reinforcement learning (RL). We evaluated the performance, parameter scales, and training efficiency of various KAN and MLP-based conservative Q-learning (CQL) on the classical D4RL benchmark for offline RL. Our study demonstrates that KAN can achieve performance close to the commonly used MLP with significantly fewer parameters. This allows us to choose the base networks according to the offline RL task requirements. © 2025 IEEE.

关键词： KAN Kolmogorov-Arnold networks MLP multilayer perceptrons offline reinforcement learning

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Throughput-oriented associated transaction assignment in sharding blockchains for IoT social data storage

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Digital Communications and Networks 2022年第6期8卷 885-899页

作者： Liping Tao Yang Lu Xu Ding Yuqi Fan Jung Yoon Kim School of Computer Science and Information Engineering Hefei University of TechnologyHefei230009China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of EducationHefei University of TechnologyHefei230009China Institute of Industry&Equipment Technology Hefei University of TechnologyHefei230009China Key Laboratory of Knowledge Engineering with Big Data(Hefei University of Technology) Ministry of EducationHefei230009China Graduate School of Game Gachon UniversitySeongnam-Si461-701South Korea

Blockchain is a viable solution to provide data integrity for the enormous volume of 5G IoT social data, while we need to break through the throughput bottleneck of blockchain. Sharding is a promising technology to solve the problem of low throughput in blockchains. However, cross-shard communication hinders the effective improvement of blockchain throughput. Therefore, it is critical to reasonably allocate transactions to different shards to improve blockchain throughput. Existing research on blockchain sharding mainly focuses on shards formation, configuration, and consensus, while ignoring the negative impact of cross-shard communication on blockchain throughput. Aiming to maximize the throughput of transaction processing, we study how to allocate blockchain transactions to shards in this paper. We propose an Associated Transaction assignment algorithm based on Closest Fit (ATCF). ATCF classifies associated transactions into transaction groups which are then assigned to different shards in the non-ascending order of transaction group sizes periodically. Within each epoch, ATCF tries to select a shard that can handle all the transactions for each transaction group. If there are multiple such shards, ATCF selects the shard with the remaining processing capacity closest to the number of transactions in the transaction group. When no such shard exists, ATCF chooses the shard with the largest remaining processing capacity for the transaction group. The transaction groups that cannot be completely processed within the current epoch will be allocated in the subsequent epochs. We prove that ATCF is a 2-approximation algorithm for the associated transaction assignment problem. Simulation results show that ATCF can effectively improve the blockchain throughput and reduce the number of cross-shard transactions.

关键词： IoT social data Blockchain Shar ding Associated transactions Transaction assi gnment

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Multi-UAV Logistics Planning Problem Based on Improved Genetic Simulated Annealing Algorithm

Multi-UAV Logistics Planning Problem Based on Improved Genet...

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International Conference on Guidance, Navigation and Control, ICGNC 2022

作者： Xue, Zhentao Chen, Jian Cao, Yuanyang Zhang, Zichao Liu, Xuzan College of Engineering China Agricultural University Beijing China Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities MNR Shanghai China Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural Resources Shenzhen China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijng China

ISBN: (纸本)9789811966125

Multi-drone logistics transportation and distribution have gradually become the focus of future logistics. So as to solve the multi-UAV logistics planning problem, the goal of this paper is to minimize the cost of take-off and variable-load flight, and builds a multi-UAV logistics planning model with constraints such as flight distance and logistics load. The Improved Genetic Simulated Annealing Algorithm(IGSAA) adds the SA mechanism on the basis of the classical GA, selectively adds non-optimal solutions and non-constrained solutions, and adopts the Improved Large Neighborhood Search Algorithm (ILNSA) to generate special algorithms with three strategies. By continuously updating the population iteration and selectively adding the probability according to the temperature change, the optimal UAV logistics planning is finally optimized. Finally, the simulation results close to reality show that compared with the initial random solution and the classical genetic algorithm, the improved genetic simulated annealing algorithm proposed in this paper reduces the flight cost of UAV by 16.78% and 8.49%, respectively, and can obtain lower transportation costs. Drone logistics solution. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Drones

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Frontiers of Artificial Intelligent and Quantitative Management: Preface for ITQM 2024

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Procedia Computer Science 2024年 242卷 1-8页

作者： Filip, Florin Gheorghe Shi, Yong Pocatilu, Paul Ciurea, Cristian-Eugen Li, Jianping Tien, James M. Berg, Daniel Romanian Academy Information Science and Technology Section Bucharest Romania Research Centre on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States Bucharest University of Economic Studies Bucharest Romania College of Engineering University of Miami Coral GablesFL33124 United States

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Cutting Learned Index into Pieces: An In-depth Inquiry into Updatable Learned Indexes

Cutting Learned Index into Pieces: An In-depth Inquiry into ...

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International Conference on data engineering

作者： Jiake Ge Boyu Shi Yanfeng Chai Yuanhui Luo Yunda Guo Yinxuan He Yunpeng Chai Key Laboratory of Data Engineering and Knowledge Engineering MOE China School of Information Renmin University of China China

Numerous high-performance updatable learned indexes have recently been designed to support the writing requirements in practical systems. Researchers have proposed various strategies to improve the availability of updatable learned indexes. However, it is unclear which strategy is more profitable. Therefore, we deconstruct the design of learned indexes into multiple dimensions and in-depth evaluate their impacts on the overall performance, respectively. Through the in-depth exploration of learned indexes, we reckon that the approximation algorithm is the most crucial design dimension for improving the performance of the learned indexes rather than the popular works that focus on the learned index structure. Moreover, this paper makes a comprehensive end-to-end evaluation based on a high-performance key-value store to answer people’s concerns about which learned index is better and whether learned indexes can outperform traditional ones. Finally, according to end-to-end and in-depth evaluation results, we give some constructive suggestions on designing a better learned index in these dimensions, especially how to design an excellent approximate algorithm to improve the lookup and insertion performance of learned indexes.

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Analyzing the Convergence of Federated Learning with Biased Client Participation 9th

Analyzing the Convergence of Federated Learning with Bias...

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19th International Conference on Advanced data Mining and Applications, ADMA 2023

作者： Tan, Lei Hu, Miao Zhou, Yipeng Wu, Di School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China School of Computing Faculty of Science and Engineering Macquarie University SydneyNSW2109 Australia

ISBN: (纸本)9783031466632

Federated Learning (FL) is a promising decentralized machine learning framework that enables a massive number of clients (e.g., smartphones) to collaboratively train a global model over the Internet without sacrificing their privacy. Though FL’s efficacy in non-convex problems is proven, its convergence amidst biased client participation lacks theoretical study. In this paper, we analyze the convergence of FedAvg on non-convex problems, which is the most renowned FL algorithm. We assume even data distribution but non-IID among clients, and elucidate the convergence rate of FedAvg in situations characterized by biased client participation. Our analysis reveals that biased client participation can significantly reduce the precision of the FL model. We validate this through trace-driven experiments, demonstrating that unbiased client participation results in 11% to 50% higher test accuracy compared to extremely biased client participation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Artificial intelligence

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