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Power Electronics, Computer Applications (ICPECA), IEEE International Conference on

作者： Mingming Gao Xinyu Wang Congying Wang Zhongxiang Zhang Shiquan Shan Yonghui Song Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications College of Electronics and Information Engineering Liaoning Technicial University Huludao Liaoning China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

A Doherty Power Amplifier (DPA) has been designed and optimized specifically for compact mobile base station deployment, operating within a frequency range of 3.3 GHz to 3.6 GHz. The amplifier utilizes the proprietary DS10-2092 LDMOS transistors developed by the company for both the carrier and peak power amplifiers. Physical test results indicate that the DPA achieves saturated output power meeting the specified requirements, with a maximum saturated drain efficiency of 41%. The drain efficiency remains stable between 26.3% and 30.7% over a 6 dB power back-off range, demonstrating its superior performance in high-efficiency and broadband applications.

关键词： Base stations Power measurement 5G mobile communication Power amplifiers Energy efficiency Power electronics Distance measurement Transistors Power generation Optimization

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Collusion Resistant Identity-based Proxy Re-encryption Scheme on Lattice 6

Collusion Resistant Identity-based Proxy Re-encryption Schem...

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6th International Conference on Computer Information Science and Application Technology, CISAT 2023

作者： Deng, Xiaohong Xie, Hua Xiong, Weizhi School of Electronics and Information Engineering Gannan University of Science and Technology Ganzhou341000 China College of Information Science Jiangxi University of Science and Technology Ganzhou341000 China Key Laboratory of Cloud Computing and Big Data Ganzhou341000 China

ISBN: (纸本)9781510668546

Proxy re-encryption plays an important role for data security in cloud computing and big data, but the traditional reencryption scheme based on the classical number theory problem cannot resist quantum attack, and the lattice based cryptosystem has been proved to have the ability to resist quantum attack, so the lattice based re-encryption scheme has always been a research hotspot. In view of the problem that collusion-resistance and multi-hop can not be satisfied at the same time in existing schemes, this paper proposes a new identity-based proxy re-encryption scheme on lattice. Firstly, the scheme uses trapdoors as private keys to achieve one-way anti-collusion. In addition, we constructs a variant of learning with errors (LWE), which uses a convert algorithm to transform the user matrix and replace the vector with a uniform matrix, so that the scheme can meet the requirements of multi-hop and encrypt multiple bits of messages at one time. Theoretical analysis and experimental simulation prove that the security of the new scheme can be reduced to a decision LWE problem, which can reach the IND-sID-CPA security in the standard model, and can improve the encryption efficiency while ensuring the security. © 2023 SPIE.

关键词： Cryptography

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Federated Unlearning with Gradient Descent and Conflict Mitigation 39

Federated Unlearning with Gradient Descent and Conflict Miti...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Pan, Zibin Wang, Zhichao Li, Chi Zheng, Kaiyan Wang, Boqi Tang, Xiaoying Zhao, Junhua The School of Science and Engineering The Chinese Univ of Hong Kong Shenzhen China The Shenzhen Institute of Artificial Intelligence and Robotics for Society China The Guangdong Provincial Key Laboratory of Future Networks of Intelligence China Department of Mathematics Department of Statistics University of Michigan United States Shenzhen Research Institute of Big Data China

ISBN: (纸本)157735897X

Federated Learning (FL) has received much attention in recent years. However, although clients are not required to share their data in FL, the global model itself can implicitly remember clients’ local data. Therefore, it’s necessary to effectively remove the target client’s data from the FL global model to ease the risk of privacy leakage and implement "the right to be forgotten". Federated Unlearning (FU) has been considered a promising way to remove data without full retraining. But the model utility easily suffers significant reduction during unlearning due to the gradient conflicts. Furthermore, when conducting the post-training to recover the model utility, the model is prone to move back and revert what has already been unlearned. To address these issues, we propose Federated Unlearning with Orthogonal Steepest Descent (FedOSD). We first design an unlearning Cross-Entropy loss to overcome the convergence issue of the gradient ascent. A steepest descent direction for unlearning is then calculated in the condition of being non-conflicting with other clients’ gradients and closest to the target client’s gradient. This benefits to efficiently unlearn and mitigate the model utility reduction. After unlearning, we recover the model utility by maintaining the achievement of unlearning. Finally, extensive experiments in several FL scenarios verify that FedOSD outperforms the SOTA FU algorithms in terms of unlearning and model utility. Copyright © 2025, Association for the Advancement of Artificia Intelligence (***). All rights reserved.

关键词： Federated learning

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PENCIL: Prototype-ENhanced ComposItional Learning for Class-Incremental Hand Gesture Recognition

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IEEE Transactions on Consumer Electronics 2025年

作者： Li, Zheng Zhang, Xueyi Guo, Yanming Cai, Siqi Lao, Mingrui National University of Defense Technology Laboratory for Big Data and Decision Changsha China National University of Singapore College of Design and Engineering Singapore Singapore National University of Defense Technology National Key Laboratory of Information Systems Engineering Changsha China

Gesture recognition systems, as an indispenable component of virtual reality technology, are typically required to continuously acquire and update knowledge in order to meet users demands for recognizing both historical and newlyregistered gestures in real-world applications. In this paper, we propose a novel yet effective Prototype-ENhanced ComposItional Learning (PENCIL) method for Class-Incremental Hand Gesture Recognition (CI-HGR) task. It attempt to achieve exemplarfree class-incremental learning through adaptive isolated parameter composition and fine-tuning. Specifically, the Task-Specific Compositional Mechanism (TCM) module in PENCIL designs the trainable indexable embedding for each sequentially-arrived tasks containing new gestures, enabling HGR model to dynamically composite different parameters based on diverse gesture inputs. Furthermore, to address the recognition confusion caused by strong similarities among different gestures representations, PENCIL involves the second module Prototype-Enhanced Hybrid Regularization (PHR). It leverage prototype pseudo samples to maintain the accuracy and stability of parameter composition in TCM. Extensive experiments demonstrate the efficacy and modelagnostic capability of our method. Moreover, PENCIL fulfils state-of-the-art performance, with remarkable accuracy boosts ( 13.33% and 22.43%) and overhead reduction (over 90%) in contrast to most competitive method. © 1975-2011 IEEE.

关键词： Virtual environments

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Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models

Rethinking the Evaluation for Conversational Recommendation ...

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2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023

作者： Wang, Xiaolei Tang, Xinyu Zhao, Wayne Xin Wang, Jingyuan Wen, Ji-Rong Gaoling School of Artificial Intelligence Renmin University of China China School of Information Renmin University of China China Beijing Key Laboratory of Big Data Management and Analysis Methods China School of Computer Science and Engineering Beihang University China

ISBN: (纸本)9798891760608

The recent success of large language models (LLMs) has shown great potential to develop more powerful conversational recommender systems (CRSs), which rely on natural language conversations to satisfy user needs. In this paper, we embark on an investigation into the utilization of ChatGPT for CRSs, revealing the inadequacy of the existing evaluation protocol. It might overemphasize the matching with ground-truth items annotated by humans while neglecting the interactive nature of CRSs. To overcome the limitation, we further propose an interactive Evaluation approach based on LLMs, named iEvaLM, which harnesses LLM-based user simulators. Our evaluation approach can simulate various system-user interaction scenarios. Through the experiments on two public CRS datasets, we demonstrate notable improvements compared to the prevailing evaluation protocol. Furthermore, we emphasize the evaluation of explainability, and ChatGPT showcases persuasive explanation generation for its recommendations. Our study contributes to a deeper comprehension of the untapped potential of LLMs for CRSs and provides a more flexible and realistic evaluation approach for future research about LLM-based CRSs. The code is available at https://***/RUCAIBox/iEvaLM-CRS. © 2023 Association for Computational Linguistics.

关键词： Computational linguistics

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Individual tree detection and counting based on high-resolution imagery and the canopy height model data

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地球空间信息科学学报（英文版） 2024年第6期27卷中插23,2162-2178页

作者： Ye Zhang Moyang Wang Joseph Mango Liang Xin Chen Meng Xiang Li Key Laboratory of Geographic Information Science(Ministry of Education)and School of Geographic Sciences East China Normal UniversityShanghaiChina Department of Transportation and Geotechnical Engineering University of Dar es SalaamDar es salaamTanzania College of Surveying and Geographic Informatics Tongji UniversityShanghaiChina Cadre School of Shanghai Municipal Bureau of Planning and Natural Resources ShanghaiChina School of Ecology and Environmental Sciences East China Normal UniversityShanghaiChina Key Laboratory of Geographic Information Science(Ministry of Education)and School of Geographic Sciences East China Normal UniversityShanghaiChina Shanghai Key Lab for Urban Ecological Processes and Eco-Restoration East China Normal UniversityShanghaiChina Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities(Ministry of Natural Resources) East China Normal UniversityShanghaiChina

Individual Tree Detection-and-Counting(ITDC)is among the important tasks in town areas,and numerous methods are proposed in this *** their many advantages,still,the proposed methods are inadequate to provide robust results because they mostly rely on the direct field *** paper presents a novel approach involving high-resolution imagery and the Canopy-Height-Model(CHM)data to solve the ITDC *** new approach is studied in six urban scenes:farmland,woodland,park,industrial land,road and residential ***,it identifies tree canopy regions using a deep learning network from high-resolution *** then deploys the CHM-data to detect treetops of the canopy regions using a local maximum algorithm and individual tree canopies using the region ***,it calculates and describes the number of individual trees and tree *** proposed approach is experimented with the data from Shanghai,*** results show that the individual tree detection method had an average overall accuracy of 0.953,with a precision of 0.987 for woodland ***,the R2 value for canopy segmentation in different urban scenes is greater than 0.780 and 0.779 for canopy area and diameter size,*** results confirm that the proposed method is robust enough for urban tree planning and management.

关键词： Individual tree detection-and-counting(ITDC) deep learning high-resolution imagery Canopy Height Model data(CHM)

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Image recognition and empirical application of desert plant species based on convolutional neural network

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Journal of Arid Land 2022年第12期14卷 1440-1455页

作者： LI Jicai SUN Shiding JIANG Haoran TIAN Yingjie XU Xiaoliang School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China Research Center on Fictitious Economy and Data Science Chinese Academy of SciencesBeijing 100190China Xinjiang Institute of Ecology and Geography Chinese Academy of SciencesUrumqi 830011China

In recent years,deep convolution neural network has exhibited excellent performance in computer vision and has a far-reaching *** plant taxonomic identification requires high expertise,which is *** nature reserves have problems such as incomplete species surveys,inaccurate taxonomic identification,and untimely updating of status *** and accurate recognition of plant images can be achieved by applying convolutional neural network technology to explore the best network *** 24 typical desert plant species that are widely distributed in the nature reserves in Xinjiang Uygur Autonomous Region of China as the research objects,this study established an image database and select the optimal network model for the image recognition of desert plant species to provide decision support for fine management in the nature reserves in Xinjiang,such as species investigation and monitoring,by using deep *** desert plant species were not included in the public dataset,the images used in this study were mainly obtained through field shooting and downloaded from the Plant Photo Bank of China(PPBC).After the sorting process and statistical analysis,a total of 2331 plant images were finally collected(2071 images from field collection and 260 images from the PPBC),including 24 plant species belonging to 14 families and 22 genera.A large number of numerical experiments were also carried out to compare a series of 37 convolutional neural network models with good performance,from different perspectives,to find the optimal network model that is most suitable for the image recognition of desert plant species in *** results revealed 24 models with a recognition Accuracy,of greater than 70.000%.Among which,Residual Network X_8GF(RegNetX_8GF)performs the best,with Accuracy,Precision,Recall,and F1(which refers to the harmonic mean of the Precision and Recall values)values of 78.33%,77.65%,69.55%,and 71.26%,*** t

关键词： desert plants image recognition deep learning convolutional neural network Residual Network X_8GF(RegNetX_8GF) Mobile Network V2(MobileNetV2) nature reserves

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Sedimentary record of climate change in a high latitude fjord—Kongsfjord

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Acta Oceanologica Sinica 2023年第1期42卷 91-102页

作者： Hang Wu Binbin Deng Jinlong Wang Sheng Zeng Juan Du Peng Yu Qianqian Bi Jinzhou Du State Key Laboratory of Estuarine and Coastal Research East China Normal UniversityShanghai200241China Monitoring Center of Aquatic Environment of Pearl River Basin Guangzhou510611China Research Centre for Eco-Environmental Engineering Dongguan University of TechnologyDongguan523808China Big Data Institute of Digital Natural Disaster Monitoring in Fujian Xiamen University of TechnologyXiamen361024China

The sedimentary record of climate change in the Arctic region is useful for understanding global *** is located in the subpolar region of the Arctic and is a suitable site for studying climate *** retreat is occurring in this region due to climate change,leading to an increase in meltwater outflow with a high debris *** August 2017,we collected a sediment Core Z3 from the central fjord near the Yellow River ***,we used the widely used chronology method of 210Pb,^(137)Cs,and other parameters to reflect the climate change record in the sedimentary environment of *** results showed that after the mid-late 1990s,the mass accumulation rate of this core increased from 0.10 g/(cm^(2)·a)to 0.34 g/(cm^(2)·a),while the flux of^(210)Pb_(ex)increased from 125 Bq/(m^(2)·a)to 316 Bq/(m^(2)·a).The higher sedimentary inventory of^(210)Pb_(ex)in Kongsfjord compared to global fallout might have been caused by sediment focusing,boundary scavenging,and riverine *** between the inventory of^(137)Cs and global fallout indicated that terrestrial particulate matter was the main source of^(137)Cs in fjord *** sedimentation rate increased after 1997,possibly due to the increased influx of glacial meltwater containing *** addition,the^(137)Cs activity,percentage of organic carbon(OC),and OC/total nitrogen concentration ratio showed increasing trends toward the top of the core since 1997,corresponding to a decrease in the mass balance of glaciers in the *** results ofδ^(13)C,δ^(15)N and OC/TN concentration ratio showed both terrestrial and marine sources contributed to the organic matter in Core *** relative contribution of terrestrial organic matter which was calculated by a two-endmember model showed an increased trend since *** these data indicate that global climate change has a significant impact on Arctic glaciers.

关键词： Kongsfjord radionuclide organic carbon/total nitrogen(OC/TN)concentration ratio δ^(13)C δ^(15)N sediment record climate change

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Angiosperm Genus Classification by RBF-SVM 1

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11th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2023

作者： Chen, Shuwen Wang, Jiaji Ni, Yiyang Shao, Jiaqi Qu, Hui Wang, Ziyi School of Physics and Information Engineering Jiangsu Second Normal University Nanjing211200 China Jiangsu Province Engineering Research Center of Basic Education Big Data Application Nanjing211200 China State Key Laboratory of Millimeter Waves Southeast University Nanjing210096 China

ISBN: (数字)9789819967063

ISBN: (纸本)9789819967056

Angiosperm genus classification performance has plateaued in the last few years. This paper proposed a novel method based on gray-level co-occurrence matrix and radial basis function kernel support vector machine for angiosperm genus classification. We collected a 300-image dataset, 100 are Hibiscus, 100 are Orchis, and the rest 100 are Prunus by digital camera. The results showed that our method achieved an accuracy of 84.73 ± 0.41%. In all, this method is promising in angiosperm genus classification. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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RSI-Mix: data Augmentation Method for Remote Sensing Image Classification 7

RSI-Mix: Data Augmentation Method for Remote Sensing Image C...

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7th International Conference on Intelligent Computing and Signal Processing, ICSP 2022

作者： Lu, Xingshun Zhang, Chao Ye, Qihong Wang, Chao Yang, Chuansheng Wang, Quanqing Zhejiang Ocean University School of Information Engineering Zhoushan China Zhejiang Ocean University Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhoushan China

ISBN: (纸本)9781665478571

data augmentation is a common strategy to improve the performance of computer vision tasks. Regrettably, current data augmentation methods are often designed for images in RGB format and few are studied for remote sensing images. In this paper, we find that the way in which remote sensing data are obtained provides a realistic possibility for cropping images to create new ones. Based on our analyses, we propose RSI-Mix for Sentinel-2 satellite image classification. RSI-Mix is designed to cut and paste two remote sensing images of the same category according to random masks. The key inspiration of RSI-Mix is that the classification of remote sensing images is not strictly based on image texture but based on band features. The information fusion of the same area from different sources is beneficial to make an area contain more band features. Experiments show that the model with RSI-Mix is more stable and has higher performance. © 2022 IEEE.

关键词： Image classification

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