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2022 International Conference on Electronic information engineering and Computer science, EIECS 2022

作者： Zhang, Jian Li, Hong Xin, Jingtao Zhu, Lianqing Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument Beijing Information Science & Technology University Beijing China Beijing Laboratory of Optical Fiber Sensing and System Beijing Information Science & Technology University Beijing China

ISBN: (纸本)9781510663312

This paper proposes a closed-loop fiber optic gyroscope control method based on the Sigma-delta modulation technique to improve the fiber optic gyroscope proportional-integral control method. Firstly, the sampling control system of digital closed-loop fiber optic gyro is established. Secondly, the fiber optic gyro digital closed-loop Sigma-delta modulator is designed and combined with the original system after the integrator. Simulation analysis is performed for both closed-loop control methods. Simulation results show that the maximum output noise amplitude of the original system is 2.8 deg/s and the maximum noise amplitude of the improved system is 0.8 deg/s under the input of a ramp signal with 100dB white noise. The noise amplitude is reduced by one order of magnitude, and the noise density is also significantly reduced. The proposed closed-loop control method of fiber optic gyro based on Sigma-delta modulation can effectively suppress the high-frequency noise of the weak signal of the gyro output, which has a promising application for the dynamic performance and noise suppression of fiber optic gyro. © 2023 SPIE.

关键词： Delta sigma modulation

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Multicomponent Similarity Graphs for Cross-Network Node Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1411-1424页

作者： Zhang, Yuhong Shi, Congmei Li, Xinzheng Zhang, Zan Hu, Xuegang School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei230009 China

Cross-network node classification aims to train a classifier for an unlabeled target network using a source network with rich labels. In applications, the degree of nodes mostly conforms to the long-tail distribution, i.e., most nodes in the network are tail nodes with sparse neighborhoods. The established methods focus on either the discrepancy cross network or the long tail in a single network. As for the cross-network node classification under long tail, the coexistence of sparsity of tail nodes and the discrepancy cross-network challenges existing methods for long tail or methods for the cross-network node classification. To this end, a multicomponent similarity graphs for cross-network node classification (MS-CNC) is proposed in this article. Specifically, in order to address the sparsity of the tail nodes, multiple component similarity graphs, including attribute and structure similarity graphs, are constructed for each network to enrich the neighborhoods of the tail nodes and alleviate the long-tail phenomenon. Then, multiple representations are learned from the multiple similarity graphs separately. Based on the multicomponent representations, a two-level adversarial model is designed to address the distribution difference across networks. One level is used to learn the invariant representations cross network in view of structure and attribute components separately, and the other level is used to learn the invariant representations in view of the fused structure and attribute graphs. Extensive experimental results show that the MS-CNC outperforms the state-of-the-art methods. Impact Statement-Node classification is an important task in graph mining. With the unavailability of labels, some researchers propose cross-network node classification, using one labeled network to assist the node classification of another unlabeled network. However, the long-tail of nodes leads to unsatisfactory performance and challenges the recent cross-network node classification m

关键词： Graphic methods

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SECodec: Structural Entropy-based Compressive Speech Representation Codec for Speech Language Models 39

SECodec: Structural Entropy-based Compressive Speech Represe...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Linqin Liu, Yaping Yu, Zhengtao Gao, Shengxiang Mao, Cunli Huang, Yuxin Wang, Wenjun Dong, Ling Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Artificial Intelligence Kunming China

ISBN: (纸本)157735897X

With the rapid advancement of large language models (LLMs), discrete speech representations have become crucial for integrating speech into LLMs. Existing methods for speech representation discretization rely on a predefined codebook size and Euclidean distance-based quantization. However, 1) the size of codebook is a critical parameter that affects both codec performance and downstream task training efficiency. 2) The Euclidean distance-based quantization may lead to audio distortion when the size of the codebook is controlled within a reasonable range. In fact, in the field of information compression, structural information and entropy guidance are crucial, but previous methods have largely overlooked these factors. Therefore, we address the above issues from an information-theoretic perspective, we present SECodec, a novel speech representation codec based on structural entropy (SE) for building speech language models. Specifically, we first model speech as a graph, clustering the speech features nodes within the graph and extracting the corresponding codebook by hierarchically and disentangledly minimizing 2D SE. Then, to address the issue of audio distortion, we propose a new quantization method. This method still adheres to the 2D SE minimization principle, adaptively selecting the most suitable token corresponding to the cluster for each incoming original speech node. Furthermore, we develop a Structural Entropy-based Speech Language Model (SESLM) that leverages SECodec. Experimental results demonstrate that SECodec performs comparably to EnCodec in speech reconstruction, and SESLM surpasses VALL-E in zero-shot text-to-speech tasks. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Encoding (symbols)

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Dynamic Syntactic Feature Filtering and Injecting Networks for Cross-lingual Dependency Parsing 39

Dynamic Syntactic Feature Filtering and Injecting Networks f...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liu, Jianjian Yu, Zhengtao Li, Ying Huang, Yuxin Gao, Shengxiang Yunnan Provincial Key Laboratory of Artificial Intelligence Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650000 China

ISBN: (纸本)157735897X

Pre-trained language models enhanced parsers have achieved outstanding performance in rich-resource languages. Cross-lingual dependency parsing aims to learn useful knowledge from high-resource languages to alleviate data scarcity in low-resource languages. However, effectively reducing the syntactic structure distributional bias and excavating the commonalities among languages is the key challenge for cross-lingual dependency parsing. To address this issue, we propose novel dynamic syntactic feature filtering and injecting networks based on the typical shared-private model that employs one shared and two private encoders to separate source and target language features. Concretely, a Language-Specific Filtering Network (LSFN) on private encoders emphasizes helpful information and ignores the irrelevant or harmful parts of it from the source language. Meanwhile, a Language-Invariant Injecting Network (LIIN) on the shared encoder integrates the advantages of BiLSTM and improved Transformer encoders to transcend language boundaries, thus amplifying syntactic commonalities across languages. Experiments on seven benchmark datasets show that our model achieves an average absolute gain of 1.84 UAS and 3.43 LAS compared with the shared-private model. Comparative experiments validate that both LSFN and LIIN components are complementary in transferring beneficial knowledge from source to target languages. Detailed analyses highlight that our model can effectively capture linguistic commonalities and mitigate the effect of distributional bias, showcasing its robustness and efficacy. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Syntactics

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Efficient and Verifiable Multi-server Framework for Secure information Classification and Storage 20th

Efficient and Verifiable Multi-server Framework for Secure...

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20th International Conference on information Security and Cryptology, Inscrypt 2024

作者： Guo, Ziqing Wang, Yaohui Jin, Xuanyu Wang, Xiuhua He, Yuanyuan Dai, Yueyue Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789819647330

With the rapid development of digital technology, the complexity and diversity of data sources present significant security and privacy challenges. To address these issues, this paper proposes an efficient and verifiable Multi-Server Framework for Secure information Classification and Storage (MSF-SCS). The framework distributes sensitive data across multiple servers, proposing an opening authority to manage classified information and a semi-honest sanitizer to ensure strong authentication while preserving user anonymity. We formalize the security model of our scheme and prove that it satisfies the no-read rule, no-write rule, and unforgeability. We instantiate the scheme and conduct comprehensive experimental evaluations, which demonstrate that MSF-SCS significantly outperforms traditional architectures in terms of scalability and efficiency, making it particularly suitable for industries with stringent data protection requirements. Future work will focus on further improving the system’s security while maintaining its high performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Authentication

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Laser polishing of a high-entropy alloy manufactured by selective laser melting

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Frontiers of Mechanical engineering 2024年第5期19卷 73-86页

作者： Xiaojun TAN Haibing XIAO Zihong WANG Wei ZHANG Zhijuan SUN Xuyun PENG Zhongmin LIU Liang GUO Qingmao ZHANG Intelligent Manufacturing and Equipment School Shenzhen Institute of Information TechnologyShenzhen 518172China Sino-German Intelligent Manufacturing School Shenzhen City PolytechnicShenzhen 518116China Guangdong Provincial Key Laboratory of Nanophotonic Functional Materials and Devices School of Information and Optoelectronic Science and EngineeringSouth China Normal UniversityGuangzhou 510006China Guangdong Provincial Key Laboratory of Industrial Ultrashort Pulse Laser Technology Shenzhen 518055China College of Materials Science and Engineering Chongqing UniversityChongqing 400045China

The selective laser melting(SLM)technique applied to high-entropy alloys(HEAs)has attracted considerable attention in recent ***,its practical application has been restricted by poor surface *** this study,the capability of laser polishing on the rough surface of a Co-free HEA fabricated using SLM was *** show that the initial SLM-manufactured(as-SLMed)surface of the Co-free HEA,with a roughness exceeding 3.0μm,could be refined to less than 0.5μm by laser ***,the microstructure,microhardness,and wear resistance of the laser-polished(LP-ed)zone were *** indicate that compared with the microhardness and wear resistance of the as-SLMed layer,those of the LP-ed layer decreased by 4%and 11%,respectively,because of the increase in grain size and reduction of the BCC *** study shows that laser polishing has an excellent application prospect in surface improvement of HEAs manufactured by SLM.

关键词： laser polishing selective laser melting high-entropy alloy surface roughness mechanical performance

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Promoting polysulfide conversions via cobalt single-atom catalyst for fast and durable lithium-sulfur batteries

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Nano Research 2023年第7期16卷 9335-9343页

作者： Ziwei Wang Yuwen Cheng Shanying Wang Jie Xu Bo Peng Dan Luo Lianbo Ma Key Laboratory of Green Fabrication and Surface Technology of Advanced Metal Materials&School of Materials Science and Engineering Anhui University of TechnologyMaanshan 243002China School of Information and Optoelectronic Science and Engineering South China Normal UniversityGuangzhou 510006China Department of Mechanical and Aerospace Engineering The Hong Kong University of Science and Technology(HKUST)Clear Water BayHong Kong 999077China

Although promising strategies have been developed to resolve the critical drawbacks of lithium-sulfur(Li-S)batteries,the intractable issues including undesirable shuttling of polysulfides and sluggish redox reaction kinetics have still been unresolved ***,a cobalt single-atom(CoSA)catalyst comprising of atomic Co distributed homogeneously within nitrogen(N)-doped porous carbon(Co-NPC)nanosphere is constructed and utilized as a separator coating in Li-S *** Co-NPC exposes abundant active sites participating in sulfur redox reactions,and remarkable catalytic activity boosting the rapid polysulfide *** a result,Li-S batteries with Co-NPC coating layer realize significantly enhanced specific capacity(1295 mAh·g^(-1)at 0.2 C),rate capability(753 mAh·g^(-1)at 3.0 C),and long-life cyclic stability(601 mAh·g^(-1)after 500 cycles at 1.0 C).Increasing the areal sulfur loading to 6.2 mg·cm^(-2),an extremely high areal capacity of 7.92 mAh·cm^(-2)is *** in situ X-ray diffraction,density functional theory calculations,and secondary ion mass spectrometry confirm the high catalytic capability of CoSA towards reversible polysulfide *** study supplies new insights for adopting single-atom catalyst to upgrade the electrochemical performance of Li-S batteries.

关键词： lithium-sulfur batteries polysulfide shuttling sluggish redox kinetics cobalt single-atom catalyst catalytic capability

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Reinforcement Learning Based Truck-and-Drone Coordinated Delivery

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第4期4卷 754-763页

作者： Wu, Guohua Fan, Mingfeng Shi, Jianmai Feng, Yanghe Central South University School of Traffic Transportation Engineering Changsha410073 China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory College of System Engineering Changsha410073 China National University of Defense Technology College of Systems Engineering Changsha410073 China

Coronavirus disease 2019 has brought a great challenge to the supply of daily necessities and medical items for home-quarantined people. Considering the unmanned operation, agility, and use of clean energy of drones, we propose a novel truck-and-drone coordinated delivery system that does not require direct human contact during the delivery process. All final deliveries are completed by the drone while the truck acts as a movable charging station and a carrier, so that the contagion risks are reduced. Moreover, we spilt the whole delivery problem into the customer-clustering problem and the routing problems of both the truck and the drone. An encoder-decoder framework combined with reinforcement learning is created to solve the routing problems without handcrafted designed heuristics. We design the different problem contexts specific to the truck routing problem and the drone routing problem. The experimental results show that the proposed approach has good generality and can consequently be applied to problems of different scales with high time efficiency. © 2020 IEEE.

关键词： Reinforcement learning

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Optimal Tracking Neuro-Control of Continuous Stirred Tank Reactor systems: A Dynamic Event-Driven Approach

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2117-2126页

作者： Yang, Xiong Zhou, Yingjiang Tianjin University Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information Engineering Tianjin300072 China Nanjing University of Posts and Telecommunications College of Automation and College of Artificial Intelligence Nanjing210023 China

It is often challenging to design an optimal tracking controller for the continuous stirred tank reactor (CSTR) system due to its nonlinear nature and physical limitations. This article presents a dynamic event-driven optimal tracking neruo-control scheme for the CSTR system with asymmetric input constraints. Initially, an improved nonquadratic cost function is introduced for the CSTR system to tackle asymmetric control restrictions. Then, a dynamic event-driven mechanism together with the event-driven Hamilton-Jacobi-Bellman equation (ED-HJBE) is proposed. To solve the ED-HJBE, a critic neural network (CNN) is constructed within the critic learning framework. The CNN's weight vector is tuned by combining the gradient descent method and the concurrent learning technique. After that, uniform ultimate boundedness of the tracking error and the CNN's weight estimation error is assured based on the Lyapunov method. Finally, experiment studies are conducted to validate the present optimal tracking neuro-control strategy. © 2023 IEEE.

关键词： Reinforcement learning

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Carrier and microstructure tuning for improving the thermoelectric properties of Ag_(8)SnSe_(6)via introducing SnBr_(2)

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Journal of Advanced Ceramics 2022年第7期11卷 1144-1152页

作者： Zhonghai YU Xiuxia WANG Chengyan LIU Yiran CHENG Zhongwei ZHANG Ruifan SI Xiaobo BAI Xiaokai HU Jie GAO Ying PENG Lei MIAO Guangxi Key Laboratory of Information Materials Electronical Information Materials and Devices Engineering Research Center of Ministry of EducationSchool of Materials Science and EngineeringGuilin University of Electronic TechnologyGuilin 541004China

The argyrodite compounds(2 A(12)/B X6 m n n^(m+)+--(Am+=Li^(+),Cu^(+),and Ag^(+);Bn^(+)=Ga^(3+),Si^(4+),Ge^(4+),Sn^(4+),P^(5+),and As^(5+);and X^(2−)=S^(2−),Se^(2−),or Te^(2−)))have attracted great attention as excellent thermoelectric(TE)materials due to their extremely low lattice thermal conductivity(κl).Among them,Ag_(8)SnSe_(6)-based TE materials have high potential for TE ***,the pristine Ag_(8)SnSe_(6)materials have low carrier concentration(<1017 cm^(−3)),resulting in low power *** this study,a hydrothermal method was used to synthesize Ag_(8)SnSe_(6)with high purity,and the introduction of SnBr_(2)into the pristine Ag_(8)SnSe_(6)powders has been used to simultaneously increase the power factor and decrease the thermal conductivity(κ).On the one hand,a portion of the Br−ions acted as electrons to increase the carrier concentration,increasing the power factor to a value of~698 mW·m^(−1)·K^(−2)at 736 *** the other hand,some of the dislocations and nanoprecipitates(SnBr_(2))were generated,resulting in a decrease ofκl(~0.13 W·m^(−1)·K^(−1))at 578 *** a result,the zT value reaches~1.42 at 735 K for the sample Ag8Sn1.03Se5.94Br0.06,nearly 30%enhancement in contrast with that of the pristine sample(~1.09).The strategy of synergistic manipulation of carrier concentration and microstructure by introducing halogen compounds could be applied to the argyrodite compounds to improve the TE properties.

关键词： Ag_(8)SnSe_(6) thermoelectric(TE)performance lattice thermal conductivity SnBr_(2) introduction hydrothermal method

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