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2022 IEEE International Conference on Unmanned systems, ICUS 2022

作者： Xie, Liwei Zhuang, Yaqiang Zhuang, Xuebin Zeng, Xiaohui School of Systems Science and Engineering Sun Yat-Sen University Guangzhou China School of Integrated Circuits and Electronics Beijing Institute of Technology Beijing Key Laboratory of Millimeter Wave and Terahertz Technology Beijing China Science and Technology On Complex Aviation Systems Simulation Laboratory Beijing China

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

In response to the existing style of deceptive jamming of Global Position System (GPS) receivers by aerial jammer carriers, a method using improved multi-layer perceptron (MLP) model is proposed to identify and classify interference sources based on the rate of change of satellite incoming angle. The work firstly constructs an airborne GPS interference source localization deployment model, then analyzes and verifies the rationality of this simulation data model, and finally emphasizes the superiority of the proposed method using the classical classification algorithm as a comparison method. The simulation results indicated that the proposed method is capable to identify the corresponding interferers with a greater rate of recognition compared with other method in the built multi-interference source environment, which provides a reference for dealing with the interference identification and classification in the networked GPS interference source deployment environment. © 2022 IEEE.

关键词： Antennas

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General theory for designing phonon transport in alloyed/doped materials

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science China(Physics,Mechanics & Astronomy) 2024年第12期67卷 133-140页

作者： Chenhan Liu Chao Wu Wei Liu Yunshan Zhao Gang Zhang Hongmin Yang Yunfei Chen Micro-and Nano-scale Thermal Measurement and Thermal Management Laboratory Key Laboratory of Numerical Simulation for Large Complex SystemsMinistry of EducationSchool of Energy and Mechanical EngineeringNanjing Normal UniversityNanjing210023China School of Electrical and Automation Engineering Nanjing Normal UniversityNanjing210023China Phonon Engineering Research Center of Jiangsu Province Center for Quantum Transport and Thermal Energy ScienceInstitute of Physics Frontiers and Interdisciplinary SciencesSchool of Physics and TechnologyNanjing Normal UniversityNanjing210023China Agency for Science Technology and ResearchInstitute of High-Performance ComputingSingapore138632Singapore Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments School of Mechanical EngineeringSoutheast UniversityNanjing211100China

Alloying/doping is a widely used technique for improving the electrical,mechanical,and optical properties of ***,this technology induces significant distortions in the lattice structure,mass distribution,and potential field,greatly enhancing phonon ***,we introduce the concept of alloying/doping path and employ crystal symmetry,lattice deformation,and electron distribution to characterize *** on this new concept,the phonon thermal transport behavior in alloyed/doped materials can be well designed,and along different alloying/doping paths,the difference in thermal conductivity can be up to 45 *** one hand,strategic alloying/doping that combines high crystal symmetry,large lattice contraction,and the same electron distribution suppresses phonon-phonon scattering phase space,induces phonon stiffening,and bolsters electronic structure symmetry,*** synergistic effects significantly improve thermal *** the other hand,random alloying/doping has a low symmetry,leading to the typical“U”shape of alloying/doping level-dependent thermal *** theory is corroborated in three-dimensional(3D)Si,2D MoS_(2),and quasi-1D TiS_(3),affirming its efficacy and broad applicability in controlling phonon transport.

关键词： phonon transport alloyed/doped materials crystal symmetry lattice contraction electronic structure

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Architecture transformation research on joint border defense systems-of-systems 2021

Architecture transformation research on joint border defense...

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2021 5th International Conference on Management Engineering, Software Engineering and Service sciences, ICMSS 2021

作者： Zhang, Mengmeng Yi, Shuanghui Lin, Menglong Chen, Honghui Science and Technology on Information Systems Engineering Laboratory China National Key Laboratory for Complex System Simulation China

ISBN: (纸本)9781450389709

Currently, with the deepening of development and open of China's border, issues of border joint defense become increasingly arduous. Traditional means of cooperation and coordination are far from being able to align requirements;information systems of different joint defense units are difficult to interconnect and interoperate;the previous results of joint defense informatization are difficult to inherit. The main reason is the lack of border joint defense information construction with a top-level design approach. Via the lens of top-level design, this paper establishes a multi-viewpoint architecture framework and transformation process, develops the as-is and to-be joint defense architecture, and finally analyzes transformation principles from the as-is architecture to to-be architecture. This paper mainly explores the architecture transformation mechanisms under the border joint defense scenario. © 2021 ACM.

关键词： Architecture

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A GNSS Spoofing Detection Method based on Sparse Decomposition Technique

A GNSS Spoofing Detection Method based on Sparse Decompositi...

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2022 IEEE International Conference on Unmanned systems, ICUS 2022

作者： He, Yuxin Zhuang, Yaqiang Zhuang, Xuebin Lin, Zijian School of System Science and Engineering Sun Yat-sen University Guangzhou China School of Integrated Circuits and Electronics Beijing Institute of Technology Beijing Key Laboratory of Millimeter Wave and Terahertz Technology Beijing China Science and Technology On Complex Aviation Systems Simulation Laboratory Beijing China

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

By broadcasting false Global Navigation Satellite System (GNSS) signals, spoofing attacks will induce false position and time fixes within the victim receiver. In this article, we propose a Sparse Decomposition (SD)-based spoofing detection algorithm in the acquisition process, which can be applied in a single-antenna receiver. In the first step, we map the Fast Fourier transform (FFT)-based acquisition result in a two-dimensional matrix, which is a distorted autocorrelation function when the receiver is under spoof attack. In the second step, the distorted function is decomposed into two main autocorrelation function components of different code phases. The corresponding elements of the result vector of the SD are the code-phase values of the spoofed and the authentic signals. Numerical simulation results show that the proposed method can not only outcome spoofing detection result, but provide reliable estimations of the code phase delay of the spoof attack. © 2022 IEEE.

关键词： Fast Fourier transforms

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A Synchronous Parallel Method with Parameters Communication Prediction for Distributed Machine Learning 19th

A Synchronous Parallel Method with Parameters Communication...

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Zeng, Yanguo Xue, Meiting Xu, Peiran Shi, Yukun Zeng, Kaisheng Zhang, Jilin Yue, Lupeng School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Key Laboratory for Modeling and Simulation of Complex Systems Ministry of Education Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China National University of Defense Technology Changsha China School of Cyberspace HangZhou Dianzi University Hangzhou China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9783031545306

With the development of machine learning technology in various fields, such as medical care, smart manufacturing, etc., the data has exploded. It is a challenge to train a deep learning model for different application domains with large-scale data and limited resources of a single device. The distributed machine-learning technology, which uses a parameter server and multiple clients to train a model collaboratively, is an excellent method to solve this problem. However, it needs much communication between different devices with limited communication resources. The stale synchronous parallel method is a mainstream communication method to solve this problem, but it always leads to high synchronization delay and low computing efficiency as the inappropriate delay threshold value set by the user based on experience. This paper proposes a synchronous parallel method with parameters communication prediction for distributed machine learning. It predicts the optimal timing for synchronization, which can solve the problem of long synchronization waiting time caused by the inappropriate threshold settings in the stale synchronous parallel method. Moreover, it allows fast nodes to continue local training while performing global synchronization, which can improve the resource utilization of work nodes. Experimental results show that compared with the delayed synchronous parallel method, the training time and quality, and resource usage of our method are both significantly improved. © ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Forecasting

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Machine Learning Security Analysis Framework Based on Threat Matrix

Machine Learning Security Analysis Framework Based on Threat...

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IEEE International Conference on Data science in Cyberspace (DSC)

作者： Hu Li Qiongmin Ma Ming Zhang Xiaohui Kuang National Key Laboratory of Science and Technology on Information System Security Beijing China National Key Laboratory for Complex Systems Simulation Beijing China

Machine Learning and Artificial Intelligence technology accelerates technological progress and promotes social development, but also brings many security problems. Machine learning models may be affected, deceived, controlled or destroyed at different stages, e.g. training and inference, which may lead to serious consequences. In view of the main stages of the life cycle of machine learning, we summarizes the related attack and defense technologies based on threat matrix, makes a comprehensive and systematic analysis on the security issues of machine learning, and puts forward several security protection suggestions for intelligent information systems.

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Relationship graph learning network for visual relationship detection 2

Relationship graph learning network for visual relationship ...

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2nd ACM International Conference on Multimedia in Asia, MMAsia 2020

作者： Li, Yanan Yu, Jun Zhan, Yibing Chen, Zhi Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China

ISBN: (纸本)9781450383080

Visual relationship detection aims to predict the relationships between detected object pairs. It is well believed that the correlations between image components (i.e., objects and relationships between objects) are significant considerations when predicting objects' relationships. However, most current visual relationship detection methods only exploited the correlations among objects, and the correlations among objects' relationships remained underexplored. This paper proposes a relationship graph learning network (RGLN) to explore the correlations among objects' relationships for visual relationship detection. Specifically, RGLN obtains image objects using an object detector, and then, every pair of objects constitutes a relationship proposal. All relationship proposals construct a relationship graph, in which the proposals are treated as nodes. Accordingly, RGLN designs bi-stream graph attention subnetworks to detect relationship proposals, in which one graph attention subnetwork analyzes correlations among relationships based on visual and spatial information, and the other analyzes correlations based on semantic and spatial information. Besides, RGLN exploits a relationship selection subnetwork to ignore redundant information of object pairs with no relationships. We conduct extensive experiments on two public datasets: the VRD and the VG datasets. The experimental results compared with the state-of-the-art demonstrate the competitiveness of RGLN. © 2021 ACM.

关键词： Semantics

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FedECCR: Federated Learning Method with Encoding Comparison and Classification Rectification 19th

FedECCR: Federated Learning Method with Encoding Comparison...

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Zeng, Yan Zheng, Hui Wang, Xin Zhang, Beibei Zhou, Mingyao Zhang, Jilin Ren, YongJian School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Key Laboratory for Modeling and Simulation of Complex Systems Ministry of Education Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China Zhejiang Lab Hangzhou311100 China Hangzhou Huawei Communication Technology Co. Ltd. Hangzhou310052 China

ISBN: (纸本)9783031545306

Federated learning is a distributed training method that integrates multi-party data information using privacy-preserving technologies through dispersed client data sets to jointly construct a global model under the coordination of a central server. However, in practical applications, there is a high degree of data distribution skewness among clients, which causes the optimization direction of the client models to diverge, resulting in model bias and reducing the accuracy of the global model. Existing methods require the calculation and transmission of much information to correct the optimization direction of the client models, or only roughly limit the deviation of the client models end-to-end, ignoring targeted processing of the internal structure of the model, resulting in unclear improvement effects. To address these problems, we propose a federated optimization algorithm FedECCR based on encoding contrast and classification correction. This algorithm divides the model into an encoder and a classifier. It utilizes prototype contrastive training of the model encoder and unbiased classification correction of the classifier. This approach notably improves the accuracy of the global model while maintaining low communication costs. We conducted experiments on multiple data sets to evaluate the validity of our method, and the quantified results showed that FedECCR can improve the global model classification accuracy by approximately 1% to 6% compared to FedAvg, FedProx, and MOON. © ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Classification (of information)

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HRSTORY: Historical News Review Based Online Story Discovery 25

HRSTORY: Historical News Review Based Online Story Discovery

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Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1

作者： Renjie Zhou Haoran Ye Jian Wan Yong Liao Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of Education Hangzhou Dianzi University Hangzhou Zhejiang China Zhejiang University of Science and Technology Hangzhou Zhejiang China University of Science and Technology of China Hefei Anhui China

ISBN: (纸本)9798400712456

Story discovery on news streams can help people quickly find story from vast amounts of news, improving the efficiency of information acquisition. Recent online story discovery methods encode text topics and then cluster articles into stories based on similarity. However, the results obtained by these methods are one-time, and clustered news cannot adaptively update in a continuous news stream. Additionally, the inadequate quality of article encoding and the presence of noise data deteriorate the performance of story discovery. To this end, we propose HRSTORY for online story discovery on news streams, which employs a historical news review method to enable news to continuously adapt to the latest environment in the stream data and make corrections and updates. Furthermore, HRSTORY captures better article embeddings through modeling multi-layer relational dependencies within the text. By using sentence-level noise masking, HRSTORY improves the relevance of news article representation to core topics and reduces the interference of noise data. Experiments on real news datasets show that HRSTORY outperforms the state-of-the-art algorithms in unsupervised online story discovery performance.

关键词： historical news

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"Why not looking backward?" a robust two-step method to automatically terminate Bayesian optimization 23

"Why not looking backward?" a robust two-step method to auto...

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Proceedings of the 37th International Conference on Neural Information Processing systems

作者： Shuang Li Ke Li Wei Li Control and Simulation Center Harbin Institute of Technology China and National Key Laboratory of Modeling and Simulation for Complex Systems China Department of Computer Science University of Exeter Exeter UK

Bayesian Optimization (BO) is a powerful method for tackling expensive blackbox optimization problems. As a sequential model-based optimization strategy, BO iteratively explores promising solutions until a predetermined budget, either iterations or time, is exhausted. The decision on when to terminate BO significantly influences both the quality of solutions and its computational efficiency. In this paper, we propose a simple, yet theoretically grounded, two-step method for automatically terminating BO. Our core concept is to proactively identify if the search is within a convex region by examining previously observed samples. BO is halted once the local regret within this convex region falls below a predetermined threshold. To enhance numerical stability, we propose an approximation method for calculating the termination indicator by solving a bilevel optimization problem. We conduct extensive empirical studies on diverse benchmark problems, including synthetic functions, reinforcement learning, and hyperparameter optimization. Experimental results demonstrate that our proposed method saves up to ≈ 80% computational budget yet is with an order of magnitude smaller performance degradation, comparing against the other peer methods. In addition, our proposed termination method is robust in terms of the setting of its termination criterion.

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